Sensitivity analysis method and device for key parameter identification of thermochemical non-equilibrium flow field

Abstract

The application discloses a kind of sensitivity analysis method and device for identifying key parameters of thermochemical non-equilibrium flow field, for ultra-high-speed thermochemical non-equilibrium flow field, according to the input parameters to be analyzed and target response, obtain input parameter sample set and target response sample set, the total effect Sobol index of each input parameter to be analyzed relative to corresponding target response is generated to input parameter sample set and response value sample set;Convergence is judged to total effect Sobol index, when meeting preset convergence condition, the sensitivity of corresponding input parameter to be analyzed is sorted, and the key parameter set having leading role to ultra-high-speed thermochemical non-equilibrium flow field is determined.The present application can realize the accurate identification of different target response and different space-time position leading sensitive parameters in ultra-high-speed thermochemical non-equilibrium flow field, with good stability and applicability.

Description

Technical Field

[0001] This invention relates to the field of aerodynamics, and in particular to a sensitivity analysis method and apparatus for identifying key parameters of thermochemical nonequilibrium flow fields. Background Technology

[0002] When hypersonic vehicles fly at high Mach numbers, the flow field is in a high-enthalpy state, and the temperature of the high-speed airflow can reach thousands to tens of thousands of Kelvin after violent compression. Under these extreme high-temperature conditions, the ideal gas assumption fails, and the vibrational energy of gas molecules is greatly excited. At the same time, complex thermochemical behaviors such as molecular dissociation, ionization, and thermal radiation occur, and the flow field exhibits significant thermochemical non-equilibrium flow characteristics. Due to the limitations of current technology, ground-based high-enthalpy testing equipment cannot fully reproduce the high-temperature thermochemical non-equilibrium flow field environment corresponding to hypersonic flight. Computational fluid dynamics (CFD) is currently the mainstream method for studying and numerically predicting such flow mechanisms. Its prediction accuracy for high-temperature gas effects directly depends on the accuracy of the physical model in characterizing the high-temperature atmospheric thermochemical non-equilibrium process.

[0003] Currently, accurate numerical prediction of ultra-high-speed thermochemical non-equilibrium flow fields still faces significant technical limitations. Existing thermochemical non-equilibrium physical models and reaction kinetic parameters are mostly based on empirical assumptions and derivations from traditional experimental data, resulting in inherent accuracy defects in the models themselves. The mainstream numerical simulation system currently uses the multi-temperature model framework established in the 1980s, with the Park two-temperature model and its associated reaction mechanism being the most widely used. The reaction rate constants in this model are mainly derived from shock tube experimental data from the 1950s to the 1970s. Limited by the testing conditions and technological capabilities at the time, the original experimental data could not accurately reflect the true characteristics of thermochemical non-equilibrium effects, leading to inherent systematic errors in existing models and severely restricting the prediction accuracy of ultra-high-speed non-equilibrium flows. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a sensitivity analysis method and apparatus for identifying key parameters of thermochemical nonequilibrium flow fields. It conducts sensitivity analysis on the core model parameters of ultra-high-speed thermochemical nonequilibrium flow fields to identify key parameters that dominate the nonequilibrium characteristics of the flow field, which is of great value for parameter calibration and mechanism modeling.

[0005] On the one hand, a sensitivity analysis method for identifying key parameters of thermochemical nonequilibrium flow fields is provided, including: Obtain the input parameters to be analyzed and the target response in the target thermochemical nonequilibrium flow field; The input parameters to be analyzed are sampled to obtain an input parameter sample set; numerical solutions and target response extraction are performed on the input parameter sample set to generate a target response quantity sample set. Based on the input parameter sample set and the response value sample set, generate the Sobol index, which represents the total effect of each input parameter to be analyzed relative to the corresponding target response. The convergence of the total effect Sobol exponent is judged. When the preset convergence condition is met, the corresponding input parameters to be analyzed are sorted by sensitivity to obtain the key parameter set of the target thermochemical nonequilibrium flow field. When the preset convergence condition is not met, the sampling is re-sampling to generate a new set of input parameter samples.

[0006] Furthermore, the input parameters to be analyzed are chemical reaction rate coefficient parameters, specifically multiple chemical reaction rate coefficients in the Park air five-component seven-reaction model that include the three-body collision effect.

[0007] Furthermore, the target response quantities include the translational temperature, vibration temperature, and mass fraction distribution of the five components of air after the shock wave.

