AU2018371678A1 - Thermal response probe and method - Google Patents

Abstract

A probe that mimics the thermal response of a product in its environment, and to a method of simulating a product's thermal response to changes in its environment using the probe.

Description

Thermal Response Probe and Method

TECHNICAL FIELD [0001] This invention relates, inter alia, to a probe that mimics the thermal response of a product in its environment, and to a method of simulating a product’s thermal response to changes in its environment using the probe.

BACKGROUND ART [0002] Temperature-controlled food product storage and transport is usually essential for avoiding food product spoilage, so as to ensure human safety and to minimise food wastage. For example, the temperature of most dairy products must be confined to a very narrow range of 37°C. If a predefined critical temperature is exceeded (7°C in this case), the food product may be defined as spoiled and unfit for human consumption.

[0003] During product storage and transport food products are typically subjected to changing ambient air temperature, which means that ambient air heat will be transferred to the (cooler) product and in this way elevate the temperature of the product. The rate of heat transfer and extent of product temperature elevation will depend on, amongst other things, the thermal properties of the product and ambient air temperature variation over time. For example, opening the door of a cooled or chilled environment/container will inevitably change the ambient air temperature of the environment/container and affect the temperature of the product. If the door is left open for too long, then the product may exceed a critical temperature and there may be spoilage of the food product.

[0004] A conventional technique for monitoring food product temperature during transport is to insert a temperature probe into the food product itself. If the probe records a reading beyond a critical temperature, then the food product may be considered spoiled. A problem with this technique, however, is that the probed food product will probably be unfit for consumption and so must be discarded. Another problem is that the probed food product may contaminate the environment/container housing the product.

[0005] The big issue in the carriage of food products in the global cold chain is that too much reliance has traditionally been placed on ambient air temperatures, instead of product

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PCT/AU2018/000231 temperatures. Temperature probes (insertion thermometers) that are inserted into food products during transport solve part of the problem, but all too often, the application is haphazard, timeconsuming and subject to abuse.

[0006] Far too many food shipments are being rejected and sent to landfill because the cargo was not kept at the right temperature, and many transport companies are not even able to prove that the food was kept at the right temperature.

[0007] SUMMARY OF THE INVENTION [0008] In some embodiments of the invention, it would be advantageous to minimize or overcome a problem described above.

[0009] According to a 1^st aspect of the present invention, there is provided a probe that simulates (mimics) the thermal response of a product to the product’s environment.

[0010] According to a 2^nd aspect of the present invention, there is provided the use of a probe for simulating (mimicking) the thermal response of a product to the product’s environment.

[0011] According to a 3^rd aspect of the present, there is provided a method of simulating (mimicking) the thermal response of a product to the product’s environment, said method comprising the step of using a probe that simulates the thermal response of the product to the product’s environment.

[0012] According to a 4^th aspect of the present invention, there is provided a probe calibrated or programmed, or that can be calibrated or programmed, to simulate the thermal response of a product to the product’s environment.

[0013] According to a 5^th aspect of the present invention, there is provided the use of a probe calibrated or programmed, or that can be calibrated or programmed, for simulating the thermal response of a product to the product’s environment.

[0014] According to a 6^th aspect of the present invention, there is provided a method of simulating the thermal response of a product to the product’s environment, said method comprising the step of using a probe calibrated or programmed, or that can be calibrated or programmed, that simulates the thermal response of the product to the product’s environment.

[0015] According to a 7^th aspect of the present invention, there is provided a probe calibrated

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PCT/AU2018/000231 or programmed, or that can be calibrated or programmed, to simulate the thermal response of a product to the product’s environment, wherein the probe is/can be calibrated or programmed based on physical and chemical properties of the product and the environment.

[0016] According to an 8^th aspect of the present invention, there is provided the use of a probe calibrated or programmed, or that can be calibrated or programmed, for simulating the thermal response of a product to the product’s environment, wherein the probe is/can be calibrated or programmed based on physical and chemical properties of the product and the environment.

[0017] According to a 9^th aspect of the present invention, there is provided a method of simulating the thermal response of a product to the product’s environment, said method comprising the step of using a probe calibrated or programmed, or that can be calibrated or programmed, that simulates the thermal response of the product to the product’s environment, wherein the probe is/can be calibrated or programmed based on physical and chemical properties of the product and the environment.

[0018] According to a 10^lh aspect of the present invention, there is provided a method of simulating the thermal response of a product to the product’s environment using a probe, wherein the method comprises the step of using a pre-assigned product probe calibration value for the product to calibrate the probe so as to simulate the thermal response of the product to the environment, wherein the product probe calibration value has been determined from physical and chemical properties of the product and the environment.

[0019] According to an 11^th aspect of the present invention, there is provided a method of simulating the thermal response of a product to the product’s environment using a probe, wherein the method comprises the steps of:

[0020] (I) assigning a product probe calibration value to the product, wherein the product probe calibration value is determined from physical and chemical properties of the product and the environment; and [0021] (II) using the product probe calibration value to calibrate the probe so as to simulate the thermal response of the product to the environment.

[0022] According to a 12^th aspect of the present invention, there is provided the use of a probe for simulating the thermal response of a product to the product’s environment, wherein the thermal response of the product is determined from physical and chemical properties of the

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PCT/AU2018/000231 product and the environment, including nutritional information on the product’s packaging or the product’s composition.

[0023] According to a 13* aspect of the present invention, there is provided the use of nutritional information on a product’s packaging or the product’s composition for determining the thermal response of the product to the product’s environment and for calibrating a probe based on the thermal response such that the probe can simulate the thermal response of the product.

[0024] According to a 14^th aspect of the present invention, there is provided a probe capable of being calibrated or programmed using a product probe calibration value.

[0025] According to a 15^th aspect of the present invention, there is provided at least one product probe calibration value, particularly when used in a method as described herein.

[0026] According to a 16^th aspect of the present invention, there is provided product composition information or nutritional information on the product’s packaging, particularly when used in a method as described herein.

[0027] According to a 17^th aspect of the present invention, there is provided a probe capable of being calibrated or programmed, for simulating the thermal response of a product to the product’s environment, said probe comprising:

[0028] a housing;

[0029] at least one temperature sensor located within the housing; and [0030] a calibrating or programming mechanism for adjusting the at least one temperature sensor such that the probe is capable of simulating the thermal response of the product to the product’s environment.

[0031] According to an 18^th aspect of the present invention, there is provided a data logger, receiver, computer software, a software product, app, computer program or user interface for monitoring or recording sensor data from a probe.

[0032] According to a 19^th aspect of the present invention, there is provided a method of monitoring or recording sensor data from a probe, said method comprising the step of using a data logger, receiver, computer software, a software product, app, computer program or user interface to monitor or record sensor data from the probe.

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PCT/AU2018/000231 [0033] According to a 20^th aspect of the present invention, there is provided computer software, a software product, app, computer program or user interface for simulating, predicting or modelling the thermal response of a product to the product’s environment, wherein the thermal response is determined from physical and chemical properties of the product and the environment.

[0034] According to a 21^st aspect of the present invention, there is provided a method of simulating, predicting or modelling the thermal response of a product to the product’s environment, said method comprising the step of determining the thermal response based on physical and chemical properties of the product and the environment.

[0035] According to a 22^nd aspect of the present invention, there is provided computer software, a software product, app, computer program or user interface for calculating or determining a product probe calibration value for a product in the product’s environment, wherein said computer software, software product, app, computer program or user interface is capable of calculating the product probe calibration value based on physical and chemical properties of the product and the environment.

[0036] According to a 23^rd aspect of the present invention, there is provided a method of calculating a product probe calibration value for a product in the product’s environment, wherein said method comprises the step of using computer software, a software product, app, computer program or user interface to calculate the product probe calibration value based on physical and chemical properties of the product and the environment.

[0037] According to a 24^th aspect of the present invention, there is provided a method of controlling the temperature of a refrigerated environment containing a product, said method comprising the step of using a probe that simulates the thermal response of the product to the product’s environment as a thermostat for controlling the temperature of the environment.

[0038] According to a 25^th aspect of the present invention, there is provided the use of a probe that simulates the thermal response of a product to the product’s refrigerated environment as a thermostat for controlling the temperature of the environment.

DETAILED DESCRIPTION OF THE INVENTION [0039] It is to be appreciated that the following detailed description will, context permitting, equally apply to the probe, method, use as well as any other aspect of the invention even if described only in respect of, say, a probe, method, use, computer software or user interface. That

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PCT/AU2018/000231 is, if a feature of the invention is described in respect of the probe, that feature could equally be a feature of a different aspect of the invention (context permitting). So, reference to a probe feature should be interpreted as a feature/step of a method etc and, likewise, a method feature/step should be interpreted as a feature of the probe etc (context permitting).

[0040] The product can be any suitable type of product requiring simulating. The product can be a perishable product. The product can be a food product. The product can be from any one of the different food groups. The product can be an ingestible product, such as a pharmaceutical. The product can be other than a food product or ingestible product. The product can be an agricultural product. The product can be a human or animal-derived product, such as tissue or an organ.

[0041] There can be more than one product present in the environment that the probe simulates. For example, there could be one or more product pallets present in the environment.

[0042] In some embodiments the product is a food product, which could include a solid, semisolid or liquid food. Examples of suitable food products include meat, seafood, dairy and fresh produce, such as cheese, yoghurt, milk, chicken, beef, and fruit and vegetables.

[0043] As mentioned, the product can be a non-food product that would spoil or become defective if exposed to an unsuitable environmental temperature - ie. when exceeding a critical environmental temperature, or exposed to an unsuitable environmental temperature for a particular period of time.

[0044] As mentioned, the thermal response of the product can be determined or calculated from physical and chemical properties of the product and the environment, the probe can be calibrated or programmed based on physical and chemical properties of the product and the environment, and the product probe calibration value can be calculated or determined from physical and chemical properties of the product and the environment. Typically this would involve the step of calculating or determining thermal properties of the product. Typically this would involve calculating or determining the likely rate of heat exchange and transfer between the environment and the product.

[0045] Any suitable physical and chemical properties of the product and the environment can be used, and any suitable method of calculation or determination can be used. Preferably, one or more of the following is used:

1. Product composition/ingredients

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2. Product mass, dimensions and geometry

3. Product packaging

4. Environment temperature

5. Environment humidity

6. Thermal conductivity (A)

7. Specific heat capacity (C_p)

8. Density (p)

9. Thermal diffusivity (a)

10. Overall heat transfer coefficient (U)

11. Convection heat transfer coefficient / heat transfer coefficient (A) [0046] The thermal properties of the product can be determined empirically and/or theoretically.

[0047] Thermal properties of the product (thermal conductivity k, specific heat capacity C_p and density p) can be calculated or determined in any suitable way. Thermal properties of the product can be calculated or determined based on the product’s composition/ingredients. Suitable methods for calculating these thermal properties are represented by equations 7, 8, 9, 10, 11 and 12 of Example 1. Suitable methods for calculating these thermal properties are represented by equations 1, 2, and 3 of Example 2. Suitable methods for calculating these thermal properties can be found in the cited references of Examples 1 and 2, the entire contents of which are incorporated herein by way of cross-reference.

[0048] As mentioned, thermal properties of the product can be calculated based on the product’s composition/ingredient list. For example, ingredients of the composition can be one or more of the mass fraction of water/moisture, protein, fat, ash and carbohydrate. Preferably all of these ingredient types are used in the thermal properties calculation. In preferred embodiments, these ingredient parameters (quantities) can be found in the nutritional table on the food product’s packaging. The mass fraction of each ingredient type can typically be found as a percentage out of lOOg.

[0049] In some instances the composition ingredients may be expressed in units other than mass fractions, yet can still be used to determine the thermal properties of the product. Such units and conversions will be familiar to those skilled in the art.

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PCT/AU2018/000231 [0050] In some embodiments, the probe can be used to predict the thermal properties of food products comprising about 5-95% weight/weight water/moisture, 1-40% weight/weight protein, 0.01-83% weight/weight fat and 0-54% weight/weight carbohydrate. One or more of these stated percentage ranges include the values of about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 1,

2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30,

31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,56,

57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,82,

83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94 and 95, as well as all 0.5 increments between the numbers 0.01 and 95.

[0051] Preferably the product composition/ingredient parameters can be found in the nutrition table displayed on the product’s packaging.

[0052] Thermal diffusivity (a) can be calculated in any suitable way. A suitable method for calculating this property/parameter is represented by equation 1 of Example 1. Suitable methods for calculating thermal diffusivity can be found in the cited references of Examples 1 and 2, the entire contents of which are incorporated herein by way of cross-reference.

[0053] Heat exchange and heat transfer to the product from the environment can be calculated in any suitable way. Suitable methods of calculation are represented by equations 2, 3, 4, 5 and 6 of Example 1. Suitable methods of calculation are represented by equations 4, 5, 6 and 7 of Example 2. Suitable methods of calculation can be found in the cited references of Examples 1 and 2, the entire contents of which are incorporated herein by way of cross-reference.

[0054] The overall heat transfer coefficient U can be calculated taking into account the packaging of the product, if such packaging is present. The packaging type and its thickness can be taken into consideration. The packaging can be in the form of, for example, foil, paper or paperboard, plastic, polymer material and/or cloth. The packaging can be more than one layer, and each layer can be a different material. The packaging material thermal conductivity and emissivity need to be known to calculate the radiation heat transfer coefficient and the conductive heat transfer. Suitable methods of calculation can be found in the cited references of Examples 1 (equations 14, 15 and 16) and 2, the entire contents of which are incorporated herein by way of cross-reference.

[0055] In some embodiments, the step of calculating or determining thermal properties of the product can involve using the product’s composition/ingredient list to calculate thermal 8

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PCT/AU2018/000231 conductivity using equation 9 of Example 1 or equation 1 of Example 2. Specific heat capacity can be calculated using equation 10 of Example 1 or equation 2 of Example 2. Next, density can be calculated using equation 13 of Example 1 or equation 3 of Example 2. Next, thermal diffusivity can be calculated using equation 1 of Example 1.

[0056] In some embodiments, the step of determining the rate of heat exchange and transfer between the environment and the product can be calculated using, for example, equations 2 to 6 of Example 1 or equations 4 to 7 of Example 2.

[0057] Regarding calibration/programming of the probe so as to establish a calibration point that simulates a product or corresponds with the product probe calibration value, this can be carried out in any suitable way and typically in a similar manner as described above for the product - namely, by determining thermal properties of the probe and calculating or determining the likely rate of heat exchange and transfer between the environment and a thermal sensor of the probe.

