Model soil construction method and device for simulating clay freezing model test

Abstract

The present application relates to the field of frozen soil temperature field and data processing technology, and discloses a method and device for constructing model soil for simulating clay freezing model tests. The method comprises: preparing salt solutions according to different mass fractions based on thermophysical similarity criteria to obtain multiple groups of solution samples, measuring parameters of the multiple groups of solution samples to obtain a target solution ratio; performing a printing process conversion on a three-dimensional structural model to obtain a model soil skeleton, injecting a salt solution with the target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil; performing a freezing cycle test on the composite model soil to obtain temperature field evolution data, and comparing and analyzing the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test. The present application achieves high-precision matching between the model soil and actual frozen soil in terms of thermal conductivity characteristics and freezing process, thereby improving the reliability and accuracy of the simulation test.

Description

Technical Field

[0001] The present application relates to the field of frozen soil temperature field and data processing, and in particular to a method and device for constructing model soil for simulating clay freezing model tests. Background Art

[0002] Simulated clay freezing model tests have become an important tool for studying frozen soils, frozen soil engineering, and their freeze-thaw properties. Research in this field primarily focuses on establishing similar experimental models to simulate and predict the thermophysical behavior of soils during freeze-thaw cycles. A common approach involves using physical model tests to study clay and its freeze-thaw effects by controlling laboratory environmental conditions such as temperature and humidity.

[0003] However, existing simulation test methods have limitations in achieving accurate simulations. Many rely primarily on simple derivational calculations based on phase analysis and experimental data, lacking in-depth modeling of the relationship between the model soil's thermophysical properties, structural design, and similarity to actual soil under freezing conditions. In particular, when constructing simulation soil models, traditional methods fail to effectively integrate the soil's three-dimensional structure, printing technology, and thermophysical similarities, compromising the accuracy of the model and the reliability of the experimental results. Summary of the Invention

[0004] The present application provides a method and device for constructing a model soil for simulating a clay freezing model test, which is used to optimize the construction method of the simulated clay freezing model soil through thermophysical similarity criteria and three-dimensional structural design, and achieve high-precision matching between the model soil and actual frozen soil in thermal conductivity characteristics and freezing process, thereby improving the reliability and accuracy of the simulation test.

[0005] In the first aspect, the present application provides a method for constructing a model soil for simulating a clay freezing model test, the method comprising: performing phase analysis on the prototype soil to obtain soil skeleton thermal conductivity and specific heat capacity reference parameters, and measuring the thermal conductivity of the prototype soil at multiple temperature points using a thermal conductivity tester to obtain temperature-thermal conductivity correspondence data; performing dimensional analysis on the temperature-thermal conductivity correspondence data to obtain a thermophysical similarity criterion, and solving the similarity criterion number through computer iterative calculation to obtain skeleton material target parameters; performing thermophysical parameter testing on candidate printing materials according to the skeleton material target parameters to obtain the target skeleton material, and performing a thermophysical parameter test on the ... The target skeleton material is structurally designed through finite element analysis to obtain a three-dimensional structural model; the salt solution is prepared according to different mass fractions according to the thermophysical similarity criterion to obtain multiple groups of solution samples, and the parameters of the multiple groups of solution samples are measured through thermal conductivity testing to obtain the target solution ratio; the three-dimensional structural model is converted into a printing process to obtain a model soil skeleton, and the salt solution with the target solution ratio is injected into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil; the composite model soil is subjected to a freezing cycle test to obtain temperature field evolution data, and the temperature field evolution data is compared and analyzed with the temperature-thermal conductivity correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test.

[0006] In a second aspect, the present application provides a model soil construction device for simulating a clay freezing model test, the model soil construction device for simulating a clay freezing model test comprising:

[0007] The measurement module is used to perform phase analysis on the prototype soil to obtain the soil skeleton thermal conductivity and specific heat capacity benchmark parameters, and to measure the thermal conductivity of the prototype soil at multiple temperature points using a thermal conductivity tester to obtain temperature-thermal conductivity correspondence data;

[0008] An analysis module is used to perform dimensional analysis on the temperature-thermal conductivity correspondence data to obtain a thermophysical similarity criterion, and solve the similarity criterion number through computer iterative calculation to obtain the target parameters of the skeleton material;

[0009] A testing module is used to test the thermophysical parameters of candidate printing materials according to the target parameters of the skeleton material to obtain the target skeleton material, and to perform structural design on the target skeleton material through finite element analysis to obtain a three-dimensional structural model;

[0010] The preparation module is used to prepare salt solutions with different mass fractions according to the thermophysical similarity criterion to obtain multiple groups of solution samples, and to measure the parameters of the multiple groups of solution samples through thermal conductivity testing to obtain the target solution ratio;

[0011] The injection module is used to convert the printing process of the three-dimensional structure model to obtain a model soil skeleton, and inject a salt solution with a target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil;

[0012] The construction module is used to conduct freezing cycle tests on composite model soil to obtain temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test.

[0013] The technical solution provided in this application uses a phase analysis of the prototype soil, combined with benchmark parameters for thermal conductivity and specific heat capacity, and dimensional analysis and similarity criteria to accurately calculate the target parameters of the skeleton material, ensuring that the thermal conductivity characteristics of the simulated soil match the freezing process of actual soil. Furthermore, through thermophysical parameter testing of the printed material and finite element analysis and design, a suitable skeleton material was obtained. The model soil skeleton was precisely constructed using 3D printing technology, ensuring structural stability and detailed fidelity. Secondly, a pressure injection technique was used to accurately inject a solution containing dissolved salts into the printed skeleton structure. Through vacuum saturation and freeze-thaw cycles, the thermophysical properties of the composite model soil were maintained, further enhancing its performance and reliability in freezing tests. A precise temperature control and measurement system, combined with real-time temperature field evolution data, effectively evaluates the similarity between the model soil and the prototype soil. This technical feature not only provides a highly accurate simulation test environment but also, through the deployment of temperature sensors, temperature acquisition, and interpolation analysis, allows real-time acquisition of the soil temperature distribution during freezing and thawing, further verifying the thermophysical similarity of the model soil. Furthermore, the error calculation for temperature and thermal conductivity provides a reliable assessment method for verifying the similarity of the model soil, ensuring the accuracy and scientific nature of the freezing model test results. By combining a temperature control system, precise material design, and printing technology, not only is the construction process of the simulated soil optimized, but the controllability and repeatability of the simulation results are also improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A schematic diagram of an embodiment of a method for constructing model soil for simulating a clay freezing model test in an embodiment of the present application;

[0016] Figure 2This is a schematic diagram of an embodiment of a model soil construction device for simulating a clay freezing model test in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiments of the present application provide a method and apparatus for constructing model soil for simulating a clay freezing model test. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, one embodiment of the method for constructing a model soil for simulating a clay freezing model test includes:

[0019] Step S101: Perform phase analysis on the prototype soil to obtain soil skeleton thermal conductivity and specific heat capacity benchmark parameters, and measure the thermal conductivity of the prototype soil at multiple temperature points using a thermal conductivity tester to obtain temperature-thermal conductivity correspondence data;

[0020] Step S102: performing dimensional analysis on the temperature-thermal conductivity relationship data to obtain a thermophysical similarity criterion, and solving the similarity criterion number through computer iterative calculation to obtain the target parameters of the skeleton material;

[0021] Step S103: performing thermophysical parameter testing on the candidate printing material according to the target parameters of the skeleton material to obtain the target skeleton material, and performing structural design on the target skeleton material through finite element analysis to obtain a three-dimensional structural model;

[0022] Step S104: preparing salt solutions with different mass fractions according to the thermophysical similarity criterion to obtain multiple groups of solution samples, and measuring parameters of the multiple groups of solution samples through a thermal conductivity test to obtain a target solution ratio;

[0023] Step S105: Perform printing process conversion on the three-dimensional structure model to obtain a model soil skeleton, and inject a salt solution with a target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil;

[0024] Step S106: Perform a freezing cycle test on the composite model soil to obtain temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test.

