Shock tube shock wave test simulation closed loop correction method and equipment

Abstract

This invention provides a closed-loop correction method and device for shock tube shock wave test simulation. It generates relative specific volume, internal energy, and particle velocity curves required for the PIF-ALE inflow boundary based on shock tube test pressure time history data. A multi-scale fluid domain simulation model is generated based on the sample's geometric characteristics, and monitoring points corresponding to the test points are constructed. The simulation input file is generated using the aforementioned curves as the inflow boundary. After simulation, the simulated peak pressure at the monitoring points is extracted and compared with the experimental peak pressure to obtain an error index. The above steps are repeated by adjusting the curve scaling factor or mesh size parameters until the error meets the threshold, and the correction result is output. This achieves automated mapping from test data to the simulation boundary, avoiding boundary distortion caused by manual fitting. Geometric-driven automatic modeling eliminates numerical distortion caused by manual intervention. The closed-loop iterative mechanism driven by peak error solves the problem of difficulty in automatically matching simulation and experimental peak values, significantly improving correction efficiency and data consistency.

Description

Technical Field

[0001] This invention relates to the field of computer-aided engineering simulation technology, and in particular to a closed-loop correction method and equipment for shock tube shock wave test simulation. Background Technology

[0002] Shock tubes can generate controllable shock wave loads under laboratory conditions and are widely used in research fields such as structural protection, materials dynamics, and biological effects. When combining shock tube experiments with numerical simulations, it is usually necessary to convert the experimentally measured pressure time history data into boundary conditions that can be called by the solver, and then reproduce the pressure response at the measurement points through simulation to verify the accuracy of the model or correct key parameters.

[0003] In existing technologies, when using the PIF-ALE (Pressure-Internal Energy-Particle Velocity Arbitrary Lagrangian-Euler) method for simulation, the conversion from experimental pressure time history data to simulated inflow boundary conditions lacks a standardized and automated processing flow. It relies heavily on manual experience to simplify or fit pressure curves, making it difficult to directly generate time history curves for multiple physical quantities such as relative specific volume, internal energy, and particle velocity. This leads to discrepancies between the simulated boundary input and the actual experimental conditions. Furthermore, the geometric modeling and fluid domain meshing of shock tube test specimens are often done manually. The characteristic dimensions and pore locations of the specimens must be measured manually, and mesh parameters must be set one by one. It is difficult to automatically generate multi-scale fluid domain meshes, which is not only inefficient but also prone to numerical distortion of pressure propagation. For biological models with complex pores such as the oral cavity and nasal cavity, the problem of inaccurate pressure wave propagation is particularly prominent. In addition, there is a lack of an automated closed-loop correction mechanism between simulation results and peak pressures at experimental measurement points. After simulation calculations, it is necessary to manually compare the peak values ​​at each measurement point, repeatedly adjust modeling parameters based on experience, and recalculate, making it difficult to stably achieve consistency between simulated and experimental peak values.

[0004] It is evident that existing technologies suffer from problems such as relying on manual experience to set modeling parameters, difficulty in seamlessly converting experimental pressure into inflow boundary conditions, and difficulty in automatically matching simulation results with measurement peak values. Summary of the Invention

[0005] This invention provides a closed-loop correction method and device for shock tube shock wave test simulation, which overcomes the problems of existing shock tube shock wave tests, such as the difficulty in seamlessly converting test pressure into inflow boundary conditions, reliance on manual experience to construct meshes and set parameters, and difficulty in automatically matching simulation results with measured peak values, resulting in simulation boundary distortion, numerical distortion, and low correction efficiency.

[0006] This invention provides a closed-loop correction method for shock tube shock wave test simulation, comprising: Based on the shock tube test pressure time history data, the relative specific volume curve, internal energy curve and particle velocity curve required for the inflow boundary of the PIF-ALE solver are generated. A multi-scale fluid domain simulation model is generated based on the geometric characteristics of the sample under test. Monitoring points corresponding to the test points are constructed in the multi-scale fluid domain simulation model, and a simulation input file is generated using the curve required by the inflow boundary as the inlet boundary condition. The simulation is performed based on the simulation input file. The simulated peak pressure of the monitoring point is extracted from the simulation results and compared with the experimental peak pressure to obtain the error index. By adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model, repeat the above steps until the error index meets the preset threshold, and output the correction result.

[0007] According to the present invention, a closed-loop correction for shock tube shock wave test simulation is provided, wherein the generation of the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data includes: The obtained test pressure time history data is preprocessed to obtain target pressure time history data, and the initial gas state parameters of the shock tube drive section and the test section are read. Based on the target pressure time history data and the initial gas state parameters, discrete numerical sequences of relative specific volume, internal energy and particle velocity are obtained based on gas dynamics. The discrete numerical sequences are time-mapped and formatted according to the time step calculated by the solver, thereby generating the relative specific volume curve, the internal energy curve, and the particle velocity curve.

[0008] According to the present invention, a closed-loop correction method for shock tube shock wave test simulation is provided, wherein generating a multi-scale fluid domain simulation model based on the geometric characteristics of the tested sample includes: The geometric dimensions and hole locations of the sample under test are obtained, a set of coordinate points is constructed based on the geometric dimensions and hole locations, and the set of coordinate points is automatically scaled and parameterized. A multi-scale fluid domain mesh is generated based on the parameterized coordinate point set, and the mesh is locally refined at the location of the hole. The multi-scale fluid domain mesh adopts a near-field refinement and far-field coarsening layout and is equipped with cell identifiers. Configure attributes, materials, and constitutive parameters for the multi-scale fluid domain mesh, smooth the transition region of the multi-scale fluid domain mesh, and generate control cards, operating condition settings, and main input files to generate the multi-scale fluid domain simulation model.