[0058] In some embodiments, the probe can be calibrated/a calibration point can be established in the following manner:

(1) The probe’s internal and external dimensions are measured.

(2) The thermal properties of the thermal medium inside the probe are found at the film temperature (average inside/initial and outside/surrounding environment temperatures). For example, glycerol and foam can be used as the thermal medium in this probe.

(3) The thermal properties of the environment are found at the film temperature.

(4) The emissivity of the probe/probe’s housing is determined.

(5) The thermal conductivity and the thickness of the probe/probe’s housing material (eg. plastic) is determined.

(6) Based on (1) and (2), the internal heat transfer coefficient is calculated.

(7) Based on (1) and (3), the external heat transfer coefficient is found.

(8) Based on (4) and temperatures (initial thermal medium and environment) the radiation heat transfer coefficient is calculated.

(9) The conductive heat transfer is found based on parameters in (5).

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PCT/AU2018/000231 (10) The overall heat transfer (U) coefficient is found from (6) to (9).

[0059] After finding the probe’s dimensions from (1), fluid thermal properties from (2) and the U value from (10), a two-dimension finite difference method (2D-FDM) can be used to map/find the temperature at any point inside the probe/housing for the whole simulation time.

[0060] The same basic procedure can be carried out for the product to thereby assign a product probe calibration value. If the product is a liquid, then steps 6 and 8 can be excluded. However, the core/centre temperature of the food product is the one of interest, and there is no need to plot all mapped temperatures except at the core and the surface.

[0061] By comparing the simulation mapping curves result with experimental/tested core temperature of the product of interest, the right match can be found.

[0062] From the matched curve the setting position/calibration point (in terms of vertical and horizontal direction) of the thermal sensor inside the housing can be readily determined.

[0063] An experiment can be carried out to test and check that the thermal response of both the calibrated probe and specific product are in agreement, and the tolerance is within the allowed range.

[0064] Preferably, the probe is not inserted into the product itself which would otherwise damage, contaminate or spoil the product. That is, preferably the probe is not an insertion thermometer.

[0065] Preferably the probe can be calibrated/programmed to simulate/mimic a core temperature of the product.

[0066] The probe can be used for simulating the product’s thermal response to changes in its environment temperature over a period of time.

[0067] The probe can transmit sensor data corresponding to the product’s thermal response to changes in its environmental temperature over a period of time.

[0068] The method can be used to monitor or record sensor data for auditing purposes, to determine whether or not the product has been kept at an acceptable temperature, or has exceeded a critical temperature or other condition.

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PCT/AU2018/000231 [0069] The period of time can correlate with the loading or unloading of the product or products into or out of the environment.

[0070] The period of time can correlate with transporting the product or products from one location to another, such as to a final destination.

[0071] In some embodiments the probe can simulate the thermal response of the product (or products) to changing environmental temperature over a period of time ranging from seconds, minutes, hours, days, weeks, months or even years.

[0072] The probe can be calibrated to simulate a wide range of products, particularly food products. In some embodiments the probe can be calibrated to simulate 2, 3, 4 or 5 or even more product types, but preferably 3 different product types (eg. 3 different types of cheeses). That is, the probe can have 3 calibration points.

[0073] Preferably, the probe can simulate a chilled product or products, as opposed to a frozen product/s.

[0074] Preferably, the probe can simulate product temperatures ranging from 0°C to +50°C (which includes all 0.5°C and 1°C increments between the upper and lower values).

[0075] In some embodiments the probe can simulate a frozen product, such as a frozen food product (eg. beef product or potato - beef patties and fries). However, the only thing that will be different from chilled product simulation is the product software simulation equations and their thermal property calculations. In the case of a frozen product, the probe can simulate temperature ranging from -50°C to 0°C (which includes all 0.5°C and 1°C increments between the upper and lower values).

[0076] In some embodiments the probe can simulate the product within an about 30% to 90% relative humidity (RH) environment.

[0077] Preferably, the probe can simulate (frozen, chilled or otherwise) product temperatures ranging from -50°C to +50°C (which includes all 0.5°C and 1°C increments between the upper and lower values).

[0078] The environment can be air, carbon dioxide or any other suitable gas or gas mixture.

[0079] The environment can be a temperature controlled environment or one that is not

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PCT/AU2018/000231 temperature controlled. If temperature controlled, the environment can be air-conditioned, chilled or cooled.

[0080] The environment can be a sealable or closable environment such as an interior of an insulated container, fridge, room, chiller room, transport container or shipping container.

[0081] The probe, method, use etc are particularly suited for product located within a temperature controlled room or container, whereby the product is loaded into or unloaded from that room or container.

[0082] The probe, method, use etc are particularly suited for product located within a refrigerated, chilled, air-conditioned or non-refrigerated vehicle or other type of vehicle or vessel, whereby the product is loaded onto or unloaded from the vehicle or vessel via one or more doors.

[0083] The probe, method, use etc can be used to predict the time that the product will take to reach a specific temperature or when the product reaches a specific temperature (due to heat transfer from the environment).

[0084] The probe, method, use etc can be used to predict when spoilage of the product occurs or is likely to occur.

[0085] The probe, method, use etc can be used for virtual monitoring of the temperature of the product over time.

[0086] The probe, method, use etc can be used to alert a user when a critical product temperature has been reached or exceeded.

[0087] The probe, method, use etc can be used to predict the required maximum time to load/unload a refrigerated or unrefrigerated road vehicle/load/container before a critical product temperature is reached.

[0088] The probe, method, use etc can be used for simulating the temperature transfer and distribution scenario at the loading/unloading process in refrigerated transport.

[0089] The probe, method, use etc can be used to accurately predict the time that the product takes to reach a specific temperature as well as the maximum allowed time for the product loading or unloading process.

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PCT/AU2018/000231 [0090] The probe, method, use etc can suit food products shipped at controlled temperatures.

[0091] The probe can be placed in contact with, adjacent to or within the general vicinity of the product/s. The method or use can comprise the step of placing more than one probe in contact with, adjacent to or within the general vicinity of the product or products.

[0092] The probe, method, use etc can comprise transmitting probe sensor data to a receiver that can include a central processing unit (CPU), display and/or user interface.

[0093] The user interface can allow the input of parameters such as the product’s temperature, ambient environmental temperature, the product’s composition/ingredients and time.

[0094] The product’s response to environmental temperature variation can be predicted by entering the product’s ingredients/composition, product initial temperature and ambient environmental temperature, time, product dimension, product geometry and the overall heat transfer coefficient.

[0095] The probe, method, use etc can comprise the probe sending sensor data to a receiver, either through a wired connection or wirelessly (e.g., Wi-Fi (WLAN) communication, Satellite communication, RF communication, infrared communication, or Bluetooth™).

[0096] The receiver can be a standalone computer, a computer network, a website interface, smart phone or other electronic device.

[0097] The receiver can have a data logging or other data recording function. The receiver can have a CPU. The receiver can have memory. The receiver can have a display screen. The receiver can have a user-friendly interface. The receiver can have a printing function.

[0098] The receiver can receive the probe sensor data and transmit it to a smart phone or website interface. The receiver can record (data log) the probe sensor data and transmit it to a smart phone or website interface. Transmission can occur through a wired connection or wirelessly, whatever the case may be (e.g., Wi-Fi (WLAN) communication, Satellite communication, RF communication, infrared communication, or Bluetooth™). In this way, users (e.g. transporters) can: maintain a watch on the product temperature in real-time; ensure that the product was always kept at the right temperature; anticipate temperature risks; and, take corrective measures in time.

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PCT/AU2018/000231 [0099] In some embodiments, a user interface is used to predict the product’s response by entering the product’s ingredients/composition, product initial temperature and ambient environmental temperature, time, product dimension, product geometry and the overall heat transfer coefficient. In some embodiments, the method can comprise using a user interface to predict the product’s response by entering also air humidity.

[00100] The probe housing can be of any suitable size, shape and construction, and can be made of any suitable material or materials, such as plastics material. The housing can be of unitary construction or can comprise two or more connectable pieces. The housing can be elongate or squat. The housing can have a central axis extending from one end of the housing to the other end. The housing can be tubular and can be of any suitable cross-section - eg. circular, ovular, rectangular, oblong, triangular et cetera.

[00101] In some embodiments the housing comprises a tube having a 1^st end and a 2^nd end. In some embodiments the 1^st end is sealed or sealable. In some embodiments the 2^nd end is sealed or sealable. In some embodiments the housing comprises a tube having a substantially blind end and an open end, and a connectable cap for substantially closing off or sealing the open end.

[00102] The housing can comprise at least one sealing member for sealing one or more ends of the tube. The sealing member can be, for example, an O-ring or gasket. An O-ring can extend about a perimeter of the second end of the tube and the cap and between the second end and cap.

[00103] The housing can comprise a grip for a hand of a person/user or tool. In some embodiments a grip can extend about one or more ends of the tube and/or cap.

[00104] In some embodiments, the housing (tube and cap) is made of plastics material.

[00105] Preferably, the housing comprises an elongate cylindrical tube having a first sealed or sealable end and a second sealed or sealable end, and a connectable or connected cap for sealing the second end. A central axis can extend through the tube or housing from one end of the tube to the other.

[00106] The housing can have a cap connectable or connected to a tube of the housing. The cap can have a central opening. The cap can have a boss extending around the central opening. The cap can have a collar extending adjacent a circumference of the cap. The cap can have openings for receiving mechanical fasteners such that the cap can be connected to an end of the tube or a flanged end of the tube. The cap can have a first surface and an opposed second surface.

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The boss and collar can extend from the first surface. The second surface can have a circumferentially extending groove for receiving a sealing member such as an O-ring. The end of the tube or flanged end of the tube can have a circumferentially extending groove for receiving an O-ring. A circumferential region of the cap can be sealingly connected to an end of the tube or flanged end of the tube.

[00107] Any suitable type of temperature sensor, which includes a thermocouple (TC), can be used. In some embodiments the probe can comprise at least one thermistor, such as a Negative Temperature Coefficient (NTC) sensor or Positive Temperature Coefficient (PTC), or a resistance temperature detector (RTD). The temperature sensor can comprise a thermistor sensor head/housing and a cable extending from the head/housing. Preferably the temperature sensor comprises at least one NTC sensor. The NTC sensor can comprise a sensor head/housing and a cable extending from the head/housing.

[00108] Preferably the sensor head is sealed within the housing in a fluid tight manner (air tight and/or liquid-tight) and this can be achieved in any suitable way. The sensor cable can extend through the first end of the housing or tube in a sealed manner. This can be achieved, for example, using a cable gland that tightens to seal an opening in the first end of the housing or tube.

[00109] The temperature sensor can comprise a heat transfer medium contained by the housing, such as a gaseous or liquid medium, within which the thermistor’s sensor head is located. Any suitable type of heat transfer medium can be used. Preferably the heat transfer medium is a liquid, and more preferably a viscous liquid. Preferred viscous liquids include polyols. Glycerol (also called glycerine and glycerin) is an example of a suitable viscous heat transfer liquid (has a 0.285 W/m K thermal conductivity at a temperature of 300 K). Other suitable types of heat transfer media may have a higher or lower thermal conductivity value.

[00110] The temperature sensor can comprise a heat transfer matrix contained within the housing. Any suitable type of matrix can be used. For example, porous sponge and porous foam are suitable matrices. The matrix can have a body of a shape and size to fill less than about 50%, about 50%, or more than about 50% of the tube. The matrix can comprise a core, channel, passageway, chamber or opening within the matrix body within which the thermistor can extend, such that the thermistor sensor head does not come into contact with the matrix body. The matrix body can be of unitary construction or can comprise more than one matrix body piece - eg. matrix

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PCT/AU2018/000231 body pieces (eg. disk-shaped) that can be stacked together.

[00111] The matrix body can be stiff or rigid, or it can be pliable or compressible and therefore require a matrix guard. Any suitable type of matrix guard can be used.

[00112] The temperature sensor can comprise at least one baffle contained within the housing for reducing stirring or agitation of the liquid heat transfer medium so as to reduce unlimited thermal currents. Any suitable type of baffle can be used. For example, porous sponge and porous foam are suitable baffles. The baffle can have a body of a shape and size to fill less than about 50%, about 50%, or more than about 50% of the tube. The baffle can comprise a core, channel, passageway, chamber or opening within the baffle body within which the thermistor can extend, such that the thermistor sensor head does not come into contact with the baffle body. The baffle body can be of unitary construction or can comprise more than one baffle body piece - eg. baffle body pieces (eg. disk-shaped) that can be stacked together.

[00113] The baffle body can be stiff or rigid, or it can be pliable or compressible and therefore require a baffle guard. Any suitable type of baffle guard can be used.

[00114] The heat transfer matrix and baffle can be one and the same.

[00115] The matrix or baffle guard can be of any suitable size, shape and construction, and can be made of any suitable material or materials. In some embodiments the guard is in the form of a frame, framework, lattice or cage that supports the matrix or baffle body within the housing and holds the matrix or baffle body in a fixed position within the housing. The guard can extend around or partway around the core, channel, passageway, chamber or opening within which the thermistor extends. The guard can extend from one end of the tube to the other. The guard can extend around or partway around the core, channel, passageway, chamber or opening such that the core, channel, passageway, chamber or opening extends radially from the central axis of the tube/housing, to accommodate any lateral movement of the sensor head.

[00116] Any suitable type of calibrating mechanism can be used for adjusting the temperature sensor.

[00117] In some embodiments, the calibrating mechanism calibrates the probe by way of moving the temperature sensor/sensor head from a first predefined location within the house to a different second predefined location within the housing. In some embodiments, the calibrating mechanism moves the temperature sensor/sensor head radially or laterally relative to the central

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PCT/AU2018/000231 axis of the tube/housing. In some embodiments, the calibrating mechanism moves the temperature sensor/sensor head perpendicularly relative to the central axis of the tube/housing. In some embodiments, the calibrating mechanism moves the temperature sensor/sensor head along the central axis of the tube/housing or along an axis parallel to the central axis. In some embodiments, the calibrating mechanism moves the temperature sensor/sensor head other than parallel to the central axis or perpendicularly of the central axis (eg. diagonally or obliquely). In some embodiments, the calibrating mechanism moves the temperature sensor/sensor head both radially or laterally relative to the central axis of the tube/housing and along the central axis of the tube/housing or along an axis parallel to the central axis.