[0025] It is understandable that the execution subject of this application can be a model soil construction device for simulating clay freezing model test, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0026] Optionally, when performing phase analysis on the prototype soil, the goal is to obtain benchmark parameters for the thermal conductivity and specific heat capacity of the soil skeleton. Phase analysis involves determining basic soil physical properties, such as particle density, porosity, and water content, through a series of experimental methods, and deriving the thermophysical parameters of the soil skeleton from these properties. The thermal conductivity and specific heat capacity of the soil skeleton are important parameters that describe the soil's thermal conductivity and heat storage capacity. To measure thermal conductivity, a thermal conductivity tester measures the thermal conductivity of the prototype soil at multiple temperature points. Specifically, the tester applies a certain temperature difference and measures the heat flow across the sample to calculate the thermal conductivity. By testing the prototype soil sample at different temperatures, temperature-thermal conductivity data can be obtained. For example, assuming the thermal conductivity is 0.5 W / (m·K) at -30°C, 0.8 W / (m·K) at 0°C, and 1.0 W / (m·K) at 20°C, a complete temperature-thermal conductivity dataset is obtained.

[0027] Dimensional analysis is performed on the temperature-thermal conductivity relationship data to derive thermophysical similarity criteria. The goal of this analysis is to unify the units of different physical quantities so that the similarity relationships between them can be applied across different experimental environments and physical systems. Dimensional analysis can lead to the development of a set of dimensionless similarity criteria, which can facilitate more accurate parameter comparisons and experimental design in actual experiments. For example, suppose the thermophysical similarity criteria derived through dimensional analysis include a dimensionless ratio expression that relates thermal conductivity to other thermophysical parameters. In this case, the similarity criterion number must be calculated. This calculation is performed using an iterative computer algorithm and numerical solutions. Assuming that a model is fitted to the temperature-thermal conductivity data, the similarity criterion values in the model can be solved by adjusting the calculation parameters and iterating repeatedly. Through these methods, the target parameters of the framework material are obtained. These parameters include the framework's thermophysical properties, such as thermal conductivity and specific heat capacity.

[0028] Based on the target parameters of the skeleton material, the candidate printing materials are tested for their thermophysical parameters. These candidate materials include polylactic acid, polystyrene copolymers, etc., and these materials are tested for thermal conductivity and specific heat capacity. By comparison, the material that matches the target skeleton material is selected to determine the target skeleton material. Specifically, by measuring the specific heat capacity and thermal conductivity of different materials, and then comparing them with the target parameters, the closest material is finally selected. After selecting the target skeleton material, the next step is to design the structure of the target skeleton material through finite element analysis. The core of this process is to geometrically design the target skeleton material according to the target parameters to obtain a three-dimensional structural model. Finite element analysis simulates the physical properties of the material such as stress and heat flow to design a three-dimensional structural model that meets the strength and thermal conduction requirements. The results of finite element analysis usually show the behavior of the structure under different working environments, for example, how to maintain the thermal conductivity of the soil during freezing.

[0029] After the 3D structural model is designed, the next step is to prepare a salt solution based on thermophysical similarity criteria. The salt solution is formulated to ensure that thermal conductivity properties similar to those of the prototype soil are simulated during freezing tests. By preparing solutions with varying mass fractions of calcium chloride and sodium chloride, multiple sets of solution samples are generated and their thermal conductivity is tested. Based on the test results, a target solution ratio that meets the thermophysical similarity criteria can be selected. After thermal conductivity tests are conducted on multiple solution sets, the target solution ratio is determined by comparing the data. For example, a solution with a calcium chloride concentration of 10% and a sodium chloride concentration of 20% might be selected as the target ratio, which produces thermal conductivity properties similar to those of the prototype soil in the experiment. Next, the target 3D structural model is converted into a model soil skeleton through a printing process. This process involves 3D printing the designed skeleton structure and then injecting a salt solution with the target solution ratio into the model soil skeleton through a vacuum saturation process to produce a composite model soil. The key to this step is to ensure that the vacuum treatment allows the solution to evenly penetrate the model soil skeleton, ensuring that every part of the model soil uniformly simulates the physical properties of frozen soil.

[0030] Finally, freezing cycle tests were conducted on the composite soil model to obtain temperature field evolution data. Freezing cycle tests simulate the temperature changes and heat conduction behavior experienced by soil during freezing. Temperature data collected by temperature sensors can be used to obtain temperature field evolution data for the composite soil model at different times and in different spaces. This data reflects the temperature distribution of the model soil during the test and facilitates comparison with the thermophysical behavior of actual frozen soil.

[0031] Numerical calculations were used to compare and analyze the temperature field evolution data with previously obtained temperature-thermal conductivity relationship data. The key to this process is to compare the two sets of data using numerical algorithms (such as least squares fitting or difference methods) and calculate the error, thereby verifying the accuracy of the simulation model. The smaller the error, the better the simulation and the more the model soil represents the freezing behavior of actual soil.

[0032] For example, when conducting a freezing cycle test on the composite model soil, assuming that at -10°C, the measured temperature is -9.8°C, while the theoretical model predicts a temperature of -10.1°C. Through numerical calculation, it can be calculated that the temperature error is 0.3°C. This error value shows that the thermal conductivity characteristics of the model soil are similar to those of the actual soil, verifying the effectiveness of the constructed composite model soil in the simulation test. From prototype soil phase analysis and thermal conductivity measurement, to skeleton material selection, three-dimensional structure design, to salt solution preparation and composite model soil construction, and finally verification through freezing cycle tests and numerical calculations, the present invention provides a scientific and efficient model soil construction method for simulating clay freezing model tests. Through precise data analysis and calculation, the high precision and high reliability of the model soil in the simulation test are ensured.

[0033] In the embodiment of the present application, by performing phase analysis on the prototype soil, combining the benchmark parameters of thermal conductivity and specific heat capacity, dimensional analysis processing and similarity criteria were used to accurately calculate the target parameters of the skeleton material, ensuring that the thermal conductivity characteristics of the simulated soil matched the freezing process of the actual soil. Furthermore, by testing the thermophysical parameters of the printed material and conducting finite element analysis and design, a suitable skeleton material was obtained, and the model soil skeleton was accurately constructed using 3D printing technology, ensuring the stability of the structure and the degree of detail restoration. Secondly, a pressure injection technique was used to accurately inject a solution containing dissolved salts into the printed skeleton structure. Through processes such as vacuum saturation and freeze-thaw cycles, the thermophysical properties of the composite model soil were ensured, further enhancing its performance and reliability in the freezing test. Through a precise temperature control and measurement system, combined with real-time monitoring of temperature field evolution data, the similarity between the model soil and the prototype soil can be effectively evaluated. This technical feature not only provides a high-precision simulation test environment, but also can obtain the temperature distribution of the soil during freezing and thawing in real time through the layout of temperature sensors, temperature acquisition and interpolation analysis, further verifying the thermophysical similarity of the model soil. Furthermore, the error calculation for temperature and thermal conductivity provides a reliable assessment method for verifying the similarity of the model soil, ensuring the accuracy and scientific nature of the freezing model test results. By combining a temperature control system, precise material design, and printing technology, not only is the construction process of the simulated soil optimized, but the controllability and repeatability of the simulation results are also improved.

[0034] In an optional embodiment, the process of executing step S101 may specifically include the following steps:

[0035] (1) The prototype soil is subjected to moisture content measurement using the drying method to obtain the dry soil mass, and the dry soil mass is subjected to soil particle specific gravity measurement using the pycnometer method to obtain the soil particle density value;

[0036] (2) Perform porosity calculation on the soil particle density value to obtain the soil porosity, and perform physical proportion processing on the soil porosity and dry soil mass to obtain the soil skeleton ratio;

[0037] (3) The prototype soil sample with soil skeleton ratio is prepared into a cylinder with a diameter of 70 mm and a height of 50 mm to obtain a standard sample, and the standard sample is dried to obtain a phase analysis sample;

[0038] (4) The thermal conductivity of the phase analysis sample is measured by a thermal conductivity tester to obtain the thermal conductivity of the soil skeleton, and the specific heat capacity of the phase analysis sample is measured by a hybrid calorimetry method to obtain the specific heat capacity of the soil skeleton, and the soil skeleton thermal conductivity and specific heat capacity benchmark parameters are obtained;

[0039] (5) The soil skeleton thermal conductivity and specific heat capacity benchmark parameters were tested at 5°C intervals in the temperature range of -30°C to 20°C to obtain the thermal conductivity data at the temperature points. The thermal conductivity data at the temperature points were then fitted to obtain the temperature-thermal conductivity correspondence data.

