CN115649455B - Method and device for judging icing based on electrothermal deicing signal - Google Patents
Method and device for judging icing based on electrothermal deicing signal Download PDFInfo
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Abstract
The present disclosure relates to a method and a device for determining icing based on an electrothermal deicing signal, wherein the electrothermal deicing signal comprises ice layer melting time, and the method comprises: determining the convective heat transfer coefficient of the surface to be measured according to the incoming flow speed; determining the ice layer melting time by using a temperature sensor; inputting the convective heat transfer coefficient and the ice layer melting time into a trained icing discrimination model to obtain the icing thickness corresponding to the measuring point of the surface to be measured, and taking the icing thickness as a discrimination result, wherein the temperature sensor is arranged on an interface between every two adjacent electrothermal deicing units in the plurality of electrothermal deicing units, and the plurality of electrothermal deicing units are arranged on the surface to be measured. The method fully utilizes the deicing signal data obtained by the existing electrothermal deicing unit to carry out deep processing, realizes the integration of electrothermal deicing and electrothermal ice detection, and is simple and quick to implement, and accurate and reliable in judgment result.
Description
Technical Field
The present disclosure relates generally to the field of deicing technology, and more particularly, to a method and apparatus for discriminating icing based on electrothermal deicing signals.
Background
Electrothermal deicing is one of the most common deicing methods for the aircraft at present, and represents the development trend of aircraft deicing prevention due to the advantages of clean energy and good maintainability. At present, a wave-sound 787 large passenger plane adopts an electrothermal deicing system to replace a hot-gas anti-icing system, so that the full electrification of the deicing system is realized.
The electrothermal deicing of the airplane is that a multilayer electrothermal deicing structure containing an electric heating unit is arranged on the front edges of wings and empennage of the airplane, the heating unit converts electric energy into heat energy, the heat energy is transferred to an icing surface through the multilayer structure, ice adhered to an interface is melted, and the purpose of ice falling is achieved under the help of aerodynamic force or inertial force.
Among the related ice control techniques, there are developed novel ice discrimination methods such as an optical fiber type ice discrimination method that can be applied to a surface having a curvature change, but each method has its own drawbacks. For example, the optical fiber type icing sensor has high sensitivity, but is sensitive to water, oil stain and dust, has a phenomenon of false alarm, has weak environmental interference resistance, and is difficult to detect a thick ice layer because the detected icing thickness is usually less than 2 mm. In addition, various icing sensors are used for judging icing conditions, and various icing sensors need to be additionally installed on the surface to be measured, so that the cost of the airplane in the aspects of design, materials and the like is increased, and the defect that the surface at the installation position of the icing sensor is uneven can be caused.
Disclosure of Invention
The invention provides a method and a device for judging icing based on an electrothermal deicing signal, which realize integration of electrothermal deicing and electrothermal ice detection by fully utilizing deicing signal data obtained by the conventional electrothermal deicing unit for deep processing, are simple and quick to implement and have accurate and reliable judgment results.
In one general aspect, there is provided a method of discriminating icing based on an electrothermal deicing signal including an ice layer melting time, wherein the method comprises: determining the convective heat transfer coefficient of the surface to be measured according to the incoming flow speed; determining the ice layer melting time by using a temperature sensor; inputting the convective heat transfer coefficient and the ice layer melting time into a trained icing discrimination model to obtain the icing thickness of the surface to be measured, and taking the icing thickness as a discrimination result, wherein the temperature sensor is arranged on an interface between every two adjacent electrothermal deicing units in the plurality of electrothermal deicing units, and the plurality of electrothermal deicing units are arranged on the surface to be measured.
Optionally, the determining a convective heat transfer coefficient of the surface to be measured according to the incoming flow velocity includes: determining the density and dynamic viscosity coefficient of the air on the surface to be measured according to the ambient temperature; determining the local Reynolds number at the surface to be measured according to the density, the dynamic viscosity coefficient and the incoming flow speed; and determining the convective heat transfer coefficient according to the ambient temperature, the density, the dynamic viscosity coefficient, the incoming flow velocity and the local Reynolds number.