[00118] The calibrating mechanism can be manually actuated or automatically actuated. If automatically actuated, then the calibrator can comprise a CPU-controlled drive or actuator for moving the temperature sensor/sensor head.

[00119] Preferably the calibrating mechanism is manually actuated by hand or using a handoperated tool.

[00120] In some embodiments, the calibrating mechanism can comprise a linear actuating mechanism for moving the sensor head from one location within the housing to another. Any suitable type of linear actuating mechanism can be used (eg. rack and pinion mechanism). Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00121] In some embodiments, the calibrating mechanism can comprise a rack and pinion mechanism for moving the sensor head from one location within the housing to another. Preferably, the rack and pinion mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00122] In some embodiments, the calibrating mechanism can comprise a worm drive mechanism for moving the sensor head from one location within the housing to another. Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00123] In some embodiments, the calibrating mechanism can comprise a lever mechanism for moving the sensor head from one location within the housing to another. Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the

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PCT/AU2018/000231 housing.

[00124] In some embodiments, the calibrating mechanism can comprise a toggle mechanism for moving the sensor head from one location within the housing to another. Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00125] In some embodiments, the calibrating mechanism can comprise a gear train mechanism for moving the sensor head from one location within the housing to another. Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00126] In some embodiments, the calibrating mechanism can comprise a chain and sprocket mechanism for moving the sensor head from one location within the housing to another. Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00127] In some embodiments, the calibrating mechanism can comprise a cam and cam follower mechanism for moving the sensor head from one location within the housing to another. Preferably, the mechanism moves the sensor head laterally within the housing relative to the central axis of the housing.

[00128] In preferred embodiments, the calibrating mechanism can comprise a dial and rack and pinion mechanism for moving the sensor head from one location within the housing to another. The dial can be operably connected to the pinion, the pinion can be operably connected to the rack, and the rack can be operably connected to the sensor head. Turning the dial from one position to another can result in the sensor head being moved from one location within the housing to another, preferably laterally or radially relative to the central axis of the housing (ie. towards a housing/tube wall or away from a housing/tube wall).

[00129] The rack and pinion mechanism can be of any suitable size, shape and construction, and can be made of any suitable material or materials, eg. plastics material. The rack can comprise a linear gear. Teeth of the linear gear can extend substantially parallel with the central axis. The rack can comprise a rack support extending from the linear gear. The rack can comprise a shaft extending from the rack support substantially parallel with the central axis of the housing. The rack can comprise a sensor clamp connected to the shaft and clamped to the temperature sensor,

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PCT/AU2018/000231 preferably the sensor head. The sensor clamp can be adjusted in position along a length of the shaft and fixed in position such that the axial position of the sensor head can be altered when required. The rack support can have a first surface from which extends the linear gear and a second opposing surface from which extends the shaft. The first and second surfaces can extend substantially perpendicularly of the central axis of the housing/tube.

[00130] The pinion can be of any suitable size, shape and construction, and can be made of any suitable material or materials, e.g. plastics material. The pinion can comprise at one end a pinion gear that meshes with the linear gear. The pinion can comprise at the other end a drive that is operably connected to the dial. The drive can be a spline drive. The pinion can comprise a shaft that extends between the pinion gear and the spline drive. The shaft can extend substantially along the central axis of the housing. One or more sealing members can extend around the shaft. Preferably a pair of O-rings extends around the shaft within grooves of the shaft, so as to seal the boss/opening in the cap of the housing.

[00131] The rack and pinion mechanism can comprise a pinion carrier for guiding the pinion relative to the rack. The pinion carrier can be of any suitable size, shape and construction, and can be made of any suitable material or materials, e.g. plastics material. The pinion carrier can be in the form of a saddle extending over and around the linear gear. The pinion carrier can comprise a pinion seat having an aperture through which pinion shaft extends such that the pinon gear abuts the rack support and engages the linear gear. The pinion seat can correspond to a middle region of the saddle. The pinion carrier can comprise at least one rack engager for engaging the rack and enabling the pinion to be moved relative to the rack without disengaging the linear gear. The rack engager can comprise a gripping region extending from each side of the pinon seat. Each gripping region can correspond to an end region of the saddle. Each gripping region can have a channel or groove for receiving a respective edge of the rack support. Preferably, opposing edges of the rack support are tapered and are slideably received within a respective channel or groove of the gripping portion. The opposing edges of the rack support can extend substantially parallel with the linear gear. The channels or grooves can extend substantially parallel with each other and with the linear gear. The pinion seat can extend substantially perpendicularly of the central axis.

[00132] The pinion carrier can comprise a connecting mechanism for connecting the pinion carrier to the housing or baffle guard or other fixed structure such that it cannot move when the calibration mechanism is actuated. In some embodiments the connecting mechanism is a notch, groove or hook that engages and connects with a complementary formation of the baffle guard or

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PCT/AU2018/000231 possible a sidewall of the housing or tube (e.g male to female, or female to male connection).

[00133] The dial can be of any suitable size, shape and construction, and can be made of any suitable material or materials. The dial can be operably connected to the pinion in any suitable way. The dial can have a centrally located spline drive engaging region for engaging the spline drive of the pinion. The drive engaging portion can be fastened to the spline drive by way of a mechanical fastener. The dial can be rotated by hand or using a tool.

[00134] The dial can comprise a knob portion that can be rotated by hand. If rotated by hand, the knob portion can be ribbed for easier gripping. The knob portion can have a circular edge that locates within a collar of the cap of the housing. Both the knob portion and collar can have markings corresponding to predefined calibration points for select products. Numbers, graduations or other indicia can correspond with positions of the dial as it is turned relative to the collar. In this way, the dial/knob can be rotated relative to the cap/collar so as to adjust the temperature sensor such that the probe is capable of mimicking the thermal properties of a select product.

[00135] If moved using a tool, then the dial can be shaped for engagement using the tool. The dial can be tamper-proof such that it can only be turned using a special tool.

[00136] The probe can further comprise a tamper-proof cover such that the dial cannot be turned. The tamper-proof cover can be of any suitable size, shape and construction. The cover can be connected to the cap of the housing using, for example, special mechanical fasteners.

[00137] The probe can include a mounting for mounting the probe housing to the product or to a fixture in the vicinity of the product. The mounting can be of any suitable size, shape and construction, and can be made of any suitable material or materials, e.g. plastics material. In one embodiment the mounting is in the form of a clip having a first end having opposed jaws that can receive the probe housing, and a second end that is fastenable to the product or fixture by way of adhesive, mechanical fasteners (e.g. screws) or the like. In a second embodiment, the mounting can be in the form of a spade/wedge/paddle/arm that is connected to the probe housing and an end of which can be inserted/wedged between 2 adjacent surfaces such as those of two products or fixture/s and/or product and fixture. The spade can be hingedly or pivotally connected relative to the probe housing such that the spade can be rotated through approximately 180° relative to the central longitudinal axis of the probe housing.

[00138] In an embodiment, the mounting can include a clip portion having a first end having

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PCT/AU2018/000231 opposed jaws that can receive the probe housing, and a second end that is pivotally connected to an end of the spade such that when the spade engages products/fixtures, the probe housing can be rotated through approximately 180° to the required orientation.

[00139] Any of the features described herein can be combined in any combination with any one or more of the other features described herein within the scope of the invention.

[00140] The reference to any prior art in this specification is not, and should not be taken as an acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge.

BRIEF DESCRIPTION OF FIGURES [00141] Various embodiments of the invention will be described with reference to the following Figures:

[00142] Figures of Example 1 [00143] Figure 1. Comparison of the predicted specific heat capacity (Q) between the measured and predicted (‘suggested’) according to the food composition, (a) moisture, (b) fat and (c) protein. The measured values (0) are within the symbol size at most points of the points.

[00144] Figure 2. Comparison of the predicted thermal conductivity (k) between the measured and predicted (‘suggested model’) according to the food composition, (a) moisture, (b) fat and (c) protein.

[00145] Figure 3. The impact of the uncertainty in the predicted thermal parameters on the calculated unwrapped cheese temperature at both the core and surface positions after 30 and 60 min exposure to the ambient temperature (20°C): (a) thermal conductivity, (b) density, (c) specific heat capacity, and (d) heat convection coefficient. The hair value used for the first three plots was 8W/m².K.

[00146] Figure 4. (a) Block diagram of the cheese sample shows the characteristic length (halflength Lch) and the radius (A), and (b) the temperature variation simulation at both core and surface in the cheese sample for 1 hour of 20°C of air temperature exposure using two different methods: FDM and HTT software. 7?=0m and T_c/i=0m represents the core temperature and 7?=0.042m and A₍/_;=0.0l4m constitute the surface temperature.

WO 2019/100101

PCT/AU2018/000231 [00147] Figure 5. A comparison between the simulated (cheese opened to air and the wrapped cheese and placed on the plastic board and stainless steel table) and experimental transient heat transfer at the core and the surface of the camembert cheese. The initial temperature was 2.7°C and the average ambient temperature was 20.24°C. Note the critical temperature for safe food transport is less than 7°C.

[00148] Figure 6. Thermal resistance network to model the wrapped Camembert cheese placed on two different layers (plastic and stainless steel).

[00149] Figure 7. Comparison of tested and simulated core and surface temperatures of Camembert cheeses at different grid Fourier number by varying the spatial step (Ax).

[00150] Figures of Example 2 [00151] Figure 8. Flow chart of the thermal response simulation process.

[00152] Figure 9. Applying the three thermal property parameters to find the thermal diffusivity in the HTTonedt software.

[00153] Figure 10. Air thermal properties at the film temperature T= 14.1°C.

[00154] Figure 11 (a) to (j). Simulation steps of the Camembert cheese using the HTTonedt software.

[00155] Figure 12. Simulation of the thermal response for the Camembert cheese given in the above example/figure using the 2D-FDM numerical method and for two different heat transfer coefficient formulae.

[00156] Figure 13 (a) and (b). The graphical user interface of a prototype program, to calculate temperature over time at both core and surface of the Camembert cheese: (a) The main window; and (b) The temperature response plot after entering the parameters shown in (a).

[00157] Figure 14. The Brie cheese and probe’s response, experimental and simulation results:

(a) The probes (NTC at 0.7R, 0L) are compared with the four Brie cheeses which are all suspended in an Esky™ (an insulated container) for 50 min ambient (26.6°C) exposure; (b) Simulation comparison of the probe; and (c) Simulation comparison of the Brie cheese.

[00158] Figure 15. The test setup: (a) The setting up of the probes and the cheeses inside

WO 2019/100101

PCT/AU2018/000231 the Esky™. The SSA-1 to SSA-3 probes and the Cheese-1-3 were in the first row and the SSB-1, SSB-2 and Cheese-4 were in the back; and (b) The ColdCube™ door opened and showing the side of the Esky™ exposed to the ambient during the transient test.

[00159] Figure 16. The heat transfer during the multi-transient test between the Cheese-4 and the probes SSB-1 and SSB-2, the cheese placed in-between two probes. In the first transient the door was opened for 20 min and then closed for 2.5 hours, then reopened twice for about 8 min with a 17 min door closing in between the last two openings.

[00160] Figure 17 (a) to (c). The Camembert cheese and its probe’s response, experimental and simulation results: (a) The probe (NTC at 0.65R, 0L) is compared with the two Camembert cheeses which are all suspended in the Esky™ for one hour ambient (26.6°C) exposure; (b) Simulation comparison of the probe; and (c) Simulation comparison of the Camembert cheese.

[00161] Figure 18. The heat transfer during the multi-transient test between the Camembert cheese and the probe beside it at 26.5°C ambient. The ColdCube™ door was opened for 8.83 min and for longer time (16.56min).

[00162] Figure 19 (a) to (c). The Cheddar cheese and its probe’s response, experimental and simulation results: (a) The probes (NTC at 0.5R, 0L) are compared with the two Cheddar cheeses which are all suspended in an Esky™ for one hour ambient (26.6°C) exposure; (b) Simulation comparison of the probe; and (c) Simulation comparison of the Cheddar cheese.

[00163] Figure 20. The heat transfer during the multi-transient test between the Cheddar cheese and the probe beside it at 26.5°C ambient. The ColdCube™ door was opened for short trip time 8.83 min and for longer trip (16.56min).

[00164] Figures of Example 3 [00165] Figure 21. Perspective view of a probe, according to an embodiment of the present invention.

[00166] Figure 22. Perspective view of the probe shown in Figure 21 but further showing a mounting, according to an embodiment of the present invention.

[00167] Figure 23. Exploded view of part of the probe shown in Figure 21.

[00168] Figure 24. Perspective view of part of the probe shown in Figure 21.

WO 2019/100101

PCT/AU2018/000231 [00169] Figure 25. Exploded view of part of the probe shown in Figure 21.

[00170] Figure 26. Exploded view of part of the probe shown in Figure 21.

[00171] Figure 27. Exploded view of part of the probe shown in Figure 21.

[00172] Figure 28. Perspective view of part of the probe shown in Figure 21.

[00173] Figure 29. Perspective view of part of the probe shown in Figure 21.

[00174] Figure 30. Exploded view of part of the probe shown in Figure 21.

[00175] Figure 31. Exploded view of part of the probe shown in Figure 21.

[00176] Figure 32. Perspective view of part of the probe shown in Figure 21.

[00177] Figure 33. Perspective view of part of the probe shown in Figure 21.

[00178] Figure 34. Perspective view of part of the probe shown in Figure 21.

[00179] Figure 35. Exploded view of part of the probe shown in Figure 21.

[00180] Figure 36. Exploded view of part of the probe shown in Figure 21.

[00181] Figure 37. Perspective view of the probe shown in Figure 21.

[00182] Figure 38. Perspective view of the probe shown in Figure 21 but further showing a mounting, according to an embodiment of the present invention.

[00183] Figure 39. Perspective view of the probe shown in Figure 21 but further showing a mounting, according to an embodiment of the present invention.

[00184] Figure 40. Perspective view of the probe shown in Figure 21 but further showing a mounting, according to an embodiment of the present invention.

[00185] Figure 41. Depiction of how the probe of Figure 40 can be mounted to a pallet.

[00186] Figure 42. Exploded view of the probe shown in Figure 21.

[00187] Figures of Example 4 [00188] Figure 43. Chicken breast temperature gain - Probe Software simulation against the

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PCT/AU2018/000231 actual product.