[0040] Optionally, the moisture content of the soil sample is measured. The drying method is a commonly used measurement method. The prototype soil sample is heated until the water in the sample is completely evaporated, and the remaining dry soil is the dry weight. The weight of the dried soil sample is subtracted from the weight of the water to obtain the mass of the dry soil. Through this process, not only the mass of the dry soil is obtained, but also the moisture content of the soil can be understood. These data are crucial for the subsequent calculation of thermophysical parameters such as soil skeleton ratio and thermal conductivity. For example, if the wet weight of a soil sample is 200 grams and the dry weight is 180 grams after drying, then the moisture content is 10%. This data provides the basis for further determination of the specific gravity of soil particles.

[0041] After determining the dry soil mass, the specific gravity of the soil particles is measured. The pycnometer method is a common method for measuring soil particle density. In this method, a dry soil sample is first placed in a pycnometer. After adding water, the volume change of the water is measured to calculate the density of the soil particles. This process reflects the volume change of the soil particles in water, thus obtaining the density of the soil particles. The porosity of the soil is calculated based on the density of the soil particles and the density of the soil mass. Porosity refers to the ratio of the volume of voids in the soil to the total volume. During the calculation process, the ratio of the soil mass density to the soil particle density reflects the distribution of solid matter and pores in the soil. From this data, the porosity of the soil can be further inferred, which is important for the subsequent calculation of the soil skeleton ratio. The porosity can be used to understand the structural characteristics and thermal conductivity of the soil.

[0042] The soil skeleton ratio is calculated from the soil particle density and porosity. The soil skeleton ratio indicates the proportion of the solid portion of the soil that does not contain water. In actual measurements, the soil skeleton ratio is calculated by combining the dry soil mass with the water mass through a phase matching method. The size of this ratio directly affects the thermal conductivity characteristics of subsequent samples and their effectiveness in simulating the freezing process. Based on the calculated soil skeleton ratio, standard samples are prepared in a cylindrical shape with a diameter of 70 mm and a height of 50 mm. These standard samples will be used for subsequent thermal conductivity and specific heat capacity measurements. During the sample preparation process, the size and shape of each sample are ensured to be consistent to enable scientific comparison and testing. The prepared samples are further dried to obtain phase analysis samples.

[0043] Phase analysis samples undergo thermal conductivity measurements. Thermal conductivity is a parameter that measures a material's ability to conduct heat. During this process, one end of the sample is heated, while a heat flow sensor monitors the heat flow at the other end, allowing the thermal conductivity to be calculated. The surface of the soil sample must be flat and its thickness uniform to obtain an accurate thermal conductivity value. The magnitude of the thermal conductivity directly affects the thermophysical properties of the model soil, which in turn affects its performance in simulated freezing tests. In addition to measuring thermal conductivity, hybrid calorimetry is used to measure the specific heat capacity of phase analysis samples. Specific heat capacity is a parameter that measures a material's ability to store heat. During the experiment, the sample is heated to a certain temperature, and the specific heat capacity is calculated by measuring the relationship between the temperature change and the amount of heat applied. The measurement of specific heat capacity provides the necessary thermophysical data for subsequent simulations of heat conduction processes.

[0044] After measuring the thermal conductivity at different temperatures, the data at these temperature points is fitted to determine the temperature-thermal conductivity relationship. Typically, this process involves using curve fitting algorithms or linear regression methods to process the experimental data. By fitting these temperature points, a functional relationship between thermal conductivity and temperature can be constructed, accurately describing the soil's heat conduction behavior at different temperatures.

[0045] The temperature-thermal conductivity relationship obtained through fitting provides theoretical support for simulating the thermophysical properties of soil during freezing. For example, assuming the thermal conductivity measured at multiple test temperatures is 0.6 W / (m·K), 0.7 W / (m·K), 0.8 W / (m·K), and so on, a linear or curvilinear relationship can be obtained through fitting. This method can predict the thermal conductivity at any temperature.

[0046] For example, assume that in a measurement of prototype soil, the dry mass obtained by the oven drying method is 180 g, the wet mass is 200 g, and the water content is 10%. The particle density measured by the pycnometer method is 2.65 g / cm³, and the bulk density is 1.6 g / cm³. Calculation yields a porosity of 39.6%, and a further calculation yields a soil skeleton ratio of 0.9, indicating that 90% of the soil consists of solid skeletal material. Based on this data, a standard sample was prepared, and its thermal conductivity was measured using a thermal conductivity meter, yielding a value of 0.75 W / (m·K). Next, the specific heat capacity of the phase analysis sample was measured using hybrid calorimetry, yielding a value of 500 J / (kg·°C). During the testing process, thermal conductivity data at various temperatures were obtained through point-by-point testing. A linear regression equation was then fitted to determine the temperature-thermal conductivity relationship, yielding the result k(T)=0.02T+0.5, where k(T) is the thermal conductivity and T is the temperature. Considering the latent heat release when liquid water is converted into solid ice near 0℃, which will affect the temperature drop in the freezing process, nonlinear fitting is used near 0℃.

[0047] In an optional embodiment, the process of executing step S102 may specifically include the following steps:

[0048] (1) The temperature-thermal conductivity relationship data is decomposed into four basic dimensions: length, time, heat, and temperature to obtain a dimensional matrix. The dimensional matrix is then subjected to linear independence analysis to obtain an independent dimension group.

[0049] (2) Substituting the Fourier heat conduction equation into the independent dimension group, the basic heat conduction equation is obtained, and the third-class boundary condition setting is performed on the basic heat conduction equation to obtain the thermophysical similarity criterion;

[0050] (3) The thermal conductivity similarity relationship is derived from the thermophysical similarity criterion to obtain the thermal conductivity ratio relationship. The time scale, length scale, density scale, and temperature scale in the thermal conductivity ratio relationship are initially assigned to obtain the similarity criterion number.

[0051] (4) The similarity criterion number is assigned a value according to the time scale of 1, the length scale of 2, the temperature scale of 1, and the density scale of 1 to obtain the numerical value of the similarity criterion number, and the dimensionless equation of the similarity criterion number is numerically solved to obtain the convergence value of the similarity criterion number;

[0052] (5) The convergence value of the similarity criterion number is converted into thermophysical parameters to obtain the target parameters of the skeleton material.

[0053] Optionally, the physical properties of the prototype soil are analyzed and mapped to the thermophysical properties of the model soil through thermophysical similarity criteria. This process involves multiple steps, including dimensional analysis, substitution of Fourier heat conduction equations, derivation of thermal conductivity ratios, assignment of similarity criteria numbers, and numerical iterative calculations. The key to each step is precise mathematical processing to ensure that the simulated model soil can accurately reflect the behavior of the actual soil in the freezing test. The temperature-thermal conductivity correspondence data is analyzed according to four basic dimensions, including length, time, heat, and temperature. In dimensional analysis, the main goal is to simplify complex physical problems into a set of dimensionless dimension groups, so that the data can be effectively processed and the relevant thermophysical similarity criteria can be derived.

[0054] Specifically, the units of temperature T (unit: K), thermal conductivity k (unit: W / (m·K)), and other related physical quantities are decomposed to obtain a dimensional matrix. The dimensional matrix represents how the unit of each quantity affects the thermal conductivity characteristics of the system. By performing linear independence analysis on the dimensional matrix, a set of independent dimensional groups can be obtained. The independent dimensional groups contain the main physical quantities that affect the thermal conductivity behavior, while irrelevant physical quantities are removed. For example, length L, time t, heat Q, temperature difference The relationship between these quantities can be extracted into a simplified matrix through dimensional analysis, which can further analyze the most critical physical quantities in the system.

[0055] After the dimensional analysis is completed, the next step is to substitute the Fourier heat conduction equation. Fourier's law describes how heat flow propagates in matter, specifically the heat flux density q and the temperature gradient By substituting the independent dimension group obtained by dimensional analysis into the Fourier heat conduction equation, the basic equation describing the heat conduction process can be derived. The general form of Fourier's law is:

[0056]

[0057] in, is the heat flux per unit area, is the thermal conductivity, is the temperature gradient.

