Post-processing method, device and equipment for hydrogen fuel flow simulation CFD and medium

Abstract

The embodiment of the invention provides a hydrogen fuel flow simulation CFD post-processing method, device, equipment and medium, and relates to the technical field of numerical simulation, and the method is realized through the following steps: executing hydrogen fuel flow CFD simulation based on an aero-engine, and generating unstructured grid data; converting the unstructured grid data into a plurality of simulation result files, sorting grid geometric information and numerical value distribution data in the simulation result files, generating a time snapshot matrix, calculating an adjoint matrix S through the time snapshot matrix, and then obtaining a mode corresponding to each feature vector of the hydrogen fuel flow field; and through a mode corresponding to each feature vector of the hydrogen fuel flow field, calculating to obtain a magnification gi and a mode coefficient amplitude ba, and carrying out dynamic reconstruction and prediction on the hydrogen fuel flow field through a mode superposition method. According to the scheme, the flow field state at any moment can be reconstructed and predicted through a modal superposition method, and the post-processing efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology, and in particular to a post-processing method, apparatus, equipment and medium for CFD simulation of hydrogen fuel flow. Background Technology

[0002] CFD simulation of hydrogen fuel flow is a key technology in the aerospace hydrogen energy utilization field, as its flow characteristics directly affect combustion efficiency and system safety. Traditional CFD-based time-averaged flow field analysis methods have significant shortcomings: First, they rely on manually set monitoring parameters, making them insensitive to microscale flow anomalies in hydrogen fuel, leading to difficulties in timely detection of safety hazards; second, conventional frequency domain analysis methods cannot effectively correlate flow modes with mixing characteristics. Existing commercial CFD post-processing software (such as Fluent and CFX) lacks dedicated analytical modules for hydrogen fuel flow, resulting in problems such as delayed mixing state assessment and insufficient early warning accuracy. Research shows that the modal distribution changes of specific frequency bands (such as the characteristic frequencies of hydrogen molecule diffusion) in the dynamic flow field are significantly correlated with mixing uniformity. This provides an important basis for developing intelligent post-processing methods for hydrogen fuel flow with multi-physics coupling analysis capabilities and is also a core technical approach to improve the operational efficiency and safety monitoring level of hydrogen energy systems. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a post-processing method for hydrogen fuel flow simulation CFD to solve the technical problems of low sensitivity in fault feature identification, poor spatiotemporal modal correlation, and insufficient diagnostic efficiency in existing traditional CFD post-processing methods. The method includes: Based on the set input variables, perform CFD simulation of hydrogen fuel flow based on aero-engines to generate unstructured mesh data; The unstructured mesh data is converted into multiple simulation result files. The mesh geometry and numerical distribution data in these files are then sorted according to physical time to generate a time snapshot matrix. Through the time snapshot matrix The adjoint matrix S is calculated, and the mode corresponding to each eigenvector of the hydrogen fuel flow field is calculated using the adjoint matrix S. ; The mode corresponding to each feature vector of the hydrogen fuel flow field The magnification was calculated. g i and modal coefficient amplitude b a Based on the magnification g i and the modal coefficient amplitude b aThe hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method.

[0004] This invention also provides a post-processing device for hydrogen fuel flow simulation CFD, to solve the technical problems of low sensitivity in fault feature identification, poor spatiotemporal modal correlation, and insufficient diagnostic efficiency in existing CFD post-processing methods. The device includes: The grid data generation module is used to perform CFD simulation of hydrogen fuel flow based on aero-engines according to the set input variables and generate unstructured grid data. The modal calculation module is used to convert the unstructured mesh data into multiple simulation result files, sort the mesh geometric information and numerical distribution data in the simulation result files according to physical time, and generate a time snapshot matrix. Through the time snapshot matrix The adjoint matrix S is calculated, and the mode corresponding to each eigenvector of the hydrogen fuel flow field is calculated using the adjoint matrix S. ; The flow field reconstruction and prediction module is used to reconstruct and predict the modes corresponding to each feature vector of the hydrogen fuel flow field. The magnification was calculated. g i and modal coefficient amplitude b a Based on the magnification g i and the modal coefficient amplitude b a The hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method.

[0005] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned post-processing method for any of the hydrogen fuel flow simulation CFD methods, thereby solving the technical problems of low sensitivity in fault feature identification, poor spatiotemporal modal correlation, and insufficient diagnostic efficiency in the traditional CFD post-processing methods of the prior art.

[0006] This invention also provides a computer-readable storage medium storing a computer program that executes any of the above-described post-processing methods for hydrogen fuel flow simulation CFD, in order to solve the technical problems of low sensitivity in fault feature identification, poor spatiotemporal modal correlation, and insufficient diagnostic efficiency in traditional CFD post-processing methods in the prior art.

