CN115408846A - Coral fine sand thermophysical parameter simplified prediction model and test device and method thereof - Google Patents
Coral fine sand thermophysical parameter simplified prediction model and test device and method thereof Download PDFInfo
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Abstract
The invention discloses a coral fine sand thermal physical parameter simplified prediction model and a test device and method thereof, belonging to the technical field of sand thermal physical parameter research in civil engineering and geotechnical engineering. The simplified prediction model of the thermophysical parameters of the coral fine sand comprises the following steps: 1) The method is suitable for a simplified prediction model k of the heat conductivity coefficient of the coral fine sand; 2) The method is suitable for a simplified prediction model c of the volume specific heat capacity of the coral fine sand; 3) The method is suitable for the simplified prediction model D of the volume specific heat capacity of the coral fine sand. The invention has the following advantages and positive effects: (1) a plurality of variables of rain intensity and dry density can be set, and the rainwater infiltration characteristic and the thermophysical rule can be comprehensively researched; (2) the operation is simple, and too many complicated steps are not needed; (3) the method is suitable for the related test of the change rule of the heat-conducting property of different positions of the coral fine sand caused by rainwater infiltration; (4) has important significance for the heat insulation of the island and reef in south China sea, the engineering design of temperature control and the research of the thermal physical characteristics of coral fine sand.
Description
Technical Field
The invention belongs to the technical field of research on sand thermal physical parameters in civil engineering and geotechnical engineering, and particularly relates to a coral fine sand thermal physical parameter simplified prediction model and a test device and method thereof.
The coral reef takes an important position in national economic development, resource development and national defense construction by unique strategic position and abundant oil gas resources and biological resources. With the construction requirements of oil development, sea reclamation engineering, fresh water resource exploitation and the like of coral reef sea areas, people gradually realize the importance of researching coral fine sand which is a special rock-soil medium, and further have deeper research on physical and mechanical properties, engineering application and the like of the coral fine sand.
In terms of thermophysics, under the condition of the same dry density, the heat conductivity coefficient of the coral fine sand is increased along with the increase of the water content; under the condition of different dry densities, the larger the dry density is, the higher the heat conductivity coefficient of the coral fine sand is, and the better the heat conductivity is. The volume heat capacity of the rock-soil body is compositionally equal to the sum of the heat capacities of solid, liquid and gas components in unit volume, and the volume specific heat capacity is increased along with the increase of the water content and the dry density. The larger the dry density is, the smaller the contact heat among the particles is, the better the temperature conductivity is, and along with the increase of the liquid phase with small heat diffusion coefficient, the increase of the heat diffusion coefficient of the coral fine sand is gradually and smoothly in a descending trend. The influence rule of the thermophysical parameters, the water content and the dry density is the basis for establishing a sandy soil model considering the mutual influence of hydraulic and thermodynamic characteristics and carrying out researches such as sandy soil solid-liquid coupling analysis and the like; the method is of great significance in demonstrating the linear relationship between the classical model of thermophysical parameters of various types of sand and coral fine sand at normal temperature. For thermophysical parameters under the normal temperature condition, prediction is generally carried out by establishing an empirical relationship with a pore ratio at present, the method is not deep enough for researching a related mechanism, the prediction values of different media are greatly different, and the prediction precision needs to be improved.
Therefore, on the premise that complete and reliable data are obtained through a test experiment, the invention provides a simplified prediction model of coral fine sand thermal physical parameters, quickly obtains the heat conductivity coefficient, the volume specific heat capacity and the thermal diffusion coefficient of coral fine sand, and provides certain basic data and reference value for research of heat conductivity mechanism of island coral sand in south China sea and subsequent prediction development technology.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a simplified prediction model of coral fine sand thermal physical parameters, a test device and a method thereof based on the existing models of heat conductivity coefficient, volume specific heat capacity and thermal diffusion coefficient for test actual measurement check and linear correction, overcomes the defects of experiment-based acquisition of the existing thermal physical parameters, tedious and slow work, insufficient applicability and the like, and conveniently and quickly acquires the thermal physical parameters of the coral fine sand.