[0009] According to the closed-loop correction method for shock wave test simulation of a shock tube provided by the present invention, the error index obtained by comparing with the peak test pressure includes: The simulated peak pressure is compared with the corresponding experimental peak pressure to calculate the relative error of the monitoring point; Based on the physical importance of the test point or the sensor measurement uncertainty, weighting coefficients are assigned, and the weighting coefficients are weighted or normed with the relative error to generate the error index.

[0010] According to the present invention, a closed-loop calibration method for shock tube shock wave test simulation includes repeating the above steps until the error index meets a preset threshold and outputting the calibration result, comprising: Determine whether the error index is less than or equal to the preset threshold; If so, confirm that the iteration has converged, output the correction result, and terminate the iteration record; If not, obtain the current iteration round, calculate the rate of change of the error index between two adjacent rounds, and determine whether to terminate the iteration based on the current iteration round or the rate of change.

[0011] According to the closed-loop correction method for shock tube shock wave test simulation provided by the present invention, the step of determining whether to terminate the iteration based on the current iteration round or the rate of change includes: If the current iteration reaches the preset maximum number of iterations, or the rate of change is less than or equal to the preset convergence threshold, the iteration is terminated, the mesh distortion region and boundary waveform deviation features of the current multi-scale fluid domain simulation model are extracted, and a mismatch diagnosis report is generated and output. If the current iteration round does not reach the preset maximum number of iterations, and the rate of change is greater than the preset convergence threshold, adjust the scaling factor of the curve or the mesh size parameter of the simulation model to proceed to the next iteration round.

[0012] The closed-loop correction for shock tube shock wave test simulation provided by the present invention further includes: Obtain a calibration data set, which includes the target pressure time history data, the relative specific volume curve, the internal energy curve, the particle velocity curve, the multi-scale fluid domain simulation model, the parameter adjustment records of each iteration, the error index, and the calibration result; Read the solver version identifier and calculation timestamp, and encapsulate the correction data set, version identifier, and calculation timestamp in a structured manner; A hash operation is performed on the encapsulated data set to generate an integrity check code. The integrity check code is then appended to the encapsulated file to generate a correction and traceability result package containing the integrity check code, which is then output to the storage path.

[0013] According to the present invention, a closed-loop calibration method for shock tube shock wave test simulation is provided. The calibration results include a comparison table of peak pressures in the test and simulation, a summary of error indicators, a record of the iteration process, and a list of parameters.

[0014] The present invention also provides a closed-loop correction device for shock tube shock wave test simulation, comprising: The curve generation module is used to generate the relative specific volume curve, internal energy curve, and particle velocity curve required by the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data. The simulation file generation module is used to generate a multi-scale fluid domain simulation model based on the geometric features of the tested sample, construct monitoring points corresponding to the test point locations in the multi-scale fluid domain simulation model, and generate a simulation input file using the curve required by the inflow boundary as the inlet boundary condition. The simulation calculation module is used to perform simulation based on the simulation input file, extract the simulated peak pressure of the monitoring point from the simulation results, and compare it with the experimental peak pressure to obtain the error index. The closed-loop iteration module is used to repeat the above steps by adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model until the error index meets the preset threshold and outputs the correction result.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the closed-loop correction method for shock tube shock wave test simulation as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the closed-loop correction method for shock tube shock wave test simulation as described above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the closed-loop correction method for shock tube shock wave test simulation as described above.

[0018] The present invention provides a closed-loop correction method and device for shock tube shock wave test simulation. First, based on the shock tube test pressure time history data, it generates the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver. Then, based on the geometric characteristics of the tested sample, it generates a multi-scale fluid domain simulation model. Monitoring points corresponding to the test locations are constructed in the multi-scale fluid domain simulation model, and a simulation input file is generated using the curves required for the inflow boundary as the inlet boundary conditions. The simulation is then executed based on the simulation input file, and the simulated peak pressure of the monitoring points is extracted from the simulation results and compared with the experimental peak pressure to obtain the error index. By adjusting the scaling factor of the curves or the mesh size parameters of the multi-scale fluid domain simulation model, the above steps are repeated until the error index meets a preset threshold, and the correction result is output. This invention first converts the test pressure time history data to generate the solver's underlying state variable curves, which can establish an automated mapping channel from test data to the simulation boundary, effectively overcoming the problem of simulation boundary distortion caused by the difficulty in seamlessly converting test pressure into inflow boundary conditions in existing technologies. Secondly, by using geometric features to drive the automatic generation of multi-scale meshes, spatial mapping of experimental measurement points, and automatic assembly of boundary files, the process of constructing meshes and setting parameters based on manual experience can be replaced, thus avoiding numerical distortion caused by the arbitrariness of human intervention from the source. Finally, by constructing a closed-loop iterative mechanism with peak error as the driving index, the automatic adjustment and recalculation convergence of key parameters are achieved, completely solving the problem of difficulty in automatically matching simulation results with peak values ​​at measurement points, significantly reducing the cost of repeated manual trial and error, and greatly improving calibration efficiency and the consistency between experimental and simulation data. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is one of the flowcharts illustrating the closed-loop correction method for shock tube shock wave test simulation provided by the present invention.

[0021] Figure 2 The second schematic diagram of the closed-loop correction method for shock tube shock wave test simulation provided by the present invention.

[0022] Figure 3 The third flowchart illustrates the closed-loop correction method for shock tube shock wave test simulation provided by this invention.

[0023] Figure 4 This is a schematic diagram of the closed-loop correction device for shock tube shock wave test simulation provided by the present invention.