[00189] Figure 44. Greek Yogurt temperature gain - Probe Software simulation against the actual product.

[00190] Figure 45. Scotch Steak tray temperature gain - Probe Software simulation against the actual product.

[00191] Figure 46. Mince meat temperature gain - Probe Software and Software simulation against the actual product.

[00192] Figure 47. Camembert temperature gain - Probe Software simulation and Software against the actual product and the probe.

[00193] Figure 48. Camembert temperature variation - Probe against the actual product.

[00194] Figure 49. Brie Cheese temperature variation - Probe against the actual product.

[00195] Figure 50. Cheddar Cheese temperature variation - Probe against the actual product.

[00196] Figure 51. Camembert Cheese temperature variation - Probe against the actual product.

[00197] Figure 52. Cheddar Cheese temperature variation - Probe against the actual product.

[00198] Figure 53. Milk temperature variation - Probe against the actual product.

[00199] Figure 54. Frozen burger temperature variation - Probe against the actual product.

[00200] Figure 55. Operation and feature flowchart.

[00201] Figures of Example 5 [00202] Figure 56. Flowchart showing steps involved for pre-calibration of the (SUPERSENSE™) probe of Example 3 so as to set a calibration point for simulating a specific product, such as a food product. The flowchart also shows steps for determining the thermal response of a product using the simulation software.

DESCRIPTION OF EMBODIMENTS

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PCT/AU2018/000231 [00203] Preferred features, embodiments and variations of the invention may be discerned from this section, which provides sufficient information for those skilled in the art to perform the invention. This section is not to be regarded as limiting the scope of any preceding section in any way.

[00204] Example 1 - Experimental Validation of New Empirical Models of the Thermal Properties of Food Products for Safe Shipping [00205] SUMMARY [00206] In this Example an improved empirical model for the thermal conductivity and specific heat capacity of a wide range of food products was derived based on the food composition (moisture, fat, protein, carbohydrate and ash). The models developed using linear regression analysis were compared with the published measured parameters in addition to previously published theoretical and empirical models. It was found that the maximum variation in the predicated thermal properties leads to less than 0.3°C temperature change. The correlation coefficient for these models was 0.96. The t-Stat test (P-value > 0.99) demonstrated that the model results are an improvement on previous works. The transient heat transfer based on the food composition and the temperature boundary conditions was found for a Camembert cheese (short cylindrical shape) using a multiple dimension finite difference method code. The result was verified using the heat transfer today (HTT) educational software which is based on finite volume method. The core temperature rises from the initial temperature (2.7°C) to the maximum safe temperature in ambient air (20.24°C) was predicted to within about 35.4±0.5 minutes. The simulation results agree very well (+0.2°C) with the measured temperature data. This improved model impacts on temperature estimation during loading and unloading the trucks and provides a clear direction for temperature control in all refrigerated transport applications.

[00207] INTRODUCTION [00208] Food waste is a global problem; the food waste from the “field to the fork” is estimated to be 50% [1], In the USA alone this equates to $165 billion per a year loss when the food is not deemed safe for humans [2], [00209] The thermal properties of food are required to evaluate the heat transferred from the surroundings to the food during the transient stages of shipping (loading and unloading). From this the time required to reach the critical temperature before the food becomes inappropriate for

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PCT/AU2018/000231 consumption and hence reduce the food waste percentage can be estimated.

[00210] The measured thermal properties (thermal conductivity, specific heat capacity, thermal diffusivity and density) data for many food groups can be found in many published documents [3-

5], In addition, many researchers present the thermal conductivity and specific heat capacity mathematically based on food composition (moisture, protein, fat, carbohydrate, ash and ice), thermal properties, mass and the volume [6, 7], Other researchers built their empirical models for thermal conductivity and specific heat capacity based on the food material compositions to predict the thermal characteristics [3, 8], There are few density empirical models, however this can be found mathematically as a function of the mass fraction and the density of each food component [7], Different empirical thermal properties models were suggested by Marschount [9] although they were applied and tested for cheddar cheese only. The thermal diffusivity (a) of the food is more difficult to find as there is no significant relationship between it and the food compositions [9], However, it is a function of the above three thermophysical parameters and can be found from the relationship [3]:

k [00211] a=— (1) [00212] where k is the thermal conductivity, p is the density and C_p is the specific heat capacity.

[00213] The prediction of the heat exchange and heat transfer of the food is very important; it helps to find the time temperature variation of the food at the transient temperature stages. A cooled container gains heat from the entered ambient air during the loading and unloading process when the doors at the loading dock are opened [10], The amount of the transferred heat to the foods depends on the ambient temperature, the convection heat coefficient (/?) and the thermal parameters of the food which are based on the food composition. Different numerical methods were used to find the behaviour of the food under specific boundary conditions as the analytical models have very limited capabilities. The most common methods are the finite difference methods (FDM), the finite element method (FEM) and finite volume method (FVM) which can be used to estimate the thermal transfer for regular and irregular food shapes and for more complicated boundary conditions [11, 12], [00214] The goals of this work were (1) to find an empirical model for thermal conductivity and specific heat capacity that can be applied for wide range of products and compare them with

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PCT/AU2018/000231 published results; (2) to find a calculation strategy which accurately predicts the required maximum time to load/unload the road vehicle before the critical temperature is reached by applying the estimated thermal properties in the FDM program to predict the heat transfer to the food; (3) to validate the simulated results with experiments for a camembert soft cheese product.

[00215]	1. TRANSIENT HEAT TRANSFER OF THE FOOD
[00216]	The transient heat conduction is a second order partial differential diffusion equation

(Heat equation) which is solved to provide the temperature variation with the time and spatial coordinates [13]:

[00217]	d2T _ i_ar dx2 a dt
[00218]	where T is the temperature and x is the distance. This equation can be used for any

geometries and can be extended to deal with two and three dimensions. However, ID and 2D analysis are usually sufficient to describe the heat transfer when one or two dimensions of the product are comparatively large. Equation (2) can be solved for the temperature distribution in the medium with defined temperatures at the boundaries (two temperature conditions for each coordinate direction).

[00219]

The transient conduction for the two multiple dimensional (cylindrical geometry) was

found for the Camembert cheese using a one-dimensional explicit FDM solution. The heat transfer for a solid cylinder can be found from the product of the solution for infinite plane wall and cylinder.

[00220]

When the door of a refrigerated trailer is opened, the heat is transferred to the food

inside the trailer via the natural/free convection of the air currents. The natural convection heat transfer coefficient relies mainly on the ambient air properties and can be determined from:

[00221]

7 Nuk zox h=~— (3) Lch

[00222] where L_ch (m) is the characteristic dimension, k (W/m.K) is the thermal conductivity of the medium and the Nu is the Nusselet number and it is function of Grashof number (Gr) and Prandtl number (Pr), the Nusselet number relation depends on many parameters such as the convection heat type (free natural or forced), the food dimension and shape (plate, cylinder, sphere, mushroomed etc.) and the flow direction to the sample [3],

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PCT/AU2018/000231 [00223] The Nusselet equation for a free natural convection flow over a vertical cylinder is [16]:

[00224]

7Gr Pr²

Nu = - _f .

[5(20 + 21 Pr)J

-.1/4

4(272 + 315 Pr)L

35(64+63 Pr)D (4) [00225] where L is the height and D is the diameter of the cylinder. However, when the ratio D/L > 35xGr'°·²’ then the Nusselet equation for a vertical flat surface can be used for the vertical cylinder case [4]:

[00226]

Nu — 0.68 +

0.67 Ra¹/⁴ [1 + (0.492/Pr)⁹/¹⁶]⁴^⁹ (5) [00227]

Nu =

0.825 +

0.387 Ra¹/⁶ [l + (0.492/Pr)⁹/¹⁶]^8/27 (6) [00228] where Ra is the Rayleigh number, it is the product of Gr and Pr numbers. Equation (5) can be applied when

10^_1<7?cz<10⁹ and Eq. (6) when 10⁹<Arz<10¹².

[00229] 2. THERMAL PROPERTIES OF THE FOOD [00230] The experimental data of both thermal conductivity and specific heat capacity properties for different food groups (dairy products, fresh meat and chicken, and seafood) from the literature were compared to a theoretical model, a published empirical model and a suggested model to determine the best approximate value.

[00231] 2.1 Thermal conductivity [00232] The analytical model by Kopelman [6] was used where the predicted values depend mainly on the food structure. The formulation was different for homogeneous, isotropic and layered systems. Different theoretical models (series, parallel, Maxwell-Eucken and Kopelman isotropic) were tested. The best theoretical model was the parallel Kopelman model which is the same for Anisotropic and layered systems. The parallel thermal conductivity (k||) of meat, fat layers on flesh and fibrous vegetables can be anticipated by this model [3, 5,14, 15]:

[00233] k\\=k_L [1-P(1k_L).

(7)

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PCT/AU2018/000231 [00234] where P is the volume fraction of the solid/discontinuous phase and Az and k_s are the thermal conductivities of liquid and solid phasers, respectively.

[00235] The same set of data was tested using the empirical model developed by Sweat [3]:

[00236] k = 0.58A_w + 0.16^ + 0.155A_pr + 0.25A_c + 0.135A_as/l (8) [00237] where X_w, X_pr, Xy Xash and X_c are the mass fraction of water, protein, fat, ash and carbohydrate, respectively.

[00238] A linear regression method was used with multi-dimensional statistical analysis [16] and the empirical model that predicts the thermal conductivity based on the food composition was:

k = 0.6527X_w + 0.209X_z + 0.0988A_pr + 0.7145X_c + 1.6806X_ash - 0.0582 (9) [00239] 2.2 Specific heat capacity [00240] The specific heat capacity in kJ/kg.K was predicted empirically using the multidimensional linear regression:

C_p = 6.099X_w + 3.97X_f + 4.22X_pr + 3.5X_C + 1.645%^ - 1.927 (10) [00241] The Choi and Okos [7] theoretical model was applied at the same temperature settings of the experimental measurement (20°C):

[00242] where the C_Pi and Xi are the specific heat and mass fraction of z^lh component, respectively and n is the number of the food components.

[00243] The empirical model by Heldman and Singh [8] was also used for specific heat capacity predictions in (J/kg.K) for food according to five different compositions at 20“C:

C_p = 4187X_W + 1549X_pr + 1675X_Z + 837X_ash+1424X_c (12) [00244] These statistical analyses were done to compare the predicted and the published models with the experimental data and to validate the suggested model. The statistical parameters used to determine the best models were: the correlation coefficient, the standard error of estimation (SEE) which measures the amount of error in the prediction of the model with respect

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PCT/AU2018/000231 to the measured values, the coefficient of determination (R²), the predictability (the mean of the difference between the experimental values and the modelled ones divide by the measured data) and the T-stat was calculated for each model as shown in Table 1.

[00245] Table 1 - Statistical comparison among three models to predicate the thermal conductivity and specific heat capacity with respect to the experimental data from literature.

Standard	Predictability
Thermal properties	Model	error of estimation (SEE)	t-Stat	Coefficient of determination (A)	Correlation coefficient (r2)
	Kopelman at 20°C	0.0364	0.2783	0.806	0.898	-0.058
Thermal
conductivity	Sweat	0.033	0.2395	0.78	0.883	0.035
Suggested	0.0182	0.9987	0.931	0.965	0.008
	Choi and Okos at	0.110	0.5559	0.93	0.964	0.006
Specific heat	20°C
capacity	Heldman	0.126	0.0416	0.927	0.963	0.043
	Suggested	0.108	0.999	0.932	0.966	-0.005

[00246] It is clear that the suggested empirical method shows the best performance among the other methods; both predicted models have a higher t-stat value of more than 0.99 which means the predicted results are very close to the measured ones and the absolute predictability is less than 0.01. These models can be used to predict successfully the thermal conductivity and the specific heat capacity for the food products with moisture (5-95%), protein (1-40%), fat (0.01-83%) and carbohydrate (0-54%).

[00247] Figures 1 and 2 show the comparison of the specific heat capacity and the thermal conductivity for all of these methods with respect to the major food composition contributions (water, protein and fat). It is clear that the k and C_p values increase with the moisture mass fraction and decrease with the fat mass fraction.

[00248] 2.3 Density [00249] There are several density prediction models for each food material [3], However, the Choi and Okos [7] is the most common model and can be applied for all food products and it is based on the food composition:

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PCT/AU2018/000231 (13) [00250] [00251] [00252] where p; and X.arc the density and mass fraction of i^th food component respectively.

RESULTS AND DISCUSSIONS

4.1 Camembert cheeses simulated parameters and results

The thermal conductivity and specific heat capacity of Camembert cheese sample [00253] were estimated using the suggested empirical models Eq. (9) and (10) while the density was calculated using Eq. (13) and the thermal diffusivity was calculated using Eq. (1). The mass composition fractions of the cheese provided by the manufacturer are shown in Table 2.

[00254] Table 2 - The thermal properties of a Camembert wrapped cheese with cylindrical shape (weight 125g, radius 0.042m and 0.028m height).

Moisture%	Protein%	Fat%	Carbohydrate%	Fibre% Ash%	Specific Heat (J/kg.K)	Thermal conductivity (W/m.K)	Density (kg/m3)
0.515	0.188	0.265	0.023	- 0.009	3155	0.384	1031.3

[00255] The change of the thermal properties on the sample core and surface temperatures was found at two different times of exposure using FDM numerical modelling. The simulation inputs assumed a two dimensional finite cylinder. The parameters used in the simulation were the same in Table 2 and the hair value was 7.3 W/m².K (//=7.3 and 7.33 W/m².K using Eq.(4) and (5), respectively). The initial temperature was assumed to be 2.7°C and the ambient temperature was 20°C. The simulation was run for 30 and 60 minutes following a step increase in ambient temperature. It was found that the maximum deviation of the estimated thermal conductivity value for many different kind of food using the suggested model was less than 0.03 W/m.K which causes less than 0.14°C and 0.1 °C temperature variations at the core and surface, respectively after 60 minutes.

[00256] The temperature at the core increases with the thermal conductivity parameter while the temperature at the surface was inversely proportional to the k values (see Figure 3a). The variation in the estimated thermal conductivity of the cheese sample by 0.1 W/m.K leads to give different reading temperature. The variation in the temperature was the same at core and surface (about 0.5°C) at both exposure times (30 and 60 min). The temperature of the sample under test

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PCT/AU2018/000231 decreases with the density parameter. The temperature variation depends on the difference between the measured and the predicted density. If the difference between the measured density and that predicted was 200 kg/m³, then the temperature variation was about 1°C at both the core and surface.

