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

Abstract

The invention relates to the technical field of frozen soil temperature fields and data processing, and discloses a model soil construction method and device for simulating a clay freezing model test. The method comprises the following steps: preparing a salt solution according to different mass fractions according to a thermophysical similarity criterion to obtain a plurality of groups of solution samples, and performing parameter measurement on the plurality of groups of solution samples to obtain a target solution ratio; performing printing process conversion on the three-dimensional structure model to obtain a model soil skeleton, and injecting a salt solution with a target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain composite model soil; and performing a freezing cycle test on the composite model soil to obtain temperature field evolution data, performing comparative analysis on the temperature field evolution data and the temperature-heat conductivity coefficient corresponding relation data through numerical calculation, and constructing the model soil meeting the requirement of the freezing model test. According to the invention, high-precision matching of the model soil and the actual frozen soil in the heat conduction characteristic and freezing process is realized, so that the reliability and accuracy of a simulation test are improved.

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 a model soil for simulating a clay freezing model test. Background Art

[0002] At present, simulated clay freezing model tests have become an important tool for studying frozen soil, frozen soil engineering and its freeze-thaw properties. Research in this field mainly focuses on simulating and predicting the thermal physical behavior of soil during the freeze-thaw process by establishing similar experimental models. A common method is to use physical model tests to study clay and its freeze-thaw effects by controlling environmental conditions such as temperature and humidity in the laboratory.

[0003] However, existing simulation test methods have certain limitations in achieving accurate simulation. Many existing methods mainly rely on simple derivation calculations based on phase analysis and experimental data, and lack in-depth modeling of the relationship between the thermophysical properties of the model soil, structural design, and its similarity to actual soil under freezing conditions. Especially when constructing simulation model soil, traditional methods fail to effectively combine the three-dimensional structure, printing technology, and thermophysical similarity of the soil, which affects 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 thermal physical similarity criteria and three-dimensional structure design, achieve high-precision matching between the model soil and actual frozen soil in thermal conduction characteristics and freezing process, thereby improving the reliability and accuracy of the simulation test.

[0005] In a first aspect, the present application provides a method for constructing a model soil for simulating a clay freezing model test, the method for constructing a model soil for simulating a clay freezing model test comprising: performing a 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 a 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 a thermophysical parameter test on a candidate printing material according to the skeleton material target parameters to obtain a 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 test 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; a freezing cycle test is carried out 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.

[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 to solve the similarity criterion number through computer iterative calculation to obtain a target parameter of the skeleton material;

[0009] A testing module is used to test the thermal physical parameters of the 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] A preparation module is used to prepare salt solutions according to 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 tests to obtain target solution ratios;

[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 carry out freezing cycle test on the composite model soil, obtain the temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation, so as to construct the model soil that meets the requirements of the freezing model test.

[0013] In the technical solution provided by the present application, by performing phase analysis on the prototype soil, combining the benchmark parameters of thermal conductivity and specific heat capacity, using dimensional analysis processing and similarity criteria, the target parameters of the skeleton material are accurately calculated to ensure that the thermal conductivity characteristics of the simulated soil match the freezing process of the actual soil. Further, by testing the thermophysical parameters of the printed material and designing the finite element analysis, a suitable skeleton material is obtained, and the model soil skeleton is accurately constructed by three-dimensional printing technology to ensure the stability of the structure and the degree of detail restoration. Secondly, the pressure injection technology is used to accurately inject the solution containing dissolved salts into the printed skeleton structure, and the thermophysical properties of the composite model soil are ensured through processes such as vacuum saturation and freeze-thaw cycles, further enhancing its performance and reliability in the freezing test. Through the precise temperature control and measurement system, combined with the real-time monitoring of the temperature field evolution data, the similarity between the model soil and the prototype soil can be effectively evaluated. This technical feature can not only provide a high-precision simulation test environment, but also obtain the temperature distribution of the soil in the freezing and thawing process in real time through the layout of temperature sensors, temperature acquisition and interpolation analysis, and further verify the thermophysical similarity of the model soil. In addition, the error calculation of temperature and thermal conductivity provides a reliable evaluation method for the similarity verification of model soil, ensuring the accuracy and scientificity of the freezing model test results. By combining the temperature control system, precise material design and printing technology, not only the construction process of the simulated soil is optimized, but also the controllability and repeatability of the simulation results are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying 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 embodiment of the present application provides a model soil construction method and device for simulating clay freezing model test. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present 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 interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. 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 device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments 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, performing phase analysis on the prototype soil to obtain soil skeleton thermal conductivity and specific heat capacity benchmark 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;

[0020] Step S102, 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 the target parameters of the skeleton material;

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

[0022] Step S104, preparing salt solutions according to 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, performing printing process conversion on the three-dimensional structure model to obtain a model soil skeleton, and injecting 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 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 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 the present application may be a model soil construction device for simulating clay freezing model test, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0026] Optionally, when the prototype soil is subjected to phase analysis, the goal is to obtain the thermal conductivity and specific heat capacity benchmark parameters of the soil skeleton. Phase analysis refers to the determination of the basic physical properties of the soil, such as soil particle density, porosity, water content, etc., through a series of experimental means, and the thermophysical parameters of the soil skeleton are derived accordingly. The thermal conductivity and specific heat capacity of the soil skeleton are important parameters that describe the thermal conductivity and heat storage capacity of the soil. For the measurement of thermal conductivity, the thermal conductivity of the prototype soil is measured at multiple temperature points by a thermal conductivity tester. Specifically, the tester calculates the thermal conductivity by applying a certain temperature difference and measuring the heat flow at both ends of the sample. By testing the prototype soil sample at different temperatures, the temperature-thermal conductivity correspondence data of the soil can be obtained. For example, assuming that the thermal conductivity is measured to be 0.5W / (m·K) at -30℃, 0.8W / (m·K) at 0℃, and 1.0W / (m·K) at 20℃. In this way, a complete temperature-thermal conductivity data set is obtained.