Optionally, said determining said convective heat transfer coefficient from said ambient temperature, said density, said kinetic viscosity coefficient, said incoming flow velocity, and said local reynolds number comprises: the convective heat transfer coefficient is determined by the following equation:
wherein,
represents a heat exchange coefficient>
Represents density,. Sup.>
Indicating incoming flow speed, <' > based on the time period>
Indicates an ambient temperature, <' > is present>
Represents a kinetic viscosity index->
Represents a characteristic length, <' > based on>
Represents the local Reynolds number>
Representing a preset critical reynolds number.
Optionally, said determining, with a temperature sensor, said ice layer melting time comprises: acquiring phase change time required by an ice layer at the temperature sensor to reach phase change temperature by using the temperature sensor, wherein the phase change temperature is the temperature when the ice phase is changed into water; determining the phase change time as the ice layer melting time.
Optionally, the acquiring a phase change time required for the ice layer at the temperature sensor to reach a phase change temperature includes: acquiring temperature data at the temperature sensor; determining a gradient of the temperature along with the time according to the temperature data, wherein the gradient is an increasing amount or a decreasing amount of the temperature in unit time; and determining that the ice layer reaches the phase change temperature at a first moment when the gradient is smaller than a preset threshold, and taking the interval duration between a second moment when the electrothermal deicing unit starts heating and the first moment as the phase change time.
Optionally, the icing discrimination model is a back propagation neural network model comprising an input layer, a hidden layer and an output layer, wherein the icing discrimination model is represented by the following equation:
wherein,
represents an output value, < > or >>
Represents an input value, < > or >>
Represents a heat exchange coefficient>
Which indicates the time for the ice layer to melt,
represents the connection weight from the input layer to the hidden layer, is->
Represents the connection weight from hidden layer to output layer, and>
represents a hidden layer threshold value>
Represents the threshold value of the output layer, is selected>
Representing an activation function.
Optionally, the input layer comprises 2 input nodes, the hidden layer comprises 20 hidden layer nodes, and the output layer comprises 1 output node, wherein the activation function is represented by the following equation:
wherein,
indicating the th in the icing discrimination model>
Is entered and/or is asserted>
Represents the node in the previous layer>
To the layer node>
And N represents the total number of input nodes. />
Optionally, the icing discrimination model is trained by: test for obtaining multiple groups of heat conversion coefficients, ice layer melting time and icing thickness through testData; training an icing discrimination model to be trained using the test data to determine if the icing discrimination model to be trained converges
、
、
And &>
Thus obtaining the trained icing discrimination model.
Optionally, the icing discrimination model to be trained has a mean square error loss value smaller than 10 through the icing discrimination model to be trained -5 To determine convergence.
In another general aspect, there is provided an apparatus for discriminating ice formation based on an electrothermal ice detachment signal including an ice layer melting time, wherein the apparatus includes: the first determining unit is configured to determine the convective heat transfer coefficient of the surface to be measured according to the incoming flow speed; a second determination unit configured to determine the ice layer melting time using a temperature sensor; and the icing judging unit is configured to input the convective heat transfer coefficient and the ice layer melting time into a trained icing judging model to obtain the icing thickness of the surface to be detected, and the icing thickness is used as a judging result, wherein the temperature sensor is arranged on an interface between every two adjacent electrothermal deicing units in the plurality of electrothermal deicing units, and the plurality of electrothermal deicing units are arranged on the surface to be detected.
Optionally, the first determining unit is configured to: determining the density and dynamic viscosity coefficient of the air on the surface to be measured according to the ambient temperature; determining a local Reynolds number at the surface to be measured according to the density, the dynamic viscosity coefficient and the incoming flow speed; and determining the convective heat transfer coefficient according to the ambient temperature, the density, the dynamic viscosity coefficient, the incoming flow velocity and the local Reynolds number.
Optionally, the first determining unit is further configured to: the convective heat transfer coefficient is determined by the following equation:
wherein,
represents a heat exchange coefficient>
Represents a density +>
Indicates the incoming flow velocity, and>
indicates an ambient temperature, <' > is present>
Represents a kinetic viscosity index->
Indicates a characteristic length, <' > or>
Represents the local Reynolds number>
Representing a preset critical reynolds number.