[00257] Eq. (13) was used to find the density of many food groups. If the food components are known then the difference between the measured and modelled density was about 5-100 kg/m³ (60 Kg/m³ estimated density difference results in less than 0.3°C temperature variation after one hour of exposure to 20°C ambient) and 5.8% average absolute percentage error (percentage error=100x (modelled-measured)/measured). If only two components are known (moisture and fat), the difference became higher up to 500 kg/m³ (27.7% average absolute percentage error) which leads to large temperature variations (3.27°C in the core and 2.46°C at the surface in 60 minutes) and results in a high prediction error.

[00258] The temperature decreased with the increasing of C_p. The maximum deviation from the measured values for more than 99% of data set was less than 200J/kg.K which results in less than 0.3 °C temperature variation at both core and surface of the sample after 30 and 60 minutes to exposure to the ambient temperature (see Figure 3c).

[00259] The natural convection coefficient from the surrounding air has the highest impact on the amount of the heat transferred to the food. A change in the ambient temperature and the air speed both directly affect the temperature (see Figure 3d).

[00260] The simulation was done for a 2D finite cylinder structure to represent the cheese sample. The temperature was mapped at two positions along the sample area: at the core (midplane) and on the surface (see Figure 4). The temperature increased rapidly in the first 30 min of the exposure (from 2.7°C to 6.7 and 11.39°C at core and surface, respectively) while in the next 30min the temperature increase of the surface was slower than the core (by about 3°C for the surface and 4.4°C for the core). The simulation was repeated using the HTT software to certify the FDM results. The FDM simulation was very close to the HTT result with approximately 0.05°C tolerance at the core.

[00261] 4.2 Simulation and Experiment results [00262] Temperature measurements were undertaken in confidence at the ΝΑΤΑ accredited facility at SuperCool Pty Ltd, Ormeau, South East Queensland. The facility is capable of logging

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PCT/AU2018/000231 up to 99 temperature sensors over periods of more than 24 hours with an accuracy of better than 0.01°C. A cylindrical shaped camembert cheese was used as an example to compare calculated results with the experimental results. The transient heat transfer of a 125g camembert cheese (code No. 6169580P) of 4.2cm radius and 2.8cm height was tested to mimic the temperature transfer and distribution scenario at the loading/unloading process in refrigerated transport.

[00263] The initial temperature of the cheese was 2.7°C and the temperature of the air was 20.24°C. The camembert cheese was wrapped (0.25mm thickness waxed paper and 20pm polypropylene pasted to the wax paper), placed on 1.5cm thickness plastic board which is set on a 3mm stainless steel table and exposed to the ambient temperature. K-type wire thermocouples (Ni/Cr-Ni/Al) were used to measure the temperature on the surface and the core of cheese and the ambient temperatures with the time. The thermocouples were connected to a data acquisition system (5690, AHLBORN) to record the temperatures every 3sec. The simulation was done for 2D finite cylinder with thermal network scheme. The heat conductivities of wrapping layers as well as the plastic board and the stainless steel table were included in the simulation for the cheese and compared with the experimental results (see Figure 5). The overall heat transfer coefficient (U) for the thermal network in Figure 6 was found from the lumped resistor model [18]:

_ 1 1

1/ hsE h_COnd h-SE kc kjj h — _ _|__L ^,Lcond j ' j a_p (14) (15) (16) [00264] where h_a is the air heat convection, hsF. is the equivalent heat convection coefficient results from three series thermal resistances (Rconv, Rcond.wrapi and Rcond.wra_P2) and hcond is the equivalent heat convection coefficient produced by the two parallel thermal resistances (Rcond.piastic and Rcond.steel)· The dw, d_Pp, d_p and d_s are the thickness of the waxed paper, polypropylene wrapping film, plastic board and stainless steel table, respectively. Kw, k_pp, k_p and ks are the thermal conductivities of the waxed paper (0.25W/m.K), PP film (0.22W/m.K), plastic (k_p=0.22W/m.K) and stainless steel (As=16W/m.K), respectively.

[00265] Simulation of the Camembert cheese was undertaken using in-house the finite difference code based on equation (2). In order to match the experimental conditions the core temperature of the cheese and the air temperature were fixed (Dirichlet boundary conditions) and

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PCT/AU2018/000231 the only air currents were convection currents.

[00266] Figure 5 shows that the simulated temperature response at the geometric centre of the cheese core of simulation with the thermal network was very close to the measured value at the first 40min of the simulation. The required times to reach the maximum safe shipping core temperature (6.9°C) were 2122 and 2090 second for the experimental and simulation with the thermal network (wrapped cheese), respectively. However, in 2D simulation when the cheese was opened to the air (i.e. without the packaging) the time was less by 5min as the wrapping reduces the transferred heat. The slopes for both experimental and thermal network simulation were very close 0.0021 and 0.0023 min/°C, respectively.

[00267] The surface temperature of the experimental result was higher than the simulated one at the beginning then the difference reduced gradually to be closer to the simulation result.

[00268] To validate the numerical simulation, the grid Fourier number was varied by changing the spatial step (zlx) between 0.25mm to 2.5mm. This results in a change in the time step of 0.78sec to 27 sec.

[00269] The root mean square error (RMSE) was calculated for each spatial step change for all data numbers (ri) to compare between the experimental (T_exp) and simulated temperatures (L™):

RMSE = Z?₌₁(T_exp - T_simf In (17) [00270] For zlx=2.5mm, the RMSE wasO.651°C at the core temperature calculation and z!T=0.66^oC at the maximum safe transportation temperature limit (7°C) (see Figure 7).

[00271] The American Cheese Society [19] recommended cheese storage temperature in the range 1.6-7.2°C and for camembert cheeses. The bacteria starts growing at 10°C [20], From the simulated and experimental results, it is evident that the maximum exposure time to the ambient temperature should be less than 35min to maintain the cheese core temperature below 7°C at the cheese core. The slopes for the experimental result and the simulation based on the thermal network modelling were very close: 0.0021 and 0.0023 sec/°C, respectively.

[00272] 5. CONCLUSIONS [00273] Empirical models were developed to predict the thermal conductivity and the specific heat capacity of the different food groups. These models show high prediction accuracy, and when

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PCT/AU2018/000231 compared to the published theoretical and empirical models, the standard error of estimation was 0.02 and 0.1 for the thermal conductivity and specific heat capacity models, respectively. The tStat parameter was very high for both suggested models (P-value > 0.99). These models were used to predict the thermal properties of the Camembert cheese. The FDM was used to simulate the heat transfer at the transient stage. 2D modelling was used to investigate the impact of the thermal properties and heat convection changes on the core and the surface of the cheese sample. It was found that no significant temperature variation (about 0.5 °C) occurred when the thermal conductivity, specific heat capacity and density varied by 0.1 W/m.K, 200 J/kg.K and 100 kg/m³, respectively. The major effect on the heat transfer resulted from changes in the heat convection coefficient.

[00274] The simulation results were compared with the HTT simulation and validated by experimental measurements to determine the temperature variations at the core and the surface of the cheese during the step change in ambient temperature. The scenario in the experiment corresponded well to the simulations after taking into consideration the impact of the cheese wrapping materials and the resting base of the cheese.

[00275] This simple analysis method provides transportation companies with all required thermal properties. The formulation accurately predicts the time that the food takes to reach a specific temperature as well as the maximum allowed time for loading/unloading process. While the technique has been applied to dairy products, it can be modified to suit all food products shipped at controlled temperatures. The model provides a very good match of the temperature profile with the experimental results. The next step in this work is producing a user friendly interface window to examine the products’ response by entering the food compositions, initial and ambient temperatures and the time. The air humidity impact on the response will be taken into account in the future works.

[00276]	Nomenclature
Cp	specific heat capacity, J Kg'1 K'1
d	thickness, m
D	diameter of the cylinder, m
Gr	Grashof number (dimensionless)
h	convection heat transfer coefficient, W m“2 K“1
k	thermal conductivity, W m'EK.'1

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k||	parallel thermal conductivity, W m^.K’1
L	length, m
Nu	Nusselet number (dimensionless)
Pr	Prandtl number (dimensionless)
R	thermal resistance, KW1
r2	correlation coefficient
R2	coefficient of determination
Ra	Rayleigh number (dimensionless)
t	time, sec
T	Temperature, K
u	overall heat transfer coefficient, W m—1
X	spatial distance, m
X	mass fraction of the component

Greek symbols

a	ambient
a	thermal diffusivity, m2.s_1
β	coefficient of thermal expansion, k’1
P	density, Kg m-3

Subscripts

a	ambient
ash	ash
c	carbohydrate
ch	characteristic length
conv	convection

cond,wrapi conduction for warp 1 cond,wrap2 conduction for warp2 cond,plastic conduction for plastic cond,steel conduction for stainless steel

f	fat
i	ith component

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1	liquid
P	plastic
PP	polypropylene film
pr	protein
s	solid
S	stainless steel
SE	series components
w	moisture
W	waxed paper

[00277] Example 2 - Experimental and Theoretical Validation for a Thermal Probe Nomenclature [00278] Summary [00279] 1. Thermal properties and thermal response of camembert cheese [00280] In this Example, empirical models for thermal conductivity and specific heat capacity that can be applied for wide range of products were found. To find the thermal response for any product (for example, the required maximum time to load/unload the truck before the critical temperature is reached), it is essential to know the thermal properties of that product. However, measuring the thermal conductivity (k\ specific heat capacity (C_p), thermal diffusivity (a) and the density (/?) needs special instruments and can be costly and time consuming. Therefore, thermal property models were estimated empirically and compared to the published empirical and theoretical models. The statistical analyses were done to compare the predicted and the published models with the experimental data and to validate the suggested model. The suggested empirical models for thermal conductivity and specific heat capacity show the best performance among the other methods; both predicted models have a higher t-stat value of more than 0.99 which means the predicted results are very close to the measured ones and the absolute predictability is less than 0.01. These models can be used to predict successfully the thermal conductivity and the specific heat capacity for the food products with moisture (5-95%), protein (1-40%), fat (0.0183%) and carbohydrate (0-54%). Then, the estimated thermal properties were entered in a software model (HTTonedt) to predict the heat transfer to the food.

[00281] The procedure to determine the thermal response of the Camembert cheese is

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PCT/AU2018/000231 summarized in the Figure 8 flowchart.

[00282] Step 1: Finding the thermal properties [00283] A. Thermal conductivity:

[00284] A linear regression method was used with multi-dimensional statistical analysis (Thiel, 2014 [16]) and the empirical model that predicts the thermal conductivity based on the food composition was:

k = 0.6527X_w + 0.209X_f + 0.0988X_pr + 0.7145X_c + 1.6806X_a - 0.0582 (1) [00285] where X_w, Xpr, Xf, Xa and X_c are the mass fraction of water, protein, fat, ash and carbohydrate, respectively. These parameters can be found from the nutrition table on the cheese pack, and the fraction of each composition can be found as a percentage out of lOOg (see Table 1). [00286] Table 1 -The thermal properties of a Camembert wrapped cheese with cylindrical shape (weight 125g, radius 0.042m and 0.028m height).

Moisture%	Protein%	Fat%	Carbohydrate% Fibre%	Ash%	Specific Heat (J/kg.K)	Thermal
conductivity (W/m.K)	Density (kg/m3)
0.515	0.188	0.265	0.023 -	0.009	3155	0.384	1031.3

[00287] B. Specific heat capacity:

[00288] The specific heat capacity in kJ/kg.K was predicted empirically using the multidimensional linear regression:

C_p = 6.099X_w + 3.97X_f + 4.22X_pr + 3.5X_C + 1.645X_a - 1.927 (2) [00289] C. Density [00290] There are several density prediction models for each food material (Rahman, 2009 [3]). However, the Choi and Okos (1986) [7] is the most common model and can be applied for all food products and it is based on the food composition:

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PCT/AU2018/000231 ^P ~ Σ^Χί/Pi ⁽³⁾ [00291] where p, and X,. are the density and mass fraction of ith food component.

[00292] The thermal diffusivity («) can be found from the software (HTTonedt) after entering the k, C_p and p values and then pressing the thermal diffusivity button (see Figure 9).

[00293] As the software deals only with one dimensional simulation, the simulation of the cheese is done twice: once regarding the height (choose plane wall) and the second for the radial heat transfer (choose the cylinder).

[00294] The characteristic length button on the top right side of Figure 9 (green line colour) represents the half height (for plane wall thermal transfer) or the radius of the cheese (for cylinder heat transfer).

[00295] Step 2: Finding the heat convection coefficient (h) [00296] The natural heat convection coefficient relies mainly on the ambient air properties and can be determined from:

Nu k h = (4) [00297] where the Nu is the Nusselt number and it is function of Grashof number (Gr) and Prandtl number (Pr), k is the thermal conductivity of the air at T (difference between the ambient and the initial temperatures), and L_c is the characterised length (equal to the cheese’s height).

[00298] The Nusselt number relation depends on many parameters such as the convection heat type (free natural or forced), the food dimension and shape (plate, cylinder, sphere, mushroomed etc.) and the flow direction to the sample (Rahman, 2009 [3]).

[00299] The Nusselt equation for a free natural convention flow over a vertical cylinder is (Bejan and Kraus, 2003 [14]):

7GrPr² Γ^/4

5(20 + 21 Pr)

4(272 + 315 Pr)L

35(64 + 63 Pr^D (5)

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PCT/AU2018/000231 [00300] where the Pr is the Prandtl number of the air at the temperature T. L and D parameters are the height and diameter of the cheese in meter, respectively. The Gr is:

gB (Ta - Ti)L² Gr =

P² (6) , i

Where <? is aavitv (9.81 m s >. B=---------:------—— is the coefficient of thermal ·- - ' 273.15 + (77+70)/2 expansion in Kelvin and Ta and Ti are the ambient and initial temperatures respectively. The υ is the Kinetic viscosity of the air at the T (rhe air properties can be found from (Fluid properties calculator). However, there are more Nusselt number relations for the cylindrical objects that can be used.

[00301] However, there are further Nusselt numbers that can be used for the vertical cylinder shapes:

( 0.387 X (GrPr)^1/6

Nu - j0.825 + _{+ (0i49}2/p_r)9/16]8/27 (7) [00302] Steps 3-8 are shown in the example below.

