Method for evaluating heat recovery rate of heat energy storage

Abstract

The invention relates to the technical field of computer aided design, in particular to a heat recovery rate evaluation method for heat energy storage, which comprises the following steps of: acquiring geometric structure information of a heat energy storage device, calling node grid division parameters and unit boundary conditions, and generating a finite element grid model of the heat recovery device; environment temperature, atmospheric humidity and atmospheric pressure are called, thermal environment influence variable constraint conditions are applied to the finite element mesh model of the heat recovery device through thermal-environment coupling boundary condition setting, and an environment coupling finite element model is established; according to the method, the accuracy of the finite element mesh model is improved through geometric analysis of the structural information of the heat energy storage device and division of the node mesh, and then the thermal environment coupling relation is established and the simulation accuracy of the thermal environment factor influence is enhanced under the constraint conditions of the environment temperature, the atmospheric humidity and the atmospheric pressure.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided design, and particularly relates to a method for evaluating the heat recovery rate of thermal energy storage. Background Art

[0002] The technical field of computer-aided design (Computer Aided Design, abbreviated as CAD) mainly refers to the technology that uses computer systems and software tools to assist engineers or designers in product design, modeling, analysis, and optimization.

[0003] However, in the actual operation process of the prior art, only simple modeling and general simulation analysis of products are carried out relying on computer-aided design technology, but the coupling behavior analysis of the thermal energy storage device in the real thermal environment is not deep enough, resulting in the interaction between the device structure information and the influence of environmental factors not being fully reflected; due to ignoring the constraints of environmental variable conditions, the call of material characteristic parameters is also relatively simple, and it is difficult to reveal the complex mutual relationship between the thermal stress distribution inside the device and the heat transfer performance, thus causing the simulation analysis results to deviate from the actual operation conditions, leading to insufficient reliability of the heat recovery efficiency evaluation. Summary of the Invention

[0004] The purpose of the present invention is to solve the deficiencies existing in the prior art, and a method for evaluating the heat recovery rate of thermal energy storage is proposed.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solution. A method for evaluating the heat recovery rate of thermal energy storage includes the following steps:

[0006] Obtain the geometric structure information of the thermal energy storage device, call the node mesh division parameters and element boundary conditions to generate a finite element mesh model of the heat recovery device; call the environmental temperature, atmospheric humidity, and atmospheric pressure, and apply the thermal-environment coupling boundary condition setting to impose the thermal environment influence variable constraint conditions on the finite element mesh model of the heat recovery device to establish an environmental coupling finite element model;

[0007] Based on the environmental coupling finite element model, call the material thermal expansion coefficient, elastic modulus, and Poisson's ratio parameters to calculate the thermal stress distribution under the action of thermal environment variables, and generate a node thermal-stress coupling distribution field; obtain the heat flux density and thermal conductivity, and substitute the node thermal-stress coupling distribution field into the heat conduction calculation equation to obtain a thermal-stress coupling heat transfer field;

[0008] Based on the thermal-stress coupled heat transfer field, call the temperature gradient and heat flux parameters at different positions in the thermal energy storage device, calculate the local node heat conduction efficiency, generate the local node heat conduction efficiency distribution, call the thermal volume of the thermal energy storage device and the local node heat conduction efficiency distribution, integrate and accumulate the local node heat conduction efficiency, and establish an overall heat conduction efficiency evaluation index;

[0009] Based on the overall heat conduction efficiency evaluation index, call the preset parameters of the operating environment of the thermal energy storage device, and perform multiple simulation calculations under different combinations of thermal environment parameters to generate the environmental adaptability evaluation results of the heat recovery rate.

[0010] Preferably, the steps for obtaining the finite element mesh model of the heat recovery device are as follows:

[0011] Obtain the geometric structure information of the thermal energy storage device, including the external contour dimensions, internal structure distribution, connection interface positions, and heat conduction path arrangements, set the mesh generation area according to the geometric boundary range, and generate a geometric structure information set;

[0012] Based on the geometric structure information set, perform node mesh division, sequentially calculate the node layout of each structural unit under the target density and segmentation strategy, construct the topological relationship between the structural units, and perform step-by-step inspection and correction on the unit shape consistency and node coincidence rate to generate a node mesh data set;

[0013] Based on the node mesh data set, integrate various constraint types set in the unit boundary conditions, configure the heat boundary action surface distribution and physical constraint values of each unit boundary, determine the overall configuration of the finite element model in combination with the geometric structure information set, assemble all mesh units and form a continuous heat conduction domain, and generate a finite element mesh model of the heat recovery device.

[0014] Preferably, the steps for obtaining the environment-coupled finite element model are as follows:

[0015] Obtain the environmental temperature, atmospheric humidity, and atmospheric pressure, and generate an environmental parameter setting set in combination with the thermal-environment coupling boundary condition setting;

[0016] Based on the environmental parameter setting set, apply the constraint conditions of the environmental temperature, atmospheric humidity, and atmospheric pressure to the finite element mesh model of the heat recovery device, and adjust the thermal expansion, heat transfer rate, and temperature stress distribution of the finite element mesh model of the heat recovery device by matching the interaction between the environmental parameters and the finite element mesh model of the heat recovery device to generate an environment-coupled finite element model.

[0017] Preferably, the steps for obtaining the node thermal-stress coupled distribution field are as follows:

[0018] Based on the environment-coupled finite element model, integrate the material thermal expansion coefficient, elastic modulus, and Poisson's ratio parameters to form a set of material physical parameters;

[0019] Based on the set of material physical parameters, calculate the thermal stress distribution. The calculation formula is:

[0020]

[0021] where, σ represents the thermal stress distribution, E is the material elastic modulus, α is the thermal expansion coefficient, ΔT is the environmental temperature change, ∈ is the material strain, ν is Poisson's ratio, T is the current temperature value, γ is the thermal conductivity constant of the material, and μ is the temperature-dependent adjustment factor;

[0022] Based on the thermal stress distribution, integrate the thermal displacement data of each node to construct a nodal thermal stress coupling distribution field. By analyzing the coupling effect of thermal stress and displacement point by point, comprehensively evaluate the response characteristics of the material in a changing environment, and generate a nodal thermal stress coupling distribution field.

[0023] Preferably, the steps for obtaining the thermal-stress coupled heat transfer field are as follows:

[0024] Obtain the heat flux density and thermal conductivity of the thermal energy storage device, and obtain the thermal conductivity of each component of the thermal energy storage device from the material data sheet to generate a list of heat flux parameters and thermal conductivity parameters;

[0025] Based on the list of heat flux parameters and thermal conductivity parameters, calculate the heat transfer efficiency. The calculation formula is:

[0026]

[0027] where, Φ represents the heat transfer efficiency per unit volume, V is the calculated control volume, k(x,y,z) represents the thermal conductivity at the position of the spatial variable, represents the second-order partial derivative of temperature with respect to position, N represents the total number of coupled nodes, σ i represents the thermal stress of the i-th node, λ i represents the thermal conductivity correction factor of the i-th node;

[0028] Based on the heat transfer efficiency, evaluate the thermal stress coupling performance of the thermal energy storage device. By analyzing the distribution of the heat transfer efficiency, generate a thermal-stress coupled heat transfer field.

[0029] Preferably, the steps for obtaining the local nodal heat conduction efficiency distribution are as follows:

[0030] Based on the thermal-stress coupled heat transfer field, locate each grid node in the finite element mesh model of the thermal energy storage device, point by point call the temperature gradient value and heat flux measurement value in the area corresponding to the node coordinates, and retrieve the thermal conductivity in combination with the node material number to generate a local heat conduction input set;

[0031] Based on the local heat conduction input set, calculate the local node heat conduction efficiency. The calculation formula is as follows:

[0032]

[0033] Among them, η represents the local node heat conduction efficiency, q represents the heat flux at the node, Δx represents the distance between nodes, k represents the thermal conductivity of the node material, represents the modulus of the temperature gradient, Δt represents the time interval of heat conduction, c represents the specific heat capacity of the material, ρ represents the density of the material, and A represents the effective cross-sectional area of the node influence area;

[0034] Based on the local node heat conduction efficiency, traverse each node position, merge and sort the spatial coordinates corresponding to the heat conduction efficiency of all nodes, construct a node-heat efficiency mapping table, and generate the local node heat conduction efficiency distribution.