The object of the invention is achieved by:
1. simplified prediction model for thermophysical parameters of coral fine sand
The simplified prediction model suitable for the coral fine sand thermal physical parameters at the normal temperature is as follows:
1. based on a Gangadhara Rao heat conductivity coefficient model, a heat conductivity coefficient simplified prediction model suitable for coral fine sand is provided through test actual measurement check and linear correction:
formula (1) wherein: rho d Is in dry density (g/cm) 3 ) (ii) a Omega is water content (%); undetermined coefficient b =53.75-127.462 rho d + 100.857ρ d 2 -26.667ρ d 3 ；
2. Selecting a De Vries model for carrying out volume specific heat capacity prediction analysis, and providing a volume specific heat capacity simplified prediction model suitable for coral fine sand through test actual measurement check and linear correction:
c＝0.22199+0.70217×(ρ b c s +ρ ω c ω θ) (2)
in equation (2): rho b Is the volume mass (kg/m) of soil 3 )；c s The mass heat capacity (coral reef sand mass specific heat capacity: 0.793 kJ/(kg. K)); rho ω c ω The heat capacity of water at normal temperature (4.18 MJ/(m) 3 K)); theta is the volumetric water content (m) of the soil 3 /m 3 )，ρ ω Is the density of water (1000 kg/m) 3 )；
3. Selecting a Baoming Dai model for thermal diffusion coefficient prediction analysis, and providing a volume specific heat capacity simplified prediction model suitable for coral fine sand through test actual measurement check and related parameter fitting:
D＝aρ d +bρ d 2 -21.405ρ d 3 +5.46ρ d 4 +c (3)
in equation (3): rho d Is in dry density (kg/m) 3 ) The a, the b and the c are all fitting related parameters, and the values of the coral fine sand binary fitting related parameters are shown in the table 1.
TABLE 1 coral fine sand fitting correlation parameter expression
In the table: omega is mass water content (%), rho d Is dry density (kg/m) 3 )。
Therefore, the experimental actual measurement check and linear correction are carried out based on the existing models of the heat conductivity coefficient, the volume specific heat capacity and the thermal diffusion coefficient, the experimental actual measurement check and linear correction can be quickly obtained, the defects that the existing thermal physical parameter obtaining is mainly based on the experiment, the work is complicated and slow, the applicability of the prediction model in coral fine sand is insufficient and the like are overcome, the thermal physical parameters of the coral fine sand are conveniently and quickly obtained, and the method has important significance for heat insulation and temperature control engineering design of island reefs in south China sea and coral sand thermal physical characteristic research.
2. Coral fine sand thermal physical parameter testing device.
The device comprises a tested object, namely coral reef sand;
the device is provided with a simulation unit and an acquisition unit;
the simulation unit consists of a rainfall device and a sand box;
the acquisition unit consists of a three-parameter sensor, a data acquisition unit and a computer;
the position and connection relation is as follows:
coral reef sand is arranged at the bottom in the sand box, and a rainfall device is arranged at the top of the sand box and is arranged on the bracket;
inserting a three-parameter sensor into the coral reef sand, and monitoring the volume water content and the temperature change;
the three-parameter sensor, the data acquisition unit and the computer are connected in sequence to realize automatic measurement.
3. Coral fine sand thermophysical parameter prediction method
The method comprises the following steps:
(1) the device is placed on a horizontal ground, and the functions of all parts can be checked to work normally;
(2) putting the prepared coral fine sand into a sand box for compaction;
(3) injecting water from the top end of the sand box to gradually and slowly raise the water level, so that coral reef sand in the sand box is fully saturated with water;
(4) inserting coral fine sand into the soil three-parameter sensor from the right side of the sand box;
(5) opening a water outlet valve of the sand box, and monitoring the change conditions of temperature and water content;
(6) and closing the rainfall device, recording experimental data, and adopting coral fine sand thermal physical parameters to simplify a prediction model to calculate parameters.
The invention has the following advantages and positive effects:
(1) a plurality of variables of rain intensity and dry density can be set, and the rainwater infiltration characteristic and the thermophysical rule can be comprehensively researched;
(2) the operation is simple, and too many complicated steps are not needed;
(3) the test method is suitable for the related test of the change rule of the heat-conducting property of the coral fine sand at different layers caused by rainwater infiltration;
(4) has important significance for heat insulation of islands and reefs in south China sea, temperature control engineering design and research on the thermophysical characteristics of coral fine sand.