[0027] The temperature-thermal conductivity correspondence data is processed by dimensional analysis to obtain the thermophysical similarity criteria. The purpose of this analysis is to unify the units of different physical quantities so that the similarity relationship between them can be applied in different test environments and physical systems. Through dimensional analysis, a set of dimensionless similarity criteria can be proposed, which can help to make more accurate parameter comparisons and experimental designs in actual experiments. For example, suppose that the thermophysical similarity criteria obtained by dimensional analysis include a dimensionless ratio expression for associating thermal conductivity with other thermophysical parameters. At this time, it is necessary to further calculate the similarity criterion number. The calculation of the similarity criterion number is realized by the iterative algorithm of the computer and solved by the numerical calculation method. Assuming that the temperature-thermal conductivity data fits a model, the similarity criterion values ​​in the model can be solved by adjusting the parameters in the calculation and iterating repeatedly. Through these methods, the target parameters of the skeleton material are obtained. These parameters include the thermophysical properties of the skeleton, such as thermal conductivity and specific heat capacity.

[0028] According to the target parameters of the skeleton material, the candidate printing materials are tested for thermophysical parameters. These candidate materials include polylactic acid, polystyrene copolymers, etc., and the thermal conductivity and specific heat capacity of these materials are tested. 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 structurally design 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 heat 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 three-dimensional structural model is designed, the next step is to prepare the salt solution according to the thermophysical similarity criterion. Here, the salt solution is prepared to ensure that the thermal conductivity characteristics similar to those of the prototype soil are simulated in the freezing test. By preparing solutions such as calcium chloride and sodium chloride with different mass fractions, multiple groups of solution samples are obtained, and the thermal conductivity of these samples is tested. According to the test results, the target solution ratio that meets the thermophysical similarity criterion can be selected. After the thermal conductivity of multiple groups of solutions is tested, 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% may be selected as the target ratio, which can produce thermal conductivity characteristics similar to those of the prototype soil in the experiment. Next, the target three-dimensional structural model is converted into a model soil skeleton through a printing process. This process involves printing the designed skeleton structure through 3D printing technology, and injecting the salt solution of the target solution ratio into the model soil skeleton through vacuum saturation treatment to obtain a composite model soil. The key to this step is to make the solution evenly penetrate into the model soil skeleton through vacuum treatment to ensure that every part of the model soil can evenly simulate the physical properties of frozen soil.

[0030] Finally, a freezing cycle test was conducted on the composite model soil to obtain the temperature field evolution data. The freezing cycle test simulates the temperature changes and heat conduction behavior of the soil during the freezing process. The temperature data collected by the temperature sensor can be used to obtain the temperature field evolution data of the composite model soil at different times and spaces. These data reflect the temperature distribution of the model soil during the test, which is helpful for comparing the thermal physical behavior of the actual frozen soil.

[0031] Through numerical calculation, the temperature field evolution data is compared and analyzed with the previously obtained temperature-thermal conductivity correspondence data. The key to this process is to compare the two sets of data through numerical algorithms (such as least squares fitting or difference method), calculate the error, and thus verify the accuracy of the simulation model. The smaller the error, the better the simulation effect, and the more the model soil can represent the freezing behavior of the actual soil.

[0032] For example, when the composite model soil is subjected to a freezing cycle test, it is assumed 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, thermal conductivity measurement, to skeleton material selection, three-dimensional structure design, to salt solution preparation and construction of composite model soil, and finally through freezing cycle tests and numerical calculations for verification, the present invention provides a scientific and efficient method for constructing model soil 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, using dimensional analysis processing and similarity criteria, the target parameters of the skeleton material are accurately calculated to ensure that the thermal conductivity characteristics of the simulated soil match the freezing process of the actual soil. Further, by testing the thermophysical parameters of the printed material and finite element analysis and design, a suitable skeleton material is obtained, and the model soil skeleton is accurately constructed by three-dimensional printing technology to ensure the stability of the structure and the degree of detail restoration. Secondly, the pressure injection technology is used to accurately inject the solution containing dissolved salts into the printed skeleton structure, and the thermophysical properties of the composite model soil are ensured through processes such as vacuum saturation and freeze-thaw cycles, further enhancing its performance and reliability in the freezing test. Through the precise temperature control and measurement system, combined with the real-time monitoring of the temperature field evolution data, the similarity between the model soil and the prototype soil can be effectively evaluated. This technical feature can not only provide a high-precision simulation test environment, but also obtain the temperature distribution of the soil in the freezing and thawing process in real time through the layout of temperature sensors, temperature acquisition and interpolation analysis, and further verify the thermophysical similarity of the model soil. In addition, the error calculation of temperature and thermal conductivity provides a reliable evaluation method for the similarity verification of model soil, ensuring the accuracy and scientificity of the freezing model test results. By combining the temperature control system, precise material design and printing technology, not only the construction process of the simulated soil is optimized, but also the controllability and repeatability of the simulation results are improved.

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

[0035] (1) The moisture content of the prototype soil is measured by the drying method to obtain the dry soil mass, and the specific gravity of the dry soil mass is measured by the specific gravity bottle method to obtain the soil particle density value;

[0036] (2) The porosity of the soil particles is calculated and processed to obtain the porosity of the soil mass, and the porosity of the soil mass and the dry soil mass are processed to obtain the soil skeleton ratio;

[0037] (3) The prototype soil sample of the 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, thereby obtaining the thermal conductivity and specific heat capacity benchmark parameters of the soil skeleton;

[0039] (5) The thermal conductivity and specific heat capacity benchmark parameters of the soil skeleton 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, the moisture content is 10%. This data provides the basis for further determination of the specific gravity of soil particles.