Optionally, the second determining unit is configured to: acquiring phase change time required by an ice layer at the temperature sensor to reach phase change temperature by using the temperature sensor, wherein the phase change temperature is the temperature when the ice phase is changed into water; determining the phase change time as the ice layer melting time.
Optionally, the second determining unit is further configured to: acquiring temperature data at the temperature sensor; determining a gradient of the temperature along with the time according to the temperature data, wherein the gradient is an increasing amount or a decreasing amount of the temperature in unit time; and determining that the ice layer reaches the phase change temperature at a first moment when the gradient is smaller than a preset threshold value, and taking the interval duration between a second moment when the electric heating deicing unit starts heating and the first moment as the phase change time.
Optionally, the icing discrimination model is a back propagation neural network model comprising an input layer, a hidden layer and an output layer, wherein the icing discrimination model is represented by the following equation:
wherein,
represents an output value, <' > based on>
Represents an input value, < > or >>
Represents a heat exchange coefficient>
Which indicates the time for the ice layer to melt,
represents the connection weight from the input layer to the hidden layer, is->
Represents the connection weight from hidden layer to output layer, and>
represents a hidden layer threshold value>
Represents the threshold value of the output layer, is selected>
Representing an activation function.
Optionally, the input layer comprises 2 input nodes, the hidden layer comprises 20 hidden layer nodes, and the output layer comprises 1 output node, wherein the activation function is represented by the following equation:
wherein,
indicating the th in the icing discrimination model>
Is entered and/or is asserted>
Represents the node in the previous layer>
To the layer node->
With a weight between, N representing the inputTotal number of nodes.
Optionally, the icing discrimination model is trained by: obtaining a plurality of groups of test data of thermal conversion coefficients, ice layer melting time and icing thickness through tests; training an icing discrimination model to be trained using the test data to determine if the icing discrimination model to be trained converges
、
、
And &>
Thus obtaining the trained icing discrimination model.
Optionally, the icing discrimination model to be trained has a mean square error loss value smaller than 10 through the icing discrimination model to be trained -5 To determine convergence.
According to the method and the device for judging icing based on the electrothermal deicing signal, after the trained icing judging model is obtained, the icing thickness can be obtained only by determining parameters such as a convective heat transfer coefficient, ice layer melting time and the like and bringing the determined parameters into the trained icing judging model, and on one hand, compared with an icing judging method in the related technology, the method is simpler and quicker, and on the other hand, compared with a method for converting the icing thickness by measuring cloud and mist parameters, the obtained icing thickness is closer to a real value; in addition, the method aims at the surface to be detected with the electric heating deicing unit, realizes the integration of electric heating deicing and electric heating ice detection by fully utilizing deicing signal data obtained by the existing electric heating deicing unit for deep processing, is simple and quick to implement, has accurate and reliable judgment results, is not easy to detect and report false alarms, is obviously superior to the scheme of judging icing through various sensors, not only saves the cost in the aspects of design, materials and the like, but also avoids the defect of surface unevenness at the installation position of the icing sensor and the like.
Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.
Drawings
The above and other objects and features of the embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings illustrating embodiments, in which:
FIG. 1 is a flow chart illustrating a method of discriminating icing based on electrothermal deicing signals according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram illustrating an electrothermal deicing unit according to an embodiment of the present disclosure;
FIG. 3 is a graph showing temperature versus time at a temperature sensor in an iced and an iceless state according to an embodiment of the present disclosure;
FIG. 4 is a block diagram illustrating an apparatus to discriminate icing based on electrothermal deicing signals according to an embodiment of the present disclosure.
In the drawings, 10, a base layer; 11. an inner insulating layer; 12. an outer insulating layer; 13. a wear layer; 14. a layer to be tested; 15. a heating layer; 16. a temperature sensor; 400. a device for discriminating icing based on the electrothermal deicing signal; 410. a first determination unit; 420. a second determination unit; 430. and an icing determination unit.