[0008] Furthermore, the input parameters to be analyzed are sampled to obtain an input parameter sample set, including: Based on the parameter variation range and sampling distribution of the input parameters to be analyzed, the Latin hypercube sampling method is used to sample them, generating multiple parameter samples to form an input parameter sample set.

[0009] Furthermore, numerical solutions and target response extraction are performed on the input parameter sample set to generate a target response quantity sample set, including: Each group of chemical reaction rate coefficient samples is input into the target ultra-high speed thermochemical non-equilibrium flow field numerical solver. The region after the shock wave is divided into grids, and numerical simulations are carried out under the corresponding working conditions to obtain the flow field calculation results corresponding to each parameter sample. The response values ​​of each target response quantity at the corresponding time or spatial location under each parameter sample are extracted from the flow field calculation results to form a target response quantity sample set.

[0010] Furthermore, based on the input parameter sample set and the response value sample set, a Sobol index is generated to represent the total effect of each input parameter to be analyzed relative to the corresponding target response, including: For each target response quantity, using the input parameter sample set and the response value sample set corresponding to the target response quantity, respectively corresponding to each grid point of the flow field after the shock wave, a second-order polynomial chaotic expansion expression is constructed that corresponds one-to-one with the translational temperature, vibration temperature and the mass fraction distribution of the five components of air as the target response quantities. Variance decomposition is performed on each polynomial chaotic expansion expression to obtain the Sobol exponent of the total effect of each input parameter to be analyzed relative to the corresponding target response.

[0011] On the other hand, a sensitivity analysis device for identifying key parameters of thermochemical nonequilibrium flow fields is provided, comprising: Acquisition module: Acquires the set of input parameters to be analyzed and the set of target response quantities in the target thermochemical nonequilibrium flow field; Processing module: Samples the input parameters to be analyzed to obtain an input parameter sample set; performs numerical solution and target response extraction on the input parameter sample set to generate a target response quantity sample set; The module for calculating the total effect Sobol index generates the total effect Sobol index of each input parameter to be analyzed relative to the corresponding target response quantity based on the input parameter sample set and the response value sample set. Judgment Module: The module performs convergence judgment on the total effect Sobol exponent. When the preset convergence condition is met, the corresponding input parameters to be analyzed are sorted by sensitivity to obtain the key parameter set of the target thermochemical nonequilibrium flow field. When the preset convergence condition is not met, the sample is resampled to generate a new set of input parameter samples.

[0012] Furthermore, an electronic device is also provided, including: Memory, used for non-transitory storage of computer-readable instructions; and Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform the method described in the first aspect above.

[0013] In another aspect, a storage medium is also provided for non-transitory storage of computer-readable instructions, wherein when the non-transitory computer-readable instructions are executed by a computer, the method described in the first aspect is performed.

[0014] In another aspect, a computer program product is also provided, including a computer program that, when run on one or more processors, is used to implement the method described in the first aspect above.

[0015] The above technical solution has the following advantages or beneficial effects: This invention provides a sensitivity analysis method and apparatus for identifying key parameters in thermochemical nonequilibrium flow fields. It performs sensitivity analysis on model parameters affecting thermochemical nonequilibrium flow fields, such as inflow conditions, reaction kinetic parameters, and energy relaxation parameters, to identify key parameters. This generates a total effect Sobol index for the corresponding target response quantity. By ranking the sensitivity of the input parameters to be analyzed that satisfy the convergence condition of the total effect Sobol index, a set of key parameters for the target thermochemical nonequilibrium flow field is obtained. This enables the identification of dominant sensitive parameters at different times or spatial locations corresponding to different target response quantities. Furthermore, convergence testing improves the stability and confidence of the sensitive parameter identification results. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0017] Figure 1 The flowchart is a sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field as described in Example 1. Figure 2 The flowchart below shows the specific steps of the sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field as described in Example 1. Figure 3 This is a schematic diagram illustrating the convergence determination of the sensitivity analysis described in Example 1; Figure 4 This is a schematic diagram of the sensitivity analysis of the target response quantity as described in Example 1, which is translational temperature. Figure 5 This is a schematic diagram of the sensitivity analysis of the target response quantity as described in Example 1, which is the vibration temperature. Figure 6 This is a schematic diagram of the sensitivity analysis of the target response quantity as mass fraction O2 described in Example 1; Figure 7 This is a schematic diagram of the sensitivity analysis of the target response quantity as mass fraction O as described in Example 1; Figure 8 This is a schematic diagram of the sensitivity analysis of the target response quantity as mass fraction N2 as described in Example 1; Figure 9 This is a schematic diagram of the sensitivity analysis of the target response quantity as the quality fraction N described in Example 1; Figure 10 This is a schematic diagram of the sensitivity analysis of the target response quantity as described in Example 1, which is the mass fraction NO. Detailed Implementation

[0018] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] In this embodiment of the invention, "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of this invention, "multiple" refers to two or more.

