Parallel computational method and apparatus for inverting lunar soil thermophysical properties using lunar surface temperature

Abstract

This invention discloses a parallel computational method and apparatus for inverting the thermophysical properties of lunar reefs from lunar surface temperature. The method includes: normalizing the time of the detected lunar surface temperature, dividing it into lunar daytime temperature and lunar nighttime temperature; expressing lunar reef density and thermal conductivity as functions of lunar reef particle size using a mathematical model; correlating lunar daytime temperature with lunar reef thermophysical properties using a thermal conductivity equation; using lunar reef particle size as the sole free parameter, and leveraging the high concurrency capabilities of the Go programming language to simulate and calculate lunar reef temperature using a high-performance computer cluster; fitting the simulated nighttime surface temperature with the detected lunar nighttime temperature to find the optimal lunar reef particle size, and simultaneously obtaining the density profile, temperature profile, and thermal conductivity profile of the lunar reef. The numerical simulation method used in this invention preserves the original depositional pattern of the lunar reef compared to laboratory experimental methods. This invention utilizes the high concurrency capabilities of the Go programming language to write a high-performance program, achieving multi-core parallel computation and improving the solution speed.

Description

Technical Field

[0001] This invention relates to the field of remote sensing data processing technology, and in particular to a parallel computing method and apparatus for inverting the thermophysical properties of lunar soil through lunar surface temperature. Background Technology

[0002] Lunar regolith is a loosely deposited layer of weathered material distributed on the lunar surface, containing rock fragments, single mineral particles, and glassy materials formed by impacts. Its physicochemical properties are crucial for understanding the lunar geological history and designing lunar spacecraft. The formation of lunar regolith is a complex and lengthy process, resulting from the combined effects of meteorite impacts, cosmic ray bombardment, and significant diurnal temperature variations leading to rock fragmentation. The thermal conductivity of lunar regolith consists of both thermal conduction and thermal radiation, which is related to the packing pattern and particle size of the solid particles. The thermophysical properties of lunar regolith are of great importance for spacecraft exploration and astronaut activities.

[0003] Since the Apollo program began, the thermophysical properties of lunar regolith (including thermal conductivity, heat capacity, density, and temperature) have attracted widespread attention. Early measurements focused primarily on lunar regolith samples returned by the Apollo missions; however, the packing method of these samples was disturbed during interplanetary transport, which affected the reliability of the experimental results.

[0004] Numerical simulation is a CPU-intensive task, and solving high-precision computational tasks often takes weeks or even months. To speed up the solution process, parallel computing is typically implemented using MPICH or OpenMPI. However, MPICH coordinates parallel computing through inter-process communication, which results in high communication latency and low parallel efficiency, while OpenMPI only supports a single computing node and cannot utilize the computing resources of multiple nodes.

[0005] Therefore, improving the reliability and efficiency of lunar soil thermophysical property measurements is an urgent technical problem to be solved. Summary of the Invention

[0006] The present invention aims to improve the reliability and efficiency of lunar soil thermophysical property measurement. To achieve the above objective, the technical solution adopted by the present invention is to provide a parallel calculation method and apparatus for inverting lunar soil thermophysical properties through lunar surface temperature.

[0007] According to a first aspect of the present invention, a parallel calculation method for inverting the thermophysical properties of lunar soil through lunar surface temperature specifically includes the following steps:

[0008] S1: The time of the detected lunar surface temperature is normalized and divided into lunar daytime temperature and lunar nighttime temperature.

[0009] S2: Using mathematical models, the density and thermal conductivity of lunar soil are expressed as functions of lunar soil particle size, and the thermal conductivity equation is used to correlate lunar surface daytime temperature with the thermophysical properties of lunar soil.

[0010] S3: Using lunar soil particle size as the only free parameter, and leveraging the high concurrency features of the Go language, a high-performance computer cluster is used to simulate and calculate lunar soil temperature.

[0011] S4: Fit the simulated nighttime surface temperature with the detected lunar surface nighttime temperature to find the optimal lunar soil particle size, and obtain the density profile, temperature profile and thermal conductivity profile of the lunar soil.

[0012] Furthermore, step S1 specifically includes:

[0013] S1.1: Normalize the time of the detected lunar surface temperature, using sunrise time as 6:00 and sunset time as 18:00, and convert the actual lunar UTC time into 24-hour lunar local time.

[0014] S1.2: The normalized temperature data is divided into lunar daytime temperature and lunar nighttime temperature. The lunar daytime temperature is used to correlate with the thermophysical properties of lunar soil, while the lunar nighttime temperature is used to compare with the simulated temperature to find the optimal lunar soil particle size.