Drawings
FIG. 1 is a block diagram showing the structure of the apparatus;
FIG. 2 is a schematic view of the structure of the apparatus;
fig. 3 is a schematic structural diagram of the simulation unit 10;
fig. 4 is a schematic structural view of the rainfall apparatus 11;
FIG. 5 is a schematic view of the construction of the sand box 12;
fig. 6 is a schematic structural view of the acquisition unit 20.
In the figure:
00-coral reef sand.
10-a unit for simulating the operation of the plant,
11-a rainfall device;
110-bracket, 111-water storage tank, 112-water control valve, 113-atomizing spray head, 114-rain cover,
115-flow meter, 116-hose, 117-water pump;
12-a sand box,
120-a flask box body, 121-a flask three-parameter sensor hole, 122-a flask water outlet,
123-sand box drain valve, 124-pulley.
20-a collection unit, wherein the collection unit,
21-three parameter sensors;
22-data collector;
23-computer.
English-translation:
1. gangadhara Rao: is the model name, named by the name of the author himself.
2. De Vries: is the model name, named by the name of the author himself.
3. Baoming Dai: is the model name, named by the name of the author himself.
Detailed Description
The following detailed description is made with reference to the accompanying drawings and examples:
1. simplified prediction model
And carrying out test actual measurement checking and linear correction based on the existing models of the heat conductivity coefficient, the volume specific heat capacity and the thermal diffusion coefficient.
1. The following 9 models were selected as the thermal conductivity model:
1) Kersten model:
in equation (1): k is a thermal conductivity (W/m.K); omega is water content (%); rho d Is in dry density (g/cm) 3 )。
2) Johansen model:
in equation (2): k is a radical of water 、k solid The heat conductivity coefficients of water and coral reef sand are respectively; n is porosity; ρ is a unit of a gradient d Is in dry density (g/cm) 3 )； S r Is the saturation.
3) Cote & Konrad model:
in equation (3): k is a radical of water 、k solid The heat conductivity coefficients of water and coral reef sand are respectively adopted; n is porosity; x and eta are parameters considering the influence of soil types and particle shapes on the dry soil thermal conductivity coefficient, and the proposal of x =0.92 and eta =1.34 is based on the coral fine sand test data; kappa is k reflecting different soil types r -S r And (4) the influence coefficient of the relationship suggests that the coral fine sand value is 3.79 through the fitting of test data.
4) Donazzi model:
in equation (4): k is a radical of formula water 、k solid The heat conductivity coefficients of water and coral reef sand are respectively adopted; n is porosity; rho d Is in dry density (g/cm) 3 )； S r Is the degree of saturation.
5) Gangadhara model:
in equation (5): ρ is a unit of a gradient d Is in dry density (g/cm) 3 ) (ii) a Omega is water content (%); the value of b is related to the soil species, and the suggested value of b =0.104 for the coral fine sand.
6) The Lu model:
in equation (6): k is a radical of formula water 、k solid The heat conductivity coefficients of water and coral reef sand are respectively adopted; n is porosity; a and b are parameters for determining the heat conduction coefficient of the dry soil, and a =0.77 and b =0.84 are respectively selected according to coral fine sand fitting data suggestions; alpha reflects the influence of soil species on Kersten variables, and 0.98 is taken for coral fine sand; s r Is the saturation.
7) The Chen model:
in equation (7): k is a radical of water 、k solid The heat conductivity coefficients of water and coral reef sand are respectively adopted; n is porosity; s. the r Is the saturation; b. c is an empirical parameter, and the proposed values for the coral reef sand are respectively b =0.132 and c =1.725
8) Paralyy model: k =0.0209 ω +0.2051 (8)
In equation (8): omega is water content (%).
9) Yang Erjing model: k =0.9784 ω 0.3008 +0.0224e 0.0453T +0.0055 (9)
In equation (9): omega is the water content (%), e is the void fraction, and T is the temperature (DEG C).