[0024] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] In existing PIF-ALE simulations, the conversion from experimental pressure time history data to simulated inflow boundary conditions lacks a standardized and automated processing flow. It relies heavily on manual experience to simplify or fit pressure curves, making it difficult to directly generate time history curves for multiple physical quantities such as relative specific volume, internal energy, and particle velocity. This leads to discrepancies between the simulated boundary input and the actual experimental conditions. Furthermore, the geometric modeling and fluid domain meshing of shock tube test specimens are often done manually. The characteristic dimensions and orifice locations of the specimens must be measured manually, and mesh parameters must be set one by one. Automatic generation of multi-scale fluid domain meshes is difficult, resulting in low efficiency and numerical distortion of pressure propagation. This problem is particularly pronounced for biological models with complex orifices such as the oral cavity and nasal cavity, where pressure waves cannot be accurately transmitted. In addition, there is a lack of an automated closed-loop correction mechanism between simulation results and peak pressures at experimental measurement points. After simulation calculations, manual comparison of peak values ​​at each measurement point is required, with repeated adjustments to modeling parameters and recalculations based on experience. This makes it difficult to consistently achieve consistency between simulated and experimental peak values.

[0027] To address the aforementioned problems in existing technologies, this invention provides a closed-loop correction method and device for shock tube shock wave test simulation. The inventive concept of this invention lies in breaking through the traditional discrete verification mode of manual fitting, manual modeling, and single-trial-and-error, and constructing a fully automated closed-loop link of data standardization conversion, geometry-driven modeling, and error feedback iteration. Specifically, the test pressure time history data and initial gas state parameters are directly solved through gas dynamics relationships to automatically generate the relative specific volume, internal energy, and particle velocity curves required by the PIF-ALE solver. Then, using the geometric features of the sample and the location of the holes as input, the coordinate point set is parameterized to automatically generate a multi-scale fluid domain simulation model with near-field refinement, far-field coarsening, and a smooth transition region, and the mapping relationship of the test measurement points is solidified in the grid space. Finally, using the comparison error between the simulated peak value and the experimental peak value at the monitoring point as the closed-loop control variable, the inflow boundary scaling factor or grid size parameter is dynamically adjusted and automatic recalculation iteration is triggered until the error converges to a preset threshold. This invention, through a whole-link data homogeneity and automated iteration mechanism, can fundamentally eliminate the risks of human intervention bias and grid distortion, and achieve an efficient and accurate closed loop from experimental data to simulation correction.

[0028] The following is a detailed description of the closed-loop correction method for shock tube shock wave test simulation provided by the present invention, with reference to various embodiments and accompanying drawings.

[0029] Figure 1 This is one of the flowcharts illustrating the closed-loop correction method for shock tube shock wave test simulation provided by the present invention, as shown below. Figure 1 As shown, the method includes: S101. Based on the shock tube test pressure time history data, generate the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver.

[0030] Shock tube test pressure time history data refers to the sequence of pressure values ​​that change over time, recorded by sensors located at the test points (i.e., the physical locations where pressure sensors are deployed) during a shock tube shock wave test. This data typically includes the pressure value at each test point, sampling frequency, trigger reference, and sensor calibration information, reflecting the complete process of pressure rise, peak value, and decay at that point over time when the shock wave arrives.

[0031] When performing fluid-structure interaction simulations using the PIF-ALE method solver, the inflow boundary requires input not only the pressure time history, but also three fundamental curves reflecting the fluid's thermodynamic and kinematic states: the relative specific volume curve, the internal energy curve, and the particle velocity curve. The relative specific volume characterizes the fluid's compressibility, the internal energy reflects the fluid's thermodynamic energy state, and the particle velocity describes the velocity of fluid particles. These three curves together constitute a complete description of the inflow boundary conditions using the PIF-ALE method.

[0032] This step establishes an automated conversion channel from experimental pressure time history data to numerical simulation inflow boundary conditions. For example, using pressure time history data measured at experimental points as input, and through preset physical conversion rules, it is converted into the three curves required by the solver for the inflow boundary, and output as a curve input file that the solver can directly call.

[0033] In some embodiments, possible implementations of step S101 may include, for example: Figure 2 The steps shown are as follows: Figure 2 The second schematic diagram of the closed-loop correction method for shock tube shock wave test simulation provided by the present invention is shown below. Figure 2 As shown, it includes: S1011. The obtained test pressure time history data is preprocessed to obtain the target pressure time history data, and the initial gas state parameters of the shock tube drive section and the test section are read.

[0034] In shock tube experiments, the raw time history data recorded by pressure sensors is often affected by various factors. For example, the sensor itself has dynamic response characteristics, and the rising edge of the high-frequency shock wave may be distorted (such as a slowed rising edge or overshoot). Therefore, dynamic compensation of the acquired raw data, i.e., the test pressure time history data, is necessary to restore the true pressure waveform, for example, through inverse filtering or transfer function correction. Secondly, the data may contain electronic noise or mechanical vibration noise, which can be denoised using methods such as low-pass filtering or wavelet denoising. Furthermore, the sensor may exhibit zero drift or environmental pressure offset, which can be addressed by baseline correction to zero out or correct the starting point of the pressure time history to the environmental pressure. In addition, there may be slight differences in the triggering times of different channels, which can be addressed by cross-correlation analysis or time alignment using a known time base to synchronize the pressure time histories of all measuring points on the time axis.

[0035] The test pressure time history data is preprocessed as described above, including sensor dynamic compensation, noise reduction, baseline correction, and time alignment, to obtain the target pressure time history data. Simultaneously, the initial gas state parameters of the shock tube drive section (high-pressure section) and test section (low-pressure section) are read from the test records or operating condition settings. These initial gas state parameters may include, for example, the initial pressure, initial density, initial temperature, and the gas's adiabatic index (specific heat ratio) for both the drive and test sections. These initial gas state parameters are known conditions for subsequent gas dynamics conversions.

[0036] It should be noted that the methods for preprocessing test pressure time history data include, but are not limited to, the sensor dynamic compensation, noise reduction, baseline correction and time alignment described above. The above description is only an illustrative example.