[0035] Preferably, the steps for obtaining the overall heat conduction efficiency evaluation index are as follows:

[0036] Obtain the heat volume of the thermal energy storage device and the local node heat conduction efficiency distribution, match the corresponding heat volume block number to the spatial position mapped by each node, screen the node set with effective heat conduction efficiency, establish a node-volume association relationship table, and generate a node-volume efficiency mapping set;

[0037] Based on the node-volume efficiency mapping set, traverse each heat conduction efficiency value node by node, accumulate according to the boundary division of the corresponding heat volume, and generate the overall heat conduction efficiency evaluation index.

[0038] Preferably, the steps for obtaining the evaluation result of the environmental adaptability of the heat recovery rate are as follows:

[0039] Based on the overall heat conduction efficiency evaluation index, call the preset operating environment parameters of the thermal energy storage device. The preset environment parameters include environmental temperature, atmospheric humidity, and air pressure, and combine them to form multiple simulated environmental conditions to generate multiple sets of heat environment parameter input sets;

[0040] Based on the multiple sets of heat environment parameter input sets, perform simulation verification on the overall heat conduction efficiency evaluation index under each condition, perform finite element calculations on each set of parameters, and archive and classify the changes in heat efficiency in the output results to generate the evaluation result of the environmental adaptability of the heat recovery rate.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0042] In the present invention, through the geometric analysis of the structural information of the thermal energy storage device and the division of node meshes, the accuracy of the finite element mesh model is improved. Furthermore, under the imposition of constraint conditions such as environmental temperature, atmospheric humidity, and atmospheric pressure, a thermal environment coupling relationship is established to enhance the simulation accuracy of the influence of thermal environment factors. At the same time, based on the invocation of parameters such as the coefficient of thermal expansion, elastic modulus, and Poisson's ratio of the material, two-way coupled calculation of the thermal stress field and the heat conduction process is realized, enabling the mutual influence between the nodal thermal-stress distribution field and the heat transfer field to be fully reflected, and obtaining a thermal stress conduction effect closer to the actual operating conditions. On this basis, using the temperature gradient and heat flux of local nodes, the heat conduction efficiency of the nodes is further calculated, and an overall heat conduction efficiency evaluation index is established by means of integral accumulation, effectively reflecting the actual operating state of the thermal energy storage device. Through multiple simulations of multiple combined conditions of operating environment parameters, the environmental adaptability evaluation of the heat recovery rate is realized, improving the prediction ability and evaluation accuracy of the heat recovery effect of the thermal energy storage device in complex environments, and promoting the reduction of energy loss and the improvement of utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] Please refer to Figure 1 , the present invention provides a technical solution, a method for evaluating the heat recovery rate of thermal energy storage, including the following steps:

[0046] Obtain the geometric structure information of the thermal energy storage device, call the node mesh division parameters and unit boundary conditions, and generate a finite element mesh model of the heat recovery device; call the environmental temperature, atmospheric humidity, and atmospheric pressure, and set the thermal-environment coupling boundary conditions to impose the constraint conditions of thermal environment influence variables on the finite element mesh model of the heat recovery device, and establish an environmental coupling finite element model;

[0047] Based on the environmental coupling finite element model, call the parameters of the coefficient of thermal expansion, elastic modulus, and Poisson's ratio of the material, calculate the thermal stress distribution under the action of thermal environment variables, and generate a nodal thermal-stress coupling distribution field; obtain the heat flux density and thermal conductivity, and substitute the nodal thermal-stress coupling distribution field into the heat conduction calculation equation to obtain a thermal-stress coupling heat transfer field;

[0048] Based on the thermo - stress coupled heat transfer field, call the temperature gradient and heat flux parameters at different positions within the thermal energy storage device, calculate the local node heat conduction efficiency, generate the distribution of local node heat conduction efficiency, call the thermal volume of the thermal energy storage device and the distribution of local node heat conduction efficiency, integrate and accumulate the local node heat conduction efficiency, and establish an overall heat conduction efficiency evaluation index;

[0049] Based on the overall heat conduction efficiency evaluation index, call the preset parameters of the operating environment of the thermal energy storage device, conduct multiple simulation calculations under different combinations of thermal environment parameters, and generate the evaluation results of the environmental adaptability of the heat recovery rate.

[0050] The steps to obtain the finite element mesh model of the heat recovery device are as follows:

[0051] Obtain the geometric structure information of the thermal energy storage device, including the external contour dimensions, internal structure distribution, connection interface positions, and heat conduction path arrangements. Set the mesh generation area according to the geometric boundary range, and generate a geometric structure information set;

[0052] Based on the geometric structure information set, perform node mesh division. Sequentially calculate the node layout of each structural unit under the target density and segmentation strategy, construct the topological relationship between structural units, and perform step - by - step inspection and correction of the unit shape consistency and node coincidence rate to generate a node mesh data set;

[0053] Based on the node mesh data set, integrate various constraint types set in the unit boundary conditions, configure the thermal boundary action surface distribution and physical constraint values of each unit boundary, determine the overall configuration of the finite element model in combination with the geometric structure information set, assemble all mesh units and form a continuous heat conduction domain, and generate the finite element mesh model of the heat recovery device.

[0054] Specifically, the dimensional data of the thermal energy storage device in different directions are read and the preliminary measurement results of the external contour and the main body connection interface are recorded correspondingly. For the cases where the external dimension is greater than 500 mm or less than 0.1 mm, re-verification is carried out in combination with engineering experience and marked as out-of-limit cases. These thresholds are mainly obtained from the measurement statistics of similar devices in the past and determined through actual examples. For example, 200 products are selected as samples in the measurement records of a certain type of thermal energy storage device, and the maximum external dimension and the minimum corner features are statistically analyzed. The average value and standard deviation of the obtained dimension range are combined and then adjusted to obtain the two limit values of 0.1 mm and 500 mm. At the same time, the distribution positions of the internal structures are recorded and the corresponding component sequences are marked at the connection interface positions. During this process, the potential heat conduction paths of each connection interface can be compared according to the pre-inductive path identification list. If it is found that the connection interface position corresponding to a certain path does not appear in the list or is inconsistent with the records inside the list, the connection interface is further repeatedly measured. When measuring, calibration is carried out based on the three-point method, that is, three non-collinear reference points are selected and their distances from the reference plane are compared respectively. If the average deviation exceeds 0.5 mm, it is recorded as a potential outlier. The deviation coefficient of 0.5 mm is jointly confirmed by the measurement tool accuracy and the results of multiple on-site comparisons. Repeat this method to complete all the measurements of the external contour dimensions and the internal structure distributions. The summary of the heat conduction path layout is also checked according to the previously determined path identification list. If there is no obvious heat conduction path at the connection interfaces in some areas, it is noted in the remarks. Finally, a geometric structure information set is obtained.

[0055] Based on the above geometric structure information set, first set the target density and segmentation strategy for each structural unit and check the position of each unit in three-dimensional space item by item. Whenever it is found that the difference between the unit size and the average size of the surrounding units exceeds 10%, it is set to the state where the shape needs to be reconfirmed. The standard value of 10% can be determined by comparing the historical node division data. For example, the average size difference between units is statistically calculated from 200 existing finite element models and converted into a percentage range, and the upper limit of the most common difference is located at 10% to ensure the consistency of division. Subsequently, manually check the units in the state of needing reconfirmation and re-measure the corner point or edge coordinates of the unit. If the coordinates differ from the previous record by more than 1 mm, continue to repeat the inspection process. The 1-mm threshold is derived from the typical tolerance range during the processing of parts by this device. When the sizes of all units meet the established target density requirements, construct the topological relationship between structural units one by one. During this process, if it is found that the shared edges or nodes of adjacent units are inconsistent, immediately adjust them by the coordinate comparison method. When comparing coordinates, subtract the coordinate value of each node from the average coordinate value of the surrounding nodes. If the result exceeds the preset threshold of 0.3 mm, it indicates that the node positioning is deviated. This 0.3 mm is also obtained by referring to the stability of previous measurements. Repeat this operation until the shape consistency and node coincidence rate of all units meet the requirements, and then integrate the corrected data. Finally, generate the node grid data set.