[0015] Furthermore, step S2 specifically includes:

[0016] S2.1: Using a mathematical model, the density of lunar soil is expressed as a function of lunar soil particle size:

[0017] The bulk density of lunar soil at depth z depends on the solid particle density G and the filling coefficient Φ(z), expressed as:

[0018] ρ(z)=GΦ(z)

[0019] The density G of solid particles depends on the abundance of FeO and TiO2:

[0020] G(FeO,TiO2)=27.3×FeO+11.0×TiO2+2773

[0021] The filling coefficient Φ(z) at depth z in lunar soil is related to pressure:

[0022]

[0023] In the formula, Φ1 and Φ2 are the minimum and maximum filling coefficients, respectively, which are 0.15 and 0.64. These two constants are independent of the lunar soil particle size; p m Δ is the transition pressure; Δ is the transition width (the logarithmic width of the transition from a low fill factor to a high fill factor), Δ = 0.58, measured by omnidirectional compression experiments; p(z) is the pressure at depth z.

[0024] p(z)=∫0 z ρ(z)gdz

[0025] In the formula, g is the gravitational acceleration of the moon.

[0026] Conversion pressure p m It can be obtained from the expression:

[0027]

[0028] In the formula, F is the cohesive force, and r is the radius of the lunar soil particles;

[0029] S2.2: Using a mathematical model, the thermal conductivity of lunar soil is expressed as a function of lunar soil particle size:

[0030] The thermal conductivity K of lunar soil is determined by the heat conduction K generated by interparticle contact. c Radiation K generated by interparticle thermal radiation r Composition, i.e., K = K c +K r ;

[0031] Assuming the lunar soil particle size remains constant, K c This can be determined using Hertzian contact theory:

[0032]

[0033] Where K0 is the thermal conductivity of the solid particles, μ is Poisson's ratio, E is Young's modulus, F is the interparticle adhesion force, and χ is a structural parameter dependent on the lunar soil particle size and the filling factor Φ.

[0034]

[0035] Where f1 = 5.18 × 10 -2 f2 = 5.26;

[0036] The adhesive force F dominates the variation of fill factor and thermal conductivity with depth, that is:

[0037] F=3πrγ

[0038] Where γ is the surface energy, and the surface energy is determined by assuming a linear relationship between surface energy and temperature:

[0039]

[0040] Among them, T 300 It is room temperature 300K, γ 300 It is the surface energy at 300K;

[0041] The radiation component K is determined based on the photon gas theory.r for:

[0042]

[0043] Where σ is the Stefan-Boltzmann constant, ∈ g e1 is the emissivity of the solid particles, and e1 is an empirical factor for the irregular shape of the solid particles, which is approximately 1.34.

[0044] S2.3: Using the thermal conductivity equation to correlate lunar surface temperature with the thermophysical properties of lunar regolith:

[0045] The temperature T of the lunar regolith varies with time t and depth z, which is governed by the heat conduction equation:

[0046]

[0047] The boundary conditions are:

[0048] Lunar soil surface

[0049] At the thermal equilibrium depth z,

[0050] In the formula, ρ is the density of lunar regolith, c is the heat capacity of lunar regolith, K is the thermal conductivity of lunar regolith, T is the temperature at depth z, σ is the emissivity of the lunar surface, and σ is the Boltzmann constant. s J0 represents the lunar surface temperature, and J0 represents the internal heat flow of the moon.

[0051] The lunar soil heat capacity is determined by an empirical function of temperature.

[0052]

[0053] Furthermore, step S3 specifically includes:

[0054] The parameters of solid particles were fixed at typical values, and only the lunar soil particle size was retained as the only free parameter affecting the thermophysical properties of lunar soil. A high-performance program was written using the high concurrency features of the Go language. The computational data was divided into blocks according to the number of CPU cores, and the program distributed the computational tasks to multiple coroutines. The PBS job script was submitted on a high-performance computer cluster, and the system waited for the system to allocate nodes for computation. Then, the threads called the CPU resources to allocate coroutines for computation, thereby achieving high parallel computing on multiple cores. The lunar soil particle size was set, and the lunar soil temperature of different lunar soil particle sizes was simulated and calculated.

[0055] Furthermore, step S4 specifically includes:

[0056] S4.1: Using the least squares method, calculate the root mean square error (RMSE) between the simulated nighttime surface temperature and the detected lunar nighttime surface temperature.