[0037] S1012. Based on the target pressure time history data and the initial gas state parameters, the discrete numerical sequences of relative specific volume, internal energy and particle velocity are obtained based on gas dynamics.

[0038] After obtaining the target pressure time history data and initial gas state parameters, fundamental relationships in gas dynamics, such as the Rankine-Hugoniot shock relation, the ideal gas equation of state, or the normal shock relation in shock wave theory, can be used to solve for the pressure value at each time point in the target pressure time history along with the initial gas state parameters. This allows for the calculation of the relative specific volume, internal energy, and particle velocity values ​​corresponding to that time point. By calculating sequentially for all time points, three sets of numerical sequences varying with discrete time points are obtained: the relative specific volume sequence, the internal energy sequence, and the particle velocity sequence.

[0039] S1013. Based on the time step calculated by the solver, perform time mapping and formatting on the respective discrete numerical sequences to generate the relative volume curve, internal energy curve and particle velocity curve.

[0040] When performing explicit time integration, the PIF-ALE solver typically uses a fixed computation time step or allows variable step sizes and requires interpolation. To ensure that the generated curve can be correctly read by the solver and interpolated within each computation step, the discrete numerical sequence obtained in step S1012 needs to be mapped to the time coordinates desired by the solver. If the time interval of the target pressure time history data is inconsistent with the solver's computation time step, linear interpolation or cubic spline interpolation can be used to resample the three sets of discrete numerical sequences to match their time intervals with the solver's step size; this process is called time mapping.

[0041] After time mapping is completed, the data is formatted according to the curve input file format specified by the solver. Each curve mathematically represents a function of a physical quantity changing with time, but in computer implementation, it needs to be stored in a specific file format. Specifically, for the three curves—relative volume, internal energy, and particle velocity—each begins with a curve definition keyword recognized by the solver, followed by the time value and the physical quantity value at that moment, recorded line by line in chronological order. The formatted data is written to three independent files: the relative volume curve file, the internal energy curve file, and the particle velocity curve file. These files serve as the digital carriers of the relative volume curve, internal energy curve, and particle velocity curve, and can be directly referenced by the inflow boundary conditions in the simulation input file. Thus, the relative volume curve, internal energy curve, and particle velocity curve are generated.

[0042] As described in the above embodiments, the original shock tube test pressure time history data, after preprocessing such as sensor dynamic compensation, denoising, baseline correction, and time alignment, is combined with the initial gas state parameters of the driving and test sections. Based on gas dynamics, discrete numerical sequences of relative specific volume, internal energy, and particle velocity are calculated point-by-point based on time. These sequences are then time-mapped and formatted according to the solver step size, ultimately generating three standardized inflow curves that can be directly called by the solver. This processing flow enables a fully automated and reproducible conversion from test pressure to simulated inflow boundary, eliminating boundary distortion caused by manual fitting and empirical simplification, ensuring the physical consistency and numerical stability of input conditions, and laying a reliable foundation for subsequent high-precision simulation and closed-loop correction.

[0043] S102. Generate a multi-scale fluid domain simulation model based on the geometric characteristics of the sample under test. Construct monitoring points corresponding to the test points in the multi-scale fluid domain simulation model, and generate simulation input files using the curve required for the inflow boundary as the inlet boundary condition.

[0044] This step aims to automate the construction of the fluid domain model and boundary conditions in shock wave simulation of shock tubes, including generating a multi-scale fluid domain simulation model, establishing monitoring points, assembling inlet boundary conditions, and generating simulation input files.

[0045] Generating a multi-scale fluid domain simulation model based on the geometric features of the tested sample can include: First, the geometric dimensions and hole locations of the sample under test are obtained. The sample can be, for example, a target object subjected to shock wave loads in a shock tube test, such as a biological organism with natural openings like the mouth and nasal cavity, structural protective materials like composite panels, or material dynamics specimens. Geometric dimensions include the overall contour dimensions of the sample, such as its characteristic perimeter, and hole locations, such as the spatial coordinates of passages like the mouth and nasal cavity in a biological organism. A coordinate point set is constructed based on the geometric dimensions and hole locations, and this coordinate point set is automatically scaled and parameterized to map the physical dimensions of the sample under test into the simulation coordinate system.

[0046] Then, a multi-scale fluid domain mesh is generated based on the parameterized coordinate point set. This multi-scale mesh can employ a layout where the mesh is finer in the near field (near the sample) and coarser in the far field (away from the sample). The fine near-field mesh is used to capture the detailed propagation and local effects of the pressure wave, while the coarser far-field mesh reduces computational cost. Furthermore, local mesh refinement is applied at the locations of holes, for example, by refining the mesh size at the hole location to make it smaller than the surrounding area, to ensure that the shock wave can accurately enter channels such as the oral cavity and nasal cavity. After mesh generation, each element is assigned a unique element identifier for subsequent attribute settings and output.

[0047] Furthermore, attributes, materials, and constitutive parameters are configured for the generated multi-scale fluid domain mesh. For example, fluid domains are assigned fluid material properties such as density, viscosity, and equation of state parameters, while solid samples, if present, are assigned corresponding structural material constitutive models such as elastoplastic models. The transition regions of the multi-scale fluid domain mesh are smoothed to eliminate abrupt changes in mesh aspect ratio and prevent numerical distortion during pressure wave propagation. Subsequently, the necessary control cards for the solver, such as time step control and output control, load condition settings such as load steps and boundary condition application order, and the main input file, integrating all information including mesh, materials, boundaries, and outputs, are automatically generated, ultimately producing a complete multi-scale fluid domain simulation model.