[0007] Compared with the prior art, the beneficial effects that at least one technical solution adopted in the embodiments of this specification can achieve include at least: Through the calculation of the flow field mode, the mode coefficient amplitude and the mode amplification, and in combination with the initial value of the flow field, the flow field state at any time can be reconstructed and predicted through the mode superposition method, thereby avoiding a large amount of repeated simulation work and improving the post-processing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0009] Figure 1 is a flow chart of a hydrogen fuel flow simulation CFD post-processing method provided by an embodiment of the present application; Figure 2 is a flow chart of a hydrogen fuel flow simulation CFD post-processing method provided by an embodiment of the present application; Figure 3 is a structural block diagram of a computer device provided by an embodiment of the present application; Figure 4 is a structural block diagram of a hydrogen fuel flow simulation CFD post-processing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application will be described in detail below with reference to the drawings.

[0011] The embodiments of the present application will be described in detail below with reference to the drawings.

[0012] In the embodiments of the present application, a hydrogen fuel flow simulation CFD post-processing method is provided, as shown in Figure 1 and Figure 2 , the method comprises: Step S101: performing hydrogen fuel flow CFD simulation based on an aero-engine according to the set input variable, to generate unstructured grid data; Step S102: Convert the unstructured mesh data into multiple simulation result files, sort the mesh geometry information and numerical distribution data in the simulation result files according to physical time, and generate a time snapshot matrix. Through the time snapshot matrix The adjoint matrix S is calculated, and the mode corresponding to each eigenvector of the hydrogen fuel flow field is calculated using the adjoint matrix S. ; Step S103: Through the modes corresponding to each eigenvector of the hydrogen fuel flow field The magnification was calculated. g i and modal coefficient amplitude b a Based on the magnification g i and the modal coefficient amplitude b a The hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method.

[0013] In practice, the unstructured mesh data is converted into multiple simulation result files through the following steps: The unstructured mesh data is analyzed to extract the mesh topology and spatial distribution data of physical quantities. The mesh topology includes node coordinates and cell connection relationships, and the physical quantities include velocity field, pressure field, and temperature field. A physical quantity mapping relationship based on cell number is established, and the physical quantities are separated by type based on the physical quantity mapping relationship. Based on the spatial distribution data, an independent simulation result file is generated for each type of physical quantity. The simulation result file includes the mesh geometry information and the numerical distribution data of the physical quantities.

[0014] In this embodiment of the invention, the following steps are used to sort the mesh geometry information and numerical distribution data in the simulation result file according to physical time, thereby generating a time snapshot matrix. Through the time snapshot matrix The adjoint matrix S is calculated as follows: The mesh geometry information and numerical distribution data in the simulation result file are sorted according to physical time to generate a time snapshot matrix. ,in, , v 1. v 2...... v N These are variables at different time steps, including velocity, pressure, temperature, and turbulence; expressed through the time snapshot matrix. Before obtaining NSnapshot matrix of one time and later N Snapshot matrix of one time ; according to the former N Snapshot matrix of one time and later N Snapshot matrix of one time , the mapping relationship of the whole sequence is obtained, the mapping matrix A of the whole sequence is reduced, and the accompanying matrix S is calculated.

[0015] In the embodiment of the present application, the mapping relationship of the whole sequence is obtained according to the former N Snapshot matrix of one time and later N Snapshot matrix of one time , the mapping relationship of the whole sequence is obtained, the mapping matrix A of the whole sequence is reduced, and the accompanying matrix S is calculated. The former N Snapshot matrix of one time is reduced in dimension by using the singular value decomposition method, a diagonal matrix Sigma composed of singular values is generated, the diagonal matrix Sigma is truncated to generate a truncated diagonal matrix Sigma, and the mapping matrix A is obtained through the unitary matrix U containing left singular vectors, the truncated diagonal matrix Sigma and the latter N Snapshot matrix of one time , wherein, J is the unitary matrix of right singular vectors, and U is the unitary matrix containing left singular vectors; the reduced accompanying matrix S is obtained through the mapping matrix A and the unitary matrix U, wherein, U * is the conjugate transpose matrix of U.

[0016] In the embodiment of the present application, the mode corresponding to each eigenvector of the hydrogen fuel flow field is calculated through the accompanying matrix S by the following steps: The eigenvalue Lambda i and eigenvector K i of the accompanying matrix S are solved by eigenvalue decomposition in-depth dynamic characteristics of the hydrogen fuel flow field; the mode N corresponding to each eigenvector of the hydrogen fuel flow field is calculated through the latter Snapshot matrix of one time , the eigenvalue K i and the truncated diagonal matrix Sigma, wherein, J is the unitary matrix of right singular vectors.