[0057]

[0058] In the formula, T m (t i ) and T(t i () represents the simulated nighttime surface temperature and the detected lunar nighttime temperature at different times, respectively, and N represents the total number of measurements;

[0059] S4.2: By fitting the simulated nighttime surface temperature with different parameters to the detected lunar surface nighttime temperature, the optimal lunar soil particle size with the minimum RMSE is found.

[0060] S4.3: Obtain the temperature profile, density profile, and thermal conductivity profile of lunar soil based on the optimal lunar soil particle size.

[0061] According to a second aspect of the present invention, a parallel computing device for inverting the thermophysical properties of lunar soil through lunar surface temperature includes the following modules:

[0062] The normalization module is used to normalize the time of the detected lunar surface temperature, which is divided into lunar daytime temperature and lunar nighttime temperature.

[0063] The data association module is used to express the density and thermal conductivity of lunar soil as functions of lunar soil particle size using mathematical models, and to associate lunar surface daytime temperature with the thermophysical properties of lunar soil using thermal conductivity equations.

[0064] The parallel simulation module is used to simulate and calculate lunar soil temperature using a high-performance computer cluster, with lunar soil particle size as the only free parameter and leveraging the high concurrency features of the Go language.

[0065] The fitting optimization module is used to fit the simulated nighttime surface temperature with the detected lunar surface and nighttime temperature to find the optimal lunar soil particle size, and at the same time obtain the density profile, temperature profile and thermal conductivity profile of the lunar soil.

[0066] Furthermore, according to a third aspect of the present invention, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the parallel calculation method for inverting the thermophysical properties of lunar soil through lunar surface temperature.

[0067] Furthermore, according to a fourth aspect of the invention, a storage medium storing a computer program that, when executed by a processor, implements the steps of the parallel calculation method for inverting the thermophysical properties of lunar soil through lunar surface temperature.

[0068] The technical solution provided by this invention has the following beneficial effects:

[0069] 1. The numerical simulation method used in this invention preserves the original deposition pattern of lunar soil compared to laboratory experimental methods.

[0070] 2. This invention uses a function of lunar soil particle size to represent the density and thermal conductivity of lunar soil, thus avoiding the strong coupling effect of these two parameters on temperature changes.

[0071] 3. This invention utilizes the high concurrency features of the Go programming language to write a high-performance program, enabling multi-core parallel computing on a computer cluster and improving computational speed. Go's coroutines have the advantages of small memory usage and fast communication speed, fully utilizing the multi-core resources of the cluster to shorten the solution time by more than ten times, significantly improving solution efficiency. Attached Figure Description

[0072] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0073] Figure 1 This is a flowchart of the parallel calculation method for inverting the thermophysical properties of lunar soil by lunar surface temperature according to the present invention;

[0074] Figure 2 This invention relates to a multi-node, multi-core parallel computing architecture model on a computer cluster.

[0075] Figure 3 This is a comparison chart of simulated lunar night temperature and measured temperature for finding the optimal particle size of lunar soil according to this invention;

[0076] Figure 4 This is a temperature (a), density (b), and thermal conductivity (c) profile of the optimal particle size of lunar soil according to the present invention;

[0077] Figure 5 This is a comparison chart of the solution time for different computational accuracies in parallel MPICH and GO languages ​​of this invention;

[0078] Figure 6 This is a structural diagram of the parallel computing device for inverting the thermophysical properties of lunar soil using lunar surface temperature, as per the present invention. Detailed Implementation

[0079] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0080] refer to Figure 1 This embodiment provides a parallel computational method for inverting lunar regolith thermophysical properties using lunar surface temperature, specifically including the following steps:

[0081] S1: The time of the detected lunar surface temperature is normalized and divided into lunar daytime temperature and lunar nighttime temperature.

[0082] In this embodiment, the specific implementation steps of step S1 are as follows:

[0083] S1.1: A lunar day-night cycle is approximately 29.53 days. The time of the detected lunar surface temperature is normalized, with sunrise at 6:00 AM and sunset at 6:00 PM, and the actual lunar UTC time is converted into 24-hour lunar local time.

[0084] S1.2: The normalized temperature data is divided into lunar daytime temperature and lunar nighttime temperature. The lunar daytime temperature is used to correlate with the thermophysical properties of lunar soil (see step S2 for details), and the lunar nighttime temperature is used to compare with the simulated temperature to find the optimal lunar soil particle size (see step S4 for details).

[0085] S2: Using mathematical models, the density and thermal conductivity of lunar soil are expressed as functions of lunar soil particle size, and the thermal conductivity equation is used to correlate lunar surface daytime temperature with the thermophysical properties of lunar soil.