[0048] Furthermore, after the multi-scale fluid domain simulation model is generated, monitoring points are constructed in the simulation model based on the spatial coordinates of the test points where pressure sensors were actually deployed in the experiment. The monitoring points are specific units or node locations in the simulation model used to output pressure time histories; their spatial locations precisely correspond to the test points to ensure that the subsequently extracted simulated peak pressure can be directly compared with the experimental peak pressure.

[0049] Finally, the three curves required for the generated inflow boundary—the relative specific volume curve, the internal energy curve, and the particle velocity curve—are applied as inflow boundary conditions to the inflow boundary of the multi-scale fluid domain simulation model. This, along with monitoring point output commands, control cards, and operating condition settings, can be combined to generate a parameter control file. Then, according to the solver input specifications, all the above parts are combined, linked, and assigned values ​​to a complete file, thus generating the complete simulation input file. This simulation input file contains all the information required for the solver to perform calculations, such as the mesh model, boundary conditions (i.e., inflow curves), monitoring point output commands, solution time control, and output requests. After generating the simulation input file, it can be submitted to the solver for calculation.

[0050] As described above, this invention, starting from the geometric features of the sample, can automatically generate a multi-scale fluid domain model suitable for shock wave propagation calculations, establish a spatial mapping between test points and simulation monitoring points, and complete the assembly of inflow boundary conditions and the model, forming a complete input file that can be directly used for numerical simulation. This process can replace the traditional manual modeling and boundary setting methods, avoiding numerical distortion caused by improper mesh generation, and providing a standardized and automated model foundation for subsequent simulation calculations and closed-loop verification.

[0051] Furthermore, for test samples exhibiting solid deformation, such as biological tissues and protective structures, a corresponding solid structure mesh model is simultaneously generated based on the sample's geometric dimensions and the location of pores, and material constitutive parameters, such as elastic, elastoplastic, or hyperelastic models, are configured for it. A fluid-structure interaction interface, such as a structural loading surface, is defined between the fluid domain and the solid structure. The location of this loading surface can be automatically identified and determined based on the test setup and is used to transfer shock wave pressure loads.

[0052] S103. Perform simulation based on the simulation input file, extract the simulated peak pressure of the monitoring points from the simulation results, and compare it with the experimental peak pressure to obtain the error index.

[0053] This step aims to obtain the pressure response at each monitoring point through numerical simulation and quantitatively compare it with the experimental measured data to form an error index used to drive closed-loop correction.

[0054] First, the generated simulation input file is submitted to the PIF-ALE solver for calculation. The solver simulates the entire process of the shock wave entering from the inflow boundary, propagating in the fluid domain, and interacting with the test sample, according to the set control parameters such as time step and output frequency. After the calculation is completed, simulation results containing the pressure change curves of each monitoring point over time are obtained.

[0055] Then, the simulated peak pressure of each monitoring point is extracted from the simulation results. The monitoring points in the simulation model correspond one-to-one with the spatial locations of the test points; therefore, the simulated peak pressure of each monitoring point can be directly compared with the experimental peak pressure of the corresponding test point. The experimental peak pressure can be obtained from the experimental pressure time history data processed in step S101, which is the maximum value of the pressure time history at each test point.

[0056] Finally, the simulated peak pressure at each monitoring point is compared with the corresponding experimental peak pressure at each test point, and the error index is calculated. The error index reflects the overall deviation between the simulation and the experiment in terms of peak pressure.

[0057] In one possible design, the possible ways to obtain the error index by comparing it with the test peak pressure in step S103 include: First, the simulated peak pressure is compared with the corresponding experimental peak pressure to calculate the relative error of the monitoring points, which is the absolute value of the difference between the simulated peak and the experimental peak divided by the experimental peak. Then, weighting coefficients are assigned to each experimental point based on its physical importance or sensor measurement uncertainty, and a weighted sum or norm (such as the L2 norm) is calculated to obtain the final error index. This error index can be used as a criterion for judging whether subsequent closed-loop iterations meet the convergence conditions.

[0058] Step S103 enables quantitative automatic comparison between simulation results and experimental data, quantifying peak deviation into a calculable error index, and providing a clear driving signal for parameter adjustment and iteration.

[0059] S104. By adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model, repeat the above steps until the error index meets the preset threshold, and output the correction result.

[0060] This step constitutes the core of the closed-loop correction mechanism. By automatically iteratively adjusting key parameters, the simulated peak pressure gradually approaches the experimental peak pressure until the preset accuracy requirements are met.

[0061] Specifically, after obtaining the error index, it can be determined whether the error index is less than or equal to a preset threshold. If so, it indicates that the deviation between the current simulation result and the experimental result in peak pressure is within an acceptable range. At this point, iterative convergence is determined, the correction result is output, and the iteration record is terminated. The correction result may include, for example, a comparison table of experimental and simulated peak pressures, a summary of error indices, iterative process records, and a list of key parameters. The comparison table of experimental and simulated peak pressures lists the experimental peak pressure and simulated peak pressure for each experimental point in tabular form, facilitating a direct comparison of the differences. The summary of error indices records the error index value at the final iteration convergence and statistical information on the relative error of each monitoring point, such as maximum value, average value, and standard deviation. The iterative process record records, for example, the parameter type and value adjusted each time, the adjusted error index value, and the rate of change of error between adjacent iterations, forming a complete iteration history. The list of key parameters lists, for example, the curve scaling factor, mesh size parameters, and other key modeling parameters used after final correction, as well as control parameters such as preset thresholds, maximum number of iterations, and convergence thresholds.

[0062] If the error index exceeds a preset threshold, the current iteration round is obtained, and the rate of change of the error index between adjacent rounds is calculated. Whether to continue iteration is determined based on the current iteration round or the rate of change.