Detailed Description
The following detailed description is provided to assist the reader in obtaining a thorough understanding of the methods, devices, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and/or systems described herein will be apparent to those skilled in the art after reviewing the disclosure of the present application. For example, the order of operations described herein is merely an example, and is not limited to those set forth herein, but may be changed as will become apparent after understanding the disclosure of the present application, except to the extent that operations must occur in a particular order. Moreover, descriptions of features known in the art may be omitted for clarity and conciseness.
The features described herein may be embodied in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and/or systems described herein, which will be apparent after understanding the disclosure of the present application.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in general dictionaries) should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense.
Further, in the description of the examples, when it is considered that detailed description of well-known related structures or functions will cause a vague explanation of the present disclosure, such detailed description will be omitted.
A method and apparatus for discriminating icing based on an electrothermal deicing signal according to an embodiment of the present disclosure will be described in detail with reference to fig. 1 to 4.
Fig. 1 is a flow chart illustrating a method of discriminating icing based on electrothermal deicing signals according to an embodiment of the present disclosure. Here, the electrothermal deicing signal may include, but is not limited to, an ice layer melting time.
Referring to fig. 1, in step S101, a convective heat transfer coefficient of the surface to be measured may be determined according to an incoming flow velocity. By way of example, the surface to be measured can be an aircraft surface, a wind turbine blade surface, a power transmission equipment surface, a surface which is easy to freeze of high-speed rails, and the like.
According to an embodiment of the present disclosure, the density of air at the surface to be measured may be determined according to the ambient temperature using the following equations (1) to (4)
The dynamic viscosity coefficient->
And the prandtl number->
And a heat conductivity->
：
Here, the number of the first and second electrodes,
representing the ambient pressure at the surface to be measured in pascals;
Which represents the ambient temperature at the surface to be measured in kelvin (K).
Then, according to the density
The dynamic viscosity coefficient->
And incoming flow velocity>
Determining the local Reynolds number at the surface to be examined using the following equation (5)>
：
Here, ,
the unit of (a) is m/s;
Represents a characteristic length in m, as an example>
But may not be limited to a value of 1.
Next, in one possible implementation, the number of bits per Plantt may be based on
Heat conductivity coefficient->
And local Reynolds number->
The convective heat transfer coefficient @isdetermined by the following equation (6)>
：
Here, ,
and representing a preset critical Reynolds number as a critical Reynolds number standard for calculating the convective heat transfer coefficient in a segmented manner. As an example, a device>
Can be but is not limited to being +>
。
In another possible implementation, the temperature may be based on ambient temperature
And density->
The dynamic viscosity coefficient->
Velocity of incoming flow
And local Reynolds number->
The convective heat transfer coefficient @isdetermined by the following equation (7)>
：
Through the equation (7), the measured flight speed (i.e. the incoming flow speed) and the ambient temperature can be directly utilized to calculate the convective heat transfer coefficient of the corresponding state, so that intermediate steps are reduced, and the calculation resources are saved.
Next, in step S102, the ice layer melting time may be determined using a temperature sensor. Here, the temperature sensor may be disposed on an interface between every two adjacent electrothermal deicing units among the plurality of electrothermal deicing units, and the plurality of electrothermal deicing units may be disposed on the surface to be measured.
According to the embodiment of the disclosure, the phase change time required for the ice layer at the temperature sensor to reach the phase change temperature can be obtained by using the temperature sensor, wherein the phase change temperature is the temperature when the ice phase is changed into water; then, the phase change time may be determined as the ice layer melting time. Further, temperature data at the temperature sensor may be acquired; then, a gradient of the temperature with time may be determined based on the temperature data, where the gradient is an increase or decrease in the temperature per unit time; then, at a first moment when the gradient is smaller than a preset threshold value, it is determined that the ice layer reaches a phase change temperature, and the interval duration between a second moment when the electrothermal deicing unit starts heating and the first moment is used as phase change time.