[0021] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0024] Example 1 This embodiment provides a sensitivity analysis method for identifying key parameters in thermochemical nonequilibrium flow fields, such as... Figure 1 As shown, it includes: Obtain the input parameters to be analyzed and the target response in the target thermochemical nonequilibrium flow field; The input parameters to be analyzed are sampled to obtain an input parameter sample set; the input parameter samples are numerically solved and the target response is extracted to generate a target response quantity sample set. Based on the input parameter sample set and the target response value sample set, the total effect Sobol index of each input parameter to be analyzed relative to the corresponding target response is generated, and the convergence of the total effect Sobol index is judged. When the preset convergence condition is met, the corresponding input parameters to be analyzed are sorted by sensitivity to obtain the set of key parameters of the target thermochemical nonequilibrium flow field; when the preset convergence condition is not met, sampling is performed again to generate a new set of input parameter samples.

[0025] This embodiment uses the one-dimensional post-shock high-temperature air thermochemical non-equilibrium flow field as the target flow field. Based on a two-temperature model (translational-rotational temperature, vibrational-electron temperature) and a Park five-component (O, N, O2, N2, NO) seven-reaction chemical reaction model, the flow field after the ultra-high-speed shock wave is numerically solved. Sensitivity analysis is then performed on the chemical reaction model parameters to identify key elementary reactions affecting the evolution of the target response. The technical solution and implementation process of this embodiment are further explained below in conjunction with this target flow field.

[0026] This embodiment provides a sensitivity analysis method for identifying key parameters in thermochemical nonequilibrium flow fields. The specific steps are as follows: Figure 2 As shown, it includes: S1: Obtain the input parameters to be analyzed and the target response quantity in the target thermochemical nonequilibrium flow field; The input parameters to be analyzed are chemical reaction rate coefficient parameters, specifically the 17 chemical reaction rate coefficients of the three-body collision effect included in the Park air five-component seven-reaction model.

[0027] The parameter variation range and sampling distribution are set for each input parameter to be analyzed; among them, the variation range of each reaction rate coefficient is set to one order of magnitude above and below the reference rate, and is characterized by a logarithmic uniform distribution.

[0028] The target response quantities include the translational temperature, vibration temperature, and mass fraction distribution of the five components of air after the shock wave. The spatial location corresponding to each target response quantity is a grid point in the flow field after the shock wave.

[0029] S2: Sampling the input parameters to be analyzed to obtain an input parameter sample set; numerically solving and extracting the target response from the input parameter sample set to generate a target response quantity sample set, specifically including: S21: Sampling the input parameters to be analyzed to obtain the input parameter sample set; Based on the parameter variation range and sampling distribution of S1, the input parameters to be analyzed are sampled to generate multiple parameter samples, forming an input parameter sample set, specifically including: Sampling was conducted within the range of 17 chemical reaction rate coefficients defined in S1. This embodiment employs the Latin hypercube sampling method, requiring a specific number of samples. N s Based on the order of the polynomial chaotic expansion expression n Total number of terms in the basis functions N b Determine the total number of terms in the basis functions. N b The calculation formula is as follows: (1) This embodiment employs a second-order polynomial chaotic expansion, and the number of input parameters to be analyzed is... d The answer is 17, therefore the total number of terms in the corresponding basis functions is... N b = 171, the number of samples meets the oversampling rate. r Determined, its expression is as follows: (2) Corresponding oversampling rate rFor the numbers 1, 2, 3, 4, and 5, 171, 342, 513, 684, and 855 parameter samples are generated respectively, forming the input parameter sample set.

[0030] S22: Perform numerical solution and target response extraction on the input parameter sample set to generate a target response quantity sample set; The chemical reaction rate coefficient samples obtained from S21 are input into the numerical solver of the target ultra-high-speed thermochemical non-equilibrium flow field. The region after the shock wave is divided into grids, and numerical simulations are carried out under the corresponding working conditions to obtain the flow field calculation results corresponding to each parameter sample.

[0031] The response values ​​of each target response quantity at the corresponding time or spatial location under each parameter sample are extracted from the flow field calculation results to form a target response quantity sample set.