[0058] When simulating clay freezing model tests, the heat conduction equation involves the relationship between temperature and heat flow, and third-type boundary conditions are necessary to describe the heat exchange between the system and the environment. In freezing tests, boundary conditions are often based on the relationship between temperature and heat flow. For example, boundary conditions may control external temperature changes, thereby affecting the temperature field evolution of the model soil. By setting these boundary conditions, thermophysical similarity criteria can be further derived. The thermal conductivity ratio relationship describes the difference in heat conduction between the model soil and the prototype soil. By establishing similarity relationships, it is possible to deduce how the thermal conductivity of the model soil and the prototype soil relate to each other under different conditions.

[0059] Assume that the thermal conductivity ratio relationship is:

[0060]

[0061] in, and are the thermal conductivity of the model soil and prototype soil, and are the characteristic lengths of the model soil and prototype soil, and are the thermodynamic temperatures of the model soil and prototype soil, and are the densities of the model soil and prototype soil, and are the freezing times of the model soil and prototype soil respectively. In this ratio relationship, 、 They are the indices of length scale, temperature scale, density scale and time scale, which describe how the thermal conductivity changes with the change of time scale, length scale and temperature scale at different length scales.

[0062] Once the temperature-thermal conductivity relationship data is obtained, a dimensional matrix must be established based on the four basic dimensions of length, time, heat, and temperature. Linear independence analysis is then used to derive independent dimensional groups. These independent dimensional groups form the basis for constructing similarity criteria. Next, these independent dimensional groups are substituted into the Fourier heat conduction equation. In the actual freezing process, due to heat exchange with the external environment, third-order boundary conditions are required to describe this process. The Fourier heat conduction equation and third-order boundary conditions allow the establishment of a complete heat transfer governing equation. Based on this complete heat transfer governing equation, similarity analysis allows the development of thermophysical similarity criteria. These similarity criteria reflect the ratio relationships that must be satisfied between the model soil and the prototype soil in terms of thermal conductivity and specific heat capacity. Specifically, by deriving similarity relationships for thermal conductivity and specific heat capacity, parameter ratio relationships can be established.

[0063] After determining the parameter ratio relationship, it is necessary to assign reasonable values to the time scale, length scale, and temperature scale. Here, the time scale is selected as 1, the length scale is selected as 2, and the temperature scale is selected as 1. These values take into account the operability of the experiment and ensure the similarity requirements. Through these specific scale ratio values, the dimensionless equation can be solved and the converged similarity constant value is finally obtained. Finally, these similarity constant values are converted into actual thermophysical parameters to obtain the target parameters that the skeleton material needs to achieve. These target parameters provide clear indicators for the subsequent selection and preparation of model soil skeleton materials.

[0064] For example, suppose a certain type of soil is being simulated, and the thermal conductivity data for that soil at different temperatures are measured. Through dimensional analysis, this data is decomposed into basic dimensions such as length, time, and temperature, resulting in a dimensional matrix. Through linear independence analysis, the independent dimension group is extracted and substituted into the Fourier heat conduction equation to derive the heat conduction equation. Applying third-kind boundary conditions, the thermophysical similarity criterion is derived. Next, based on the actual experimental conditions, values are assigned for time ratios, length ratios, density ratios, and temperature ratios to obtain a preliminary similarity criterion number. Assume the values are 1 for the time ratio, 2 for the length ratio, and 1 for the temperature ratio. Based on this, a numerical iterative algorithm is used to optimize the convergence value of the similarity criterion number and use it to transform the target parameters of the skeleton material, such as thermal conductivity and specific heat capacity, to obtain model soil properties that accurately simulate actual soil.

[0065] In an optional embodiment, the process of executing step S103 may specifically include the following steps:

[0066] (1) Parameter matching processing is performed on the target parameters of the skeleton material to obtain three candidate printing materials: polymer materials polylactic acid, acrylonitrile-butadiene-styrene copolymer, and polyethylene terephthalate. The thermal conductivity coefficient of the three candidate printing materials is tested to obtain the thermal conductivity of the printing materials;

[0067] (2) Conducting a specific heat capacity test on the candidate printing material to obtain the specific heat capacity of the printing material, and performing a target parameter comparison process on the thermal conductivity coefficient and the specific heat capacity of the printing material to obtain the target skeleton material;

[0068] (3) Perform grid unit design processing on the target skeleton material to obtain grid unit parameters, and perform stress calculation processing on the grid unit parameters to obtain grid structure force data;

[0069] (4) Perform strength check on the grid structure stress data to obtain the structural safety factor, and perform threshold judgment on the structural safety factor to obtain a qualified grid structure;

[0070] (5) Perform spatial expansion processing on the qualified grid structure to obtain structural parameters, and then perform geometric transformation processing on the structural parameters to obtain a three-dimensional structural model.

[0071] Optionally, three candidate printing materials are selected, including polylactic acid (PLA), acrylonitrile-butadiene-styrene copolymer (ABS), and polyethylene terephthalate (PET). These materials are selected because they have different thermophysical properties and can simulate the skeleton characteristics of soil. By conducting experiments on these materials, their thermal conductivity and specific heat capacity are measured, and finally a suitable material is selected as the target skeleton material. The thermal conductivity of these three candidate materials is tested. During the thermal conductivity test, each material is measured multiple times using a thermal conductivity tester to obtain its thermal conductivity. These measurement results are:

[0072] The thermal conductivity of PLA is 0.15W / (m·K);

[0073] The thermal conductivity of ABS is 0.21W / (m·K);

[0074] The thermal conductivity of PET is 0.30W / (m·K).

[0075] Based on these thermal conductivity test results, PET was initially selected as the target skeleton material because its thermal conductivity is closer to the heat transfer characteristics of actual soil. The specific heat capacity of these materials was then tested. Specific heat capacity is the amount of heat absorbed per unit mass of material per degree of temperature increase. This is typically performed using differential scanning calorimetry (DSC).

[0076] Assume that the specific heat capacities of the three materials obtained through testing are as follows:

[0077] The specific heat capacity of PLA is 1300 J / (kg·K).

[0078] The specific heat capacity of ABS is 1500 J / (kg·K).

[0079] The specific heat capacity of PET is 1800 J / (kg·K).

[0080] These data further confirmed PET as a suitable framework material, as its specific heat capacity is similar to that of actual soil, meeting the requirements for model soil. A mesh element design was then performed for this material. The goal of mesh design is to divide the material into multiple small elements of a specific size and perform stress calculations on these elements to ensure that the framework material structure can withstand external loads and temperature fluctuations. During mesh design, each element is typically set to 1 mm × 1 mm × 1 mm. After the mesh design is completed, stress calculations are performed. Stress calculations assess the stress state of each mesh element under external forces and are typically performed using finite element analysis (FEA). FEA numerically calculates the stress distribution within each mesh element, helping to assess the overall strength of the material. Assuming a calculated maximum stress of 100 MPa, stress analysis of each element further assesses whether the material meets the structural strength requirements. After the stress calculations yield mesh element stress data, a strength check is performed. This check ensures that the mesh structure can withstand stress and temperature fluctuations under experimental conditions. Strength checks typically employ the von-Mises strength criterion or the maximum normal stress criterion. Calculate the safety factor based on the strength check results. The safety factor is the ratio between the maximum stress of the material and the actual stress, and is defined as:

[0081] Safety factor = maximum stress / maximum actual stress

[0082] Assuming the calculated maximum stress is 150 MPa and the maximum actual stress is 100 MPa, the safety factor is:

[0083] Safety factor = 150MPa / 100MPa = 1.5

[0084] If the safety factor is greater than 1, it indicates that the structure can withstand actual stresses and meets safety standards. If it is less than 1, redesign is required. Once the strength check has been completed and the grid structure is confirmed to be safe, spatial expansion and geometric transformation are performed. Spatial expansion involves expanding the two-dimensional grid cells into a three-dimensional structure, thereby obtaining a complete skeleton structure. Geometric transformation involves adjusting the geometric shape of the three-dimensional structure based on actual needs to make it more suitable for experimental conditions.