[0041] After determining the mass of dry soil, the specific gravity of soil particles is determined. The pycnometer method is a common method for measuring the density of soil particles. In the pycnometer method, a dry soil sample is first placed in a pycnometer, and after adding water, the density of the soil particles is calculated by measuring the volume change of water. This process reflects the volume change of soil particles in water, thereby obtaining the density of soil particles. The porosity of the soil is calculated based on the density of soil particles and the density of the soil body. Porosity refers to the ratio of the void volume in the soil body to the total volume. In the calculation process, the ratio of the soil density to the soil particle density reflects the distribution of solid matter and pores in the soil body. Through these data, the porosity of the soil body can be further inferred, which is of great significance 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 body.

[0042] The soil skeleton ratio is calculated by soil particle density and porosity. The soil skeleton ratio indicates the proportion of the solid part of the soil that does not contain water. In actual measurement, the dry soil mass and the water mass are combined by the phase matching method to calculate the soil skeleton ratio. The size of this ratio directly affects the thermal conductivity characteristics of the subsequent samples and the effect of its simulated freezing process. According to the calculated soil skeleton ratio, the standard samples are prepared into 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, ensure that the size and shape of each sample are consistent so that scientific comparison and testing can be carried out. The prepared samples will be further dried to obtain phase analysis samples.

[0043] The thermal conductivity of the phase analysis sample is measured. Thermal conductivity is a parameter that measures the heat conduction capacity of a material. In this process, one end of the sample is heated, and the other end is monitored by a heat flow sensor to calculate the thermal conductivity. When measuring, it is necessary to ensure that the surface of the soil sample is flat and the thickness is consistent, so that an accurate thermal conductivity value can be obtained. The size of the thermal conductivity directly affects the thermophysical properties of the model soil, which in turn affects its performance in the simulated freezing test. In addition to the measurement of thermal conductivity, hybrid calorimetry is used to measure the specific heat capacity of the phase analysis sample. Specific heat capacity is a parameter that measures the heat storage capacity of a material. In the experiment, the sample is heated to a certain temperature, and then the specific heat capacity is calculated by measuring the relationship between its temperature change and the amount of heat applied. The measurement of specific heat capacity provides the necessary thermophysical data for the subsequent simulation of the heat conduction process.

[0044] After measuring the thermal conductivity at different temperatures, the data of these temperature points are fitted to obtain the corresponding relationship between temperature and thermal conductivity. Usually, this process uses curve fitting algorithms or linear regression methods to process experimental data. Through the fitting of these temperature points, a functional relationship between thermal conductivity and temperature can be constructed, thereby accurately describing the thermal conduction behavior of soil at different temperatures.

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

[0046] For example, suppose that in the measurement of the prototype soil, the dry soil mass obtained by the drying method is 180 grams, the wet soil mass is 200 grams, and the water content is 10%; the soil particle density measured by the pycnometer method is 2.65g / cm³, and the soil density is 1.6g / cm³. By calculation, the porosity of the soil is 39.6%, and the soil skeleton ratio is further calculated to be 0.9, indicating that 90% of the soil is solid skeleton material. Based on these data, a standard sample was prepared, and its thermal conductivity was measured by a thermal conductivity tester to be 0.75W / (m·K). Then, the specific heat capacity of the phase analysis sample was measured by the hybrid calorimetry method, and the specific heat capacity was 500J / (kg·℃). During the test, the thermal conductivity data at different temperatures were obtained by temperature point testing, and the temperature-thermal conductivity relationship was fitted by the linear regression method. The fitting result is 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℃, it 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 according to the four basic dimensions of length, time, heat, and temperature to obtain a dimensional matrix, and a linear independence analysis is performed on the dimensional matrix to obtain an independent dimensional 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 thermal physical similarity criterion;

[0050] (3) The thermal conductivity similarity relation is derived from the thermophysical similarity criterion to obtain the thermal conductivity ratio relation, and the time scale, length scale, density scale and temperature scale in the thermal conductivity ratio relation 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 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 criterion numbers, and numerical iteration 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 are analyzed according to four basic dimensions, including length, time, heat, and temperature. In dimensional analysis, the main goal is to simplify the complex physical problem 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 a 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] in, is the heat flux density per unit area, is the thermal conductivity, is the temperature gradient.

[0057] When simulating the freezing model test of clay, the heat conduction equation involves the relationship between temperature and heat flow, and the third type of boundary conditions are necessary conditions to describe the heat exchange between the system and the environment. In freezing tests, boundary conditions are usually the relationship between temperature and heat flow. For example, boundary conditions may affect the evolution of the temperature field of the model soil by controlling the external temperature change. By setting these boundary conditions, the thermophysical similarity criteria can be further derived. The thermal conductivity ratio relationship will describe the difference in heat conduction between the model soil and the prototype soil. By establishing a similarity relationship, it can be deduced how the thermal conductivity of the model soil and the prototype soil is related to each other under different conditions.

[0058] Assume that the thermal conductivity ratio relationship is:

[0059] 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 time of the model soil and the 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 under different length scales.

[0060] After obtaining the temperature-thermal conductivity correspondence data, it is first necessary to establish a dimensional matrix based on the four basic dimensions of length, time, heat and temperature, and obtain independent dimensional groups through linear independence analysis. These independent dimensional groups are 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 the existence of heat exchange with the external environment, the third-class boundary conditions need to be introduced to describe this process. Through the Fourier heat conduction equation and the third-class boundary conditions, a complete heat transfer control equation can be established. Based on the complete heat transfer control equation, the thermophysical similarity criteria can be obtained through similarity analysis. These similarity criteria reflect the ratio relationship that the model soil and the prototype soil must satisfy in terms of thermal conductivity and specific heat capacity. Specifically, through the derivation of the thermal conductivity similarity relationship and the specific heat capacity similarity relationship, the parameter ratio relationship can be established.

[0061] After determining the parameter ratio relationship, it is necessary to reasonably assign values ​​to the time scale, length scale, and temperature scale. Here, the time scale is selected as 1, the length scale is 2, and the temperature scale is 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 can be 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.