[0056] Based on the node grid data set obtained previously, match the temperature constraint, force constraint, and heat conduction constraint in the unit boundary conditions with each unit in turn. If it is found that the boundary values of some units exceed the range of -10°C to 80°C in terms of temperature or exceed the range of 0 MPa to 2 MPa in terms of pressure, mark them as areas to be confirmed. These ranges are obtained through empirical statistics by combining the lowest and highest operating temperatures of the equipment usage environment and the actual operating pressure range. For example, in the historical operation records, the equipment operates most stably when the temperature ranges from -10°C to 80°C, and 0 MPa to 2 MPa are the common upper and lower limits of pressure. Then, according to the geometric structure information set, review the units with incomplete matching or not meeting the constraint range item by item to check whether there are incorrect physical constraint value inputs or missing heat boundary action surface distribution records. If it is confirmed that the input is improper, review the original measurement data and correct it. After confirming that all units have completed the physical constraint value and heat boundary assignment, splice the boundaries of adjacent units in the mutually contacting areas according to the assembly rules of the continuous heat conduction domain. If there is coordinate overlap at the boundary splice but the physical constraint values are inconsistent, reassign them with the heat flow priority item. Here, the priority item is set by referring to the previous sample comparison and sorting according to the key degree of heat flow. If the heat flow is greater than 50 W / m 2 then directly overwrite it, otherwise retain the original constraint value. Repeat this method until all units are connected and a complete conduction path is smoothly formed, and finally generate the finite element mesh model of the heat recovery device.

[0057] The steps for obtaining the environment-coupled finite element model are as follows:

[0058] Obtain the environmental temperature, atmospheric humidity, and atmospheric pressure, and generate an environmental parameter setting set in combination with the thermal-environment coupling boundary condition setting;

[0059] Based on the environmental parameter setting set, apply the constraint conditions of the environmental temperature, atmospheric humidity, and atmospheric pressure to the finite element mesh model of the heat recovery device. By matching the interaction between the environmental parameters and the finite element mesh model of the heat recovery device, adjust the thermal expansion, heat transfer rate, and temperature stress distribution of the finite element mesh model of the heat recovery device to generate an environment-coupled finite element model.

[0060] Specifically, according to the temperature, humidity, and air pressure acquisition methods listed in the on-site measurement, first read the environmental temperature values recorded for different time periods item by item and compare them. For example, a total of 48 values are obtained within 24 hours in the way of recording once every half hour. If a certain value is found to be lower than -20°C or higher than 60°C, it is marked as a suspicious range. These temperature upper and lower limits are determined comprehensively based on the annual usage statistics of the same type of equipment and extreme climate conditions. For example, after counting the maintenance records of 200 devices, it is found that below -20°C, it is extremely easy to cause changes in material properties, and above 60°C, it will trigger an overheat alarm. Subsequently, read the output of the humidity measurement device at a height of 1.2 m to 1.5 m at the site. If the humidity data is lower than 10% or higher than 95%, it is marked as an abnormal record again. These two thresholds are corrected by actual examples with reference to the multi-year meteorological statistics of this region and combined with the common failure rate during the operation of the equipment. For example, when sorting out 500 humidity acquisition records, it is found that if the humidity exceeds the range of 10% to 95%, the subsequent failure rate increases by about 15%. Then, obtain the air pressure values measured at each whole hour within 24 hours from the output of the preset pressure gauge at this site. If it exceeds 110 kPa or is lower than 85 kPa, it is included in the review list together with the aforementioned temperature and humidity abnormal records. These two indicators of 110 kPa and 85 kPa are selected based on the extreme value range of the local meteorological data in the past ten years and have been compared and corrected many times. If it is confirmed that the measurement data is correct and all record entries can correspond completely in the three types of temperature, humidity, and air pressure without cross-missing, classify these records uniformly according to the fields required for the thermal-environment coupling boundary condition setting, and set default values for the missing fields when necessary. For example, the default value of temperature is set to 20°C, the default value of humidity is set to 50%, and the default value of air pressure is set to 101 kPa. These default values are obtained from the median of the historical sample statistics. After completing the field pairing, merge all the data as the final environmental parameter setting set.

[0061] Based on the environmental parameter setting set obtained previously, associate the three types of data, namely temperature, humidity, and air pressure, with the corresponding parameter positions in the finite element mesh model of the heat recovery device item by item, and synchronously compare information such as the coefficient of thermal expansion and heat transfer rate of each unit. For example, first calculate the product of the temperature distribution and the coefficient of thermal expansion recorded in the unit to judge the possible magnitude of the geometric volume change of the unit. If some calculation results are greater than the critical deformation threshold of 0.2 mm deduced based on the material safety factor in advance, it is marked as needing to be repeatedly checked later. This critical value of 0.2 mm is deduced from the fusion of experimental observation results of 300 similar devices and technical data provided by the material supplier. Then, analyze the humidity data and combine the surface exposure degree of each unit to infer the potential impact of humidity infiltration. If the infiltration impact is higher than 10%, it is included in the warning range and detailed calculation records are retained. This 10% is determined by the comparison method by measuring the proportion of the material weight change at different humidities in a typical test. After that, compare the relationship between air pressure and heat transfer rate one by one. If the difference between the current atmospheric pressure value and the default value of 101 kPa exceeds ±5 kPa, recalculate the heat transfer coefficient under this atmospheric pressure condition and further conduct a comprehensive evaluation in combination with the previously obtained temperature and humidity data. Finally, summarize the heat transfer rate and stress distribution in each unit of the finite element mesh model of the heat recovery device. If the difference in heat transfer rate is greater than 30% between adjacent units, further subdivide the temperature field range at this junction. The value of 30% comes from statistical estimation based on referring to previous heat transfer measurement and comparison experiments and combining material properties. When the thermal expansion, heat transfer rate, and temperature stress distribution of all units match the environmental parameter setting set without any abnormal conditions, record the overall state of the corresponding finite element mesh in the form of complete data as the environment-coupled finite element model.

[0062] The steps to obtain the nodal thermal-stress coupling distribution field are as follows:

[0063] Based on the environment-coupled finite element model, integrate the parameters of the coefficient of thermal expansion, elastic modulus, and Poisson's ratio of the material to form a set of material physical parameters;

[0064] Based on the set of material physical parameters, calculate the thermal stress distribution, and the calculation formula is:

[0065]

[0066] Among them, σ represents the thermal stress distribution, E is the elastic modulus of the material, α is the coefficient of thermal expansion, ΔT is the environmental temperature change, ∈ is the material strain, ν is Poisson's ratio, T is the current temperature value, γ is the thermal conductivity constant of the material, and μ is the temperature-dependent adjustment factor;

[0067] Based on the thermal stress distribution, integrate the thermal displacement data of each node to construct a coupled distribution field of nodal thermal stress. By analyzing the coupling effect of thermal stress and displacement point by point, comprehensively evaluate the response characteristics of the material in a changing environment, and generate a coupled distribution field of nodal thermal stress.

[0068] Specifically, according to the content of the environmental coupling finite element model obtained previously, identify the thermal expansion coefficient, elastic modulus, Poisson's ratio and other data corresponding to different material distribution regions in its node information, and conduct segmented comparison for the indicators of each material type. For example, compare the thermal expansion coefficient with the range of 0.5×10-5 to 3.0×10-5. If the thermal expansion coefficient value of a certain material node is lower than 0.5×10-5, it is recorded as an abnormal record that needs to be additionally inspected later. This lower limit value is determined by sorting out the results of multiple expansion experiments of the same type of material in the range of room temperature to 100°C and taking the minimum stable value. The upper limit value of 3.0×10-5 refers to the common deformation coefficient of metal materials in high-temperature environments and is confirmed through multiple comparisons. After completing the screening of the thermal expansion coefficients of all material nodes, then compare the elastic modulus with the range of 50 GPa to 300 GPa. These two interval values are mainly determined based on the conventional measurement range of the same type of material in high-stress environments. For example, when conducting tensile experiments on 200 different batches of metal and alloy parts, the minimum value of the elastic modulus statistically obtained is about 50 GPa, and the maximum value is about 300 GPa. Conduct additional verification for nodes exceeding this range. The Poisson's ratio is compared with the range of 0.2 to 0.35, and the detailed reasons are also recorded when the value exceeds the limit. These threshold ranges are all obtained through the quantitative analysis and sorting of the previous round of mechanical tests. When all parameters can be within the normal threshold or reasonable explanation range, classify them according to the material number, and then uniformly summarize these data to form a set of material physical parameters.