Aiming at the existing electric heating deicing system, the existing electric heating deicing units in the system are utilized, only a plurality of temperature sensors are needed to be arranged at key positions of the electric heating deicing units, the temperature signal change of points to be measured between the electric heating deicing units is monitored, and the icing signal on the surface of the electric heating deicing is obtained by adopting a depth analysis processing method, so that the electric heating deicing system has great convenience and advantages for equipment such as an active aircraft.
Next, in step S103, the convective heat transfer coefficient and the ice layer melting time may be input into the trained icing discrimination model to obtain the icing thickness of the surface to be measured, and the icing thickness is used as a discrimination result.
According to the embodiment of the disclosure, the applicant finds that a very strong nonlinear relation exists between the icing thickness, the convective heat transfer coefficient and the ice layer melting time through long-term practice, so that an icing judgment model can be constructed by adopting a neural network theory. In one possible implementation, the icing discrimination model is a Back-propagation (BP) neural network model, which includes an input layer, a hidden layer, and an output layer, and employs a nonlinear transformation function
The function acts as an activation function. As an example, the icing discrimination model may be represented by the following equations (8) and (9):
here, ,
represents an output value, < > or >>
Represents an input value, < > or >>
Represents a heat exchange coefficient>
Which indicates the time for the ice layer to melt,
represents the connection weight from the input layer to the hidden layer, is->
Represents the connection weight from hidden layer to output layer, and is greater than or equal to>
Represents a hidden layer threshold value>
Represents an output layer threshold value>
Representing an activation function.
According to an embodiment of the present disclosure, in the icing discrimination model described above, the input layer may include 2 input nodes, the hidden layer may include 20 hidden layer nodes, and the output layer may include 1 output node. As an example, the activation function can be represented by the following equations (10) and (11):
here, ,
indicating the th in the icing discrimination model>
Is entered and/or is asserted>
Represents the node in the previous layer>
To the layer node->
And N represents the total number of input nodes.
According to an embodiment of the present disclosure, the icing discrimination model may be trained by: obtaining a plurality of groups of test data of thermal conversion coefficients, ice layer melting time and icing thickness through tests; training the icing discrimination model to be trained by using the test data to determine that the icing discrimination model to be trained converges
、
、
And &>
Thus obtaining the trained icing discrimination model. In one possible implementation, the loss function of the icing criteria model may be a mean square error function, in other words, the icing criteria model to be trained may determine convergence by the mean square error loss value of the icing criteria model to be trained being less than a loss threshold. As an example, the loss threshold may be, but is not limited to, 10 -5 。
By training the icing discrimination model to be trained, according to embodiments of the present disclosure, in the event of model convergence,
、
、
and &>
May be determined, but is not limited to, as follows:
for a better understanding of the above embodiments, reference is made to fig. 2 and 3 below.
Fig. 2 is a schematic diagram illustrating an electrothermal deicing unit according to an embodiment of the present disclosure.
Referring to fig. 2, the electrothermal deicing unit may include a base layer 10, an inner insulating layer 11, a heating layer 15, an outer insulating layer 12, a wear layer 13, a layer to be measured 14, a temperature sensor 16, and a processor (not shown). As shown in fig. 2, the base layer 10, the inner insulating layer 11, the heating layer 15, the outer insulating layer 12, the wear layer 13, and the layer to be measured 14 may be sequentially disposed from bottom to top, where the temperature sensor 16 may be disposed on an interface between adjacent electrothermal deicing units, and a gap is provided between the heating layers 15 of the adjacent electrothermal deicing units. Further, the layer to be measured 14 is an ice/water layer, and in actual operation, a temperature signal can be collected by the temperature sensor 16 to determine the icing condition of the surface to be measured. Still further, the processor may receive temperature data transmitted by the temperature sensor 16 and visualize the temperature data, the visualized data employing a temperature-time curve. The temperature-time curves at the temperature sensor 16 in the ice and non-ice states according to an embodiment of the present disclosure are described below with reference to fig. 3.
FIG. 3 is a graph showing temperature versus time at a temperature sensor in ice and no ice states, according to an embodiment of the disclosure.