[0062] For example, suppose a certain type of soil is being simulated, and the thermal conductivity data of the soil at different temperatures are measured. Through dimensional analysis, these data are decomposed into basic dimensions such as length, time, and temperature to obtain 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, and the third-class boundary conditions are applied to derive the thermophysical similarity criterion. Then, according to the actual experimental conditions, the time ratio, length ratio, density ratio, and temperature ratio are assigned to obtain a preliminary similarity criterion number. Assume that the assignment is time ratio 1, length ratio 2, and temperature ratio 1. On this basis, through the numerical iterative algorithm, the convergence value of the similarity criterion number is optimized and used to convert the target parameters of the skeleton material, such as thermal conductivity and specific heat capacity, to obtain the model soil characteristics that can accurately simulate the actual soil.

[0063] In an optional embodiment, the process of executing step S103 may specifically include the following steps:

[0064] (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 of the three candidate printing materials is tested to obtain the thermal conductivity of the printing materials;

[0065] (2) Performing a specific heat capacity test on the candidate printing materials to obtain the specific heat capacity of the printing materials, and performing a target parameter comparison process on the thermal conductivity coefficient and the specific heat capacity of the printing materials to obtain the target skeleton material;

[0066] (3) Performing mesh unit design processing on the target skeleton material to obtain mesh unit parameters, and performing stress calculation processing on the mesh unit parameters to obtain mesh structure force data;

[0067] (4) Perform strength verification 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;

[0068] (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.

[0069] 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 determined, 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 their thermal conductivity. These measurement results are:

[0070] The thermal conductivity of PLA is 0.15W / (m·K);

[0071] The thermal conductivity of ABS is 0.21W / (m·K);

[0072] The thermal conductivity of PET is 0.30W / (m·K).

[0073] Based on the test results of these thermal conductivity coefficients, PET was initially selected as the target skeleton material because its thermal conductivity coefficient is closer to the heat transfer characteristics of actual soil. The specific heat capacity of these materials was tested, which refers to the amount of heat absorbed by a unit mass of material per degree of increase in temperature. The specific heat capacity test is usually performed using a differential scanning calorimeter (DSC).

[0074] Assume that the specific heat capacities of the three materials obtained through testing are as follows:

[0075] The specific heat capacity of PLA is 1300 J / (kg·K).

[0076] The specific heat capacity of ABS is 1500 J / (kg·K).

[0077] The specific heat capacity of PET is 1800 J / (kg·K).

[0078] These data help further confirm PET as the target skeleton material because its specific heat capacity is similar to that of actual soil and meets the requirements of model soil. The mesh unit design is performed on the material. The purpose of mesh design is to divide the material into multiple small units according to a certain size and perform stress calculations on these units to ensure that the structure of the skeleton material can withstand external loads and temperature changes. When designing the mesh, the size of each unit is usually set to 1mm×1mm×1mm. After the mesh design is completed, stress calculation is performed. Stress calculation is used to evaluate the stress state of each mesh unit under external force, and is usually completed using the finite element analysis (FEA) method. Finite element analysis can calculate the stress distribution of the mesh unit through numerical methods, thereby helping to evaluate the overall strength of the material. Assuming that the calculated maximum stress is 100MPa, by analyzing the stress of each unit, it is possible to further evaluate whether the material meets the structural strength requirements. After the stress calculation obtains the stress data of the mesh unit, strength verification is required. The purpose of strength verification is to ensure that the mesh structure can withstand changes in stress and temperature under experimental conditions. Strength verification usually uses the von-Mises strength criterion or the maximum normal stress criterion for analysis. The safety factor is calculated based on the results of the strength check. The safety factor is the ratio between the maximum stress of the material and the actual stress, and is defined as:

[0079] Safety factor = maximum stress / maximum actual stress

[0080] Assuming that the calculated maximum stress is 150MPa and the maximum actual stress is 100MPa, the safety factor is:

[0081] Safety factor = 150MPa / 100MPa = 1.5

[0082] If the safety factor is greater than 1, it means that the structure can withstand the actual stress and meets the safety standards. If it is less than 1, it needs to be redesigned. Once the strength check is passed and the safety of the grid structure is confirmed, the next step is to carry out spatial expansion and geometric transformation. Spatial expansion is to expand the two-dimensional grid unit into a three-dimensional structure to obtain a complete skeleton structure. Geometric transformation is to adjust the geometric shape of the three-dimensional structure according to actual needs to make it more in line with experimental conditions.

[0083] For example, in the process of spatial expansion, it is assumed that each unit of the grid is expanded into a layer-by-layer structure, and the number of layers is adjusted according to the experimental requirements to obtain a suitable three-dimensional structure. Through this process, the final three-dimensional structure model can be obtained, which will be used for subsequent simulated freezing experiments.

[0084] For example, suppose PET is selected as the target skeleton material, and its thermal conductivity is 0.30W / (m·K) and its specific heat capacity is 1800J / (kg·K) through testing. Next, the material is divided into small units of 1mm×1mm×1mm according to the grid design scheme, and finite element analysis is performed on these units to calculate the stress distribution of each unit.

[0085] After the stress calculation, it is assumed that the calculated maximum stress is 100 MPa and the safety factor is 1.5, indicating that the structure meets the strength requirements. Then, in the spatial expansion process, each grid unit is expanded into 10 layers, each layer consists of 100 units, and finally a three-dimensional structural model suitable for the freezing test is constructed.

[0086] In an optional embodiment, the process of executing step S104 may specifically include the following steps:

[0087] (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;

[0088] (2) Conducting conductivity measurement on deionized water to obtain reference solvent parameters, preparing solutions of calcium chloride according to six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30%, and preparing solutions of sodium chloride according to six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30%, to obtain multiple groups of solution samples;

[0089] (3) Performing density measurement processing on multiple 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;

[0090] (4) measuring the thermal conductivity of multiple groups of solution samples using a thermal conductivity tester to obtain solution thermal conductivity data, and performing similarity criterion matching on the solution thermal conductivity data to obtain candidate solution samples;

[0091] (5) The specific heat capacity of the selected solution samples is measured to obtain the specific heat capacity data of the solution, and the specific heat capacity data of the solution is comprehensively evaluated to obtain the target solution ratio.