[00303] 2. Camembert cheese example > To find the /rvalue, apply the parameters in Table 1 into Eq. (1) k(W/m.K) = 0.6527(0.515) + 0.209(0.265) + 0.0988(0.188) +

0.7145(0.023) + 1.6806(0.009) - 0.0582 = 0.3835 > To find the C_P value, apply the parameters m Table 1 into Eq. (2)

C_p(kj/kg.K) = 6.099(0.515) + 3.97(0.265) + 4.22(0.188) +

3.5(0.023) + 1.645(0.009) - 1.927 = 3.1547 [00304] The resultant value from above relation is in kJ/kg.K so it should be multiplied by

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PCT/AU2018/000231

1000 to convert it to J/kg.K as the one shown in Table 3.

The density property can be found from Eq. (,3). the pi for the pure moisture (water), protein, fat. carbohydrate and ash are:

p_w = 997.18 + 3.1439 X 10^_37 - 3.7574 X 10^_37² = 993.8927 p_c = 15599.1 - 0.310467 = 1589.8 /ty = 925.59 — 0.417577 = 913.0629 p_pr = 1330 - 0.51847 = 1314.4 p_a = 2423.8 - 0.280637 = 2415.4

The composition density was found (for example) at 30 ^:C and equivalent density for the product is 1031.3(kg,nf) and can be found from Eq. (3) as shown below:

0.515

0.023 ⁹ Σί₁Χ</ρ< ^V \993.8927/ '^{Wi5t<?r +} (1589.8

0.265 \ ( 0.188 \ .913.0629/ \^{fat +} (1314.4/ ^^rotein

0.009 ,

2415.4/ z' The h value for a Camembert cheese has 3.2⁼C initial temperature and for 25C ambient the T will be: (25+3.2) 2=14.1 ^:C and rhe air properties at T is shown in Figure (3 ).

[00305] The Camembert cheese (125g) has outer dimensions (0.081m diameter and 0.028m height) and inner dimensions (0.08m diameter and 0.027m height). So, the Nu will be calculated using Eq. 5, although for the short cylinder the Gr (Eq. 6) and h (Eq. 4) will be found in terms of diameter rather than the height:

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PCT/AU2018/000231

Gr =

9.81 * 0.0035 * (25 - 3.2) * 0.081³ (1.45 * 10-⁵)² = 1.875 * 10⁶

Nu=1 [7 * 1.875 * 10⁶ * 0.71²!⁴

5(20 + 21 * 0.71)

4(272 + 315 * 0.71) * 0.028 ——------------------= 18.79

35(64+ 63 * 0.71) * 0.081

The h parameter is:

18.79 * 0.02573

0.081

5.86 W/m²K [00306] However, all dairy products are packaged, the packaging reduces the heat transfer slightly, and should be considered in the h value and when finding the overall heat transfer coefficient U.

1 first layer thickness

U h3 first layer conductivity

Second layer thickness

Second layer conductivity

0.25 X 10~³ 40 X 10“⁶

5.86⁺ (+25 ⁺ 0+6 = 5.82 W/m²K [00307] There is another simpler formula that can be used to find the h value of the

Camembert and Brie cheeses, that provides similar results (see Figure 12):

h = 1.42 X (/?/D)⁰·²⁵ = 5.71 W/m²K (8) [00308] To enter all the calculated parameters, first open the software, press the dimensional input (see Figure Ila) and the input dimensional value appears. Choose the geometry and surface. In the case of the Camembert cheese, the simulation needs to be done twice (to get the equivalent 2D heat transfer), to get more accurate results that are closer to reality. Choose the plane wall and insert the characteristic length (half value of the height in the software) as shown in Figure 11b.

[00309] Under the material properties, insert all the calculated thermal properties (k, C_p and p)

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PCT/AU2018/000231 then press the thermal diffusivity button to find the a (see Figure 11c). Then, enter the h value and press the Biot Number button to find its value (see Figure lid).

[00310] The input parameter, Biot (Bi) and Fourier (Fo) numbers depend on the thermal properties (predicted theoretically or empirically) of the food sample, the dimensions of the sample (thickness for slab geometry and the radius for cylindrical and spherical shapes), the natural heat convention coefficient, h, and the time t (Rahman,2009 [3]):

at ^Fo=f (9) [00311] The Biot number is:

Bl =

h.L_c (10) [00312] Under the stopping criterion, there are two ways for the simulation to be ended:either when it reaches the specified time of exposition (chosen by the user), or the target temperature of the product. Usually, we choose the specified elapsed time and insert the total time of simulation in seconds, and press the Fourier Number to calculate it (see Figure lie). 3600 seconds was chosen as a simulation time so the heat transfer will be simulated and scanned through the height of the cheese for one hour, and the temperature can be found at any time and for different positions along the cheese height.

[00313] Finally, the initial cheese and ambient temperatures in degrees are entered and the Update/Return button is pressed as illustrated in Figure Ilf.

[00314] After that, the software takes you to the main window. Pressing the start button starts the simulation (see Figure 1 Ig).

[00315] At the bottom left side of the window, the computed data is displayed and represented by the Theta, relating to the distance (see the green rectangular in Figure 1 Ig).

Tf-T_a

The Theta equals-------, the 77 is the tarset temperature that is determined. Bv scrolline the i — i_a bar the Theta can be found at different positions (see Figure llh).

[00316] In Figure 1 Ig, the Theta was found at the core of the cheese (D=0), while in Figure llh the Theta was found at the mid-way point between the core and the surface (D=0.5). The Theta can be found at the cheese surface after scrolling the bar to D=l.

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PCT/AU2018/000231 [00317] The same scenario is repeated for the cylindrical dimension, except you will need to swap from the plane wall to cylinder and insert the radius of the cheese as a characteristic length in the geometry and surface section, and update both Biot and Fourier numbers by simply entering them in the input dimensional window (see Figure Hi) and pressing the Update/Return button at the bottom of the window. This will return you to the main window. To run the simulation the start button is pressed (see Figure 1 Ij).

[00318] To find the temperature at any distance, first the Theta value of both dimensions should be multiplied to get the right temperature for a 2D object. To find the temperature at the cheese core:

_ Tf-Tg . η n _ ^Tf~²³ .

^r Tp-Tg ^{1 2} 3.2-25

0.6674 x 0.8635 = IlEL _t γ = 12.44°C

3.2-25 / [00319] However, a program was written to replace the software and run all the calculations and simulation in one program package. The finite difference method was used to find the Transient heat transfer of the product (see Figure 12).

[00320] 3. Thermal properties and thermal response of the probe (known as ‘SUPERSENSE’) [00321] The same process for the cheese will be followed for te probe simulation, although extra parameters will be added in consideration of the vial material as well as the heat transfer coefficient. The combination of the radiation heat (from the vial surface), the external heat convection (due to air fluid) and the internal convection (due to thermal liquid inside the vial, in this instance being Glycerol) will be considered. Furthermore, the vial thickness and its material thermal conductivity will be taken into account when the overall heat convection is calculated.

[00322] The following parameters must be known before starting the U value calculations.

[00323] 1. The vial properties: the dimension (length, thickness and inner and outer diameters) and the material properties (thermal conductivity and emissivity). Here we are using white PVC plastic which has 0.28 W/m.K thermal conductivity and 0.93 emissivity.

[00324] 2. The thermal properties of the external fluid (the outside fluid that surrounds the probe, which is air). The air properties should be found at the average temperature as explained before (see Figure 10).

[00325] 3. The thermal properties of the internal fluid (the thermal liquid inside the PVC vial, which is Glycerol in this case). This can be found using relevant thermal equations (Yaws,

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1998 [22]).

[00326] The overall heat convection which will be used as the convection coefficient in HTTonedt is:

1 PVC vial thickness

U hi. ho + hr PVC thermal conductivity [00327] where h, and h₀ are the internal and external heat coefficients and the h_r is the radiation heat coefficient.

[00328] If a sponge was placed inside the vial then the /?, parameter will be half of the one without the sponge.

[00329] The h_o can be found using equations 4-6 and entering the vial dimensions instead of the cheese ones. The air properties will be the same.

The hi can be calculated from the following Nusselt number and applied in Eq. 4:

Nu = 0.55//°²⁵ (11) where R_a is found for the thermal properties of the Glycerol from:

gp(Ta - Ti)L³ (12)

The radiation hear transfer coefficient h_r is:

h_r = ε_οσ(Τα -Ti)(Ta² -Ti²) (13) where σ is the Stefan-Boltzman constant (5.67 IO’⁵) and ε₀ is rhe emissivity for the probe surface material, the temperatures will be in Kelvin.

[00330] The HTTonedt windows parameters will be for Glycerol thermal properties and the vial internal dimensions. The characteristic length parameter will be the inner probe radius and half internal length for cylindrical and plane wall geometries, respectively.

[00331] However, for the coming software package the HTTonedt we will be using as a numerical simulation program code will be embedded in the software and appearing as a friendly user interface window (see Figure 13). The cheese name/barcode, initial product temperature, ambient temperature and the time of exposure are the only required parameters to get the temperature of the product at the centre and surface at any time.

[00332] 4. Probe for the three cheeses: experimental and simulation results [00333] A probe that mimics the product thermal response is determined for each cheese type (Camembert, Brie and Cheddar). The multi-transient test (oscillation test) and 1 hour

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PCT/AU2018/000231 transient are done to examine the probe’s performance and compare it with the product that predicts its temperature behaviour, as demonstrated below:

[00334] a. Brie Cheese:

[00335] The probe with the same described properties (PVC vial has 53.5mm inner diameter and 155-160mm long, filled with Glycerol and foam/sponge) has anNTC sensor to find the inside temperature accurately. The position of the NTC is at a point 0.7R, 0L (radially: 18.725mm from the centre and vertically: at the mid length of the vial) which gives the same product thermal response, as found in advance using the numerical analysis as shown in Figure

14. The NTC is fixed in its position using a spider made from PMMA. All of the products were suspending in an insulated Esky™ container (see Figure 15). The multi-transient test was also carried out to predict the temperature for short trailer trips when the door is opened during product delivery, when loaded and unloaded (see Figure 15b and 16).

[00336] b. Camembert Cheese:

[00337] A probe with the same properties as described above has an NTC sensor to find the inside temperature precisely. The position of the NTC is at a point 0.65R, 0L (radially: 17.387mm from the centre and vertically: at the mid length of the vial) which gives the same product thermal response, as found in advance using the numerical analysis as demonstrated in Figure 17. The NTC is fixed in its position using a plastic spider. All of the products were suspended in the Esky™ container (see Figure 15). The multi-transient test was also done to predict the temperature for short trailer trips when the door is opened during the product delivery, when loaded and unloaded (see Figure 15b and 18).

[00338] c. Cheddar Cheese:

[00339] Cheddar cheese is a hard cheese and has a slower thermal response than the above soft cheeses (Brie and Camembert). The probe for this cheese has a different NTC position. The NTC is set at a point 0.5R, 0L (radially: 13.375mm from the centre and vertically: at the mid length of the vial) which gives the same product thermal response, as found in advance using numerical analysis as demonstrated in Figure 19. All of the products were suspended in the Esky™ container (see Figure 15). The multi-transient test was also done to predict the temperature for short trailer trips when the door is opened during the product delivery, when loaded and unloaded (see Figures 15b and 20).

[00340] Example 3 -Probe (known as ‘SUPERSENSE’) [00341] This Example describes a probe 100, according to an embodiment of the present invention.

[00342] The probe 100 includes a housing 1, a temperature sensor 2 having a sensor head

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PCT/AU2018/000231 located within the housing 1, and a calibrating mechanism 3 for adjusting the sensor head such that the probe 1 is capable of mimicking/simulating the thermal response of a product 101 (see Figure 41). The position of the sensor head can be adjusted axially at the time of assembly and radially by the end user through the use of the calibrating mechanism 3. By adjusting the position, the end user can simulate the thermal response/thermal properties of products (product pallet 101 in Figure 41) being transported.

[00343] The housing 1 is made of plastics material. The housing 1 includes a cylindrical tube 10 having a first end 11 and a second end 12. A grip 113 extends around each end of the tube 10. The second end 12 of the tube 10 has a flange 12. The flange 12 has a circumferentially extending groove 12a within which is located an O-ring 12b (see Figures 35 and 36). A central axis 102 extends through the tube 10 from one end of the tube 10 to the other (see Figures 35 and 37).

[00344] As best seen in Figures 35 to 37, the housing 1 includes a cap 13 connected to the second end 12 of the tube 10. The cap 13 has a first surface and a second surface. The cap 13 has a boss 13a extending from the first surface around a central opening 13b. The cap 13 has a collar 13c extending from the first surface adjacent a circumference of the cap 13. The second surface has a circumferentially extending groove within which is located the O-ring 12b. The cap 13 has openings for receiving mechanical fasteners (not labelled) such that the cap 13 can be connected to the flange 12 of the tube 10, whereby the circumferential region of the cap 13 is sealingly connected to the flange 12.

[00345] The temperature sensor 2 includes a NTC sensor 2a that has a sensor head 20 (sensor housing) and a cable 21 extending from the head 20.

[00346] The sensor head 20 is sealed within the housing 10 in a fluid tight manner, and the sensor cable 21 extends through the first end 11 of the tube 10 in a sealed manner using a cable gland 23 (see Figure 23).

[00347] The temperature sensor 2 includes a heat transfer medium 2b contained by the housing 1 within which the thermistor’s sensor head 20 is located (see Figure 34). The medium is a polyol such as glycerol.

[00348] The temperature sensor 2 includes a baffle 2c contained within the housing 10 for minimising stirring or agitation of the medium 2b so as to reduce unwanted thermal currents. The baffle 2c has a body sized and shaped to fill most of the housing 1. The baffle 2c has a body made of porous sponge and has a core 24 (see Figures 26 to 28) within which the sensor head 20 is located. The baffle 2c body consists of disk-shaped baffle body pieces that are stacked together (see Figure 26).

[00349] The temperature sensor 2 includes a baffle guard 2d in the form of a cage that holds 48

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PCT/AU2018/000231 the baffle 2c body in a fixed position within the housing 1 (see Figures 26-28). The guard 2d extends partway around the core 24 and from one end of the tube 10 to the other. The guard 2d also extends around the core 24 such that the core 24 extends radially from the central axis 102 of the tube 10, to accommodate any intentional lateral movement of the sensor head 20.