[0086] In this embodiment, the specific implementation steps of step S2 are as follows:

[0087] S2.1: Using a mathematical model, the density of lunar soil is expressed as a function of lunar soil particle size:

[0088] The bulk density of lunar soil at depth z depends on the solid particle density G and the filling coefficient Φ(z), expressed as:

[0089] ρ(z)=GΦ(z)

[0090] The density G of solid particles depends on the abundance of FeO and TiO2 (in wt%):

[0091] G(FeO,TiO2)=27.3×FeO+11.0×TiO2+2773

[0092] The abundance of FeO and TiO2 in lunar soil particles is obtained mainly by two methods. One is to measure lunar soil samples returned from the moon in the laboratory, and the other is to calculate it using remote sensing spectral data measured by orbiting or landing probes.

[0093] The filling coefficient Φ(z) at depth z in lunar soil is related to pressure:

[0094]

[0095] In the formula, Φ1 is the minimum filling coefficient for randomly loosely packed lunar soil particles under no load pressure, and Φ2 is the maximum filling coefficient for completely compacted lunar soil particles under sufficiently high load pressure, which are 0.15 and 0.64 respectively. These two constants are independent of the lunar soil particle size; p m It is the conversion pressure; Δ is the transition width (the logarithmic width of the transition from a low fill factor to a high fill factor), and the transition width Δ = 0.58 is measured by omnidirectional compression experiments;

[0096] Without additional loads on the lunar surface, the pressure in the lunar regolith should be primarily hydrostatic pressure. The hydrostatic pressure p(z) of the lunar regolith within the lunar surface depends on the depth z:

[0097] p(z)=∫0 z ρ(z)gdz

[0098] In the formula, g is the gravitational acceleration of the moon. The change in the filling coefficient in lunar soil is essentially caused by the change in hydrostatic pressure with depth.

[0099] Conversion pressure p m It is the pressure converted from other pressures to the pressure at the particle contact point, inversely proportional to the particle cross-section and directly proportional to the filling factor, which can be obtained from the expression:

[0100]

[0101] In the formula, F is the adhesive force, which is the adhesive force between the contacting lunar soil particles, and r is the radius of the lunar soil particles;

[0102] S2.2: Using a mathematical model, the thermal conductivity of lunar soil is expressed as a function of lunar soil particle size:

[0103] The thermal conductivity K of lunar soil is determined by the heat conduction K generated by interparticle contact. c Radiation K generated by interparticle thermal radiation r Composition, i.e., K = K c +K r ;

[0104] Assuming the lunar soil particle size remains constant, K c This can be determined using Hertzian contact theory:

[0105]

[0106] Where K0 is the thermal conductivity of the solid particles, μ is Poisson's ratio, E is Young's modulus, F is the interparticle adhesion force, and χ is a structural parameter dependent on the lunar soil particle size and the filling factor Φ.

[0107]

[0108] Where f1 = 5.18 × 10 -2 f2 = 5.26;

[0109] The adhesive force F dominates the variation of the fill factor and thermal conductivity with depth. Here, the John-Kendall-Robert model is used to determine this parameter, namely:

[0110] F=3πrγ

[0111] Here, γ is the surface energy, a measure of the adhesive strength of materials. Early work only determined the surface energy of silica particles at room temperature to 300 K. To assess the relationship between surface energy and temperature, the assumption of a linear relationship between surface energy and temperature was used to determine the surface energy:

[0112]

[0113] Among them, T 300 It is room temperature 300K, γ 300 It is the surface energy at 300K;

[0114] The radiation component K is determined based on the photon gas theory. r for:

[0115]

[0116] Where σ is the Stefan-Boltzmann constant, ∈ g e1 is the emissivity of the solid particles, and e1 is an empirical factor for the irregular shape of the solid particles, which is approximately 1.34.

[0117] S2.3: Using the thermal conductivity equation to correlate lunar surface temperature with the thermophysical properties of lunar regolith:

[0118] The temperature T of the lunar regolith varies with time t and depth z, which is governed by the heat conduction equation:

[0119]

[0120] The boundary conditions are:

[0121] The heat conduction and absorption of solar radiation on the lunar surface are balanced with thermal radiation.

[0122] At depth z, the thermal conductivity of the lunar regolith is in equilibrium with the heat flow inside the moon.