[0085] For example, during spatial expansion, each cell of the grid is assumed to expand layer by layer, and the number of layers is adjusted according to experimental requirements to obtain a suitable three-dimensional structure. Through this process, the final three-dimensional structural model can be obtained, which will be used for subsequent simulated freezing experiments.

[0086] For example, suppose PET is selected as the target skeleton material, and testing indicates that its thermal conductivity is 0.30 W / (m·K) and its specific heat capacity is 1800 J / (kg·K). Next, the material is divided into small 1mm×1mm×1mm cells according to the mesh design scheme. Finite element analysis is performed on these cells to calculate the stress distribution of each cell.

[0087] After the stress calculation, assuming a maximum stress of 100 MPa and a safety factor of 1.5, indicating that the structure met the strength requirements, each grid cell was then expanded into 10 layers, each consisting of 100 cells, to ultimately construct a three-dimensional structural model suitable for the freezing test.

[0088] In an optional embodiment, the process of executing step S104 may specifically include the following steps:

[0089] (1) Analyze and process the thermal conductivity requirements of the solution based on the thermophysical similarity criterion to obtain the solution ratio benchmark, and perform purity testing on the inorganic salt to obtain purity data;

[0090] (2) Conducting conductivity measurement on deionized water to obtain reference solvent parameters, preparing solutions of calcium chloride at six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30%, and preparing solutions of sodium chloride at six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30%, to obtain multiple groups of solution samples;

[0091] (3) Density measurement is performed on multiple groups of solution samples to obtain solution density data, and freezing point calculation is performed on the solution density data to obtain freezing point temperature data;

[0092] (4) The thermal conductivity of multiple groups of solution samples is measured by a thermal conductivity tester to obtain solution thermal conductivity data, and the solution thermal conductivity data is matched using similarity criteria to obtain candidate solution samples;

[0093] (5) The specific heat capacity of the selected solution samples is measured to obtain the solution specific heat capacity data, and the solution specific heat capacity data is comprehensively evaluated to obtain the target solution ratio.

[0094] Alternatively, solutions of different concentrations have different thermal conductivities, and therefore the thermal conductivities of solutions of different concentrations need to be analyzed according to the thermophysical similarity criterion. The thermophysical similarity criterion compares the thermophysical parameters of the prototype soil with the similarity of the model soil based on a proportional relationship to determine the solution parameters required for the model soil.

[0095] Through analysis, a benchmark for solution ratios was derived, requiring the thermal conductivity of the solution to match that of the prototype soil in the freezing test. To this end, the concentration of the solution needs to be adjusted based on the thermal conductivity of the prototype soil to ensure that its performance in the simulated soil is as close to that of the prototype soil as possible. After determining the thermal conductivity requirements for the solution, the next step is to conduct purity testing on the inorganic salts used. The purity of the inorganic salts directly affects the thermal conductivity of the solution, so the purity of the salts used must be accurately tested before the solution is prepared. The purity of the inorganic salts is tested using high-precision analytical instruments such as X-ray fluorescence spectrometers (XRF) or flame photometers to ensure that they meet experimental standards.

[0096] Purity data obtained from testing allows for accurate solution preparation. For example, if calcium chloride (CaCl2) and sodium chloride (NaCl) are selected as inorganic salts, solutions are prepared at varying mass fractions. Common concentrations include 5%, 10%, 15%, 20%, 25%, and 30%. The preparation of these solutions requires accurate mass and volume calculations to ensure the concentrations meet experimental requirements.

[0097] For example, to prepare 1L of a 10% calcium chloride solution, 100g of calcium chloride needs to be dissolved in 900mL of deionized water to obtain the target solution. This process requires precise control of the solution concentration for subsequent thermal conductivity and specific heat capacity testing.

[0098] After the solution is prepared, the next step is to measure the density of multiple groups of solution samples. The density of a solution describes the specific gravity relationship between the solute and solvent in the solution. The density data provides a basis for the subsequent calculation of thermophysical parameters. Use a high-precision density meter to measure the density of solutions of different concentrations to obtain the corresponding density data. Based on this density data, the freezing point can be further calculated. The freezing point temperature is an important parameter that describes the temperature change of a solution during the freezing process. The freezing point of a solution is closely related to its concentration. Based on the known solution density data, the freezing point of the solution can be calculated using an empirical formula or by consulting relevant standard tables. For example, for calcium chloride solution and sodium chloride solution of different concentrations, assume that the freezing point of the calcium chloride solution is -5°C at a concentration of 10%, -10°C at 20%, and -12°C at 25%.

[0099] The calculation of this freezing point data helps ensure that the selected solution can achieve the required low-temperature freezing conditions during the simulated freezing test, ensuring that the freezing behavior of the solution in the frozen state is consistent with that of actual soil.

[0100] Next, the thermal conductivity of multiple solution samples was tested. Thermal conductivity is an important indicator of the solution's heat conduction capacity and directly affects the performance of the model soil in the freezing test. Using a thermal conductivity tester, such as the laser flash method or the steady-state heat flow method, the thermal conductivity of the solution samples was accurately measured. Through the thermal conductivity test, the thermal conductivity data of solutions of different concentrations were obtained, for example:

[0101] The thermal conductivity of a 5% solution is 0.45 W / (m·K);

[0102] The thermal conductivity of a 10% solution is 0.50 W / (m·K);

[0103] The thermal conductivity of a 15% solution is 0.55 W / (m·K);

[0104] The thermal conductivity of a 20% solution is 0.60 W / (m·K);

[0105] The thermal conductivity of a 25% solution is 0.65 W / (m·K);

[0106] The thermal conductivity of a 30% solution is 0.70 W / (m·K).

[0107] This data is then matched against thermophysical similarity criteria to ensure that the solution's thermal conductivity meets the requirements for simulating actual soil. This similarity matching determines the solution concentration most suitable for simulating the soil freezing process. Finally, a differential scanning calorimeter (DSC) is used to measure the solution's specific heat capacity. Specific heat capacity is an important parameter that describes a solution's ability to absorb heat, affecting its thermal stability and performance during freezing tests. By testing the specific heat capacity of solutions of varying concentrations, the following results are obtained:

[0108] The specific heat capacity of a 5% solution is 300 J / (kg·K);

[0109] The specific heat capacity of a 10% solution is 320 J / (kg·K);

[0110] The specific heat capacity of a 15% solution is 340 J / (kg·K);

[0111] The specific heat capacity of a 20% solution is 360 J / (kg·K);

[0112] The specific heat capacity of a 25% solution is 380 J / (kg·K);

[0113] The specific heat capacity of a 30% solution is 400 J / (kg·K).

[0114] These specific heat capacity data will be comprehensively evaluated with the target parameters, and the optimal solution ratio will be calculated by the weighted average method or weighted scoring method. Taking into account the thermal conductivity, specific heat capacity, density and freezing point of the solution, the target solution ratio is determined. For example, 20% calcium chloride solution and 10% sodium chloride solution are selected as the optimal ratio. For example, suppose the thermal conductivity, specific heat capacity and density of solutions with different concentrations from 5% to 30% are measured, and these data are combined to perform similarity criterion matching. Through experimental data, the thermal conductivity of 20% calcium chloride solution is 0.60W / (m·K), the specific heat capacity is 360J / (kg·K), and the freezing point is -10°C, which meets the requirements of the freezing test. Ultimately, this solution was selected as the target solution ratio.

[0115] In an optional embodiment, the process of executing step S105 may specifically include the following steps:

[0116] (1) Slice the three-dimensional structure model to obtain layered data, and perform printing path planning on the layered data to obtain printing trajectory parameters;

[0117] (2) The printing trajectory parameters are processed to set the printing temperature, and the nozzle temperature is obtained as 230°C. The nozzle temperature is then matched with the printing platform temperature, and the platform temperature is obtained as 60°C.

[0118] (3) The printing speed is calculated based on the nozzle temperature and platform temperature, and the printing speed is 60 mm per second. The layer thickness setting process is also performed on the printing speed, and the layer spacing is 0.2 mm, thus obtaining the model soil skeleton;

[0119] (4) The model soil skeleton is vacuum-extracted to obtain a skeleton in an exhaust state, and a salt solution with a target solution ratio is pressure-injected to obtain a skeleton in an infiltrated state;

[0120] (5) The saturation of the infiltrated skeleton is measured to obtain the filling rate data, and the filling rate data is tested to obtain the composite model soil.