[0092] Optionally, solutions of different concentrations have different thermal conductivities, and therefore the thermal conductivities of solutions of different concentrations need to be analyzed according to a thermophysical similarity criterion. The thermophysical similarity criterion compares the similarity of thermophysical parameters of the prototype soil with that of the model soil based on a proportional relationship, thereby determining the solution parameters required for the model soil.

[0093] Through analysis, the solution ratio benchmark is obtained, requiring the thermal conductivity of the solution to match the thermal conductivity of the prototype soil in the freezing test. To this end, the concentration of the solution needs to be adjusted according to the thermal conductivity of the prototype soil to ensure that its performance in the simulated soil is as close to the prototype soil as possible. After determining the thermal conductivity requirements of the solution, the next step is to test the purity of the inorganic salts used. The purity of the inorganic salts directly affects the thermal conductivity of the solution, so before the solution is prepared, the purity of the salts used is accurately tested. By using high-precision analytical instruments such as X-ray fluorescence spectrometers (XRF) or flame photometers, the purity of the inorganic salts is tested to ensure that they meet the experimental standards.

[0094] The purity data obtained from the test can accurately prepare the solution. Assuming that calcium chloride (CaCl2) and sodium chloride (NaCl) are selected as inorganic salts, the solution preparation will be carried out according to different mass fractions. Common ratios include solution samples with different concentrations such as 5%, 10%, 15%, 20%, 25% and 30%. The preparation process of these solutions involves accurate mass and volume calculations to ensure that the concentration of the solution meets the experimental requirements.

[0095] For example, if you need to prepare 1L of 10% calcium chloride solution, you need to dissolve 100g of calcium chloride in 900mL of deionized water to obtain the target solution. This process requires precise control of the concentration of the solution for subsequent thermal conductivity and specific heat capacity testing.

[0096] After the solution is prepared, the next step is to measure the density of multiple groups of solution samples. The density of the solution describes the specific gravity relationship between the solute and the solvent in the solution, and 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. With these density data, the freezing point can be further calculated. The freezing point temperature is an important parameter that describes the temperature change of the solution during the freezing process, and the freezing point of the 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 solutions and sodium chloride solutions of different concentrations, it is assumed 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%.

[0097] The calculation of this freezing point data helps to 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 the actual soil.

[0098] Next, the thermal conductivity of multiple groups of solution samples was tested. Thermal conductivity is an important indicator of the thermal conductivity of the solution, which directly affects the performance of the model soil in the freezing test. Use a thermal conductivity tester, such as the laser flash method or the steady-state heat flow method, to accurately measure the thermal conductivity of the solution sample. Through the thermal conductivity test, the thermal conductivity data of solutions of different concentrations are obtained, for example:

[0099] The thermal conductivity of a 5% solution is 0.45 W / (m·K);

[0100] The thermal conductivity of a 10% solution is 0.50 W / (m·K);

[0101] The thermal conductivity of 15% solution is 0.55W / (m·K);

[0102] The thermal conductivity of a 20% solution is 0.60 W / (m·K);

[0103] The thermal conductivity of the 25% solution is 0.65 W / (m·K);

[0104] The thermal conductivity of 30% solution is 0.70W / (m·K).

[0105] These data will be matched with the thermophysical similarity criteria to ensure that the thermal conductivity of the solution meets the simulation requirements of the actual soil. Through the similarity criteria matching, it is determined which concentration of solution is most suitable for simulating the freezing process of the soil. Finally, the specific heat capacity of the solution is measured using a differential scanning calorimeter (DSC). The specific heat capacity is an important parameter that describes the ability of a solution to absorb heat, which affects the thermal stability of the solution and its performance in the freezing test. By testing the specific heat capacity of solutions of different concentrations, the following results can be obtained:

[0106] The specific heat capacity of a 5% solution is 300 J / (kg·K);

[0107] The specific heat capacity of a 10% solution is 320 J / (kg·K);

[0108] The specific heat capacity of a 15% solution is 340 J / (kg·K);

[0109] The specific heat capacity of a 20% solution is 360 J / (kg·K);

[0110] The specific heat capacity of a 25% solution is 380 J / (kg·K);

[0111] The specific heat capacity of a 30% solution is 400 J / (kg·K).

[0112] These specific heat capacity data will be comprehensively evaluated with the target parameters, and the optimal solution ratio will be calculated by weighted average method or weighted scoring method. The target solution ratio is determined by comprehensively considering the parameters such as the thermal conductivity, specific heat capacity, density and freezing point of the solution. For example, 20% calcium chloride solution and 10% sodium chloride solution are selected as the optimal ratio. For example, suppose that the thermal conductivity, specific heat capacity and density of solutions with different concentrations from 5% to 30% are measured, and these data are combined for 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. In the end, this solution was selected as the target solution ratio.

[0113] In an optional embodiment, the process of executing step S105 may specifically include the following steps:

[0114] (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;

[0115] (2) The print trajectory parameters are processed to set the print temperature, and the nozzle temperature is obtained to be 230°C. The nozzle temperature is then matched with the print platform temperature, and the platform temperature is obtained to be 60°C.

[0116] (3) The print speed is calculated based on the nozzle temperature and platform temperature, and the print speed is 60 mm per second. The layer thickness is set based on the print speed, and the layer spacing is 0.2 mm, thus obtaining the model soil skeleton.

[0117] (4) vacuum-evacuating the model soil skeleton to obtain a skeleton in an evacuated state, and pressure-injecting a salt solution having a target solution ratio to obtain a skeleton in an infiltrated state;

[0118] (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.