Referring to fig. 3, at the position of the temperature sensor 16, in both the frozen state and the ice-free state, as can be seen from the graph of the temperature change with time, when the heating layer 15 is heated in the frozen state, the temperature rises with time, and when the temperature reaches around the phase change temperature 273K, a phase change step occurs in which the temperature no longer changes with time because the ice needs to absorb latent heat during the conversion into water without changing the temperature; however, in the ice-free state, the temperature between the air-abrasion layer 13 gradually increases as the heating layer 15 heats up, and the increasing process will not generate a phase change step similar to the icing state; therefore, whether the ice layer is frozen or not can be judged by judging whether the phase change step occurs or not, and the ice layer melting time can be further determined under the condition that the phase change step occurs. In the temperature-time relation at the temperature sensor 16, in any time interval, when the gradient of the temperature along with the time change is smaller than a preset threshold value, the icing of the surface to be detected is judged; otherwise, no ice is formed. Here, the gradient of the temperature change with time is defined as an increase or decrease amount of the temperature per unit time, and the temperature of latent heat absorbed during the phase change of ice into water is not changed, so that the preset threshold value may be 0K/s in theory, however, in actual operation, the temperature sensor has an error, and other errors may cause inaccurate reading of the temperature parameter, and therefore, the setting range of the preset threshold value may be a value around 0K/s, which is not limited by the present disclosure.
According to the method for judging icing based on the electrothermal deicing signal, after a trained icing judging model is obtained, the icing thickness can be obtained only by determining parameters such as convective heat transfer coefficient, ice layer melting time and the like and bringing the determined parameters into the trained icing judging model, on one hand, compared with an icing judging method in the related technology, the method is simpler and quicker, and on the other hand, compared with a method for converting the icing thickness by measuring cloud and mist parameters, the obtained icing thickness is closer to a true value; in addition, aiming at the surface to be detected provided with the electrothermal deicing unit, the deicing signal data obtained by the existing electrothermal deicing unit is fully utilized for deep processing, so that the integration of electrothermal deicing and electrothermal ice detection is realized, the implementation is simple and quick, the judgment result is accurate and reliable, the problem of detection and false alarm is not easy to occur, the method is obviously superior to the scheme of judging icing through various sensors, the cost in the aspects of design, materials and the like is saved, and the defect of uneven surface at the installation position of the icing sensor and the like is avoided.
FIG. 4 is a block diagram illustrating an apparatus to discriminate icing based on electrothermal deicing signals according to an embodiment of the present disclosure. The device for discriminating icing based on electrothermal deicing signals according to the embodiments of the present disclosure may be implemented in a computing device having sufficient computing power. Here, the electrothermal deicing signal may include, but is not limited to, an ice layer melting time.
Referring to fig. 4, an apparatus 400 for discriminating icing based on electrothermal deicing signals may include a first determination unit 410, a second determination unit 420, and an icing discrimination unit 430.
The first determining unit 410 may determine the convective heat transfer coefficient of the surface to be measured according to the incoming flow velocity.
The second determination unit 420 may determine the ice layer melting time using a temperature sensor. Here, the temperature sensor may be disposed on an interface between every two adjacent electrothermal deicing units among the plurality of electrothermal deicing units, and the plurality of electrothermal deicing units may be disposed on the surface to be measured.
The icing discriminating unit 430 may input the convective heat transfer coefficient and the ice layer melting time into the trained icing discriminating model to obtain the icing thickness of the surface to be measured, and use the icing thickness as a discrimination result.
According to an embodiment of the present disclosure, the first determining unit 410 may determine the density and the dynamic viscosity coefficient of air at the surface to be measured according to the ambient temperature; determining the local Reynolds number at the surface to be measured according to the density, the dynamic viscosity coefficient and the incoming flow speed; and determining the convective heat transfer coefficient according to the ambient temperature, the density, the dynamic viscosity coefficient, the incoming flow speed and the local Reynolds number.
According to an embodiment of the present disclosure, the first determination unit 410 may also determine the convective heat transfer coefficient through equation (7) as described above.
According to an embodiment of the present disclosure, the second determining unit 420 may acquire a phase transition time required for the ice layer at the temperature sensor to reach the phase transition temperature using the temperature sensor; the phase change time was determined as the ice layer melting time. Here, the phase transition temperature is a temperature at which ice phase changes to water.