[00350] The calibrating mechanism 3 calibrates the probe 100 by way of moving the sensor head 20 from a first predefined location within the house 1 to a different second predefined location within the housing 1. The calibrating mechanism 3 is manually actuated by hand (as per the embodiment of Figure 35) or using a hand-operated tool (as per the tamperproof embodiment of Figure 36).

[00351] The calibrating mechanism 3 includes a dial 3a and rack and pinion mechanism 3b, 3c, 3d for moving the sensor head 20 from one location within the housing 1 to another. The dial 3a is operably connected to the pinion 3 b, the pinion 3b is operably connected to the rack 3 c, and the rack is operably connected to the sensor head 20. Turning the dial 3a from one position to another results in the sensor head 20 being moved from one location within the housing/tube 1/10 to another - laterally or radially relative to the central axis 102 of the housing 1 (ie. towards the housing/tube sidewall 1/10 or away from the housing/tube sidewall 1/10).

[00352] As seen in Figure 25, the rack 3c includes a linear gear 30. Teeth 31 of the linear gear 30 extend substantially parallel with the central axis 102. The rack 3c includes a rack support 32 extending from the linear gear 30. The rack support 32 has a first surface from which extends the linear gear 30 and a second opposing surface from which extends an axial adjustment shaft 33. The shaft 33 extends substantially parallel with the central axis 102 of the housing 1/tube.

[00353] The rack 3c includes a sensor clamp 34 connected to the shaft 33 and clamped to the sensor head 20. The sensor clamp 34 can be adjusted in position along a length of the shaft 33 and fixed in position using a mechanical fastener (see Figure 42) such that the axial position of the sensor head 20 can be altered during assemblage.

[00354] The pinion 3b includes at one end a pinion gear 40 that meshes with the linear gear 30 and, at the other end, a spline drive 41. The pinion 3b includes a shaft 42 that extends between the pinion gear 40 and the spline drive 41 and extends along the central axis 102 of the housing/tube 1/10. The pinion 3b includes a pair of O-rings 43 extending within grooves around the shaft 42, so as to seal the boss 13a/opening 13b in the cap 13 of the housing 1.

[00355] The rack and pinion mechanism includes a pinion carrier 3d for guiding the pinion 3b relative to the rack 3c. The pinion carrier 3d is in the form of a saddle 3d extending over and around the linear gear 30. The pinion carrier 3d includes a pinion seat 45 having an aperture 46 through which pinion shaft 42 extends such that the pinon gear 40 abuts the rack support 32 and

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PCT/AU2018/000231 engages the linear gear 30. The pinion seat 45 corresponds to a middle region of the saddle 3d. [00356] The pinion carrier 3d includes a pair of rack engagers 48, 49 for engaging the rack 3c and enabling the pinion 3b to be moved relative to the rack 3c without disengaging the linear gear 30. Each rack engager 48, 49 has a gripping region extending from each side of the pinon seat 45. Each gripping region 48, 49 corresponds to an end region of the saddle 3d. Each gripping region 48, 49 has a channel/groove 50, 51 for receiving a respective edge of the rack support 32. Opposing edges 36, 37 of the rack support 32 are tapered and are slideably received within a respective channel 50, 51 of the gripping portion 48, 49 (see Figure 25). The opposing edges 36, 37 of the rack support 32 extend substantially parallel with the linear gear 30. The channels 50, 51 extend substantially parallel with each other and with the linear gear 30. The pinion seat 45 extends substantially perpendicularly of the central axis 102.

[00357] The pinion carrier 3d comprises a connecting mechanism in the form of formations 52 for connecting the pinion carrier 3d to the baffle guard 2d such that it cannot move when the calibration mechanism 3 is actuated.

[00358] Referring now to Figures 35 and 37, the dial 3a has a centrally located articulated spline drive engaging region 60 for engaging the spline drive 41 of the pinion 3b. The drive engaging portion 60 is fastened to the spline drive 41 by way of a mechanical fastener (see Figure 35). The dial 3a can be rotated by hand (see the embodiment of Figure 35) or using a tool (see the embodiment of Figure 36).

[00359] As seen in Figures 35 and 37 the dial 3a includes a ribbed knob portion 61 that can be rotated by hand. The knob portion 61 has a circular edge 62 that locates within the collar 13c of the cap 13 of the housing 1. Both the knob portion 61 and collar 13 have markings corresponding to predefined calibration points for select products. In this way, the dial 3a/knob 61 can be rotated relative to the cap 13/collar 13c so as to adjust the temperature sensor 2 such that the probe 100 is capable of mimicking/simulating the thermal properties/thermal response of one or more select products - preferably up to 3 products.

[00360] If moved using a tool, then the dial 65 can be shaped differently for engagement using the tool, as seen in Figure 36. The dial 65 can be tamper-proof such that it can only be turned using a special tool.

[00361] As seen in Figure 36, the probe 100 can further comprise a tamper-proof cover 66, 66a such that the dial 65 cannot be turned. The tamper-proof cover 66, 66a is connected to the cap 13 of the housing 1 using special mechanical fasteners.

[00362] When assembled as shown in Figure 37, manual turning of the dial 3a results in rotation of the spline drive 41 and pinion gear 40, causing the rack 3c to be moved laterally 50

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PCT/AU2018/000231 relative to the central axis 102 of the tube 10, thereby causing the NTC sensor head 20 to be moved relative to the central longitudinal axis 102 of the tube 10. In this way, by turning the dial 3a to preset positions relative to the cap 13 of the housing 1, the NTC sensor head 20 is laterally re-positionable within the housing 1 so as to mimic/simulate the thermal properties/thermal response of one or more products. That is, the sensor head 20 may need to be positioned closer to a side wall of the tube 10 so as to simulate the thermal response of a first product, and the NTC sensor head 20 may need to be positioned closer to the central axis 102 of the tube 10 in order to simulate the thermal response of a second product.

[00363] Steps for assembling the probe 100 include the following: the sponge baffle 2c is stacked around the baffle guard 2d as seen in Figures 27 and 28; the baffle guard 2d is inserted into the tube 10 as shown in Figure 27; the wired NTC sensor 2a is threaded through the first end 11 of the tube as shown in Figure 23 and pulled through as shown in Figures 27 and 28 (although threading and pulling through the NTC sensor 2a can be done prior to inserting the baffle guard 2d into the tube 10); the pinion 3b, pinion carrier 3d and rack 3c are assembled as shown in Figure 25; the NTC sensor head 20 is secured to the sensor clamp 34 in the desired position along the adjustment shaft 33 as shown in Figures 25, 30, 31 and 32; the pinion carrier 3d is engaged with the baffle guard 2d as shown in Figures 34 to 36; the cable gland is tightened to seal the end 11 of the tube 10 as shown in Figures 23 and 33; the tube 10 is filled with a viscous heat-absorbing liquid 2b as shown in Figure 34; the cap 13 is sealingly engaged with the flanged tube end 12 as shown in Figures 35 to 37, such that the spline drive 41 sealingly extends through the boss 13a of the cap 13 as shown in Figures 35 and 36 (so that the medium 2b is fully sealed within the housing

1); and the dial 3a (or dial 65) is secured to the spline drive 41 such that it can rotate relative to the collar 13c as shown in Figures 35 to 37.

[00364] As seen in Figures 22 and 38 to 41, the probe 100 can include a mounting 4 for mounting the probe housing 1 to the product 101 or to a fixture in the vicinity of the product, such as a wall.

[00365] As seen in Figure 39, that mounting 4 can be in the form of a clip 4a having a first end 70 having opposed jaws 70 that can receive the probe housing 1, and a second end 71 that is fastenable to the product or fixture by way of adhesive, mechanical fasteners (e.g. screws) or the like.

[00366] As seen in Figures 38 to 41, the mounting 4 can include a clip portion 73 having a first end having opposed jaws 73 that can receive the probe housing 1, and a second end 74 that is pivotally connected by pivot pin 76 to a spade/wedge 75 such that when the spade 75 engages products/fixtures, the probe housing 1 can be rotated through approximately 180° to the required 51

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PCT/AU2018/000231 orientation. The spade 75 be inserted/wedged between two adjacent surfaces such as those of two products or fixture/s and/or product and fixture.

[00367] Example 4 - Use of the Probe and Related (‘SUPERSENSE’) Software [00368] This Example describes use of the probe of Example 3 for the carriage of food products.

[00369] Introduction [00370] The big issue in the carriage of food products in the global cold chain is that too much reliance has traditionally been placed on air temperatures, instead of product temperatures.

[00371] Temperature probes that are inserted into food during transport solve part of the problem, but all too often, their application is haphazard, time-consuming and subject to abuse.

[00372] Far too many food shipments are being rejected because the cargo was not kept at the right temperature, and many transport companies are not even able to prove that the food was kept at the right temperature.

[00373] The probe as exemplified concerns measurement of actual product temperatures. This is based on the simple and well known fact that air temperatures can vary quite dramatically within the refrigerated space of a truck body or trailer, while the product temperatures can maintain their temperature and vary only slightly.

[00374] The probe as exemplified will eliminate the necessity to insert probes into the cargo. Instead, the probe will sit on top or near the pallet of food product and can be calibrated/programmed to mimic the core temperature of that product.

[00375] It is an intelligent probe that can be calibrated to suit a wide range of food products.

[00376] The temperature data collected by the probe can be sent to a temperature recorder which can then send the data on to a mobile phone or website interface, enabling transporters to ensure that their cargo has always been kept at the right temperature. Temperature risks can be anticipated and corrective measures can be taken in time.

[00377] Figure 55 show an operation and feature flowchart, according to an embodiment of the present invention.

[00378] Features [00379] The probe monitors temperature in real time and can be calibrated to imitate the thermal response of the core temperature of food products under extreme environmental conditions. The sensor design is based on the unique thermal properties of each product.

[00380] Mathematical formulae were devised to take into account each individual food’s nutritional content as published on the food label, its mass and geometry as well as the type and thickness of the packaging.

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PCT/AU2018/000231 [00381] The probe can monitor the average food core temperature of the whole trailer by placing it inside the refrigerated food transport and in more than one zone.

[00382] As an option, the probe includes an articulated flat arm/spade for placement inside or between pallets or boxes, without penetrating the product, to accurately simulate the core temperature of the food inside the pallet.

[00383] The probe particularly suits products with narrow range transported food temperatures, like cheese (2°C to 5°C) with a tolerance of less than +/- 0.5°C.

[00384] The probe as exemplified can be adjusted/calibrated for use on 3 different food types, for example.

[00385] The probe as exemplified can transmit warnings through the logger/recorder/receiver to mobile phone or management destinations when the product temperature found by a probe reaches a maximum safe transportation safe limit when the door is left open or the refrigeration is faulty.

[00386] The probe can be adapted for monitoring temperatures of food (including chilled, frozen food or other) and surrounding environment from -50°C to +50°C and 30% to 90% relative humidity environment.

[00387] Additional benefits of the probe as exemplified [00388] - Replaces the traditional bayonet probe, eliminating the necessity to insert probes into the cargo.

[00389] - Can be connected to any data logger.

[00390] - No battery, no Wi-Fi and no Bluetooth.

[00391] - Is a time-saving solution for traffic managers, loading dock personnel and drivers.

[00392] - Reduces risk of food rejection.

[00393] - Makes recording temperatures easier than other methods.

[00394] - Can send multiple pieces of data to the logger/recorder/receiver, warning about potential temperature lapses in food within the refrigerated space.

[00395] - Saves money.

[00396] - Saves food.

[00397] - Prevents food loss and waste.

[00398] - Suitable for wide-range of products such as medical and pharmaceutical products.

[00399] Until now, bayonet probes have been the tried and tested means of ensuring food temperature compliance, but depending on how they are handled, they can be unreliable and inaccurate.

[00400] Thermal Response Software for Use in Connection with the Probe

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PCT/AU2018/000231 [00401] Thermal response software can be available through a login portal and can be made available for online rental at two levels - standard and advanced.

[00402] The software, driven by specially designed and complex mathematical science, is a smart method of predicting and modelling food product temperatures in a variety of changeable transport or cold store conditions, ft means that the characteristics of a proposed new food line can be fed into the software to predict its thermal response to various ambient temperatures over various transit timeframes.

[00403] With the software this data is now available long before investment is applied to production, packaging and even details like the water or fat content of the food.

[00404] The thermal response software is an essential planning tool for any food company planning to bring a new chilled product to market. With the knowledge it can provide to the food industry, a great deal of the guesswork of food presentation and packaging can be eliminated. [00405] Those who could benefit include:

[00406] - Process designers - in transport logistics, quality control and HACCP compliance will be better equipped to devise chilled food rollouts while minimising the potential for failure.

[00407] - Auditors - investigating why loads are rejected and how to solve this problem.

[00408] - Food designers - knowing the thermal effects of ambient temperatures and transit times on food of any mass or shape of chilled food could lead to savings.

[00409] - Food packagers - knowing the thermal effects of different styles of packaging before going to market could greatly impact on the food’s ability to maintain temperature in transport.

[00410] Table 1 shows the thermal properties of different foods calculated using the software, using the information data on the packaging. The graphs of Figures 43 to 47 also show the simulation results by the software against the actual product.

[00411] Table 1 - Theoretical thermal properties of different foods

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Info from the package

Results calculated by Software

Items	Water%	Protein%	Fat%	Ash%	Carb%	k (W/m.K)	Cp (J/Kg.K)	P (Kg/m3)
Yogurt dots, l50g	79.05	5	1,3	0.15	'4.5	0.572	3067	1064
=ull Cream Pauls m? k. 1 _	88/	3.8	2	0.2	5.9	0.57	3896	1025.5
Yogurt, Greek, ' Kg	78.05	4.9	9,5	0.25	0.7	0.532	3692	1025
Bee' scusage/SAussie JO.sSKgJ	59.9	12.4	18.9	1.1	7.7	0.458	3288	1044.6
Cn’cken breast (O.55Kg)	/ 4.7	23.1	1.2		0	0.472	3868	1058
Bee1 burger/4nome style |O.5K.g]	66.7	1 6.5	1-1.2	0.8	’ .8	0.449	3477	1034
Bee' Scolcn steak ;0.3SKg·	62.6	17.2	19.4	0.8	0	0.42	3400	1023.5
M'nce bee' 'eat (1 Kg;	01.3	19.9	17	0.8	1	0.418	3375	1037.4
Camembert, 125g	51.5	17.3	2i.5	2.4	2.3	0.407	3' 16	1037
Brie 12 5g	48.9	17.3	32	1.7	0.1	0.374	3087	10’ 8.5
Cassie Cheddar. 250c.	39.71	24.8	33	1.49	1	0.327	291 1	103?