[0123] In the formula, ρ is the density of lunar regolith, c is the heat capacity of lunar regolith, K is the thermal conductivity of lunar regolith, T is the temperature at depth z, ∈ is the emissivity of the lunar surface, σ is the Boltzmann constant, and T s J0 represents the lunar surface temperature, and J0 represents the internal heat flow of the moon.

[0124] The lunar soil heat capacity is determined by an empirical function of temperature.

[0125]

[0126] S3: Using lunar soil particle size as the only free parameter, and leveraging the high concurrency features of the Go language, a high-performance computer cluster is used to simulate and calculate lunar soil temperature.

[0127] The specific implementation steps of step S3 are as follows:

[0128] S3.1: Fix the parameters of solid particles to typical values ​​(Table 1), and retain only the lunar soil particle size as the only free parameter affecting the thermophysical properties of lunar soil.

[0129] Table 1 Typical values ​​of physical parameters of lunar soil particles

[0130]

[0131] S3.2: Utilizing the high concurrency features of the Go language, a high-performance program is written to divide the lunar regolith depth (0-5m) into corresponding segments based on the number of CPU cores required for computation. This divides the computational data into blocks, and the program distributes the computational tasks of these blocks to multiple coroutines. The PBS job script is submitted to a high-performance computing cluster, awaiting system allocation of nodes for computation. Threads then call CPU resources to allocate coroutines for computation, thereby achieving highly parallel computing on multiple cores. Figure 2 );

[0132] S3.3: Based on the particle size distribution of lunar soil samples collected from the moon, set the lunar soil particle size value, with a range of 10 to 1000 micrometers, and simulate and calculate the lunar soil temperature for different lunar soil particle sizes.

[0133] S4: Fit the simulated nighttime surface temperature with the detected lunar surface nighttime temperature to find the optimal lunar soil particle size, and obtain the density profile, temperature profile and thermal conductivity profile of the lunar soil.

[0134] The specific implementation steps of step S4 are as follows:

[0135] S4.1: Using the least squares method, calculate the root mean square error (RMSE) between the simulated nighttime surface temperature and the detected lunar nighttime surface temperature.

[0136]

[0137] In the formula, T m (t i ) and T(t i () represents the simulated nighttime surface temperature and the detected lunar nighttime temperature at different times, respectively, and N represents the total number of measurements;

[0138] S4.2: By fitting simulated nighttime surface temperatures with different parameters to the detected lunar nighttime surface temperatures, the optimal lunar soil particle size with the minimum RMSE is found. Figure 3 );

[0139] When searching for the optimal lunar soil particle size, calculations were performed in groups according to the order of magnitude of the particle size values. First, the optimal particle size in the 100-micrometer range was found. Then, the particle size accuracy was increased to find the optimal lunar soil particle sizes in the 10-micrometer and 1-micrometer ranges, respectively.

[0140] like Figure 3 As shown, after three sets of calculations, the optimal particle size of lunar soil at the micrometer level was found to be 15 micrometers, with an error of only 0.07K.

[0141] S4.3: Obtain the temperature profile, density profile, and thermal conductivity profile of lunar soil based on the optimal lunar soil particle size. Figure 4 ).

[0142] The program calculates the output file based on the input lunar soil particle size, including temperature, density, and thermal conductivity data of the lunar soil at different depths over time. Different profile maps can be drawn using this data according to different needs.

[0143] like Figure 4 As shown, Figure (a) is a cross-sectional view of the temperature of lunar soil at different times of the day with depth, namely the temperature cross-sections at midnight, sunrise, noon and sunset; Figure (b) is a cross-sectional view of the average density of lunar soil at the same depth throughout the day, showing the average density of lunar soil with depth; similarly, Figure (c) is a cross-sectional view of the average thermal conductivity of lunar soil with depth.

[0144] like Figure 5 As shown, the solution time of MPICH and the Go language parallelism of this invention with different computational precisions is compared.

[0145] When using the same parallel computing method and the same number of CPU cores, the accuracy of depth is doubled, but the solution time increases by nearly tenfold.

[0146] MPICH coordinates parallel computing through inter-process communication. When the number of CPU cores exceeds five, the latency of inter-process communication begins to gradually offset, and even surpasses, the acceleration effect of multi-core parallel computing. When using the five most efficient cores for calculation with a depth accuracy of 0.01 meters, the calculation time for one month's soil particle size takes nearly ten minutes. When the depth accuracy is 0.001 meters, the solution time takes more than ten days, and finding the optimal particle size through calculation of three sets of parameters takes a month and a half.