According to an embodiment of the present disclosure, the second determination unit 420 may also acquire temperature data at the temperature sensor; determining the gradient of the temperature along with the time according to the temperature data; and determining that the ice layer reaches the phase change temperature at the first moment when the gradient is smaller than the preset threshold, and taking the interval duration between the second moment when the electric heating deicing unit starts heating and the first moment as the phase change time. Here, the gradient is an increase or decrease in the temperature per unit time.
According to an embodiment of the present disclosure, the icing discrimination model is a back propagation neural network model, and may include an input layer, a hidden layer, and an output layer. Here, the icing discrimination model can be represented by equations (8) and (9) as described above.
According to an embodiment of the present disclosure, the input layer may include 2 input nodes, the hidden layer may include 20 hidden layer nodes, and the output layer may include 1 output node. Here, the activation function can be represented by equations (10) and (11) as described above.
According to an embodiment of the present disclosure, the icing discrimination model may be trained by: obtaining a plurality of groups of test data of thermal conversion coefficients, ice layer melting time and icing thickness through tests; training the icing discrimination model to be trained by using the test data to determine that the icing discrimination model to be trained converges
、
、
And &>
Thus obtaining the trained icing discrimination model.
According to the embodiment of the disclosure, the icing discrimination model to be trained can pass through the icing discrimination model to be trained, and the mean square error loss value is less than 10 -5 To determine convergence.
According to the method and the device for judging icing based on the electrothermal deicing signal, after a trained icing judgment model is obtained, the icing thickness can be obtained only by determining parameters such as a convective heat transfer coefficient, ice layer melting time and the like and bringing the determined parameters into the trained icing judgment model, on one hand, compared with an icing judgment method in the related technology, the method is simpler and quicker, and on the other hand, compared with a method for converting the icing thickness by measuring cloud and mist parameters, the obtained icing thickness is closer to a real value; in addition, aiming at the surface to be detected provided with the electrothermal deicing unit, the deicing signal data obtained by the existing electrothermal deicing unit is fully utilized for deep processing, so that the integration of electrothermal deicing and electrothermal ice detection is realized, the implementation is simple and quick, the judgment result is accurate and reliable, the problem of detection and false alarm is not easy to occur, the method is obviously superior to the scheme of judging icing through various sensors, the cost in the aspects of design, materials and the like is saved, and the defect of uneven surface at the installation position of the icing sensor and the like is avoided.
Although a few embodiments of the present disclosure have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined in the claims and their equivalents.
Claims (8)
1. A method for discriminating ice based on an electrothermal ice detachment signal, wherein the electrothermal ice detachment signal comprises an ice layer melting time, wherein the method comprises:
determining the convective heat transfer coefficient of the surface to be measured according to the incoming flow speed;
determining the ice layer melting time by using a temperature sensor;
inputting the convective heat transfer coefficient and the ice layer melting time into a trained icing discrimination model to obtain the icing thickness of the surface to be measured, taking the icing thickness as a discrimination result,
the temperature sensor is arranged on an interface between every two adjacent electrothermal deicing units in the electrothermal deicing units, and the electrothermal deicing units are arranged on the surface to be detected;
wherein said determining the ice layer melting time with the temperature sensor comprises:
acquiring phase change time required by an ice layer at the temperature sensor to reach phase change temperature by using the temperature sensor, wherein the phase change temperature is the temperature when the ice phase is changed into water;
determining the phase change time as the ice layer melting time;
the icing discrimination model is a back propagation neural network model and comprises an input layer, a hidden layer and an output layer, wherein the icing discrimination model is expressed by the following equation:
wherein,
represents an output value, < > or >>
Represents an input value, < > or >>
Represents a heat exchange coefficient>
Indicates the time of ice thawing>
Represents the connection weight from the input layer to the hidden layer, is->
Represents the connection weight from hidden layer to output layer, and is greater than or equal to>
Represents a hidden layer threshold value>
Represents an output layer threshold value>
Representing an activation function.