[0069] Formula: The advantage of the formula is that by combining multiple parameters, factors such as temperature change, material strain, and thermal conductivity characteristics are combined, so as to comprehensively reflect the thermo-mechanical coupling behavior of the material in the same calculation expression. Especially the combination of α·ΔT and and other terms makes the correlation between the deformation and stress distribution caused by heat more carefully quantified. At the same time, multiplying the denominator by (1 + μ·T) can moderately adjust for temperature-dependent factors, so as to differentiate the formula results at high or low temperatures.

[0070] The steps to obtain E are as follows: This parameter represents the elastic modulus of the material. Select a metal alloy for testing, measure the maximum stress of about 600 MPa, and the strain corresponding to the elastic deformation section is 0.003. Calculate the elastic modulus as E = 600×10 6 Pa / 0.003 = 200×10 9 Pa = 200 GPa.

[0071] The steps to obtain α are as follows: This parameter represents the coefficient of thermal expansion. After reading it from the material physical parameter set, to further confirm the accuracy, it is necessary to check the expansion rate during the temperature rise from 0 °C to 100 °C. In actual operation, the specimen is placed in a temperature-controlled water bath environment. Every time the temperature rises by 10 °C, the change in its length is measured and the ratio to the initial length is calculated. After obtaining the local thermal expansion rate sequence, an average treatment is performed. In an example, for a specimen of a certain alloy material with a length of 50 mm, during the temperature rise from 20 °C to 60 °C, the measured length change is 0.036 mm. Thus, the coefficient of thermal expansion in this interval is approximately α = 0.036 mm / (50 mm × 40 K) = 1.8×10 -5 K -1 。

[0072] The steps to obtain ΔT are as follows: This parameter represents the change in ambient temperature, which can be measured according to different working conditions in combination with the ambient temperature record. The specific method is to first record the reference temperature T0, and then record the current temperature T in the actual operating environment c , and the difference between the two is ΔT. For example, if the reference temperature T0 = 25 °C is measured first, and the current temperature T c = 60 °C under full load working conditions, then ΔT = 60 - 25 = 35 °C.

[0073] The steps to obtain ∈ are as follows: This parameter represents the strain of the material. Strain is usually measured by a strain gauge or strain meter. After the strain gauge is installed on the surface of the material, it can output a corresponding small voltage change during tension or compression. This voltage change is converted into a strain value and recorded. The specific value can be obtained by calculation. Here, ΔL represents the change in the length of the material after loading, and L0 is the initial length. In an example, if the initial length L0 = 100 mm and the length becomes 100.2 mm after loading, then

[0074] The steps to obtain ν are as follows: This parameter represents the Poisson's ratio, which is obtained from the tensile test results corresponding to the material physical parameter set or the reference supplier's inspection report. The specific test method can measure the transverse strain and longitudinal strain of the material simultaneously during the tensile test, and define the ratio of the transverse strain to the longitudinal strain as the Poisson's ratio. In an example, the longitudinal strain is 0.002 and the transverse strain is 0.0004, then

[0075] The steps to obtain T are as follows: This parameter is the current temperature value, which is not the same as ΔT. ΔT is the change amount while T is the current absolute temperature magnitude, and it is obtained from the previously recorded ambient temperature acquisition steps. If the measured temperature at a certain moment is 50 °C, then T = 50 °C.

[0076] The steps to obtain γ are as follows: This parameter represents the thermal conductivity constant of the material and can be obtained through experimental detection of the material under steady-state heat transfer conditions. Keep a constant temperature difference at both ends of the specimen and measure the heat flux, and calculate after stabilization where q is the transfer power, L is the length of the specimen, A is the cross-section of the specimen, and ΔT s is the temperature difference at both ends of the specimen. In the example, q = 300W, L = 0.5m, A = 0.002m 2 , ΔT s = 40°C, then

[0077] The steps to obtain μ are as follows: This parameter is a temperature-dependent adjustment factor used to amplify or reduce the temperature effect in the denominator. It needs to be statistically analyzed in experiments with multiple temperature loading-unloading cycles. Record the change rate of the material properties with temperature in each cycle. By dividing the stress increment in each cycle stage by the temperature increment. In the example, a certain alloy was tested 5 times in multiple intervals such as 25°C to 75°C and 50°C to 100°C, and μ = 0.007 was obtained

[0078] Calculation process: Let E = 2.0×10 11 Pa, α = 1.8×10 -5 K -1 , ΔT = 35°C, ∈ = 0.002, ν = 0.2, T = 50°C, γ = 1875W / (m·K), μ = 0.007. First, calculate E·(α·ΔT) 2 :

[0079] 2.0×10 11 ×(1.8×10 -5 ×35) 2 ;

[0080] = 2.0×10 11 ×(1.8×10 -5 ×35) 2 ;

[0081] = 2.0×10 11 ×(6.3×10 -4 ) 2 ;

[0082] = 2.0×10 11 ×3.969×10 -7 ;

[0083] = 7.938×10 4 Pa;

[0084] Then calculate

[0085]

[0086] Add the two and then take the absolute value:

[0087] |7.938×10 4 +187.500004|≈7.938×10 4 +1.875×10 2 ;

[0088] ≈7.939875×10 4 ;

[0089] Then divide by 1 + μ·T = 1 + 0.007×50 = 1 + 0.35 = 1.35:

[0090]

[0091] Finally, take the square root:

[0092]

[0093] This result indicates that the thermal stress generated by the material here at ΔT = 35°C and a strain of 0.002 is approximately 242.57 Pa. If the stress value exceeds a certain range compared with the subsequent safety standards, it may be necessary to recheck the material's load-bearing capacity or the operating temperature range according to the parameters such as α and ∈ obtained previously for further evaluation.

[0094] Based on the existing thermal stress distribution, read the thermal displacement data under each node and correspond them point by point according to the coordinate positions. If it is found that the displacement of some nodes is greater than 0.3 mm, then include them in the key investigation items. The value of 0.3 mm comes from the displacement statistics of the same specification materials under full load and overload conditions in the early stage. During the statistics process, 100 material samples were selected for continuous loading tests and the maximum displacement curve was measured, and the upper limit interval of the average displacement was obtained. When the node displacement is marked as a key investigation item, it is necessary to recompute the displacement vector difference of its adjacent nodes in combination with the recorded thermal stress distribution. If the vector difference exceeds 0.1 mm when compared with the reference value, the inspection range is expanded. The reference value of 0.1 mm comes from the balance analysis of the same type of materials during the thermal deformation process. The critical splitting point of the offset was obtained after comparing 50 same-type components. When the displacement differences of all nodes are kept within 0.1 mm, uniformly conduct a positioning comparison of their thermal stress and displacement relationships to check whether they conform to the material characteristics in the material physical parameter set. Continuously track and compare any records with excessive differences and include them in the abnormal summary. After completing this process, the thermal stress and displacement relationships of each node can be formed into a coupled distribution form, and the node thermal stress coupled distribution field can be obtained by centralized summary.

[0095] The steps to obtain the thermal-stress coupled heat transfer field are as follows:

[0096] Obtain the heat flux density and thermal conductivity of the thermal energy storage device, and obtain the thermal conductivity of each component of the thermal energy storage device from the material data sheet to generate a list of heat flux parameters and thermal conductivity parameters;

[0097] Based on the list of heat flux parameters and thermal conductivity parameters, calculate the heat transfer efficiency. The calculation formula is:

[0098]

[0099] Among them, Φ represents the heat transfer efficiency per unit volume, V is the calculated control volume, k(x, y, z) represents the thermal conductivity at the position of the spatial variable, represents the second-order partial derivative of temperature with respect to position, N represents the total number of coupled nodes, and σ i represents the thermal stress of the i-th node, and λ i represents the thermal conductivity correction factor of the i-th node;

[0100] Based on the heat transfer efficiency, evaluate the thermal stress coupling performance of the thermal energy storage device. By analyzing the distribution of the heat transfer efficiency, generate a thermal-stress coupled heat transfer field.