[0119] Optionally, after the three-dimensional structure model is established, the model is sliced ​​and divided into multiple thin layers. This process converts the three-dimensional structure into two-dimensional slice data so that the printing of each layer can be accurately controlled. Through slicing, different layer information can be obtained. The thickness of each layer is determined by the design requirements, usually with a spacing of 0.2mm to 0.4mm, depending on the printing accuracy requirements and the characteristics of the printing material used. Through slicing, the original three-dimensional model is decomposed into several layered data, thereby providing basic data for subsequent printing path planning. Once the slice data is generated, the next step is printing path planning. The planning of the printing path is to calculate the optimal printing path through an algorithm based on the geometric shape of each layer. The path planning should ensure that the movement of the print head is minimized, reduce unnecessary backtracking and blank lines, and thus improve printing efficiency. In path planning, factors such as the movement speed of the nozzle and the bonding strength between layers need to be considered to ensure that each layer is tightly connected to ensure the stability and strength of the overall structure.

[0120] Once the print path planning is completed, the next step is to set the print temperature. The nozzle temperature is critical to the print quality. Too high or too low nozzle temperature will affect the material's adhesion, fluidity, and print details. According to the characteristics of the selected target skeleton material, in this solution, the nozzle temperature is usually set to 230°C. This temperature is adjusted according to the melting point and fluidity of the material to ensure that the material can be melted and extruded smoothly during the printing process. However, the nozzle temperature is only one factor in the printing process. In order to ensure good adhesion between layers during the printing process, the temperature of the printing platform must be adjusted. In the process of platform temperature matching, the platform temperature is usually set to 60°C, which can effectively reduce the risk of material warping and deformation during the printing process. The platform temperature matches the nozzle temperature, which can ensure that the material will not flow too fast due to excessively high temperature, nor will it solidify too fast due to too low temperature, ensuring the adhesion between the printed layers.

[0121] During the printing process, in addition to the nozzle temperature and platform temperature, the printing speed is also an important parameter that affects the printing quality. The printing speed determines the deposition rate of the material and the connectivity between layers. In this solution, the printing speed is usually set to 60 mm per second, which ensures uniform deposition of the material while maintaining good printing accuracy. Too fast a printing speed may cause uneven material accumulation and affect the structural strength, while too slow a printing speed may affect production efficiency.

[0122] In order to further optimize the printing quality, the layer thickness setting is a factor that must be considered. The layer thickness directly determines the printing accuracy and the degree of detail restoration. Usually, the layer thickness is set at about 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 also the bonding strength between each layer can be improved to ensure that the final constructed model soil skeleton has sufficient stability. After printing, 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 compactness and structural stability of the skeleton material. Under vacuum conditions, the pores of the material are compressed, and excess bubbles and air are removed, thereby improving the density and mechanical properties of the model soil skeleton.

[0123] Next, the target solution (e.g., a salt solution with a suitable ratio) is injected into the model skeleton by pressure injection. Pressure injection ensures that the solution is evenly filled into every void in the model soil skeleton, which is crucial for subsequent freezing tests. Salt solutions with different mass fractions can simulate the physical and thermophysical properties of soil under different environmental conditions, ensuring the accuracy of the simulated soil in the experiment. After the solution is injected, the porosity of the model skeleton will change, further affecting its thermal conductivity characteristics.

[0124] After the salt solution injection 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 adopts the weighing method, that is, by measuring the wet weight and dry weight of the model skeleton, the mass of the salt solution absorbed by it is calculated, so as to obtain 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 salt solution in the model skeleton. The filling rate is usually required to meet certain standards to ensure the effectiveness of the simulated soil. The methods for testing the filling rate usually include volume method and mass method. By comparing with the theoretical calculated value, it is judged 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.

[0125] After saturation determination and filling rate inspection, the model soil skeleton obtained is the composite model soil. This composite model soil not only has the structural strength of the skeleton material, but also contains the thermophysical properties of the solution, which enables it to accurately simulate the thermal conduction behavior of the actual soil in the freezing test. Through this treatment, the thermophysical properties of the model soil are effectively adjusted, providing a reliable basis for the freezing test.

[0126] For example, assuming that PET is used as the target skeleton material, the 3D model is divided into multiple 0.2 mm 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 compactness. Then, the 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 performance of the obtained composite model soil in the freezing test is close to that of the actual soil, and can effectively simulate the thermal conductivity characteristics of the soil under low temperature conditions. This process shows how to ensure that the physical and thermophysical properties of the model soil meet the experimental requirements through precise printing technology, solution injection and saturation control.

[0127] In an optional embodiment, the process of executing step S106 may specifically include the following steps:

[0128] (1) 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 temperature sensors are arranged on the test device to obtain the measurement point layout;

[0129] (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;

[0130] (3) The composite model soil is frozen for 24 hours to obtain a frozen state sample, and the frozen state sample is thawed to obtain a freeze-thaw cycle sample;

[0131] (4) Perform temperature collection processing on the measurement point arrangement to obtain measured temperature data, and perform temperature field interpolation processing on the measured temperature data to obtain temperature field evolution data;

[0132] (5) The temperature field evolution data and the temperature-thermal conductivity correspondence data are processed by error calculation to obtain similarity verification results. The similarity verification results are evaluated to obtain a model soil that meets the requirements of the freezing model test.

[0133] Optionally, the composite model soil needs to be filled into the test box according to a certain size. The size of the test box is 1.0 m × 0.6 m × 0.5 m, which is large enough to accommodate the composite model soil to be tested and provide reasonable space for temperature control. By filling the composite model soil into the test box, the soil samples can be evenly distributed and the behavior of the soil can be fully simulated in subsequent tests. During the layout of the test box, the test device is equipped with temperature sensors, that is, multiple temperature sensors are evenly arranged in the test box to monitor the temperature changes in real time. These sensors generally use thermocouples or RTD (resistance temperature detector) sensors, which can provide high-precision temperature measurement. The arrangement of sensors usually includes the four corners and the middle part of the test box to ensure that the temperature changes at different positions in the box can be fully reflected. Through the arrangement of temperature sensors, the data obtained will provide a basis for subsequent temperature field analysis. One of the keys to the test device is the precise control of temperature. When conducting simulated clay freezing model tests, temperature control must be accurate to ensure that the experimental conditions are consistent with the actual soil behavior. According to the test requirements, the cold source temperature is first controlled and set to -25°C. This temperature setting is to simulate the temperature change 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 ambient 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 accurately controlled.