2. The method of claim 1, wherein determining the convective heat transfer coefficient of the surface to be measured from the incoming flow velocity comprises:
determining the density and dynamic viscosity coefficient of the air on the surface to be measured according to the ambient temperature;
determining the local Reynolds number at the surface to be measured according to the density, the dynamic viscosity coefficient and the incoming flow speed;
and determining the convective heat transfer coefficient according to the ambient temperature, the density, the dynamic viscosity coefficient, the incoming flow velocity and the local Reynolds number.
3. The method of claim 2, wherein said determining said convective heat transfer coefficient from said ambient temperature, said density, said kinetic viscosity coefficient, said incoming flow velocity, and said local reynolds number comprises:
the convective heat transfer coefficient is determined by the following equation:
wherein,
represents a heat exchange coefficient>
Represents density,. Sup.>
Indicating incoming flow speed, <' > based on the time period>
Indicates an ambient temperature, <' > is present>
Represents a kinetic viscosity index->
Indicates a characteristic length, <' > or>
Represents the local Reynolds number>
Representing a preset critical reynolds number.
4. The method of claim 1, wherein said obtaining a phase change time required for an ice layer at said temperature sensor to reach a phase change temperature comprises:
acquiring temperature data at the temperature sensor;
determining a gradient of the temperature along with the time according to the temperature data, wherein the gradient is an increasing amount or a decreasing amount of the temperature in unit time;
and determining that the ice layer reaches the phase change temperature at a first moment when the gradient is smaller than a preset threshold value, and taking the interval duration between a second moment when the electric heating deicing unit starts heating and the first moment as the phase change time.
5. The method of claim 1, wherein the input layer includes 2 input nodes, the hidden layer includes 20 hidden layer nodes, and the output layer includes 1 output node, wherein,
the activation function is represented by the following equation:
wherein,
indicating the th in the icing discrimination model>
Is entered and/or is asserted>
Represents the node in the previous layer>
To the layer node->
The weight value of the time interval between the first time interval and the second time interval,Nrepresenting the total number of input nodes.
6. The method of claim 5, wherein the icing discrimination model is trained by:
obtaining a plurality of groups of test data of the thermal conversion coefficient, the ice layer melting time and the icing thickness through tests;
training an icing discrimination model to be trained using the test data to determine if the icing discrimination model to be trained converges
、
、
And &>
Thereby obtaining trainedAnd (5) an icing discrimination model.
7. The method of claim 6, wherein the icing discrimination model to be trained has a loss of mean square error value of less than 10 across the icing discrimination model to be trained -5 To determine convergence.
8. An apparatus for discriminating ice based on an electrothermal ice detachment signal, the electrothermal ice detachment signal comprising an ice layer melting time, wherein the apparatus comprises:
the first determining unit is configured to determine the convective heat transfer coefficient of the surface to be measured according to the incoming flow speed;
a second determination unit configured to determine the ice layer melting time using a temperature sensor;
an icing judging unit configured to input the convective heat transfer coefficient and the ice layer melting time into a trained icing judging model to obtain an icing thickness of the surface to be measured, and to use the icing thickness as a judging result,
the temperature sensor is arranged on an interface between every two adjacent electrothermal deicing units in the electrothermal deicing units, and the electrothermal deicing units are arranged on the surface to be detected;
wherein the second determination unit is configured to:
acquiring phase change time required by an ice layer at the temperature sensor to reach phase change temperature by using the temperature sensor, wherein the phase change temperature is the temperature when the ice phase is changed into water;
determining the phase change time as the ice layer melting time;
the icing discrimination model is a back propagation neural network model and comprises an input layer, a hidden layer and an output layer, wherein the icing discrimination model is expressed by the following equation:
wherein,
represents an output value, < > or >>
Represents an input value, < > or >>
Represents a heat exchange coefficient>
Indicates the time of ice thawing>
Represents the connection weight from the input layer to the hidden layer, is->
Represents the connection weight from hidden layer to output layer, and is greater than or equal to>
Represents a hidden layer threshold value>
Represents an output layer threshold value>
Representing an activation function. />
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