[0147] In Go programs, threads allocate CPU resources to coroutines, which in turn handle computation and communication. Go coroutines offer advantages such as low memory usage and fast communication. Even with dozens of cores, the acceleration from parallel computation remains significant, fully utilizing the multi-core resources of the cluster. When using 30 cores for computation with a depth accuracy of 0.01 meters, the solution time is only 1 minute. With a depth accuracy of 0.001 meters, finding the optimal particle size using three sets of parameters takes only 3 days.

[0148] Therefore, by using parallel computing methods in the Go language, the solution time was reduced by more than ten times, significantly improving the solution efficiency.

[0149] The key points of implementing the above method in this invention are as follows:

[0150] 1. In step S2, the effects of thermal conductivity and density on temperature change are strongly coupled. Using a function of lunar soil particle size to represent these two parameters can avoid the strong coupling effect of these two parameters on temperature change.

[0151] 2. In step S3, the data is divided into blocks according to the lunar soil depth, and the high concurrency features of the Go language are used to distribute the data to multiple goroutines to achieve multi-core parallel computing on a computer cluster and improve the computing speed.

[0152] Furthermore, to better implement the above method, this embodiment also provides a parallel computing device for inverting the thermophysical properties of lunar soil through lunar surface temperature, such as... Figure 6 As shown, the device specifically includes the following modules:

[0153] Normalization module 1 is used to normalize the time of the detected lunar surface temperature, which is divided into lunar daytime temperature and lunar nighttime temperature.

[0154] Data association module 2 is used to express the density and thermal conductivity of lunar soil as functions of lunar soil particle size using mathematical models, and to associate lunar surface daytime temperature with the thermophysical properties of lunar soil using thermal conductivity equations;

[0155] Parallel simulation module 3 is used to simulate and calculate lunar soil temperature using a high-performance computer cluster, with lunar soil particle size as the only free parameter and leveraging the high concurrency features of the Go language.

[0156] The fitting optimization module 4 is used to fit the simulated nighttime surface temperature with the detected lunar surface nighttime temperature to find the optimal lunar soil particle size, and at the same time obtain the density profile, temperature profile and thermal conductivity profile of the lunar soil.

[0157] Furthermore, to better implement the above method, this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the parallel calculation method for inverting lunar soil thermophysical properties through lunar surface temperature. These steps specifically include: normalizing the time of the detected lunar surface temperature, dividing it into lunar daytime temperature and lunar nighttime temperature; expressing the density and thermal conductivity of lunar soil as functions of lunar soil particle size using a mathematical model, and correlating the lunar daytime temperature with the lunar soil thermophysical properties using a thermal conductivity equation; using the lunar soil particle size as the only free parameter, and utilizing the high concurrency characteristics of the Go language, using a high-performance computer cluster to simulate and calculate the lunar soil temperature; fitting the simulated nighttime surface temperature with the detected lunar nighttime temperature to find the optimal lunar soil particle size, and simultaneously obtaining the density profile, temperature profile, and thermal conductivity profile of the lunar soil.

[0158] Furthermore, to better implement the above method, this embodiment also provides a storage medium storing a computer program. When executed by a processor, this computer program implements the steps of the parallel calculation method for inverting lunar soil thermophysical properties through lunar surface temperature. These steps specifically include: normalizing the time of the detected lunar surface temperature, dividing it into lunar daytime temperature and lunar nighttime temperature; using a mathematical model to express the density and thermal conductivity of lunar soil as functions of lunar soil particle size, and using a thermal conductivity equation to correlate the lunar daytime temperature with the lunar soil thermophysical properties; using lunar soil particle size as the only free parameter, leveraging the high concurrency characteristics of the Go language, and using a high-performance computer cluster to simulate and calculate the lunar soil temperature; fitting the simulated nighttime surface temperature with the detected lunar nighttime temperature to find the optimal lunar soil particle size, and simultaneously obtaining the density profile, temperature profile, and thermal conductivity profile of the lunar soil.

[0159] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0160] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the unit claims listing several devices, several of these devices may be embodied by the same hardware item. The use of the terms first, second, and third, etc., does not indicate any order and can be interpreted as identifiers.