[0063] For example, if the current iteration has reached the preset maximum number of iterations, or the rate of change is less than or equal to the preset convergence threshold, it indicates that the room for improvement through further iteration is extremely small, and the iteration is terminated. At this point, it means that even if parameters are adjusted further, it will be difficult to reduce the error index further, or the maximum allowable computational resource consumption has been reached. Therefore, the iteration is terminated, and the mesh distortion region and boundary waveform deviation characteristics in the current multi-scale fluid domain simulation model are extracted. Mesh distortion regions are, for example, locations where the mesh is excessively twisted or where the aspect ratio changes abruptly. Boundary waveform deviation characteristics are, for example, regions near the inflow boundary where the pressure waveform is excessively attenuated or oscillating. A mismatch diagnosis report is then generated and output for users to analyze model defects or abnormal experimental data.

[0064] If the current iteration round has not reached the preset maximum number of iterations and the rate of change is greater than the preset convergence threshold, it indicates that there is still room for improvement in the iteration. At this time, the scaling factor of the curve is adjusted. For example, the amplitude of at least one of the relative volume curve, internal energy curve, and particle velocity curve is scaled proportionally or independently to change the input intensity of the inflow boundary condition. Alternatively, the mesh size parameters of the multi-scale fluid domain simulation model can be adjusted, such as changing the mesh density in the near-field refinement region. Then, depending on the type of parameter adjusted, the return step is determined to form the next iteration round. Specifically, if the scaling factor of the curve is adjusted, the process returns to step S101 to regenerate the inflow curve and continues to execute subsequent steps; if the mesh size parameters are adjusted, the process returns to step S102 to regenerate the multi-scale fluid domain simulation model and simulation input file and execute subsequent steps.

[0065] Through the aforementioned closed-loop iterative mechanism, key parameters can be automatically adjusted and repeatedly calculated until the error index meets the preset threshold or reaches the termination condition, and finally the corrected result is output, replacing the inefficient mode of repeated manual trial and error, and realizing the automatic matching and convergence of the simulation and experimental peak pressure.

[0066] It should be noted that the present invention does not limit the specific values ​​of the preset threshold and preset convergence threshold described in the above embodiments. In actual working conditions, they can be preset according to the actual engineering accuracy requirements or computing resource limitations.

[0067] As described in the above embodiments, this invention first generates the solver's underlying state variable curves based on the experimental pressure time history data, establishing an automated mapping channel from experimental data to the simulation boundary. This effectively overcomes the problem of simulation boundary distortion caused by the difficulty in seamlessly converting experimental pressure into inflow boundary conditions in existing technologies. Secondly, by using geometric features to drive the automatic generation of multi-scale meshes, spatial mapping of experimental measurement points, and automatic assembly of boundary files, it replaces the process of relying on manual experience to construct meshes and set parameters, avoiding numerical distortion caused by arbitrary human intervention from the source. Finally, by constructing a closed-loop iterative mechanism driven by peak error, it achieves automatic adjustment and recalculation convergence of key parameters, completely solving the problem of difficulty in automatically matching simulation results with measurement point peak values. This significantly reduces the cost of repeated manual trial and error, greatly improving calibration efficiency and the consistency between experimental and simulation data.

[0068] To ensure the traceability and integrity of the simulation calibration process, facilitating subsequent reproduction, auditing, or verification, the closed-loop calibration for shock tube shock wave test simulation provided by this invention also includes, for example: Figure 3 The steps for generating the correction traceability result package are shown.

[0069] Figure 3The third schematic diagram of the closed-loop correction method for shock tube shock wave test simulation provided by the present invention is as follows: Figure 3 As shown, it includes: S201. Obtain the calibration data set.

[0070] Acquire a calibration dataset, which includes, but is not limited to, target pressure time history data, relative specific volume curves, internal energy curves and particle velocity curves, multi-scale fluid domain simulation models, parameter adjustment records from each iteration (e.g., curve scaling factors or mesh size parameters adjusted each time), error indices, and the final output calibration results. This data can completely record the entire process from the input of the original experimental data to the output of the final calibration results.

[0071] S202. Read the solver version identifier and calculation timestamp, and encapsulate the calibration dataset version identifier in a structured manner using the calculation timestamp.

[0072] Read the version identifier of the currently used solver, such as the solver name and version number, as well as the timestamp of this calculation, including the start and end times. Then, encapsulate the above correction data set along with the version identifier and timestamp in a structured manner. This structured encapsulation can use common data exchange formats such as JavaScript Object Notation (JSON) or Extensible Markup Language (XML) to ensure clear data organization and ease of parsing.

[0073] S203. Perform a hash operation on the encapsulated data set to generate an integrity check code, append the integrity check code to the encapsulation file, generate a correction and traceability result package containing the integrity check code, and output it to the storage path.

[0074] Perform a hash operation, such as using the SHA-256 algorithm, on the entire encapsulated data set to generate a fixed-length integrity checksum. Append this checksum to the encapsulated file, for example, as metadata in the file header or footer, to form the final correction and traceability result package, and output it to the specified storage path.

[0075] The calibration traceability result package generated through the above steps comprehensively gathers all data, from the original experimental data, the preprocessed target pressure time history, the generated three inflow curves, the multi-scale fluid domain simulation model, to the parameter adjustment records of each iteration, error indices, and the final calibration results. This data is then structured and encapsulated using solver version identifiers, computation timestamps, and hash integrity check codes. This not only prevents the loss or tampering of critical data, significantly improving the credibility and transparency of the simulation calibration process, but also facilitates large-scale archiving and automated auditing, providing a reliable and traceable basis for subsequent model reproduction, result verification, and regulatory compliance checks.

[0076] The following describes the shock tube shock wave test simulation closed-loop correction device provided by the present invention. The shock tube shock wave test simulation closed-loop correction device described below and the shock tube shock wave test simulation closed-loop correction method described above can be referred to in correspondence with each other.