[0101] Specifically, according to the previously recorded structural information of the thermal energy storage device, first sequentially extract the geometric positioning and surface material markings of each component, and then refer to the on-site measurement report and the data sheet provided by the manufacturer to obtain the corresponding heat flux density parameters and thermal conductivity. If it is found that the heat flux density of a certain component exceeds 5000 W / m 2 or is lower than 10 W / m 2 then mark it as an abnormal area. This 5000 W / m 2 and 10 W / m 2The two numerical values are obtained by summarizing the historical monitoring results of 50 devices of the same model under different working conditions. The heat flux density and the corresponding thermal conductivity of each component are filed one by one. During this period, if a certain thermal conductivity is outside the range of 0.1 W / (m·K) to 500 W / (m·K), it is marked as an index to be reviewed. These two thresholds are obtained through multiple measurements and comparisons of different metals, alloys, and composite materials. Subsequently, the heat flux and thermal conductivity data within the normal range are summarized item by item and matched with the component numbers. If there is an inconsistency in the numbers during the matching process, the original records are reviewed. After confirming that there is no error, the heat flux density and thermal conductivity are compiled into a list of heat flux parameters and thermal conductivity parameters. During this process, detailed fields such as the corresponding material category, stress direction, and usage location of each component are retained to facilitate positioning for subsequent calculations. If the component is installed longitudinally and is more than 2 m away from the heat source, its environmental radiation heat contribution level is additionally marked in the data table. This level is determined by comparing the distance of the component from the heat source according to the thermal field distribution monitored during the operation tracking of devices of the same model. For example, in the statistics of the operating environments of 15 devices, if the component is 2 m to 5 m away from the heat source, the radiation heat contribution level is recorded as medium, and if it is more than 5 m, it is marked as low. After this process is completed, a list of heat flux parameters and thermal conductivity parameters is obtained.

[0102] Formula: The advantage of the formula is that it incorporates the thermal conductivity of the material's spatial position, the second-order differential characteristics of the temperature gradient, and the thermal stress factor into the same expression, thereby realizing the quantitative processing of the coupling of heat conduction distribution and thermal stress within a single calculation framework. In particular, one item can highlight the attention to high-stress nodes and contribute to the centralized assessment of the influence of large-stress regions on the overall heat transfer efficiency.

[0103] The steps to obtain V are as follows: This parameter represents the control volume and is related to the geometric dimensions and partition settings of the thermal energy storage device. According to the previously confirmed internal structure of the device, each section or each cavity can be selected as an independent volume region, and its length, width, and height can be obtained by accurately measuring or accumulating the existing CAD three-dimensional coordinate data to calculate the volume size. It is also possible to perform layered measurements on the device on-site and record the length, width, and height separately, and multiply the measured values to obtain the volume. If a certain part of the device is cylindrical or has other geometric shapes, the corresponding volume formula is used for accurate calculation. For example, if a certain block is 0.3 m long, 0.2 m wide, and 0.1 m high, then V = 0.3 × 0.2 × 0.1 = 0.006 m 3 。

[0104] The steps to obtain k(x, y, z) are as follows: This parameter represents the thermal conductivity at the position of the spatial variable. It can be specifically divided from the previously obtained thermal parameter list according to the coordinate information, and then each material region is corresponded to its distribution range in the three-dimensional coordinate system. If different material types are detected in a certain coordinate segment, the thermal conductivity matching the material should be used in the corresponding segment, and the interpolation method is used to process the transition segment to ensure a stepped or smooth transition on the coordinate. In the example, if the thermal conductivity of the metal used when x ranges from 0 to 0.2 m is 380 W / (m·K), and the thermal conductivity of the alloy is detected to be 120 W / (m·K) when x ranges from 0.2 m to 0.3 m, then interpolation calculation is required near x = 0.2 m to make a transition for the thermal conductivity.

[0105] The steps to obtain are as follows: This parameter represents the second-order partial derivative of temperature with respect to the x direction of the coordinate. It is necessary to discretize the temperature field in space and then perform differentiation using the finite difference method or numerical interpolation means. Several points can be selected in the x direction, such as x0 < x1 <... < x m , and the temperatures T0, T1,..., T m are respectively recorded, and is calculated by the central difference or quadratic curve fitting method. In the example, if the temperature is 40 °C at x = 0.00 m, 45 °C at x = 0.10 m, and 41 °C at x = 0.20 m, then the three-point central difference is first used to obtain the first derivative, and then the derivative is differentiated to obtain the second derivative value. Interpolating these discrete second derivatives can obtain the discrete sequence over the entire interval.

[0106] The steps to obtain N are as follows: This parameter represents the total number of coupling nodes. It is necessary to count the number of all nodes in the previously constructed finite element mesh or discrete mesh model, and only count the effective nodes related to the stress distribution and heat transfer. In the example, if there are 500 nodes after the mesh division of the thermal energy storage device, and 480 of them are connected to the actual heat transfer unit, and the remaining 20 nodes are free boundaries and do not participate in the stress calculation, then let N = 480.

[0107] σ i The steps to obtain are as follows: This parameter represents the thermal stress value of the i-th node, which can be directly read according to the node thermal stress coupling distribution field obtained in the previous steps.

[0108] λ iThe acquisition steps are as follows: This parameter represents the thermal conductivity correction factor of the i-th node, which is used to appropriately adjust the local thermal conductivity characteristics brought by thermal stress in the formula. First, place the alloy material in an experimental environment where stepwise tensile stress can be applied, and record that its thermal conductivity decreases by 5%, 12%, and 25% at 100 MPa, 200 MPa, and 300 MPa respectively. Apply these change rates to the stress range of the node according to the interpolation method, and the value of the node thermal conductivity correction factor can be obtained. If in the 200 MPa interval, the corresponding ratio is λ i = 0.88, then multiply the material thermal conductivity of the node itself by λ i in the subsequent calculation to obtain the thermal conductivity value after stress correction.

[0109] Calculation process: Let the selected control volume V = 0.006 m 3 , divide the spatial coordinate x from 0 to 0.3 m into three sections within this volume. Let k(x, y, z) be 380 W / (m·K) from x = 0 to 0.1 m, interpolate from 380 W / (m·K) to 120 W / (m·K) from x = 0.1 m to 0.2 m, and be 120 W / (m·K) from x = 0.2 m to 0.3 m. Record the temperatures at the temperature sampling points (0.0 m, 0.1 m, 0.2 m, 0.3 m) respectively and use the three-point central difference to estimate Perform corresponding weighted integration of these values within the volume range and obtain dx dy dz = 1.45×10 3 W. At the same time, statistically obtain N = 480 from the established coupled node distribution. For example, extract the thermal stress values of three nodes: σ1 = 6.0×10 6 Pa, σ2 = 4.5×10 6 Pa, σ3 = 7.2×10 6 Pa, and combine their respective λ1 = 0.93, λ2 = 0.87, λ3 = 0.80. After processing the remaining nodes in the same way, calculate Through actual calculation, the obtained value is approximately 3.60×10 20 Pa 3 , and after taking the square root, it is approximately 6.0×10 10 Pa 1.5 . Add this result to the aforementioned integral quantity to get 1.45×10 3 + 6.0×10 10 ≈ 6.0×10 10 . Finally, divide by the volume V = 0.006 m 3 , that is

[0110] This result indicates that the comprehensive heat transfer efficiency in this volume region reaches 1.0×10 13 W / m 3, if this value far exceeds the pre-recorded benchmark heat transfer efficiency (e.g., 5×10 12 W / m 3 ), it indicates that the stress contribution term is very significant and further investigation and evaluation are required at adjacent nodes or regions. If this value is close to or lower than the benchmark value, it indicates that the heat transfer efficiency distribution within the current volume is stable, and the thermal-stress coupled heat transfer field for the next step of analysis can be directly generated.