[00412] Validation and Verification [00413] A number of theoretical models have been developed to predict certain food product thermal behaviour/thermal response in different temperature conditions. These models have become the fundamental logic to develop the software.

[00414] In addition, a large number of tests in various scenarios have been undertaken to experimentally verify the probe against the actual and the software simulation results. These scenarios were designed to mimic the real life of a food product journey from production to the supermarket shelves. The main variables were ambient temperature and humidity, also the amount of time the products were exposed to unrefrigerated ambient conditions. Figures 43 to 47 show the simulation by the software against the actual product, and Figures 48 to 54 show that probe against the actual product in various temperature conditions.

[00415] Example 5 - Calibration of the probe (known as ‘SUPERSENSE’) [00416] This Example describes how the thermal properties of the probe 100 of Example 3 can be calculated and the sensor head 20 of the temperature sensor 2 can be positioned correctly within the housing 10 such that the probe can simulate a product of interest, such as a food product. Once correctly positioned, the sensor head 20 will correlate with one calibration point of the probe. In a preferred embodiment, the probe 100 will have three calibration points whereby the sensor head 20 will have 3 specific positions within the housing 10.

[00417] The flow chart in Figure 56 shows steps involved for calibration of the SUPERSENSE™ probe in Example 3 so as to set a calibration point for simulating a specific product. The flowchart also shows steps for determining the thermal response of a product - which will needed to correlate with the probe. The steps in Figure 56 are explained in more detail below (but different step numbering is used from the numbering in the Figure).

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PCT/AU2018/000231 [00418] SUPERSENSE thermal parameter calculations:

[00419] (1) The probe’s internal and external dimensions are measured.

[00420] (2) The thermal properties of the heat transfer medium inside the probe are found at the film temperature (average inside and outside temperatures).

[00421] (3) The thermal properties of the environment are found at the film temperature.

[00422] (4) The emissivity of the probe manufacturing material is determined.

[00423] (5) The thermal conductivity and the thickness of the probe material (plastic) is determined.

[00424] (6) Based on 1 and 2, the internal heat transfer coefficient is calculated.

[00425] (7) Based on 1 and 3, the external heat transfer coefficient is found.

[00426] (8) Based on 4 and temperatures (fluid and environment) the radiation heat transfer coefficient is calculated.

[00427] (9) The conductive heat transfer is found based on parameters in 5.

[00428] (10) The overall heat transfer (U) coefficient is found from 6 to 9.

[00429] The same procedure is carried out for the product in order to determine the product probe calibration value. If the product is a liquid, then steps 6 and 8 are excluded. However, the core temperature is the one of interest, and there is no need to plot all mapping temperatures except the core and the surface.

[00430] SUPERSENSE probe thermal response mapping [00431] After finding the probe dimensions from (1), fluid thermal properties from (2) and the U value from (10), a two-dimension finite difference method (2D-FDM) is used to map/find the temperate at any point inside the probe housing for the whole simulation time.

[00432] The thermal response of the food under test (FUT) can be determined practically or using a simulation software. For both methods, the initial (product core temperature) and boundary conditions (surrounding environment) should be defined and maintained, as the same boundary conditions will be used for the SUPERSENSE™ simulation. However, more details are required to be determined and known in case of simulating the food thermal response using the software such as thermal properties, packaging and physical properties (see the left-side flowchart).

[00433] By comparing the simulation mapping curve results with experimental/tested core temperature of the product of interest, the right match can be found. From the matched curve the setting position (in terms of vertical and horizontal direction) of the thermal sensor inside the housing can be readily determined to establish the calibration point.

[00434] An experiment can be carried out to test and check that the thermal response of both

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PCT/AU2018/000231 the calibrated probe and specific product are in agreement, and the tolerance is within the allowed range.

[00435] In the present specification and claims, the word ‘comprising’ and its derivatives including ‘comprises’ and ‘comprise’ include each of the stated integers but does not exclude the inclusion of one or more further integers.

[00436] Reference throughout this specification to ‘one embodiment’ or ‘an embodiment’ means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearance of the phrases ‘in one embodiment’ or ‘in an embodiment’ in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more combinations.

[00437] In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. It is to be understood that the invention is not limited to specific features shown or described since the means herein described comprises preferred forms of putting the invention into effect. The invention is, therefore, claimed in any of its forms or modifications within the proper scope of the appended claims (if any) appropriately interpreted by those skilled in the art.

Cited References

1. Smil, V. (2000) Feeding the World: A Challenge for the 21st Century, MIT Press Cambridge.

2. Gunders, D. (2012) Wasted: how America is losing up to 40 percent of its food from farm to fork to landfill. NRDC, Issue Paper, Natural Resources Defense Council. http://www.nrdc.org/food/wasted-food.asp, accessed on 20 Dec. 2016.

3. Rahman, M.S. (2009) Food properties handbook, CRC Press, 2^nd Ed.

4. ASHRAE (2009) Handbook fundamental (I-P) Ed, chapter 4, American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc.

5. Berk, Z. (2009) Food process engineering and technology, Elsevier, 2nd Ed.

6. Kopelman, I.J. (1966) Transient heat transfer and thermal properties in food system, PhD thesis, Michigan State University.

7. Choi, Y., Okos, M.R. (1986) Effects of Temperature and Composition on the Thermal Properties of Foods. In Food Engineering and Process Applications, M. LeMaguer and P. Jelen, eds. Elsevier Applied Science, London 1, 93-101.

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8. Heldman, D.R, Singh (1981) R.P., Food Process Engineering, 2nd Ed., Avi Pub. Co.

9. Marschoun, L.T, Muthukumarappan K., Gunasekaran S. (2006) Thermal properties of Cheddar cheese: experimental and modelling. International Journal of Food Properties 4 (3), 383-403.

10. Vigneault, C., Thompson J., Wu S., Hui K.P , LeBlanc D.I. (2009) Transportation of fresh horticultural produce, Postharvest Technol. Hortic. Crops, 2, 1-24.

11. Wang, L., Sun, D. (2003) Recent developments in numerical modelling of heating and cooling processes in the food industry - a review. Trends in Food Science and Technology 14, 408-423.

12. lezzi, R., Francolino, S., Mucchetti, G. (2011) Natural convective cooling of cheese: Predictive model and validation of heat exchange simulation. Journal of Food Engineering 106, 88-94.

13. Incropera, F.P., Dewitt, D.P., Bergman, T.L., Lavine, A.S. (2007) Fundamental of heat and mass transfer, John Wily and Sons, 6^lh Ed.

14. Bejan, A., Kraus, A.D. (2003) Heat transfer handbook, Chapter 7, John Wiley & Sons, Inc.

15. Sahin, S., Sumnu, S. (2006) Physical properties of foods. Springer Science.

16. Thiel, D.V. (2014) Research methods for engineers, Cambridge University Press.

17. University of Virginia, One-Dimensional Transient Conduction, http://www.faculty.virginia.edu/ribando/modules/OneDTransient/, accessed on 15 Dec. 2016.

18. Earle, R.L. (1983) Unit operations in food processing, chapter 5, Pergamon Press, 2nd Ed.

19. American Cheese Society, Tips on storage cheese, http://www.cheesesociety.org/i-heartcheese/tips-for-cheese-lovers/#Storing, accessed on 16 Dec. 2016.

20. Fox, P.F., Guinee, T. P., Cogan, T. M., McSweeney, P.L. (2000) Fundamental of cheese science, Aspen Publishers, Inc.

21. ASHARE (2006) Handbook refrigeration (I-P) Ed, chapter 9, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.

22. Yaws, C. Chemical Properties Handbook, McGraw-Hill Education - Europe, 1998.

23. Fluid Properties Calculator website from University of Waterloo in Canada, http://www.mhtl.uwaterloo.ca/old/onlinetools/airprop/airprop.html

Claims (20)

1. (1) simulating or mimicking the thermal response of a product to the product’s environment, said method comprising the step of using a probe that simulates the thermal response of the product to the product’s environment;

   (1) a probe for simulating or mimicking the thermal response of a product to the product’s environment;

   (1) that simulates or mimics the thermal response of a product to the product’s environment;

   1. A probe:
2. (2) simulating the thermal response of a product to the product’s environment, said method comprising the step of using a probe calibrated or programmed, or that can be calibrated or programmed, that simulates the thermal response of the product to the product’s environment;

   (2) a probe calibrated or programmed, or that can be calibrated or programmed, for simulating the thermal response of a product to the product’s environment;

   2. Use of:

   (2) calibrated or programmed, or that can be calibrated or programmed, to simulate the thermal response of a product to the product’s environment;
3. (3) simulating the thermal response of a product to the product’s environment, said method comprising the step of using a probe calibrated or programmed, or that can be calibrated or programmed, that simulates the thermal response of the product to the product’s environment, wherein the probe is/can be calibrated or programmed based on physical and chemical properties of the product and the environment;

   3. A method of:

   (3) a probe calibrated or programmed, or that can be calibrated or programmed, for simulating the thermal response of a product to the product’s environment, wherein the probe is/can be calibrated or programmed based on physical and chemical properties of the product and the environment; or (4) a probe that simulates the thermal response of a product to the product’s refrigerated

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   PCT/AU2018/000231 environment as a thermostat for controlling the temperature of the environment, wherein:

   said probe is capable of simulating or mimicking a core temperature of the product; and said probe is not adapted to penetrate the product itself

   (3) calibrated or programmed, or that can be calibrated or programmed, to simulate the thermal response of a product to the product’s environment, wherein the probe is/can be calibrated or programmed based on physical and chemical properties of the product and the environment; or (4) capable of being calibrated or programmed, for simulating the thermal response of a product to the product’s environment, said probe comprising:

   a housing;

   at least one temperature sensor located within the housing; and a calibrating or programming mechanism for adjusting the at least one temperature sensor such that the probe is capable of simulating the thermal response of the product to the product’s environment, wherein:

   said probe is capable of simulating or mimicking a core temperature of the product; and said probe is not adapted to penetrate the product itself
4. 4. The probe of claim 1, the use of claim 2 or the method of claim 3, wherein the thermal response of the product is determined or calculated based on thermal properties of the product and the likely rate of heat exchange and transfer between the environment and the product.

   (4) simulating the thermal response of a product to the product’s environment using a probe, wherein the method comprises the step of using a pre-assigned product probe calibration value for the product to calibrate the probe so as to simulate the thermal response of the product to the environment, wherein the product probe calibration value has been determined from physical and chemical properties of the product and the environment;
5. 5. The probe, the use or the method of claim 4, wherein the thermal response of the product is determined or calculated based on one or more of the following parameters: product composition/ingredient list; product mass, dimensions and geometry; product packaging; environment temperature; environment humidity; thermal conductivity (A); specific heat capacity (C_p); density (p); thermal diffusivity (a); overall heat transfer coefficient (t/); and convection heat transfer coefficient / heat transfer coefficient (Λ).

   (5) simulating the thermal response of a product to the product’s environment using a probe, wherein the method comprises the steps of:

   (a) assigning a product probe calibration value to the product, wherein the product probe calibration value is determined from physical and chemical properties of the product and the environment; and (b) using the product probe calibration value to calibrate the probe so as to simulate the thermal response of the product to the environment;
6. 6. The probe, the use or the method of claim 5, wherein the thermal response of the product is determined or calculated based on the product’s composition/ingredient list, including one or more of the mass fraction of water/moisture, protein, fat, ash and carbohydrate.

   (6) simulating, predicting or modelling the thermal response of a product to the product’s

   WO 2019/100101

   PCT/AU2018/000231 environment, said method comprising the step of determining the thermal response based on physical and chemical properties of the product and the environment; or (7) controlling the temperature of an environment containing a product, said method comprising the step of using a probe that simulates the thermal response of the product to the product’s environment as a thermostat for controlling the temperature of the environment, wherein: said probe is capable of simulating or mimicking a core temperature of the product; and said probe is not adapted to penetrate the product itself.
7. 7. The probe, the use or the method of claim 6, wherein the product’s composition/ingredient list is determined from a nutritional table on the product’s packaging.
8. 8. The probe, the use or the method of claim 7, wherein the product comprises about 5-95% weight/weight water/moisture, 1-40% weight/weight protein, 0.01-83% weight/weight fat and 054% weight/weight carbohydrate.

   WO 2019/100101

   PCT/AU2018/000231
9. 9. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 8, wherein calibration or programming of the probe so as to establish a calibration point that simulates the product or corresponds with the product probe calibration value, involves determining thermal properties of the probe and calculating or determining the likely rate of heat exchange and transfer between the environment and a thermal sensor of the probe.
10. 10. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 9, wherein the probe is adapted to simulate the product’s thermal response to changes in its environment temperature over a period of time.
11. 11. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 10, wherein the probe is adapted to transmit sensor data corresponding to the product’s thermal response to changes in its environmental temperature over a period of time.
12. 12. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 11, wherein the probe is used to monitor or record sensor data for auditing purposes, to determine whether or not the product has been kept at an acceptable temperature, or has exceeded a critical temperature or other condition.
13. 13. The probe, use or method of claim 10 or 11, wherein the period of time correlates with the loading or unloading of the product into or out of the environment.
14. 14. The probe, use or method of claim 10 or 11, wherein the period of time correlates with transporting the product from one location to another, such as to a final destination.
15. 15. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 14, wherein the product is a perishable product.
16. 16. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 15, wherein the product is a food product.
17. 17. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 16, wherein the environment is that of a refrigerated, chilled, airconditioned or non-refrigerated vehicle or other type of vehicle or vessel, whereby the product is loaded onto or unloaded from the vehicle or vessel via one or more doors.

    WO 2019/100101

    PCT/AU2018/000231
18. 18. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 17, wherein the probe is used to predict the time that the product will take to reach a specific temperature or when the product reaches a specific temperature due to heat transfer from the environment.
19. 19. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 18, wherein the probe is used to predict when spoilage of the product occurs or is likely to occur.
20. 20. The probe of claim 1, the use of claim 2, the method of claim 3, or the probe, use or method of any one of claims 4 to 19, wherein the probe is used to predict the required maximum time to load/unload a refrigerated or unrefrigerated road vehicle/load/container before a critical product temperature is reached.