[0121] Optionally, after the 3D structural model is created, it is sliced into multiple thin layers. This process converts the 3D structure into 2D slice data, enabling precise control of the printing of each layer. Slicing yields different layer information, with the thickness of each layer determined by the design requirements, typically with a spacing of 0.2mm to 0.4mm, depending on the required printing accuracy and the properties of the printing material used. Slicing decomposes the original 3D model into several layers, providing the foundation for subsequent printing path planning. Once the slice data is generated, the next step is printing path planning. Print path planning involves using an algorithm to calculate the optimal printing path based on the geometry of each layer. This path planning minimizes print head movement, reduces unnecessary backtracking and blank lines, and thus improves printing efficiency. Path planning also considers factors such as the nozzle speed and the bond strength between layers to ensure a tight connection between layers, ensuring the stability and strength of the overall structure.

[0122] Once the print path planning is complete, the next step is to set the print temperature. Excessively high or low nozzle temperatures can affect the material's adhesion, fluidity, and print detail. Based on the properties of the selected target skeleton material, in this solution, the nozzle temperature is typically set to 230°C. This temperature is adjusted based on the material's melting point and fluidity to ensure smooth melting and extrusion during printing. However, nozzle temperature is only one factor in the printing process. To ensure good adhesion between layers during printing, the temperature of the print platform must be adjusted. During platform temperature matching, the platform temperature is typically set to 60°C, which effectively reduces the risk of material warping and deformation during printing. Matching the platform temperature to the nozzle temperature ensures that the material neither flows too quickly due to excessively high temperatures nor solidifies too quickly due to excessively low temperatures during printing, thus ensuring good adhesion between printed layers.

[0123] During the printing process, in addition to nozzle and platform temperatures, print speed is also a key parameter affecting print quality. Print speed determines the material deposition rate and interlayer connectivity. In this solution, the print speed is typically set to 60 mm / s, which ensures uniform material deposition while maintaining good print accuracy. A faster print speed may result in uneven material deposition, affecting structural strength, while a slower print speed may affect production efficiency.

[0124] In order to further optimize the printing quality, the layer thickness setting must be considered. The layer thickness directly determines the printing accuracy and the degree of detail restoration. Usually, the layer thickness is set at around 0.2mm, which can effectively ensure the accurate presentation of details during the printing process. Through reasonable layer thickness control, not only can the printing error be reduced, but the bonding strength between each layer can also be improved, ensuring that the final constructed model soil skeleton has sufficient stability. After printing is completed, the model soil skeleton enters the next stage of processing, namely vacuum extraction and pressure injection. First, the printed model soil skeleton is vacuum-extracted. The purpose of this process is to remove air and moisture from the model skeleton to ensure the density and structural stability of the skeleton material. Under vacuum conditions, the pores of the material are compressed, removing excess bubbles and air, thereby improving the density and mechanical properties of the model soil skeleton.

[0125] Next, the target solution (e.g., a suitably proportioned salt solution) is injected into the model skeleton via pressure injection. Pressure injection ensures that the solution evenly fills every void in the model soil skeleton, which is crucial for subsequent freezing tests. Salt solutions with varying mass fractions simulate the physical and thermophysical properties of soil under varying environmental conditions, ensuring the accuracy of the simulated soil during the experiment. After solution injection, the porosity of the model skeleton changes, further affecting its thermal conductivity.

[0126] After the injection of the salt solution is completed, the next step is to measure the saturation of the infiltrated skeleton. Saturation refers to the proportion of the material filled with the solution, which directly affects the thermophysical properties of the model soil. Saturation determination usually uses the weighing method, that is, by measuring the wet weight and dry weight of the model skeleton, the mass of the salt solution it absorbs is calculated, thereby obtaining the saturation of the skeleton. Once the saturation data is obtained, the filling rate data is then tested. The filling rate refers to the degree of filling of the model skeleton with salt solution. It is usually required that the filling rate meet certain standards to ensure the effectiveness of the simulated soil. The methods for testing the filling rate usually include volumetric method and mass method. By comparing with the theoretical calculated value, it is determined whether the filling meets the requirements. If the filling rate is too low, the concentration of the salt solution needs to be readjusted or re-injected.

[0127] After saturation determination and filling rate testing, the resulting model soil skeleton is the composite model soil. This composite model soil not only possesses the structural strength of the skeleton material but also incorporates the thermophysical properties of the solution, enabling it to accurately simulate the thermal conductivity behavior of actual soil during the freezing test. This treatment effectively modulates the thermophysical properties of the model soil, providing a reliable foundation for freezing tests.

[0128] For example, assuming PET is used as the target skeleton material, the three-dimensional model is divided into multiple 0.2mm layers through slicing, and path planning is performed. The nozzle temperature is set to 230°C, the platform temperature is set to 60°C, and the printing speed is set to 60 mm per second. After printing is completed, the model soil skeleton is vacuum-evacuated to ensure its density. Then, a calcium chloride solution is pressure-injected at a mass fraction of 20%. After injection, the saturation of the model skeleton is 95%, and the filling rate meets the standard requirements. The resulting composite model soil performs close to actual soil in the freezing test and can effectively simulate the thermal conductivity characteristics of soil under low temperature conditions. This process demonstrates how to ensure that the physical and thermophysical properties of the model soil meet experimental requirements through precise printing technology, solution injection and saturation control.

[0129] In an optional embodiment, the process of executing step S106 may specifically include the following steps:

[0130] (1) The composite model soil is filled into a test box with a size of 1.0 m × 0.6 m × 0.5 m to obtain a test device, and temperature sensors are arranged on the test device to obtain a measurement point layout;

[0131] (2) The test device is temperature-controlled to obtain a cold source temperature of -25°C, and the ambient temperature is controlled to obtain an ambient temperature of 20°C;

[0132] (3) The composite model soil was frozen for 24 hours to obtain a frozen state sample, and the frozen state sample was thawed to obtain a freeze-thaw cycle sample;

[0133] (4) Perform temperature collection processing on the measurement point arrangement to obtain the measured temperature data, and perform temperature field interpolation processing on the measured temperature data to obtain the temperature field evolution data;

[0134] (5) The temperature field evolution data and the temperature-thermal conductivity correspondence data are processed by error calculation to obtain similarity verification results, and the similarity verification results are evaluated to obtain the model soil that meets the requirements of the freezing model test.

[0135] Optionally, the composite model soil needs to be filled into a test chamber to a specific size. The test chamber dimensions are 1.0 m × 0.6 m × 0.5 m, which is sufficient to accommodate the composite model soil to be tested and provide adequate space for temperature control. Filling the test chamber with the composite model soil ensures uniform distribution of the soil sample and adequately simulates soil behavior in subsequent tests. During the test chamber layout, the test apparatus is equipped with temperature sensors. Multiple temperature sensors are evenly distributed throughout the chamber to monitor temperature changes in real time. These sensors typically use thermocouples or RTDs (resistance temperature detectors), which provide highly accurate temperature measurements. Sensors are typically located in the four corners and the center of the chamber to comprehensively reflect temperature variations at various locations within the chamber. The data obtained from these temperature sensor placements provides the basis for subsequent temperature field analysis. Precise temperature control is a key component of the test apparatus. When conducting simulated clay freezing model tests, precise temperature control is essential to ensure that experimental conditions align with actual soil behavior. Based on the test requirements, the cold source temperature is first controlled and set to -25°C. This temperature setting is designed to simulate the temperature changes of actual soil in a freezing environment, ensuring that the thermophysical behavior of the composite model soil at low temperatures can be tested. At the same time, the control of the ambient temperature is also crucial. In a freezing environment, the surrounding temperature should be maintained at 20°C to simulate the normal temperature environment of the ground or air. This temperature setting ensures that there is an appropriate temperature difference between the model soil and the surrounding environment when it is frozen, thereby achieving an effective freezing experiment. Through precise temperature control equipment, such as an adjustable temperature thermostat or a liquid nitrogen cooling system, the cold source temperature and ambient temperature in the test can be precisely controlled.