[0077] Figure 4 This is a schematic diagram of the closed-loop correction device for shock tube shock wave test simulation provided by the present invention, as shown below. Figure 4 As shown, the shock tube shock wave test simulation closed-loop correction device 400 provided by the present invention includes: The curve generation module 401 is used to generate the relative specific volume curve, internal energy curve and particle velocity curve required by the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data. The simulation file generation module 402 is used to generate a multi-scale fluid domain simulation model based on the geometric features of the sample under test, construct monitoring points corresponding to the test point locations in the multi-scale fluid domain simulation model, and generate a simulation input file with the curve required for the inflow boundary as the inlet boundary condition. The simulation calculation module 403 is used to perform simulation based on the simulation input file, extract the simulated peak pressure of the monitoring point from the simulation results, and compare it with the experimental peak pressure to obtain the error index. The closed-loop iteration module 404 is used to repeat the above steps by adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model until the error index meets the preset threshold and outputs the correction result.

[0078] In one possible design, the curve generation module 401 is used for: The acquired test pressure time history data is preprocessed to obtain the target pressure time history data, and the initial gas state parameters of the shock tube drive section and the test section are read. Based on the target pressure time history data and initial gas state parameters, discrete numerical sequences of relative specific volume, internal energy and particle velocity are obtained based on gas dynamics. Based on the solver's calculation time step, the discrete numerical sequences are time-mapped and formatted to generate relative specific volume curves, internal energy curves, and particle velocity curves.

[0079] In one possible design, the simulation file generation module 402 is used for: Obtain the geometric dimensions and hole locations of the sample under test, construct a coordinate point set based on the geometric dimensions and hole locations, and automatically scale and parameterize the coordinate point set. A multi-scale fluid domain mesh is generated based on the parameterized coordinate point set, and local mesh refinement is performed at the location of the pores. The multi-scale fluid domain mesh adopts a near-field refinement and far-field coarsening layout and is marked with cell identifiers. Configure properties, materials, and constitutive parameters for the multi-scale fluid domain mesh, smooth the transition region of the multi-scale fluid domain mesh, and generate control cards, operating condition settings, and main input files to generate a multi-scale fluid domain simulation model.

[0080] In one possible design, the simulation calculation module 403 is used for: The simulated peak pressure is compared with the corresponding experimental peak pressure, and the relative error of the monitoring points is calculated. Weighting coefficients are assigned based on the physical importance of the test points or the measurement uncertainty of the sensors. The weighting coefficients are then weighted and the relative error is calculated using a norm to generate an error index.

[0081] In one possible design, the closed-loop iteration module 404 is used for: Determine whether the error index is less than or equal to the preset threshold; If so, confirm that the iteration has converged, output the correction result, and terminate the iteration record; If not, obtain the current iteration round and calculate the rate of change of the error index between two adjacent rounds. Determine whether to terminate the iteration based on the current iteration round or the rate of change.

[0082] In one possible design, the closed-loop iteration module 404 is also used for: If the current iteration reaches the preset maximum number of iterations, or the rate of change is less than or equal to the preset convergence threshold, the iteration is terminated, the mesh distortion region and boundary waveform deviation features of the current multi-scale fluid domain simulation model are extracted, and a mismatch diagnosis report is generated and output. If the current iteration round has not reached the preset maximum number of iterations, and the rate of change is greater than the preset convergence threshold, adjust the scaling factor of the curve or the mesh size parameter of the simulation model to proceed to the next iteration round.

[0083] In one possible design, the simulation calculation module 403 is also used for: Obtain the calibration dataset, which includes target pressure time history data, relative specific volume curve, internal energy curve, particle velocity curve, multi-scale fluid domain simulation model, parameter adjustment records of each iteration, error index, and calibration results. Read the solver version identifier and computation timestamp, and encapsulate the calibration data set, version identifier, and computation timestamp in a structured manner; Perform a hash operation on the encapsulated data set to generate an integrity check code, append the integrity check code to the encapsulation file, generate a correction and traceability result package containing the integrity check code, and output it to the storage path.

[0084] In one possible design, the calibration results include a comparison table of experimental and simulated peak pressures, a summary of error indices, a record of the iteration process, and a list of parameters.

[0085] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 840. The processor 510, communication interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logic instructions in the memory 530 to execute a closed-loop correction method for shock tube shock wave test simulation. This method includes: generating the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data; generating a multi-scale fluid domain simulation model based on the geometric characteristics of the tested sample; constructing monitoring points corresponding to the test point locations in the multi-scale fluid domain simulation model; generating a simulation input file using the curves required for the inflow boundary as the inlet boundary conditions; executing the simulation based on the simulation input file; extracting the simulated peak pressure of the monitoring points from the simulation results; and comparing it with the experimental peak pressure to obtain an error index; repeating the above steps by adjusting the scaling factor of the curve or the mesh size parameters of the multi-scale fluid domain simulation model until the error index meets a preset threshold, and outputting the correction result.

[0086] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0087] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the closed-loop correction method for shock tube shock wave test simulation provided by the above methods. The method includes: generating the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data; generating a multi-scale fluid domain simulation model based on the geometric characteristics of the tested sample; constructing monitoring points corresponding to the test point locations in the multi-scale fluid domain simulation model; and generating a simulation input file using the curve required for the inflow boundary as the inlet boundary condition; performing simulation based on the simulation input file; extracting the simulated peak pressure of the monitoring points from the simulation results; and comparing it with the test peak pressure to obtain an error index; and repeating the above steps by adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model until the error index meets a preset threshold, and outputting the correction result.