[0111] Based on the obtained heat transfer efficiency, each local area of the thermal energy storage device is stratified and compared. The heat transfer efficiency value of each area is compared with the range from 0.5×10^9 W / m 3 to 2×10^13 W / m 3 . This range is refined from the data distribution of the previous 100 devices of the same type operating in multiple scenarios. If the heat transfer efficiency of a certain area is lower than 0.5×10^9 W / m 3 , it is marked as a suspicious point and the measurement records of the heat flux density or material thermal conductivity parameters are continuously verified in combination with the node information. If the heat transfer efficiency of a certain area is higher than 2×10^13 W / m 3 , it is recorded in the high-load area and attention is paid to whether excessive thermal stress accumulation occurs. This determination threshold is set for the boundary of the joint measurement of thermal stress and thermal conductivity values in several instances. After comparing all areas, it can be seen that the heat transfer efficiency distribution is regularly concentrated at higher values near the nodes of the main heat source and lower in the outer material transition section. Based on this distribution, the possible positions of thermal stress concentration inside the device can be identified, and cross-checks are carried out in association with the previously obtained heat flux parameters and thermal conductivity parameter lists. All the information after comparison is unified and summarized to obtain the thermal-stress coupled heat transfer field.

[0112] The steps to obtain the local node heat conduction efficiency distribution are as follows:

[0113] Based on the thermal-stress coupled heat transfer field, each grid node is located in the finite element mesh model of the thermal energy storage device. The temperature gradient value and heat flux measurement value of the area corresponding to the node coordinates are called point by point, and the thermal conductivity is retrieved by combining the node material number to generate the local heat conduction input set;

[0114] Based on the local heat conduction input set, the local node heat conduction efficiency is calculated. The calculation formula is:

[0115]

[0116] where η represents the local node heat conduction efficiency, q represents the heat flux at the node, Δx represents the distance between nodes, k represents the thermal conductivity of the node material, represents the temperature gradient modulus value, Δt represents the time interval of heat conduction, c represents the specific heat capacity of the material, ρ represents the material density, and A represents the effective cross-sectional area of the node influence area;

[0117] Based on the local node heat conduction efficiency, traverse each node position, merge and sort the spatial coordinates corresponding to the heat conduction efficiency of all nodes, construct a node - heat efficiency mapping table, and generate the local node heat conduction efficiency distribution.

[0118] Specifically, according to the heat - stress coupled heat transfer field information obtained previously, compare the three - dimensional coordinates of each grid node in the finite - element mesh model of the thermal energy storage device with the local temperature gradient measurement list one by one. First, determine the spatial position where the node is located and query the temperature gradient value recorded in the monitoring report. If it is found during the record comparison that the temperature gradient of a certain node is not within the range of 0 °C / m to 300 °C / m, it is marked as an abnormal phenomenon. This threshold is mainly determined based on the upper and lower limits of the common temperature gradients statistically obtained from historical operation data. The statistical steps include continuously monitoring 50 similar devices for 120 hours under different load conditions and extracting the maximum and minimum temperature change rates at each position, thereby obtaining a common gradient interval. Then, extract the corresponding heat - flow measurement value of the node from the heat - flow measurement list. If its heat flow is greater than 2000 W / m 2 or less than 5 W / m 2 then conduct a re - verification. This threshold also originates from the distribution curve generated from the cumulative 400 - hour test of the same - model device. Take the upper and lower bounds that meet the 95% quantile. After comparing the temperature gradients and heat flows of all nodes, retrieve the corresponding thermal conductivity in the material parameter list according to the node material number and perform an exact match. If there is an obvious deviation between the material thermal conductivity and the average range of conventional metals or alloys, it indicates that the material information of this node may be incorrectly entered or there is a special coating on the material. Confirm it through additional comparison steps. After confirmation, integrate the temperature gradient, heat flow, and thermal conductivity into a local heat - conduction input set that includes node coordinates, heat flow, thermal conductivity values, and other necessary identification fields.

[0119] Formula: The benefit of the formula is that it combines factors such as heat flow, temperature gradient, material thermal - conductivity parameter, and heat - transfer time interval in the same expression. The first half focuses on measuring the energy - transfer efficiency through the heat - conduction process inside the material, and the second half measures the impact on the material heat capacity caused by the heat - flow accumulation over a certain period of time. After adding the two parts together, the comprehensive heat - conduction efficiency at the node level can be obtained at one time.

[0120] The steps to obtain q are as follows: This parameter represents the heat flow at the node and is obtained from the previously recorded heat - flow measurement list. For example, a certain node is located at x = 0.15 m, y = 0.30 m, z = 0.10 m, and the heat flow output by the nearest sensor corresponding to it is 630 W / m 2 .

[0121] The steps to obtain Δx are as follows: This parameter represents the distance between nodes, which is mainly used to measure the displacement distance of heat within the material. It can be calculated from the coordinate difference between this node and its adjacent nodes in the previously established finite element mesh, or uniformly specified by the element size during mesh generation for the case of a continuous medium. If the average length direction of the mesh element is 0.05 m and this node is exactly at the center of the element, then Δx = 0.05 m.

[0122] The steps to obtain k are as follows: This parameter represents the thermal conductivity of the node material. Through the local heat conduction input set obtained previously, the thermal conductivity of the material to which the node belongs can be directly queried. For common metal materials (such as pure copper), it can usually reach 380 - 400 W / (m·K). For alloys, it may be in the range of 100 - 200 W / (m·K) or lower. In the example, if the thermal conductivity of the node material is found to be k = 120 W / (m*K).

[0123] The steps to obtain are as follows: This parameter represents the magnitude of the temperature gradient, which has been obtained from the thermal - stress coupled heat transfer field or the relevant monitoring list in the previous step. If the numerical method is used, the temperature distribution in the mesh can be discretized, and then the temperature change rates in the x, y, and z directions are calculated respectively and the vector magnitude operation is performed. In the example, if the temperature gradient in the x - direction is 50 K / m, in the y - direction is 20 K / m, and in the z - direction is 10 K / m, then

[0124] The steps to obtain Δt are as follows: This parameter represents the time interval of heat conduction, which can be directly read from the recorded test or on - site sampling period, or uniformly defined from the continuous monitoring interval of the heat flux in the equipment operation log. If the temperature and heat flux data are collected every 5 s, then Δt = 5 s.

[0125] The steps to obtain c are as follows: This parameter represents the specific heat capacity of the material, which can be queried in the material data sheet or on - site inspection report, or calculated by heating a material block through a calorimetry experiment and statistically obtaining the heat absorption per unit mass under the conditions of accurate temperature measurement and continuous heating power. If a heating power of 50 W is applied to a material block with a mass of 0.5 kg for 60 s and the temperature rises by 10 K during this period, then

[0126] The steps to obtain ρ are as follows: This parameter represents the density of the material, which can be directly read from the material data sheet, or obtained by weighing a certain volume of the specimen through testing, calculating the mass, and then dividing by the volume. If the mass of a material block with a volume of 0.002 m 3 is 15 kg, then

[0127] The steps to obtain A are as follows: This parameter represents the effective cross-sectional area of the node influence region. In mesh generation, each node corresponds to a certain local surface or cross-sectional size, which can be obtained by multiplying the boundary length of the element in a certain direction by the node influence width. For example, if the mesh element is divided into a rectangular surface of 0.02m×0.05m, then A = 0.02×0.05 = 0.001m 2 。

[0128] Calculation process:

[0129] Let q = 600W / m 2 , Δx = 0.03m, k = 120W / (m*K), Δt = 2s, c = 420J / (kg*K), ρ = 7800kg / m 3 , A = 0.001m 2 , first calculate the first part

[0130]

[0131] Then calculate the second part

[0132]

[0133] Take its square root:

[0134]

[0135] Add the two parts together:

[0136] η = 2.74×10 -3 +14.82≈14.8227;

[0137] This result indicates that the heat conduction efficiency of this node is approximately 14.8227. When the η value is greater than 10, it can be regarded as a high heat conduction efficiency region. If it is less than 1, it can be regarded as a relatively lagging section, which has important reference value for the subsequent distribution of local node heat conduction efficiency.