[0032] S3: Based on the input parameter sample set and the response value sample set, generate the total effect Sobol index of each input parameter to be analyzed relative to the corresponding target response, and determine the convergence of the total effect Sobol index; specifically including: S31: For each target response quantity, using the input parameter sample set obtained in S21 and the response value sample set corresponding to the target response quantity in S22, respectively corresponding to each grid point of the flow field after the shock wave, construct a second-order polynomial chaotic expansion expression that corresponds one-to-one with the translational temperature, vibration temperature and the mass fraction distribution of the five components of air as the target response quantities.

[0033] When the same target response corresponds to multiple preset time or spatial locations, an independent polynomial chaotic expansion is performed on the target response at each preset location.

[0034] S32: Based on the variance decomposition of each independent polynomial chaotic expansion expression in S31, the Sobol exponent of the total effect of each input parameter to be analyzed relative to the corresponding target response is obtained; Among them, variance decomposition was performed on each independent polynomial chaotic expansion expression to obtain the Sobol exponent of the total effect of the 17 input parameters to be analyzed relative to the corresponding target response. S T,j The calculation formula is as follows: (3) in, D The total variance of the output. D T , j In order to be with the first j The variance of the total effect related to each input parameter; both the total variance and the variance of the main effects can be obtained analytically from the polynomial chaotic expansion expression.

[0035] S4: Perform convergence judgment on the total effect Sobol exponent. When the preset convergence condition is met, sort the corresponding input parameters to be analyzed by sensitivity to obtain the key parameter set of the target thermochemical nonequilibrium flow field. When the preset convergence condition is not met, resampling is performed to generate a new set of input parameter samples.

[0036] Based on the Sobol exponents corresponding to the converged target response quantities in S3, the sensitivity ranking of the input parameters to be analyzed is performed to identify the sensitive parameters corresponding to each target response quantity; and based on the sensitivity ranking results of multiple target response quantities, the set of key parameters that have a dominant influence on the evolution of the target ultra-high-speed thermochemical non-equilibrium flow field is determined.

[0037] S41: Perform a convergence test on the Sobol exponent of the total effect obtained in S3; To assess the convergence of the sensitivity analysis results, the convergence of the Sobol index of the total effect obtained in S3 was determined.

[0038] This embodiment calculates the average change in the Sobol exponent of the total effect of all input parameters across adjacent batches of samples, and examines whether this global quantity gradually stabilizes as the number of samples increases. For the first... m In the nth sampling iteration j The formula for calculating the absolute change of the total effect Sobol exponent relative to the previous iteration for the input parameters to be analyzed is as follows: (4) (5) in, Indicates the first m In the nth sampling iteration j The total Sobol exponent corresponding to each uncertain parameter This represents the absolute change in the Sobol exponent of the total effect of the input parameter between two adjacent sampling iterations. Indicates the first m The average value of the Sobol exponent change of the total effect of all input parameters to be analyzed under each sampling iteration. During incremental sampling, if... If the sensitivity analysis results continue to decrease as the sample size increases and eventually stabilize, then the results are considered to have converged.

[0039] The convergence judgment result described in this embodiment is as follows: Figure 3 As shown, according to Figure 3 It can be seen that when the sample size reaches 3 N b At that point, the error of the average total effect Sobol exponent was less than 1%, and the change in error was small after further increasing the sample size.

[0040] Taking into account both stability and economy, this embodiment ultimately adopts 3 N b The number of samples is used to construct a polynomial chaotic expansion expression.

[0041] S42: When the preset convergence condition is met, the corresponding input parameters to be analyzed are sorted by sensitivity to obtain the set of key parameters of the target thermochemical nonequilibrium flow field.

[0042] In one embodiment, key parameters for translational and vibrational temperatures include O2 and N2. 2O+N2 and N2+O The reaction rate coefficient of NO+N This indicates that the reaction is reversible; for the distribution of O2 and O, key parameters include O2 + N2. 2O + N2, O2 + O 2O+O, N2+O NO+N and NO+N The reaction rate coefficient of N + O + N; for the distribution of N2 and N, the key parameter is N2 + O The reaction rate coefficient of NO + N; for the distribution of NO, key parameters include N2 + O. NO+N and NO+N The reaction rate coefficient of N+O+N.