The volume specific heat capacity model is selected from the following 2 models:
10 De Vries model:
c＝ρ b c s +ρ ω c ω θ (10)
11 Xiaoming Xu model:
the thermal diffusion coefficient model was chosen as follows:
12 Baoming Dai model:
D＝a+bω+cω 2 +dω 3 +eρ d +fρ d 2 (12)
in the formula: omega is mass water content (%), rho d Is dry density (kg/m) 3 ) And a, b, c, d, e and f are all fitting related parameters.
And enumerating and analyzing each heat conductivity coefficient model, the volumetric specific heat capacity model, the thermal diffusion coefficient model and the related coefficient of the basic parameter, and selecting the optimal model for correction by combining the comparison of the measured data and the prediction numerical analysis so as to be suitable for the thermal physical parameter prediction model.
13 Predicted values and measured values are closest based on the Gangadhara Rao model, and the distribution form of a model curve and the measured data values are more concentrated along with the increase of the dry density. Therefore, a Gangadhara Rao model is selected for correction, and a heat conductivity coefficient simplified prediction model suitable for coral fine sand is provided:
equation (13) in which: rho d Is in dry density (g/cm) 3 ) (ii) a Omega is water content (%); undetermined coefficient b =53.75-127.462 rho d + 100.857ρ d 2 -26.667ρ d 3 。
14 Based on the actually measured data, the correlation coefficient of the model is higher than that of the De Vries model, and a simplified prediction model of volume specific heat capacity suitable for coral fine sand is provided through test actually measured checking and linear correction:
c＝0.22199+0.70217×(ρ b c s +ρ ω c ω θ) (14)
in equation (14): rho b Is the volume mass (kg/m) of soil 3 )；c s The mass heat capacity (coral reef sand mass specific heat capacity: 0.793 kJ/(kg. K)); rho ω c ω The heat capacity of water at normal temperature (4.18 MJ/(m) 3 K)); theta is the volumetric water content (m) of the soil 3 /m 3 )，ρ ω Is the density of water (1000 kg/m) 3 )。
15 Selecting a Baoming Dai model to perform thermal diffusion coefficient prediction analysis, and providing a simplified prediction model of volume specific heat capacity suitable for coral fine sand through test actual measurement check and related parameter fitting:
D＝aρ d +bρ d 2 -21.405ρ d 3 +5.46ρ d 4 +c (15)
in equation (15): ρ is a unit of a gradient d Is dry density (kg/m) 3 ) The a, the b and the c are all fitting related parameters, and the values of the coral fine sand binary fitting related parameters are shown in the table 1.
TABLE 1 coral fine sand fitting correlation parameter expression
In the table: omega is mass water content (%), rho d Is in dry density (kg/m) 3 )。
Based on experimental data of coral fine sand, the section lists and compares the thermal physical parameter model and the correlation coefficient of the basic parameter one by one, further analyzes the influence factors which have the most outstanding contribution to the thermal conductivity coefficient, and selects the optimal model for correction according to the size of the correlation coefficient and by combining with the image so as to be suitable for the thermal physical parameter prediction model. The main data indexes are shown in table 2, table 3 and table 4.
The invention is further described below with reference to specific examples:
the sample required by the test is from a certain coral reef island in the sea area of south China sea. The trend that the heat conductivity coefficient of the coral sand with different particle sizes changes along with the water content and the dry density is approximately the same through an indoor preliminary test, and the coral fine sand with uniform particle sizes has small discreteness and strong regularity, so that the coral fine sand with the particle size of 0.075-0.25 mm is selected to carry out a thermal parameter test through screening in subsequent tests. Tests show that the specific gravity of the coral fine sand is 2.81, and the dry density is 1.156-1.554 g/cm 3 Samples with different dry densities and water contents were prepared with a porosity of 0.448 to 0.591 and a saturated water content of 23.1% to 41.1%, and the water content change rate was set at Δ ω =0.5% at low water contents (0 to 10%), and then from Δ ω =1.0% to 30% at water contents.