[0134] After the test chamber is filled and the temperature is set, the next step is to freeze the composite model soil for 24 hours. The purpose of this process is to freeze the model soil to a set low temperature to simulate the actual soil freezing state. In this process, the change of the temperature field is very important. Through the early layout of temperature sensors, the temperature change of the soil can be monitored in real time to ensure that the entire sample is evenly frozen.

[0135] After the frozen state sample is completed, the next step is thawing treatment, which is to thaw by slowly raising the temperature to room temperature (20°C). The thawing process needs to strictly control the rate to avoid uneven thermal expansion or cracking of the model soil due to too fast temperature change. By controlling the thawing rate and temperature change, the real process of freeze-thaw cycle can be simulated to obtain freeze-thaw cycle samples. These freeze-thaw cycle samples provide necessary experimental data for subsequent experimental analysis and verify the thermophysical behavior of the composite model soil during the freezing process. In the process of freezing and thawing, temperature acquisition is a key step. Through the deployed temperature sensors, the real-time temperature data of each measuring point are collected. These measured temperature data include the temperature distribution of each layer in the test box, reflecting the temperature changes at different locations during the freezing and thawing process. The temperature acquisition system records synchronously through the data recorder and computer system to ensure the integrity and accuracy of the data. Once the measured temperature data is obtained, the temperature field interpolation processing is required. The purpose of the interpolation processing is to expand the measured data to the entire test space through numerical methods so as to accurately describe the temperature field evolution of the entire model soil. Commonly used interpolation methods include linear interpolation and spline interpolation. These methods can infer the temperature changes of other unmeasured points based on the existing measurement point data, thereby obtaining complete temperature field data. The analysis of temperature field evolution data can reveal the temperature change trend of the model soil at different times and locations, which is crucial for the subsequent verification of the accuracy of the thermal physics model. For example, during the freezing process, the temperature change curve usually shows a gradual decrease, while during the thawing process, it will show a gradual increase in temperature. By interpolating these temperature change data, more accurate temperature field evolution data can be obtained.

[0136] Finally, similarity verification is performed. By comparing the temperature field evolution data with the temperature-thermal conductivity correspondence data, error calculation can be performed to verify whether the thermophysical properties of the model soil meet the experimental requirements. The temperature-thermal conductivity correspondence data is usually obtained through experimental measurements, which reflects the thermal conductivity of materials at different temperatures.

[0137] The purpose of similarity verification is to ensure that the thermal conductivity behavior of the simulated soil is similar to that of the actual soil. Error calculation usually uses mean square error or mean absolute error as the evaluation criterion. For example, assuming that at a certain temperature point, the thermal conductivity of the actual soil is 0.5W / (m·K), while the thermal conductivity of the simulated model soil is 0.45W / (m·K), then the error is 0.05W / (m·K). By calculating the error over the entire temperature range, a comprehensive error value is obtained, which can reflect the thermal physical similarity between the simulated soil and the prototype soil.

[0138] For example, suppose that when conducting a simulated freezing test, a 1.0m×0.6m×0.5m test box is selected, filled with composite model soil, and temperature sensors are placed in various corners of the box. During the temperature control process, the cold source temperature is set to -25°C and the ambient temperature is 20°C. By freezing the model soil for 24 hours, the temperature changes at different depths are recorded, and interpolation processing is performed to obtain complete temperature field evolution data. By comparing with the known temperature-thermal conductivity relationship data, the error at a certain key time point is calculated to be 0.02W / (m·K), indicating that the thermal physical properties of the simulated soil are very close to those of the actual soil and can meet the requirements of the freezing model test.

[0139] 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 the embodiment of the present application, one embodiment of the model soil construction device for simulating the clay freezing model test includes:

[0140] 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;

[0141] An analysis module is used to perform dimensional analysis on the temperature-thermal conductivity correspondence data to obtain a thermophysical similarity criterion, and to solve the similarity criterion number through computer iterative calculation to obtain a target parameter of the skeleton material;

[0142] A testing module is used to test the thermal physical parameters of the 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;

[0143] A preparation module is used to prepare salt solutions according to 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 tests to obtain target solution ratios;

[0144] 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;

[0145] The construction module is used to carry out freezing cycle test on the composite model soil, obtain the temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation, so as to construct the model soil that meets the requirements of the freezing model test.

[0146] Through the synergy of the above components, the prototype soil was analyzed for phases, combined with the benchmark parameters of thermal conductivity and specific heat capacity, and dimensional analysis 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, through the testing of the thermophysical parameters of the printed materials and the finite element analysis and design, a suitable skeleton material was obtained, and the model soil skeleton was accurately constructed through 3D printing technology to ensure the stability of the structure and the degree of detail restoration. Secondly, the pressure injection technology was used to accurately inject the 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 the precise temperature control and measurement system, combined with the real-time monitoring of the temperature field evolution data, the similarity between the model soil and the prototype soil can be effectively evaluated. This technical feature can not only provide a high-precision simulation test environment, but also 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, and further verify the thermophysical similarity of the model soil. In addition, the error calculation of temperature and thermal conductivity provides a reliable evaluation method for the similarity verification of model soil, ensuring the accuracy and scientificity of the freezing model test results. By combining the temperature control system, precise material design and printing technology, not only the construction process of the simulated soil is optimized, but also the controllability and repeatability of the simulation results are improved.

[0147] Those skilled in the art can 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.