[0043] When the change in the Sobol index of the total effect and the change in the sensitivity ranking of each input parameter to be analyzed meet the preset convergence condition as the number of input parameter samples increases, the sensitivity analysis result of the corresponding target response is determined to be converged. Based on the Sobol exponent distribution of the total effect obtained after convergence in S41, the sensitivity results can be sorted and analyzed using the Park five-component air seven-reaction model (including 17 reaction rate coefficients for three-body collisions) as the input parameter, and the post-shock translational and vibrational temperature distributions and the five-component air distribution as the target response quantities. This allows for the identification of the sensitive parameters for each target response quantity at different spatial locations. The sensitivity results using the post-shock translational and vibrational temperature distributions as the target response quantities are as follows: Figure 4-5 As shown, Figure 4 This represents the results of a sensitivity analysis where the target response is translational temperature. Figure 5 This represents the sensitivity analysis results with vibration temperature as the target response quantity. The sensitivity analysis results with the five-component distribution of air as the target response quantity are shown below. Figure 6-10 As shown, Figure 6 This represents the results of a sensitivity analysis where the target response is the O2 mass fraction. Figure 7 This represents the results of a sensitivity analysis where the target response is a mass fraction of 0. Figure 8The results of the sensitivity analysis are represented by the target response quantity as N² mass fraction. Figure 9 The result of the sensitivity analysis is represented by the target response quantity being N mass fractions. Figure 10 This represents the sensitivity analysis results with the target response quantity being the NO mass fraction.

[0044] For ease of illustration, input parameters with a total effect Sobol exponent value less than 0.1 are represented by gray curves. According to... Figure 4-10 The results show that the key parameters for translational and vibrational temperatures include O2 and N2. 2O+N2 and N2+O The reaction rate coefficient of NO + N; for the distribution of O2 and O, key parameters include O2 + N2. 2O + N2, O2 + O 2O+O, N2+O NO+N and NO+N The reaction rate coefficient of N + O + N; for the distribution of N2 and N, the key parameter is N2 + O The reaction rate coefficient of NO + N; for the distribution of NO, key parameters include N2 + O. NO+N and NO+N The reaction rate coefficient of N + O + N. According to Figure 4-10 The results show that the dominant sensitivity parameters differ for different target response quantities, and the sensitivity ranking of the same target response quantity at different spatial locations also changes with the flow direction. The near-leading region behind the shock wave is mainly affected by oxygen molecule dissociation-related reactions, while in the downstream region, the displacement reaction N2+O... The influence of NO+N and some NO dissociation-related reactions gradually increases.

[0045] S43: When the preset convergence condition is not met, resampling is performed to generate a new set of input parameter samples.

[0046] If the preset convergence condition is not met, return to step 21 to continue sampling, generate a new set of input parameter samples, and repeat steps 3 to 4 until the preset convergence condition is met.

[0047] In the ultra-high-speed thermochemical non-equilibrium flow field described in this embodiment, different target response quantities and their corresponding sensitivities at different spatial locations vary. This embodiment can identify the sensitivity of key parameters affecting the characteristics of the thermochemical non-equilibrium flow field by conducting sensitivity analysis on different target response quantities.

[0048] Example 2 This embodiment provides a sensitivity analysis device for identifying key parameters of thermochemical nonequilibrium flow fields, including: Acquisition module: Acquires the set of input parameters to be analyzed and the set of target response quantities in the target thermochemical nonequilibrium flow field; Processing module: Samples the input parameters to be analyzed to obtain an input parameter sample set; performs numerical solution and target response extraction on the input parameter sample set to generate a target response quantity sample set; The module for calculating the total effect Sobol index generates the total effect Sobol index of each input parameter to be analyzed relative to the corresponding target response quantity based on the input parameter sample set and the response value sample set. Judgment Module: The module performs convergence judgment on the total effect Sobol exponent. When the preset convergence condition is met, the corresponding input parameters to be analyzed are sorted by sensitivity to obtain the key parameter set of the target thermochemical nonequilibrium flow field. When the preset convergence condition is not met, the sample is resampled to generate a new set of input parameter samples.

[0049] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0050] The proposed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative, and the division of the modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed.

[0051] Example 3 This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the method described in Embodiment 1.

[0052] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0053] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0054] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0055] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0056] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0057] Example 4 This embodiment also provides a storage medium for storing computer instructions, which, when executed by a processor, complete the method described in Embodiment 1.