Example 1:
it is apparent from table 2 that the coefficient of thermal conductivity is highest in relation to saturation, and is closer to the coefficient of water content, and secondly, the coefficient of thermal conductivity is greater in relation to natural density, and is smallest in relation to dry density. The actually measured data has the highest correlation coefficient with a Cote & Konrad model and the lowest correlation coefficient with a Gangadhara model, but the invalid thermal conductivity value of the Gangadhara model at low water content is removed, and the predicted value of the model is closest to the actually measured value, so that the Gangadhara model is selected for correction.
TABLE 2 correlation coefficient of coral fine sand thermal conductivity coefficient model and basic parameters
Example 2:
as can be seen from table 3, the correlation coefficient of the volume specific heat capacity and the saturation of the coral fine sand is the largest, the correlation coefficient of the water content and the proximity thereof are larger than the thermal conductivity, and the analysis shows that the volume heat capacity of the water content is at the first of three phases, the proportion contribution of the water content to the saturation is larger, so that the correlation coefficient of the saturation and the correlation coefficient of the water content are closer, and the correlation coefficient of the measured data and the De Vries model is higher. Therefore, the De Vries model is subjected to linear correction, and a prediction model for calculating the volume heat capacity of the coral fine sand is calculated.
TABLE 3 correlation coefficient between coral fine sand volumetric specific heat capacity model and basic parameters
Example 3:
a Baoming Dai model is selected and substituted into the actually measured data to carry out binary fitting on the correlation coefficient of the model, and the correlation coefficient of the model with the dry density is the largest, the correlation coefficient of the model with the dry density is the second order of the natural density, the saturation and the water content are the smallest, and the thermal diffusion coefficient, the water content and the correlation coefficient of the saturation are greatly reduced compared with the thermal conductivity and the volume specific heat capacity. Because the gas phase thermal diffusion coefficient is maximum and the liquid phase is minimum, the contribution ratio of the liquid phase to the coral fine sand thermal diffusion coefficient is suddenly reduced.
TABLE 4 correlation coefficient of coral fine sand thermal diffusion coefficient model and basic parameters
2. Device
1. General of
Referring to fig. 1 and 2, the device comprises a tested object, namely coral reef sand 00;
the simulation unit 10 and the acquisition unit 20 are arranged;
the simulation unit 10 consists of a rainfall device 11 and a sand box 12;
the acquisition unit 20 consists of a three-parameter sensor 21, a data acquisition unit 22 and a computer 23;
the position and connection relation is as follows:
inserting a three-parameter sensor 21 into the coral reef sand 00, and monitoring the volume water content and the temperature change;
the three-parameter sensor 21, the data acquisition unit 22 and the computer 23 are connected in sequence to realize automatic measurement.
2. Functional unit
1) Analog unit 10
As shown in fig. 3, the simulation unit 10 is composed of a rainfall device 11 and a sand box 12;
(1) Rainfall device 11
As shown in fig. 4, the rainfall device 11 comprises a bracket 110, a water storage tank 111, a water control valve 112, an atomizing nozzle 113, a rain cover 114, a flow meter 115, a hose 116 and a water pump 117;
the position and connection relation is as follows:
a water pump 117 is arranged in the water storage tank 111, a rain cover 114 and an atomizing nozzle 113 are arranged on the bracket 110, and the atomizing nozzle 113 is aligned with the water storage tank 111;
the water pump 117, the flow meter 115, the water control valve 112, the rain cover 114 and the atomizer 113 are connected in sequence by a hose 116.
The functions are as follows: rainfall is carried out on the coral reef sand 00, water is provided by the water storage tank 111, water power is provided by the water pump 117, the rainfall is controlled by the water control valve 112, the amount of water passing is counted by the flowmeter 115, the whole process of the rainfall device 11 is connected by the hose 116, and the height of the support 130 enables the spray head 113 to rainfall the coral reef sand 00 at different heights.
(2) Sand box 12
As shown in fig. 5, the flask 12 includes a flask box 120, a flask three-parameter sensor port 121, a flask drain port 122, a flask drain valve 123, and a pulley 124;
the position and connection relation is as follows:
the sand box body 120 is a cuboid container with an open top, and 3 sand box three-parameter sensor orifices 122 are arranged on the right side of the sand box body 120 from top to bottom in sequence;
a flask water outlet 125 and a flask water outlet valve 126 are also arranged at the bottom of the rear side of the flask box 120;
the sand box 12 is located below the rain box 11.