[0136] After the test chamber is filled and the temperature is set, the composite model soil is subjected to a 24-hour freezing treatment. This process aims to freeze the model soil to a set low temperature, simulating the actual freezing state of soil. During this process, temperature field changes are crucial. Preliminary deployment of temperature sensors allows for real-time monitoring of soil temperature changes, ensuring uniform freezing across the entire specimen.

[0137] After the frozen specimens are completed, they are thawed by slowly raising the temperature to room temperature (20°C). The thawing rate must be strictly controlled to avoid uneven thermal expansion or cracking of the model soil due to rapid temperature changes. By controlling the thawing rate and temperature variation, a realistic freeze-thaw cycle can be simulated, resulting in freeze-thaw cycle samples. These freeze-thaw cycle samples provide essential experimental data for subsequent experimental analysis and verify the thermophysical behavior of the composite model soil during the freezing process. Temperature acquisition is a key step in the freezing and thawing process. Temperature sensors are deployed to collect real-time temperature data at each measurement point. This measured temperature data includes the temperature distribution of each layer within the test chamber, reflecting the temperature changes at different locations during the freezing and thawing process. The temperature acquisition system simultaneously records the data using a data logger and a computer system to ensure data integrity and accuracy. Once the measured temperature data are obtained, the temperature field needs to be interpolated. The purpose of interpolation is to numerically extend the measured data to the entire test space to accurately describe the temperature field evolution of the entire model soil. Common interpolation methods include linear interpolation and spline interpolation. These methods can infer the temperature changes at other unmeasured points based on the existing measured point data, thereby obtaining complete temperature field data. Analysis of temperature field evolution data can reveal the temperature trends of the model soil at different times and locations, which is crucial for subsequent verification of the accuracy of the thermal physics model. For example, during the freezing process, the temperature change curve typically shows a gradual decrease, while during the thawing process, the temperature gradually increases. By interpolating these temperature change data, more accurate temperature field evolution data can be obtained.

[0138] Finally, similarity verification is performed. By comparing the temperature field evolution data with the temperature-thermal conductivity relationship data, error calculations can be performed to verify whether the thermophysical properties of the model soil meet the experimental requirements. Temperature-thermal conductivity relationship data are usually obtained through experimental measurements and reflect the thermal conductivity of the material at different temperatures.

[0139] The goal of similarity verification is to ensure that the thermal conductivity behavior of the simulated soil is similar to that of the actual soil. Error calculations typically use mean square error (MSE) or mean absolute error (MAE) as evaluation criteria. For example, if the thermal conductivity of the actual soil is 0.5 W / (m·K) at a certain temperature, while the thermal conductivity of the simulated model soil is 0.45 W / (m·K), the error is 0.05 W / (m·K). By calculating the error over the entire temperature range, a comprehensive error value is obtained, which reflects the thermophysical similarity between the simulated soil and the prototype soil.

[0140] For example, suppose a simulated freezing test involves a 1.0 m × 0.6 m × 0.5 m test chamber filled with a composite model soil and temperature sensors placed throughout the chamber. During temperature control, the cold source temperature is set at -25°C and the ambient temperature is 20°C. The model soil is frozen for 24 hours, and temperature changes at different depths are recorded and interpolated to obtain complete temperature field evolution data. Comparison with known temperature-thermal conductivity relationship data reveals a calculated error of 0.02 W / (m·K) at a critical time point, indicating that the simulated soil closely matches the thermophysical properties of actual soil and meets the requirements of the freezing model test.

[0141] The above describes the model soil construction method for simulating the clay freezing model test in the embodiment of the present application. The following describes the model soil construction device for simulating the clay freezing model test in the embodiment of the present application. Figure 2 In one embodiment of the present application, a model soil construction device for simulating a clay freezing model test includes:

[0142] The measurement module is used to perform phase analysis on the prototype soil to obtain the soil skeleton thermal conductivity and specific heat capacity benchmark parameters, and to measure the thermal conductivity of the prototype soil at multiple temperature points using a thermal conductivity tester to obtain temperature-thermal conductivity correspondence data;

[0143] An analysis module is used to perform dimensional analysis on the temperature-thermal conductivity correspondence data to obtain a thermophysical similarity criterion, and solve the similarity criterion number through computer iterative calculation to obtain the target parameters of the skeleton material;

[0144] A testing module is used to test the thermophysical parameters of candidate printing materials according to the target parameters of the skeleton material to obtain the target skeleton material, and to perform structural design on the target skeleton material through finite element analysis to obtain a three-dimensional structural model;

[0145] The preparation module is used to prepare salt solutions with different mass fractions according to the thermophysical similarity criterion to obtain multiple groups of solution samples, and to measure the parameters of the multiple groups of solution samples through thermal conductivity testing to obtain the target solution ratio;

[0146] The injection module is used to convert the printing process of the three-dimensional structure model to obtain a model soil skeleton, and inject a salt solution with a target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil;

[0147] The construction module is used to conduct freezing cycle tests on composite model soil to obtain temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test.

[0148] Through the collaborative efforts of these components, the prototype soil was analyzed for physical properties. Using benchmark parameters for thermal conductivity and specific heat capacity, dimensional analysis and similarity criteria were employed to accurately calculate the target parameters for the framework material, ensuring that the thermal conductivity characteristics of the simulated soil matched those of the actual soil during freezing. Furthermore, through thermophysical parameter testing of the printed material and finite element analysis and design, a suitable framework material was identified. The model soil framework was then precisely constructed using 3D printing technology, ensuring structural stability and detailed fidelity. Secondly, a pressure injection technique was used to precisely inject a solution containing dissolved salts into the printed framework. Through vacuum saturation and freeze-thaw cycles, the thermophysical properties of the composite model soil were maintained, further enhancing its performance and reliability during freezing tests. A precise temperature control and measurement system, combined with real-time temperature field evolution data, effectively assessed the similarity between the model soil and the prototype soil. This technical feature not only provides a highly accurate simulated test environment but also, through the placement of temperature sensors, temperature acquisition, and interpolation analysis, allows for real-time acquisition of the soil temperature distribution during freezing and thawing, further verifying the thermophysical similarity of the model soil. Furthermore, the error calculation for temperature and thermal conductivity provides a reliable assessment method for verifying the similarity of the model soil, ensuring the accuracy and scientific nature of the freezing model test results. By combining a temperature control system, precise material design, and printing technology, not only is the construction process of the simulated soil optimized, but the controllability and repeatability of the simulation results are also improved.