[0058] Example 5 This embodiment also provides a computer program product, including a computer program that, when run on one or more processors, implements the method described in Embodiment 1.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A sensitivity analysis method for identifying key parameters in a thermochemical nonequilibrium flow field, characterized in that, include: Obtain the input parameters to be analyzed and the target response in the target thermochemical nonequilibrium flow field; The input parameters to be analyzed are sampled to obtain the input parameter sample set; Numerical solutions and target response extraction are performed on the input parameter sample set to generate a target response quantity sample set; Based on the input parameter sample set and the response value sample set, generate the Sobol index, which represents the total effect of each input parameter to be analyzed relative to the corresponding target response. The convergence of the total effect Sobol exponent is judged. When the preset convergence condition is met, the sensitivity of the corresponding input parameters to be analyzed is sorted to obtain the set of key parameters of the target thermochemical nonequilibrium flow field. If the preset convergence condition is not met, resampling is performed to generate a new set of input parameter samples.

2. The sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field according to claim 1, characterized in that, The input parameters to be analyzed are chemical reaction rate coefficient parameters, specifically multiple chemical reaction rate coefficients in the Park air five-component seven-reaction model that include the three-body collision effect.

3. The sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field according to claim 1, characterized in that, The target response quantities include the translational temperature, vibration temperature, and mass fraction distribution of the five components of air after the shock wave.

4. The sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field according to claim 1, characterized in that, The input parameters to be analyzed are sampled to obtain an input parameter sample set, including: Based on the range of parameter variation and sampling distribution of the input parameters to be analyzed, the Latin hypercube sampling method is used to sample them, generating multiple parameter samples to form an input parameter sample set.

5. The sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field according to claim 1, characterized in that, Numerical solutions and target response extraction are performed on the input parameter sample set to generate a target response quantity sample set, including: Each group of chemical reaction rate coefficient samples is input into the target ultra-high speed thermochemical non-equilibrium flow field numerical solver. The region after the shock wave is divided into grids, and numerical simulations are carried out under the corresponding working conditions to obtain the flow field calculation results corresponding to each parameter sample. The response values ​​of each target response quantity at the corresponding time or spatial location under each parameter sample are extracted from the flow field calculation results to form a target response quantity sample set.

6. The sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field according to claim 1, characterized in that, Based on the input parameter sample set and the response value sample set, a Sobol index is generated to represent the total effect of each input parameter to be analyzed relative to the corresponding target response, including: For each target response quantity, using the input parameter sample set and the response value sample set corresponding to the target response quantity, respectively corresponding to each grid point of the flow field after the shock wave, a second-order polynomial chaotic expansion expression is constructed that corresponds one-to-one with the translational temperature, vibration temperature and the mass fraction distribution of the five components of air as the target response quantities. Variance decomposition is performed on each polynomial chaotic expansion expression to obtain the Sobol exponent of the total effect of each input parameter to be analyzed relative to the corresponding target response.

7. A sensitivity analysis device for identifying key parameters of thermochemical nonequilibrium flow fields, characterized in that, include: Acquisition module: Acquires the set of input parameters to be analyzed and the set of target response quantities in the target thermochemical nonequilibrium flow field; Processing module: Samples the input parameters to be analyzed to obtain a sample set of input parameters; Numerical solutions and target response extraction are performed on the input parameter sample set to generate a target response quantity sample set; The module for calculating the total effect Sobol index generates the total effect Sobol index of each input parameter to be analyzed relative to the corresponding target response quantity based on the input parameter sample set and the response value sample set. Judgment module: performs convergence judgment on the total effect Sobol exponent. When the preset convergence condition is met, the corresponding input parameters to be analyzed are sorted by sensitivity to obtain the set of key parameters of the target thermochemical nonequilibrium flow field. If the preset convergence condition is not met, resampling is performed to generate a new set of input parameter samples.

8. An electronic device, characterized in that, include: Memory is used to store computer-readable instructions in a non-transitory manner. as well as Processor, for executing the computer-readable instructions, When the computer-readable instructions are executed by the processor, they perform a sensitivity analysis method for identifying key parameters of a thermochemical non-equilibrium flow field as described in any one of claims 1-6.

9. A storage medium, characterized in that, Non-transitory storage of computer-readable instructions, wherein, when executed by a computer, the non-transitory computer-readable instructions perform a sensitivity analysis method for identifying key parameters of a thermochemical non-equilibrium flow field as described in any one of claims 1-6.

10. A computer program product, characterized in that, The method includes a computer program that, when running on one or more processors, implements a sensitivity analysis method for identifying key parameters of a thermochemical nonequilibrium flow field as described in any one of claims 1-6.

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