The functions are as follows: the coral reef sand 00 is placed and is the main body for test, and the three-parameter sensor opening 121 of the right sand box is used for inserting the three-parameter sensor 21.
2) Acquisition unit 20
As shown in fig. 6, the acquisition unit 20 includes a three-parameter sensor 21, a data acquisition unit 22 and a computer 23;
the connection relation is as follows:
the three-parameter sensor 21 is inserted into coral reef sand 00 through a sand box three-parameter sensor orifice 122 on the right side of the sand box 12, and the conductivity, the temperature and the volume water content are measured;
the data acquisition unit 22 acquires the data measured by the three-parameter sensor 21 and transmits the data to the computer 23;
the computer 23 displays the acquired data;
after the rainfall is finished, the thermal physical law of the sand box 12 is explored and analyzed.
3. The working mechanism is as follows:
the device simulates coral reef sand 00 water transfer of circulating rainfall in different stratum depths.
The experiment begins, measures coral reef sand 00 initial volume moisture content and temperature value at sand box three-parameter sensor drill way 121 department, later opens the sand box drain valve 126 of sand box 12 rear side, uses rainfall device 11 evenly to spray coral reef sand 00.
When the rainwater reaches the orifices 121 of the three parameter sensors at the top, stopping rainfall, and calculating coral reef sand 00 thermal physical parameter values by adopting a coral fine sand thermal physical parameter simplified prediction model.
And (3) continuously raining, stopping raining when the rainwater reaches the orifice 121 of the third-parameter sensor at the second position, and calculating coral reef sand 00 thermal physical parameter values at the top and the second layer by adopting a coral fine sand thermal physical parameter simplified prediction model.
And (3) continuously raining, stopping raining when the rainwater reaches the third three-parameter sensor orifice 121, and calculating three coral reef sand 00 thermal physical parameter values by adopting a coral fine sand thermal physical parameter simplified prediction model.
And then simulating rainfall again, judging that the sand layer is completely saturated when water flows out from the sand box water outlet 125, stopping rainfall, and calculating three coral reef sand 00 thermal physical parameter values by adopting a coral fine sand thermal physical parameter simplified prediction model.
The three-parameter sensor 21 inserted into the right side of the sand box 12 can collect rainfall and the whole moisture content and temperature change until the sand layer is filled with moisture.
After the test, the process of coral reef sand 00 water migration and heat conductivity change in the rainfall infiltration process can be counted, calculated and analyzed through the recorded data and by using drawing software.
3. Method of producing a composite material
The coral fine sand with different dry densities can be placed in the sand box, then the rainfall device is started, the three-parameter sensor is placed in the sand box, the temperature and the volume water content of sand layers in different depths can be monitored at all times, when the sand layers are not completely saturated, rainwater permeates from top to bottom, so that the volume water contents of different depths of the sand box are different, and the heat conduction performances of different sand layers are different, at the moment, the heat conduction coefficient model k, the volume specific heat capacity model c and the thermal diffusion coefficient model D provided by the invention are adopted, the self-control can be realized based on the known dry density, the water content can be measured by the sensor, so that the dry density and the water content are substituted into the three models, the heat conduction coefficient, the volume specific heat capacity and the thermal diffusion coefficient of different depths can be obtained, and the model test is carried out for the practical significance of further researching and reflecting the influence of rainfall on the heat conduction performances of different depths of the sand layers.