[0161] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A parallel computational method for inverting lunar regolith thermophysical properties from lunar surface temperature, characterized in that, Includes the following steps: S1: The time of the detected lunar surface temperature is normalized and divided into lunar daytime temperature and lunar nighttime temperature. S2: Using mathematical models, the density and thermal conductivity of lunar soil are expressed as functions of lunar soil particle size, and the thermal conductivity equation is used to correlate lunar surface daytime temperature with the thermophysical properties of lunar soil. S3: Using lunar soil particle size as the only free parameter, and leveraging the high concurrency features of the Go language, a high-performance computer cluster is used to simulate and calculate lunar soil temperature. S4: Fit the simulated nighttime surface temperature with the detected lunar surface nighttime temperature to find the optimal lunar soil particle size, and obtain the density profile, temperature profile and thermal conductivity profile of the lunar soil. Step S2 specifically includes: S2.1: Using a mathematical model, the density of lunar soil is expressed as a function of lunar soil particle size: The bulk density of lunar regolith at depth z depends on the solid particle density G and the packing factor. , represented as: The density G of solid particles depends on the abundance of FeO and TiO2: The filling coefficient at depth z in lunar soil Related to stress: In the formula, and These are the minimum and maximum fill factors, respectively; It is the conversion pressure; Δ is the transition width; This is the pressure at depth z: In the formula, g is the gravitational acceleration of the moon, and the converted pressure is... It is obtained from the following expression: In the formula, F is the cohesive force, and r is the radius of the lunar soil particles; S2.2: Using a mathematical model, the thermal conductivity of lunar soil is expressed as a function of lunar soil particle size: The thermal conductivity K of lunar soil is determined by the heat conduction K generated by interparticle contact. c Radiation K generated by interparticle thermal radiation r Composition, that is ; Assuming the lunar soil particle size remains constant, K c This is determined using Hertzian contact theory: Where K0 is the thermal conductivity of the solid particles, μ is Poisson's ratio, E is Young's modulus, F is the interparticle adhesion force, and χ is a coefficient dependent on lunar soil particle size and filling factor. Structural parameters: Where f1 = 5.18 × 10⁻², f2 = 5.26; The adhesive force F dominates the variation of fill factor and thermal conductivity with depth, that is: Where γ is the surface energy, and the surface energy is determined by assuming a linear relationship between surface energy and temperature T: Among them, T 300 It is room temperature 300 K, γ 300 It is the surface energy at 300 K; The radiation component K is determined based on the photon gas theory. r for: Where σ is the Stefan-Boltzmann constant, e1 is the emissivity of the solid particle, and e1 is an empirical factor for the irregular shape of the solid particle. S2.3 Using the thermal conductivity equation to correlate lunar surface temperature with the thermophysical properties of lunar soil: The temperature T of the lunar regolith varies with time t and depth z, which is governed by the heat conduction equation: The boundary conditions are: Lunar soil surface At the thermal equilibrium depth z, In the formula, Here, c is the density of lunar soil, k is the heat capacity of lunar soil, K is the thermal conductivity of lunar soil, and T is the temperature at depth z. For lunar surface emissivity, Boltzmann's constant, J0 represents the lunar surface temperature, and J0 represents the internal heat flow of the moon. The heat capacity of lunar soil is determined by an empirical function of temperature. 。 2. The parallel calculation method for inverting lunar regolith thermophysical properties from lunar surface temperature according to claim 1, characterized in that, Step S1 specifically includes: S1.1: Normalize the time of the detected lunar surface temperature, using sunrise time as 6:00 and sunset time as 18:00, and convert the actual lunar UTC time into 24-hour lunar local time. S1.2: The normalized temperature data is divided into lunar daytime temperature and lunar nighttime temperature. The lunar daytime temperature is used to correlate with the thermophysical properties of lunar soil, while the lunar nighttime temperature is used to compare with the simulated temperature to find the optimal lunar soil particle size.

3. The parallel calculation method for inverting lunar regolith thermophysical properties from lunar surface temperature according to claim 1, characterized in that, S3 specifically includes: The parameters of solid particles were fixed at typical values, and only the lunar soil particle size was retained as the only free parameter affecting the thermophysical properties of lunar soil. A high-performance program was written using the high concurrency features of the Go language. The computational data was divided into blocks according to the number of CPU cores, and the program distributed the computational tasks to multiple coroutines. The PBS job script was submitted on a high-performance computer cluster, and the system waited for the system to allocate nodes for computation. Then, the threads called the CPU to allocate coroutines for computation, thereby achieving high parallel computing on multiple cores. The lunar soil particle size was set, and the lunar soil temperature of different lunar soil particle sizes was simulated and calculated.

4. The parallel calculation method for inverting lunar regolith thermophysical properties from lunar surface temperature according to claim 1, characterized in that, The parameters of the solid particles include: minimum filling factor, maximum filling factor, conversion pressure, thermal conductivity, Poisson's ratio, Young's modulus, emissivity, irregular shape empirical factor, and surface energy.