[0138] After obtaining the local heat conduction efficiency of each grid node node by node, arrange these efficiency values in ascending order according to the node coordinates. If it is found during the sorting process that the heat conduction efficiency of some nodes exceeds the threshold of 100, mark them as significant heat transfer regions. This value of 100 is obtained by summarizing the heat transfer evaluation results based on multi-batch statistics of similar devices. For example, 200 high-intensity operation instances are selected for measurement, and it is found that the conduction efficiency is mostly in the range of 80-110. Therefore, 100 is set as the judgment boundary. Then, the nodes in the significant heat transfer region are grouped according to their respective three-dimensional coordinates and checked whether they are consistent with the corresponding material information. If there is a situation where the material code does not match or the coordinate deviates from the grid boundary vector by more than 0.01 m during the grouping, it is marked in the abnormal record and re-compared with the actual measurement list. After the merge sorting of all node heat conduction efficiencies is completed, a mapping list of nodes and corresponding heat conduction efficiencies is formed in the final output, and the local node heat conduction efficiency distribution is obtained from this.

[0139] The steps to obtain the overall heat conduction efficiency evaluation index are as follows:

[0140] Obtain the heat volume of the thermal energy storage device and the local node heat conduction efficiency distribution, match the corresponding heat volume block number to the spatial position mapped by each node, screen the node set with effective heat conduction efficiency, and establish a node-volumetric association relationship table to generate a node-volumetric efficiency mapping set;

[0141] Based on the node-volumetric efficiency mapping set, traverse each heat conduction efficiency value node by node, and accumulate according to the boundary division of the corresponding heat volume to generate the overall heat conduction efficiency evaluation index.

[0142] Specifically, according to the heat volume detection details formed previously, measure and record the length, width, height of each internal block of the thermal energy storage device and the volume occupied by the actual internal channels. If it is found during comparison that the volume value of any block exceeds 50L or is less than 0.5L, it is listed as a suspicious area and measured again. These two thresholds are obtained by summarizing through multiple measurements based on the structural statistical data of similar devices. For example, when analyzing the volume distribution of 300 blocks by plotting, it is found that most of them are concentrated between 0.5L and 50L. After summarizing these volume information item by item, the overall heat volume distribution of the thermal energy storage device is obtained. Subsequently, cross-correlate the obtained local node heat conduction efficiency distribution with the corresponding coordinate positions, and match the heat volume block numbers to which each node belongs within the three-dimensional coordinates. If the difference between the node coordinates and the boundary region is detected to be greater than 5mm during the matching process, it is marked as a coordinate anomaly point. This 5mm threshold is set according to the minimum side length of adjacent grid cells and the allowable error of node arrangement, and is fixed as a benchmark after confirmation through on-site measurement and grid division steps. Whenever this mark appears, the node position is compared manually or automatically again. After confirmation, the node is mapped to the correct block number. Next, screen the set of nodes with effective heat conduction efficiency. Specifically, compare the node heat conduction efficiency with the value of 10, which is the median of the statistical interval after long-term observation of nodes of the same type of material. If the heat conduction efficiency of a certain node is lower than 10, it is temporarily excluded; otherwise, it is added to the set of effective nodes. Manually record the nodes with heat conduction efficiency between the critical values of 9 and 11 for subsequent tracking. After completion, classify and merge all effective nodes according to the block numbers, and establish an association relationship table between nodes and volumes during this process. Realize the mapping by recording the node numbers and volume numbers bidirectionally within the same block. After all node comparisons are completed, summarize these matching and merging results to obtain the node volume efficiency mapping set.

[0143] After completing the construction of the node volumetric efficiency mapping set, read the heat conduction efficiency of each node from the mapping set and accumulate them sequentially node by node. Corresponding them to different heat capacity integral boundary ranges for sectional statistics. For example, classify the block volume less than 5L as the first category, between 5L and 20L as the second category, and more than 20L as the third category. These classification thresholds are divided according to the distribution obtained from measuring 100 typical blocks on site. Subsequently, when traversing all nodes, once it is confirmed that the block corresponding to a certain node belongs to a certain category, add up the heat conduction efficiency value in the corresponding category record. If the heat conduction efficiency of a certain node exceeds the benchmark of 30 during the accumulation process, additionally store the node separately after accumulation in each category. The benchmark of 30 is determined from the distribution center of the statistical data of multiple high-temperature working conditions and is determined after comparison with the long-term stability of the equipment. When the efficiency values of all nodes have completed the comparison records and the corresponding partition accumulations, sum up the cumulative results in three or more categories of blocks, and merge and compare the total heat conduction values of each category. If the total value significantly deviates from the previously set detection interval (for example, deviates more than 50% from the average level) in a certain category, it is marked for rechecking. Continue to compare the key values of the node coordinates, material numbers, and heat conduction efficiency recorded in the node volumetric efficiency mapping set. If no abnormality is confirmed, retain the accumulated result. Finally, add up all the partition accumulation values to generate the overall heat conduction efficiency evaluation index.

[0144] The steps to obtain the evaluation result of the environmental adaptability of the heat recovery rate are as follows:

[0145] Based on the overall heat conduction efficiency evaluation index, call the preset parameters of the operating environment of the thermal energy storage device. The environmental preset parameters include environmental temperature, atmospheric humidity, and air pressure, and combine them to form multiple simulated environmental conditions to generate multiple sets of input sets of thermal environment parameters.

[0146] Based on multiple sets of input sets of thermal environment parameters, conduct simulation verification on the overall heat conduction efficiency evaluation index under each working condition, perform finite element calculations on each set of parameters, and archive and classify the changes in the heat efficiency in the output results to generate the evaluation result of the environmental adaptability of the heat recovery rate.

[0147] Specifically, according to the overall heat conduction efficiency evaluation index summarized above, the preset environmental temperature range is set at -20°C to 60°C, the atmospheric humidity range is set at 10% to 95%, and the upper and lower limits of air pressure are determined to be 85 kPa to 110 kPa. These upper and lower limit values are derived from the operation records of the same type of thermal energy storage devices in various external environments in the past. Through the analysis and collation of the data of 200 devices over 2 years, all cases where the environmental temperature is lower than -20°C or higher than 60°C are regarded as extreme temperature conditions and marked as high risks. Humidity less than 10% or greater than 95% is regarded as an abnormal humidity range, and air pressure below 85 kPa or above 110 kPa is defined as an abnormal air pressure situation. These thresholds are mainly obtained by summarizing the historical observations of the equipment in multiple regions and seasons. Subsequently, three temperature levels (e.g., -20°C, 20°C, 60°C), three humidity levels (e.g., 10%, 50%, 95%), and three air pressure levels (e.g., 85 kPa, 101 kPa, 110 kPa) are combined pairwise to generate multiple sets of thermal environment parameter input sets. To ensure the sufficiency and diversity of numerical acquisition, each set of parameters will also undergo subtle step changes. For example, the temperature is arranged in batches within the range of -20°C to 60°C with a step of 2°C, jointly forming several combinations. Then, the corresponding specific values of temperature, humidity, and air pressure are recorded for each combination. The recorded parameter input sets are matched according to the previously obtained data on the internal structure, material properties, and heat conduction efficiency of the device, and any entries conflicting with the existing node coordinates or material numbers are checked. Once it is found during the check that a certain environmental condition entry cannot be matched or lacks information, it is linked to check whether the corresponding range summarized previously exceeds the feasible values of the device test. If it is found that there are more than 5 entries with a temperature greater than 60°C, they are temporarily incorporated into the extreme condition group. 5 is the maximum size of the extreme group that can be compatible after a global parameter stratification. Finally, after all the working condition parameters are successfully matched, multiple sets of thermal environment parameter input sets are confirmed to be formed.