[0148] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 simulating the clay freezing model test comprises: The prototype soil was subjected to phase analysis to obtain the soil skeleton thermal conductivity and specific heat capacity benchmark parameters, and the thermal conductivity of the prototype soil was measured at multiple temperature points using a thermal conductivity tester to obtain the 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 the target parameters of the skeleton material; Conducting thermophysical parameter tests on candidate printing materials according to target parameters of the skeleton material to obtain the target skeleton material, and conducting 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 parameters of the multiple groups of solution samples are measured through thermal conductivity tests to obtain target solution ratios; The three-dimensional structure model is converted by 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 test was carried out on the composite model soil to obtain the temperature field evolution data. The temperature field evolution data was compared and analyzed with the temperature-thermal conductivity correspondence data through numerical calculation to construct a model soil that meets the requirements of freezing model test.

2. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The prototype soil is subjected to phase analysis to obtain soil skeleton thermal conductivity and specific heat capacity benchmark parameters, and the prototype soil is subjected to multi-temperature point thermal conductivity measurement by a thermal conductivity tester to obtain temperature-thermal conductivity correspondence data, including: The prototype soil is subjected to a moisture content measurement process according to a drying method to obtain a dry soil mass, and the dry soil mass is subjected to a soil particle specific gravity measurement process according to a pycnometer method to obtain a soil particle density value; Performing porosity calculation processing on the soil particle density value to obtain soil porosity, and performing physical proportion processing on the soil porosity and dry soil mass to obtain soil skeleton ratio; The prototype soil sample of the soil skeleton ratio is subjected to a cylindrical preparation process 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; The physical phase analysis sample is subjected to thermal conductivity measurement processing by a thermal conductivity tester to obtain the thermal conductivity of the soil skeleton, and the physical phase analysis sample is subjected to specific heat capacity measurement processing by a hybrid calorimetry to obtain the specific heat capacity of the soil skeleton, and the thermal conductivity coefficient and specific heat capacity reference parameters of the soil skeleton are obtained; The thermal conductivity and specific heat capacity benchmark parameters of the soil skeleton are tested at intervals of 5°C in a temperature range of -30°C to 20°C to obtain temperature point thermal conductivity data, and the temperature point thermal conductivity data are fitted to obtain the temperature-thermal conductivity correspondence data.

3. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The temperature-thermal conductivity correspondence relationship data is subjected to dimensional analysis to obtain a thermophysical similarity criterion, and the similarity criterion number is solved by computer iterative calculation to obtain the target parameters of the skeleton material, including: Decomposing the temperature-thermal conductivity correspondence data according to four basic dimensions of length, time, heat, and 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 dimension 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 thermal physical similarity criterion and boundary condition; The thermal physical similarity criterion is subjected to a thermal conductivity similarity relationship derivation process to obtain a thermal conductivity ratio relationship formula, and the time scale, length scale, density scale and temperature scale in the thermal conductivity ratio relationship formula are subjected to an initial value assignment process to obtain the similarity criterion number; The similarity criterion number is assigned a value according to the time scale being 1, the length scale being 2, the temperature scale being 1, and the density scale being 1 to obtain a value of the similarity criterion number, and a dimensionless equation of the similarity criterion number is numerically solved to obtain a convergence value of the similarity criterion number; The convergence value of the similarity criterion number is processed by thermophysical parameter conversion to obtain the target parameters of the skeleton material.

4. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The method of testing the thermal physical parameters of the candidate printing materials 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 includes: Performing parameter matching processing on the target parameters of the skeleton material to obtain three candidate printing materials, namely, polymer materials polylactic acid, acrylonitrile-butadiene-styrene copolymer, and polyethylene terephthalate, and performing thermal conductivity test processing on the three candidate printing materials to obtain thermal conductivity of the printing materials; Performing a specific heat capacity test process 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.

5. The method for constructing model soil for simulating clay freezing model test according to claim 1, characterized in that: The method comprises preparing salt solutions according to 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 thermal conductivity test 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 benchmark, and the inorganic salt is subjected to a purity detection process to obtain purity data; Conducting conductivity measurement processing on deionized water to obtain reference solvent parameters, preparing solutions of calcium chloride according to six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30%, and preparing solutions of sodium chloride according to six mass fractions of 5%, 10%, 15%, 20%, 25%, and 30%, to obtain the plurality of groups of solution samples; Performing density measurement processing on the multiple 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 multiple groups of solution samples by 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 solution samples to be selected; The specific heat capacity of the selected solution sample is measured to obtain solution specific heat capacity data, and the solution specific heat capacity data is comprehensively evaluated to obtain the target solution ratio.

6. 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 processing on the layered data to obtain printing trajectory parameters; The printing trajectory parameters are processed for printing temperature setting, and the nozzle temperature is obtained to be 230° C., and the nozzle temperature is processed for printing platform temperature matching, and the platform temperature is obtained to be 60° C.; The nozzle temperature and the 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 to obtain the model soil skeleton; The model soil skeleton is subjected to vacuum evacuation treatment to obtain an evacuated 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.

7. 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 the measurement point arrangement; The test device is temperature-controlled to obtain a cold source temperature of -25°C, and the ambient temperature is temperature-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 measuring point arrangement to obtain measured temperature data, and performing temperature field interpolation processing on the measured temperature data to obtain temperature field evolution data; The temperature field evolution data and the temperature-thermal conductivity correspondence data are subjected to error calculation processing to obtain similarity verification results, and the similarity verification results are evaluated to obtain model soil that meets the requirements of the freezing model test.

8. 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 as described in any one of claims 1 to 7, characterized in that: The model soil construction device for simulating the clay freezing model test comprises: 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; An analysis module is used to perform dimensional analysis on the temperature-thermal conductivity correspondence data to obtain a thermophysical similarity criterion, and to solve the similarity criterion number through computer iterative calculation to obtain a target parameter of the skeleton material; A testing module is used to test the thermal physical parameters of the 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; A preparation module is used to prepare salt solutions according to 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 tests to obtain target solution ratios; 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 carry out freezing cycle test on the composite model soil, obtain the temperature field evolution data, and compare and analyze the temperature field evolution data with the temperature-thermal conductivity coefficient correspondence data through numerical calculation, so as to construct the model soil that meets the requirements of the freezing model test.

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