5. The parallel calculation method for inverting lunar regolith thermophysical properties from lunar surface temperature according to claim 1, characterized in that, S4 specifically includes: S4.1: Using the least squares method, calculate the root mean square error (RMSE) between the simulated nighttime surface temperature and the detected lunar nighttime surface temperature. In the formula, and These represent the simulated nighttime surface temperature and the detected lunar nighttime temperature at different times, respectively, with N representing the total number of measurements. S4.2: By fitting the simulated nighttime surface temperature with different parameters to the detected lunar nighttime temperature, the optimal lunar soil particle size with the minimum RMSE is found. S4.3: Obtain the temperature profile, density profile, and thermal conductivity profile of lunar soil based on the optimal lunar soil particle size.

6. A parallel computing device for inverting the thermophysical properties of lunar soil using lunar surface temperature, characterized in that, Includes the following modules: The normalization module is used to normalize the time of the detected lunar surface temperature, which is divided into lunar daytime temperature and lunar nighttime temperature. The data association module is used to express the density and thermal conductivity of lunar soil as functions of lunar soil particle size using mathematical models, and to associate lunar surface daytime temperature with the thermophysical properties of lunar soil using thermal conductivity equations. The parallel simulation module is used to simulate and calculate lunar soil temperature using a high-performance computer cluster, with lunar soil particle size as the only free parameter and leveraging the high concurrency features of the Go language. The fitting optimization module is used to fit the simulated nighttime surface temperature with the detected lunar surface and nighttime temperature to find the optimal lunar soil particle size, and at the same time obtain the density profile, temperature profile and thermal conductivity profile of the lunar soil. The density and thermal conductivity of lunar regolith are expressed as functions of lunar regolith particle size using mathematical models. The thermal conductivity equation is used to correlate lunar surface daytime temperature with the thermophysical properties of lunar regolith, specifically including: The density of lunar soil is expressed as a function of lunar soil particle size using a mathematical model: The bulk density of lunar regolith at depth z depends on the solid particle density G and the packing factor. , represented as: The density G of solid particles depends on the abundance of FeO and TiO2: The filling coefficient at depth z in lunar soil Related to stress: In the formula, and These are the minimum and maximum fill factors, respectively; It is the conversion pressure; Δ is the transition width; This is the pressure at depth z: In the formula, g is the gravitational acceleration of the moon, and the converted pressure is... It is obtained from the following expression: In the formula, F is the cohesive force, and r is the radius of the lunar soil particles; The thermal conductivity of lunar soil is expressed as a function of lunar soil particle size using a mathematical model: The thermal conductivity K of lunar soil is determined by the heat conduction K generated by interparticle contact. c Radiation K generated by interparticle thermal radiation r Composition, that is ; Assuming the lunar soil particle size remains constant, K c This is determined using Hertzian contact theory: Where K0 is the thermal conductivity of the solid particles, μ is Poisson's ratio, E is Young's modulus, F is the interparticle adhesion force, and χ is a coefficient dependent on lunar soil particle size and filling factor. Structural parameters: Where f1 = 5.18 × 10⁻², f2 = 5.26; The adhesive force F dominates the variation of fill factor and thermal conductivity with depth, that is: Where γ is the surface energy, and the surface energy is determined by assuming a linear relationship between surface energy and temperature T: Among them, T 300 It is room temperature 300 K, γ 300 It is the surface energy at 300 K; The radiation component K is determined based on the photon gas theory. r for: Where σ is the Stefan-Boltzmann constant, e1 is the emissivity of the solid particle, and e1 is an empirical factor for the irregular shape of the solid particle. The thermal conductivity equation was used to correlate lunar surface temperature with the thermophysical properties of lunar soil: The temperature T of the lunar regolith varies with time t and depth z, which is governed by the heat conduction equation: The boundary conditions are: Lunar soil surface At the thermal equilibrium depth z, In the formula, Here, c is the density of lunar soil, k is the heat capacity of lunar soil, K is the thermal conductivity of lunar soil, and T is the temperature at depth z. For lunar surface emissivity, Boltzmann's constant, J0 represents the lunar surface temperature, and J0 represents the internal heat flow of the moon. The heat capacity of lunar soil is determined by an empirical function of temperature. 。 7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the parallel computation method for inverting the thermophysical properties of lunar soil from lunar surface temperature as described in any one of claims 1-5.

8. A storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the parallel computation method for inverting the thermophysical properties of lunar soil from lunar surface temperature as described in any one of claims 1-5.

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