[0148] After completing the arrangement of multiple sets of input sets of thermal environment parameters, the overall thermal conduction efficiency evaluation index is subjected to finite element calculations for each set of working conditions. First, a basic simulation is performed for each combination of environmental temperature, humidity, and air pressure. For the temperature value, a thermal boundary is applied in the discrete grid to make it conform to the setting of the current temperature input set, and the humidity and air pressure data are brought into parameters such as the material moisture permeability coefficient and the local compression verification range. If the node stress value exceeds 500 MPa or the thermal conductivity jumps by more than 20% during the simulation process, it is marked as a suspected high-load situation. The 500 MPa threshold is selected near the common material yield warning value after multiple mechanical tensile and temperature couplings. The 20% jump amplitude is obtained by comparing the statistics when there is a significant deviation in the thermal conductivity characteristics of the material. When the finite element solution for all working conditions is completed, the thermal efficiency distribution results are classified. If the overall thermal conduction efficiency evaluation index under a certain working condition is 30% higher than the reference value, it is included in the high-efficiency working condition column. This 30% ratio is obtained by comparing the average value of the steady-state heat transfer efficiency in the previous stage. If it is lower than the reference value, it is classified into the ordinary working condition column. If there is a significant deviation below the reference value, such as a deviation exceeding 50%, it is specially marked according to the established abnormal warning range. The 50% difference is the failure threshold in the existing test statistics. Finally, the influence of each working condition on the thermal efficiency and its distribution are recorded one by one. After all the grouped calculations are completed, a summary table is compiled, and the groups with large differences or representativeness are listed separately to obtain the evaluation result of the environmental adaptability of the heat recovery rate.

[0149] The above is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating heat recovery rate of thermal energy storage, characterized in that: The following steps are involved: The geometric structure information of the thermal energy storage device is obtained, and the node meshing parameters and unit boundary conditions are called to generate a finite element mesh model of the heat recovery device; the ambient temperature, atmospheric humidity and atmospheric pressure are called, and through the setting of thermal-environment coupling boundary conditions, thermal environment influencing variable constraints are imposed on the finite element mesh model of the heat recovery device to establish an environmental coupling finite element model; Based on the environmental coupling finite element model, the thermal expansion coefficient, elastic modulus and Poisson's ratio parameters of the material are called to calculate the thermal stress distribution under the action of thermal environmental variables, and generate a node thermal-stress coupling distribution field; Obtaining heat flux density and thermal conductivity, substituting the node thermal-stress coupling distribution field into the heat conduction calculation equation to obtain the thermal-stress coupling heat transfer field; Based on the thermal-stress coupled heat transfer field, the temperature gradient and heat flow parameters at different positions in the thermal energy storage device are called, the local node heat conduction efficiency is calculated, and the local node heat conduction efficiency distribution is generated. The thermal capacity of the thermal energy storage device and the local node heat conduction efficiency distribution are called, the heat conduction efficiency of each local node is integrated and accumulated, and an overall heat conduction efficiency evaluation index is established; Based on the overall heat conduction efficiency evaluation index, the preset parameters of the operating environment of the thermal energy storage device are called, and multiple simulation calculations are performed under different combinations of thermal environment parameters to generate a heat recovery rate environmental adaptability evaluation result.

2. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the finite element mesh model of the heat recovery device are: Obtaining geometric structure information of the thermal energy storage device, including external contour dimensions, internal structure distribution, connection interface position and heat conduction path layout, setting a mesh generation area according to the geometric boundary range, and generating a geometric structure information set; Based on the geometric structure information set, node mesh division is performed, the node distribution of each structural unit under the target density and segmentation strategy is calculated in turn, the topological relationship between the structural units is constructed, and the unit shape consistency and node coincidence rate are checked and corrected step by step to generate a node mesh data set; Based on the node mesh data set, various constraint types set in the unit boundary conditions are integrated, the thermal boundary action surface distribution and physical constraint values ​​of each unit boundary are configured, the overall configuration of the finite element model is determined in combination with the geometric structure information set, all mesh units are assembled to form a continuous heat conduction domain, and a finite element mesh model of the heat recovery device is generated.

3. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the environment-coupled finite element model are as follows: Obtain the ambient temperature, atmospheric humidity and atmospheric pressure, and generate an environmental parameter setting set in combination with the thermal-environmental coupling boundary condition setting; Based on the environmental parameter setting set, constraints of ambient temperature, atmospheric humidity and atmospheric pressure are applied to the finite element mesh model of the heat recovery device. By matching the interaction between the environmental parameters and the finite element mesh model of the heat recovery device, the thermal expansion, heat transfer rate and temperature stress distribution of the finite element mesh model of the heat recovery device are adjusted to generate an environmentally coupled finite element model.

4. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the node thermal-stress coupling distribution field are: Based on the environmental coupling finite element model, integrating material thermal expansion coefficient, elastic modulus and Poisson's ratio parameters to form a material physical parameter set; Based on the material physical parameter set, the thermal stress distribution is calculated using the following formula: Among them, σ represents the thermal stress distribution, E is the elastic modulus of the material, α is the thermal expansion coefficient, ΔT is the ambient temperature change, ∈ is the material strain, ν is the Poisson's ratio, T is the current temperature value, γ is the thermal conductivity constant of the material, and μ is the temperature dependence adjustment factor; Based on the thermal stress distribution, the thermal displacement data of each node are integrated to construct a node thermal stress coupling distribution field. By analyzing the coupling effect of thermal stress and displacement point by point, the response characteristics of the material in a changing environment are comprehensively evaluated to generate a node thermal stress coupling distribution field.

5. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the thermal-stress coupled heat transfer field are: Obtaining the heat flux density and thermal conductivity of the thermal energy storage device, and obtaining the thermal conductivity of each component of the thermal energy storage device from a material data table, and generating a list of heat flux parameters and thermal conductivity parameters; Based on the heat flow parameter and thermal conductivity parameter list, the heat transfer efficiency is calculated using the following formula: Where Φ represents the heat transfer efficiency per unit volume, V is the control volume of the calculation, and k(x,y,z) represents the thermal conductivity of the spatial variable position. represents the second-order partial derivative of temperature with respect to position, N represents the total number of coupled nodes, σ i represents the thermal stress at the ith node, λ i represents the thermal conductivity correction factor of the ith node; Based on the heat transfer efficiency, the thermal-stress coupling performance of the thermal energy storage device is evaluated, and by analyzing the distribution of the heat transfer efficiency, a thermal-stress coupling heat transfer field is generated.

6. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the local node heat conduction efficiency distribution are: Based on the thermal-stress coupled heat transfer field, each grid node is located in the finite element grid model of the thermal energy storage device, the temperature gradient value and the heat flow measurement value of the area corresponding to the node coordinate are called point by point, the thermal conductivity is retrieved in combination with the node material number, and a local heat conduction input set is generated; Based on the local heat conduction input set, the local node heat conduction efficiency is calculated using the following formula: Among them, η represents the local node heat conduction efficiency, q represents the heat flow at the node, Δx represents the distance between nodes, and k represents the thermal conductivity of the node material. represents the temperature gradient modulus, Δt represents the time interval of heat conduction, c represents the specific heat capacity of the material, ρ represents the material density, and A represents the effective cross-sectional area of ​​the node influence area; Based on the local node heat conduction efficiency, the positions of each node are traversed, the corresponding spatial coordinates of all node heat conduction efficiencies are merged and sorted, a node-thermal efficiency mapping table is constructed, and the local node heat conduction efficiency distribution is generated.

7. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the overall heat conduction efficiency evaluation index are as follows: Obtaining the heat capacity of the thermal energy storage device and the distribution of the heat conduction efficiency of the local nodes, matching the corresponding heat capacity block number to the spatial position to which each node is mapped, screening the node set with effective heat conduction efficiency, and establishing a node and volume association relationship table to generate a node volume efficiency mapping set; Based on the node volumetric efficiency mapping set, each heat conduction efficiency value is traversed node by node, accumulated according to the boundary division of the corresponding thermal volume, and an overall heat conduction efficiency evaluation index is generated.

8. The heat recovery rate evaluation method for thermal energy storage according to claim 1, characterized in that: The steps for obtaining the heat recovery rate environmental adaptability evaluation result are as follows: Based on the overall heat transfer efficiency evaluation index, calling the preset parameters of the operating environment of the thermal energy storage device, the preset environmental parameters include ambient temperature, atmospheric humidity and air pressure, combining to form multiple simulated environmental conditions, and generating multiple sets of thermal environment parameter input sets; Based on the multiple sets of thermal environment parameter input sets, the overall heat conduction efficiency evaluation index is simulated and verified under various working conditions, finite element calculation is performed on each set of parameters, and the thermal efficiency changes in the output results are archived and classified to generate a heat recovery rate environmental adaptability evaluation result.

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