[0149] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0150] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for constructing model soil for simulating clay freezing model test, characterized in that: The model soil construction method for the simulated clay freezing model test includes: The prototype soil is subjected to phase analysis to obtain the thermal conductivity coefficient of the soil skeleton and the specific heat capacity of the soil skeleton, and the thermal conductivity coefficient of the prototype soil is measured at multiple temperature points by a thermal conductivity tester to obtain the temperature-thermal conductivity correspondence data, including: the prototype soil is subjected to moisture content determination according to the drying method to obtain the dry soil mass, and the dry soil mass is subjected to soil particle specific gravity determination according to the specific gravity bottle method to obtain the soil particle density value; the soil particle density value is subjected to porosity calculation to obtain the soil porosity, and the soil porosity and dry soil mass are subjected to phase matching to obtain the soil skeleton ratio; the prototype soil sample of the soil skeleton ratio is subjected to a 70 mm diameter A cylindrical preparation process is performed on the sample with a diameter of 50 mm and a height of 50 mm to obtain a standard sample, and the standard sample is dried to obtain a phase analysis sample; the thermal conductivity of the phase analysis sample is measured using a thermal conductivity tester to obtain the thermal conductivity of the soil skeleton, and the specific heat capacity of the phase analysis sample is measured using a mixed calorimetry method to obtain the specific heat capacity of the soil skeleton; the thermal conductivity and specific heat capacity of the soil skeleton are tested at intervals of 5°C in a temperature range of -30°C to 20°C to obtain thermal conductivity data at multiple temperature points, and the thermal conductivity data at multiple temperature points are fitted to obtain temperature-thermal conductivity correspondence data; The temperature-thermal conductivity correspondence data is subjected to dimensional analysis to obtain the thermophysical similarity criterion, and the similarity criterion number is solved by computer iterative calculation to obtain the target parameters of the model soil skeleton material, including: decomposing the temperature-thermal conductivity correspondence data according to the four basic dimensions of prototype soil sample length, prototype soil sample freezing time, prototype soil sample density, and prototype soil sample temperature to obtain a dimensional matrix, and performing linear independence analysis on the dimensional matrix to obtain an independent dimensional group; substituting the Fourier heat conduction equation into the independent dimensional group to obtain a basic heat conduction equation, and setting the basic heat conduction equation according to the third-class boundary condition to obtain the thermophysical similarity criterion and boundary conditions; deriving the thermal conductivity similarity relationship of the thermophysical similarity criterion to obtain the thermal conductivity ratio relationship, wherein the thermal conductivity ratio relationship is: ； in, is the thermal conductivity of the model soil, is the thermal conductivity of the prototype soil, is the sample length of the model soil, is the sample length of the prototype soil, is the sample temperature of the model soil, is the sample temperature of the prototype soil, is the sample density of the model soil, is the sample density of the prototype soil, is the freezing time of the model soil sample, is the freezing time of the prototype soil sample is the length scale index, is the temperature scale index, is the density scale index, is the time scale index; The time scale, length scale, density scale, and temperature scale in the thermal conductivity ratio relationship are initially assigned values to obtain the similarity criterion number. The similarity criterion number is assigned values according to the time scale of 1, the length scale of 1 / 2, the temperature scale of 1, and the density scale of 1 to obtain the numerical value of the similarity criterion number. The dimensionless equation of the similarity criterion number is numerically solved to obtain the convergence value of the similarity criterion number. The convergence value of the similarity criterion number is converted into thermophysical parameters to obtain the target parameters of the model soil skeleton material. Conduct thermophysical parameter tests on candidate printing materials based on target parameters of the skeleton material to obtain the target skeleton material, and perform structural design on the target skeleton material through finite element analysis to obtain a three-dimensional structural model; According to the thermophysical similarity criterion, salt solutions are prepared according to different mass fractions to obtain multiple groups of solution samples, and the parameters of the multiple groups of solution samples are measured through thermal conductivity tests to obtain the target solution ratio; The three-dimensional structure model is converted into a printing process to obtain a model soil skeleton, and a salt solution with a target solution ratio is injected into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil; Freezing cycle tests were carried out on the composite model soil to obtain temperature field evolution data. The temperature field evolution data were compared and analyzed with the temperature-thermal conductivity correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test.

2. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The method of performing thermophysical parameter testing on candidate printing materials according to target parameters of the skeleton material to obtain a target skeleton material, and performing structural design on the target skeleton material through finite element analysis to obtain a three-dimensional structural model includes: Performing parameter matching processing on the target parameters of the skeleton material to obtain three candidate printing materials: polymer materials polylactic acid, acrylonitrile-butadiene-styrene copolymer, and polyethylene terephthalate; and performing thermal conductivity testing processing on the three candidate printing materials to obtain the thermal conductivity of the printing materials; Performing a specific heat capacity test on the candidate printing material to obtain the specific heat capacity of the printing material, and performing a target parameter comparison process on the thermal conductivity coefficient and the specific heat capacity of the printing material to obtain the target skeleton material; Performing grid unit design processing on the target skeleton material to obtain grid unit parameters, and performing stress calculation processing on the grid unit parameters to obtain grid structure force data; Performing strength verification processing on the force data of the grid structure to obtain a structural safety factor, and performing threshold judgment processing on the structural safety factor to obtain a qualified grid structure; The qualified grid structure is spatially expanded to obtain structural parameters, and the structural parameters are geometrically transformed to obtain the three-dimensional structural model.

3. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The salt solution is prepared according to different mass fractions according to the thermophysical similarity criterion to obtain multiple groups of solution samples, and the parameters of the multiple groups of solution samples are measured through thermal conductivity testing to obtain the target solution ratio, including: The thermal physical similarity criterion is subjected to a solution thermal conductivity requirement analysis process to obtain a solution ratio, and the inorganic salt is subjected to a purity test process to obtain purity data; Conductivity measurement is performed on deionized water to obtain solvent parameters, and solutions are prepared according to six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30% for calcium chloride, and solutions are prepared according to six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30% for sodium chloride to obtain the multiple groups of solution samples; performing density measurement processing on the plurality of groups of solution samples to obtain solution density data, and performing freezing point calculation processing on the solution density data to obtain freezing point temperature data; Performing thermal conductivity measurement processing on the plurality of solution samples using a thermal conductivity tester to obtain solution thermal conductivity data, and performing similarity criterion matching processing on the solution thermal conductivity data to obtain candidate solution samples; The specific heat capacity of the candidate solution sample is measured to obtain solution specific heat capacity data, and based on the solution density data and the freezing point temperature data, the solution specific heat capacity data is comprehensively evaluated by weighted averaging to obtain the target solution ratio.

4. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The three-dimensional structure model is subjected to printing process conversion to obtain a model soil skeleton, and a salt solution with a target solution ratio is injected into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil, including: Slicing the three-dimensional structure model to obtain layered data, and performing printing path planning on the layered data to obtain printing trajectory parameters; The printing trajectory parameters are processed for printing temperature setting to obtain a nozzle temperature of 230° C., and the nozzle temperature is processed for printing platform temperature matching to obtain a platform temperature of 60° C. The nozzle temperature and platform temperature are processed for printing speed calculation to obtain a printing speed of 60 mm per second, and the printing speed is processed for layer thickness setting to obtain a layer spacing of 0.2 mm, thereby obtaining the model soil skeleton; The model soil skeleton is subjected to vacuum extraction treatment to obtain an exhaust state skeleton, and the salt solution with the target solution ratio is subjected to pressure injection treatment to obtain an infiltrated state skeleton; The saturation measurement process is performed on the infiltrated state skeleton to obtain filling rate data, and the filling rate data is inspected to obtain the composite model soil.

5. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The freezing cycle test is performed on the composite model soil to obtain temperature field evolution data, and the temperature field evolution data is compared and analyzed with the temperature-thermal conductivity correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test, including: The composite model soil is filled into a test box according to the size of 1.0 m × 0.6 m × 0.5 m to obtain a test device, and the temperature sensors are arranged on the test device to obtain a measurement point layout; The test device is temperature-controlled to obtain a cold source temperature of -25°C, and the ambient temperature is controlled to obtain an ambient temperature of 20°C; Freezing the composite model soil for 24 hours to obtain a frozen state sample, and thawing the frozen state sample to obtain a freeze-thaw cycle sample; Performing temperature acquisition processing on the measurement point arrangement to obtain measured temperature data, and performing temperature field interpolation processing on the measured temperature data to obtain temperature field evolution data; Error calculation processing is performed on the temperature field evolution data and the temperature-thermal conductivity coefficient correspondence data to obtain a similarity verification result, and the similarity verification result is evaluated to obtain a model soil that meets the requirements of the freezing model test.

6. A model soil construction device for simulating a clay freezing model test, used to implement the model soil construction method for simulating a clay freezing model test according to any one of claims 1 to 5, characterized in that: The model soil construction device for simulating the clay freezing model test includes: The measurement module is used to perform phase analysis on the prototype soil to obtain the soil skeleton thermal conductivity and soil skeleton specific heat capacity, and to measure the thermal conductivity of the prototype soil at multiple temperature points using a thermal conductivity tester to obtain temperature-thermal conductivity correspondence data; The analysis module performs dimensional analysis on the temperature-thermal conductivity relationship data to obtain the thermophysical similarity criterion, and solves the similarity criterion number through computer iterative calculation to obtain the target parameters of the model soil skeleton material; A testing module is used to test the thermophysical parameters of candidate printing materials according to the target parameters of the skeleton material to obtain the target skeleton material, and to perform structural design on the target skeleton material through finite element analysis to obtain a three-dimensional structural model; The preparation module is used to prepare salt solutions with different mass fractions according to the thermophysical similarity criterion to obtain multiple groups of solution samples, and to measure the parameters of the multiple groups of solution samples through thermal conductivity testing to obtain the target solution ratio; The injection module is used to convert the printing process of the three-dimensional structure model to obtain a model soil skeleton, and inject a salt solution with a target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil; The construction module is used to conduct freezing cycle tests on composite model soil to obtain temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation to construct a model soil that meets the requirements of the freezing model test.

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