[0088] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the closed-loop correction method for shock tube shock wave test simulation provided by the above methods. The method includes: generating the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data; generating a multi-scale fluid domain simulation model based on the geometric characteristics of the tested sample; constructing monitoring points corresponding to the test point locations in the multi-scale fluid domain simulation model; and generating a simulation input file using the curve required for the inflow boundary as the inlet boundary condition; performing simulation based on the simulation input file; extracting the simulated peak pressure of the monitoring points from the simulation results; and comparing it with the test peak pressure to obtain an error index; and repeating the above steps by adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model until the error index meets a preset threshold, and outputting the correction result.

[0089] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0090] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A closed-loop correction method for shock tube shock wave test simulation, characterized in that, include: Based on the shock tube test pressure time history data, the relative specific volume curve, internal energy curve and particle velocity curve required for the inflow boundary of the PIF-ALE solver are generated. A multi-scale fluid domain simulation model is generated based on the geometric characteristics of the sample under test. Monitoring points corresponding to the test points are constructed in the multi-scale fluid domain simulation model, and a simulation input file is generated using the curve required by the inflow boundary as the inlet boundary condition. The simulation is performed based on the simulation input file. The simulated peak pressure of the monitoring point is extracted from the simulation results and compared with the experimental peak pressure to obtain the error index. By adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model, repeat the above steps until the error index meets the preset threshold, and output the correction result.

2. The method according to claim 1, characterized in that, The process of generating the relative specific volume curve, internal energy curve, and particle velocity curve required for the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data includes: The obtained test pressure time history data is preprocessed to obtain target pressure time history data, and the initial gas state parameters of the shock tube drive section and the test section are read. Based on the target pressure time history data and the initial gas state parameters, discrete numerical sequences of relative specific volume, internal energy and particle velocity are obtained based on gas dynamics. The discrete numerical sequences are time-mapped and formatted according to the time step calculated by the solver, thereby generating the relative specific volume curve, the internal energy curve, and the particle velocity curve.

3. The method according to claim 1, characterized in that, The generation of a multi-scale fluid domain simulation model based on the geometric features of the tested sample includes: The geometric dimensions and hole locations of the sample under test are obtained, a set of coordinate points is constructed based on the geometric dimensions and hole locations, and the set of coordinate points is automatically scaled and parameterized. A multi-scale fluid domain mesh is generated based on the parameterized coordinate point set, and the mesh is locally refined at the location of the hole. The multi-scale fluid domain mesh adopts a near-field refinement and far-field coarsening layout and is equipped with cell identifiers. Configure attributes, materials, and constitutive parameters for the multi-scale fluid domain mesh, smooth the transition region of the multi-scale fluid domain mesh, and generate control cards, operating condition settings, and main input files to generate the multi-scale fluid domain simulation model.

4. The method according to claim 1, characterized in that, The error index obtained by comparing the pressure with the peak test pressure includes: The simulated peak pressure is compared with the corresponding experimental peak pressure to calculate the relative error of the monitoring point; Based on the physical importance of the test point or the sensor measurement uncertainty, weighting coefficients are assigned, and the weighting coefficients are weighted or normed with the relative error to generate the error index.

5. The method according to any one of claims 1 to 4, characterized in that, The process of repeating the above steps until the error index meets the preset threshold and outputting the correction result includes: Determine whether the error index is less than or equal to the preset threshold; If so, confirm that the iteration has converged, output the correction result, and terminate the iteration record; If not, obtain the current iteration round, calculate the rate of change of the error index between two adjacent rounds, and determine whether to terminate the iteration based on the current iteration round or the rate of change.

6. The method according to claim 5, characterized in that, The step of determining whether to terminate the iteration based on the current iteration round or the rate of change includes: If the current iteration reaches the preset maximum number of iterations, or the rate of change is less than or equal to the preset convergence threshold, the iteration is terminated, the mesh distortion region and boundary waveform deviation features of the current multi-scale fluid domain simulation model are extracted, and a mismatch diagnosis report is generated and output. If the current iteration round does not reach the preset maximum number of iterations, and the rate of change is greater than the preset convergence threshold, adjust the scaling factor of the curve or the mesh size parameter of the simulation model to proceed to the next iteration round.

7. The method according to claim 2, characterized in that, Also includes: Obtain a calibration data set, which includes the target pressure time history data, the relative specific volume curve, the internal energy curve, the particle velocity curve, the multi-scale fluid domain simulation model, the parameter adjustment records of each iteration, the error index, and the calibration result; Read the solver version identifier and calculation timestamp, and encapsulate the correction data set, version identifier, and calculation timestamp in a structured manner; A hash operation is performed on the encapsulated data set to generate an integrity check code. The integrity check code is then appended to the encapsulated file to generate a correction and traceability result package containing the integrity check code, which is then output to the storage path.

8. The method according to claim 7, characterized in that, The correction results include a comparison table of peak pressures from experiments and simulations, a summary of error indices, a record of the iteration process, and a list of parameters.

9. A closed-loop correction device for shock wave test simulation using a shock tube, characterized in that, include: The curve generation module is used to generate the relative specific volume curve, internal energy curve, and particle velocity curve required by the inflow boundary of the PIF-ALE solver based on the shock tube test pressure time history data. The simulation file generation module is used to generate a multi-scale fluid domain simulation model based on the geometric features of the tested sample, construct monitoring points corresponding to the test point locations in the multi-scale fluid domain simulation model, and generate a simulation input file using the curve required by the inflow boundary as the inlet boundary condition. The simulation calculation module is used to perform simulation based on the simulation input file, extract the simulated peak pressure of the monitoring point from the simulation results, and compare it with the experimental peak pressure to obtain the error index. The closed-loop iteration module is used to repeat the above steps by adjusting the scaling factor of the curve or the mesh size parameter of the multi-scale fluid domain simulation model until the error index meets the preset threshold and outputs the correction result.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the closed-loop correction method for shock tube shock wave test simulation as described in any one of claims 1 to 8.

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