Claims (3)
1. The coral fine sand thermophysical parameter simplified prediction model is characterized in that:
is suitable for use at normal temperature;
1) Based on a Gangadhara Rao heat conductivity coefficient model, a heat conductivity coefficient simplified prediction model suitable for coral fine sand is provided through test actual measurement check and linear correction:
formula (1) wherein: ρ is a unit of a gradient d Dry density (g/cm 3); omega is water content (%); undetermined coefficient b =53.75-127.462 rho d +100.857ρ d 2 -26.667ρ d 3 ；
2) Selecting a De Vries model for carrying out volume specific heat capacity prediction analysis, and providing a volume specific heat capacity simplified prediction model suitable for coral fine sand through test actual measurement check and linear correction:
c＝0.22199+0.70217×(ρ b c s +ρ ω c ω θ) (2)
in equation (2): rho b Is the volume mass (kg/m) of soil 3 )；c s The mass heat capacity (the mass-to-heat ratio of the coral reef sand is 0.793 kJ/(kg.K)); rho ω c ω The heat capacity of water at normal temperature (4.18 MJ/(m) 3 K)); theta is the volumetric water content (m) of the soil 3 /m 3 )，ρ ω Is the density of water (1000 kg/m) 3 )；
3) Selecting a Baoming Dai model for thermal diffusion coefficient prediction analysis, and providing a volume specific heat capacity simplified prediction model suitable for coral fine sand through test actual measurement check and related parameter fitting:
D＝aρ d +bρ d 2 -21.405ρ d 3 +5.46ρ d 4 +c (3)
in equation (3): rho d Is in dry density (kg/m) 3 ) A, b and c are all fitting related parameters, and the values of the coral fine sand binary fitting related parameters are as follows:
a＝-13.894+681.241ω 2 -1313.908ω 3
b＝28.658-275.765ω 2
omega is mass water content (%), rho d Is in dry density (kg/m) 3 )。
2. The test device for the coral fine sand thermophysical parameter simplified prediction model based on claim 1, is characterized in that:
including coral reef sand (00);
the device is provided with a simulation unit (10) and a collection unit (20);
the simulation unit (10) consists of a rainfall device (11) and a sand box (12);
the acquisition unit (20) consists of a three-parameter sensor (21), a data acquisition unit (22) and a computer (23);
the position and connection relation is as follows:
coral reef sand (00) is arranged at the bottom in the sand box (12), and a rainfall device (11) is arranged at the top of the sand box (12) and is arranged on the bracket;
inserting a three-parameter sensor (21) into the coral reef sand (00) to monitor the volume water content and the temperature change;
the three-parameter sensor (21), the data acquisition unit (22) and the computer (23) are connected in sequence to realize automatic measurement.
3. The coral fine sand thermal physical parameter prediction method according to claim 1 or 2, characterized by comprising the steps of:
(1) the device is placed on a horizontal ground, and the functions of all parts can be checked to work normally;
(2) placing the prepared coral fine sand into a sand box for compaction;
(3) injecting water from the top end of the sand box to gradually and slowly raise the water level so that the coral reef sand in the sand box is fully saturated with water;
(4) inserting coral fine sand into the soil three-parameter sensor from the right side of the sand box;
(5) opening a water outlet valve of the sand box, and monitoring the change conditions of temperature and water content;
(6) and closing the rainfall device, recording experimental data, and adopting coral fine sand thermal physical parameters to simplify a prediction model to calculate parameters.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109117452A (en) * | 2018-07-13 | 2019-01-01 | 西安理工大学 | The improved thermal coefficient design methods based on soil physics basic parameter |
| WO2021239152A1 (en) * | 2020-05-29 | 2021-12-02 | 中国华能集团清洁能源技术研究院有限公司 | System and method for measuring equivalent geothermal temperature |
| CN114813828A (en) * | 2022-04-25 | 2022-07-29 | 河海大学 | Novel micro-thermal test method for determining thermophysical property parameters of aquifer |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109117452A (en) * | 2018-07-13 | 2019-01-01 | 西安理工大学 | The improved thermal coefficient design methods based on soil physics basic parameter |
| WO2021239152A1 (en) * | 2020-05-29 | 2021-12-02 | 中国华能集团清洁能源技术研究院有限公司 | System and method for measuring equivalent geothermal temperature |
| CN114813828A (en) * | 2022-04-25 | 2022-07-29 | 河海大学 | Novel micro-thermal test method for determining thermophysical property parameters of aquifer |
Non-Patent Citations (3)
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| 张楠;夏胜全;侯新宇;王照宇;: "土热传导系数及模型的研究现状和展望", 岩土力学 * |
| 甄作林;朱江鸿;张虎元;马国梁;盖玉玺;: "砂土导热性能测试与预测研究", 地下空间与工程学报 * |
| 皇甫红旺;晋华;: "含水率对工程常用土导热系数影响的试验研究", 水电能源科学 * |
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