[0017] In this embodiment of the invention, the modes corresponding to each feature vector of the hydrogen fuel flow field are realized through the following steps. The magnification was calculated. g i and modal coefficient amplitude b a Based on the magnification g i and the modal coefficient amplitude b a The hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method: The mode corresponding to each feature vector of the hydrogen fuel flow field and the variables of the first time step v 1. Calculate the modal coefficient amplitude. b a ,in, ; through the first i Characteristic frequencies of the first-order modes w i and the adjoint matrix S of the first i eigenvalues ​​of order Lambda i Calculate the first i Magnification of the order g i ,in, , t The change in time step; based on the first i Characteristic frequencies of the first-order modes w i The first i Magnification of the order g i and the modal coefficient amplitude b a The hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method.

[0018] In this embodiment of the invention, the following steps are used to achieve the goal based on the first... i Characteristic frequencies of the first-order modes w i The first i Magnification of the order g i and the modal coefficient amplitude b a The hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method: ,in, It is the reconstructed time snapshot matrix. e It is a natural constant. r The modal order used for reconstruction, the dynamics mode of the order of i b i the modal coefficient amplitude of the order of i t is a time step, w i the characteristic frequency of the mode of the order of i

[0019] Specifically, the following steps are implemented: Step (1), a monitoring environment of variables such as speed, pressure, temperature, etc. is constructed.

[0020] In the CFD simulation, the full-variable output of the speed, pressure, temperature and turbulence parameters of the full field grid points is configured, the adaptive time step strategy is adopted to ensure that the sampling frequency meets the sampling requirement of the modal frequency, the parallel I / O technology is used to store the transient flow field data in a specified format, and a data quality control process including variable range checking and physical reasonableness verification is established, thereby providing a complete time-space flow field data basis for subsequent modal analysis.

[0021] Specifically, in the CFD simulation of the hydrogen fuel flow of the aero-engine, first, the full-variable output of the speed (axial / radial / tangential components), pressure (static pressure / total pressure), temperature and turbulence parameters (such as turbulent kinetic energy, dissipation rate) of the full flow field grid points is configured in the solver, and the key flow regions such as the blade surface, the tip clearance and the wake area of the aero-engine are monitored; the adaptive time step strategy based on the impeller speed is adopted to ensure that the sampling frequency meets the requirement of more than 2 times the highest concerned modal frequency; the parallel post-processing IO technology is used to store the transient flow field data in time steps, and the control process including the pressure-temperature correlation check, the vorticity reasonableness verification, etc. is established, thereby forming a complete time-space flow field database, and providing a data basis for subsequent turbomachinery fault modal analysis.

[0022] Step (2), the variable storage format in the output result file (the storage form of the time-space flow field database) is converted, and the specific implementation is as follows: ​​​Aiming at the unstructured grid data file output by CFD software, a special format conversion module is developed. The module first parses the grid topology structure (including node coordinates, element connection relationship, and other geometric information) and the distribution data of each grid geometric information and numerical distribution data (velocity, pressure, temperature, etc.) in the original result file; then a mapping relationship table of grid number and physical variable is established, and sparse matrix storage technology is used to efficiently organize large-scale grid data; finally, different physical variables are separated according to the field separation principle and output as independent format files, each file containing complete grid geometric information and corresponding single physical field data, while retaining metadata such as time step number. This processing method not only ensures that specific variable data can be quickly located and read during subsequent analysis, but also reduces the memory overhead of single file processing through the data division strategy.

[0023] Specifically, for the unstructured grid data output by the CFD simulation of hydrogen fuel flow in an aero-engine, a professional format conversion and variable storage scheme is developed. First, the original data file is completely parsed, and the grid topology structure (including node coordinates, element connection relationship) and the spatial distribution data of the velocity field (three components), pressure field, temperature field, and other physical quantities are accurately extracted; then a physical quantity mapping relationship based on element number is established, and large-scale grid data is efficiently organized using sparse matrix format; finally, each physical quantity is separated by type and output as an independent text format file (simulation result file). That is, each file contains complete grid geometric information and numerical distribution of corresponding physical quantities, while retaining key metadata such as time step number and speed. This divide-and-conquer storage strategy not only ensures that target variables can be quickly located and read during subsequent dynamic mode analysis, but also significantly reduces the memory overhead of single processing by separating data, making it particularly suitable for processing large-scale transient flow field data generated by high-precision simulation of turbomachinery.

[0024] Step (3), forming a time snapshot matrix of simulation results and performing linear mapping, the specific method is as follows: In the above step, the simulation result file (independent text format file) of turbomachinery after storage format conversion is sorted according to physical time to form a time snapshot matrix as shown in the following formula, where v represents the variable, subscript N represents the time snapshot moment.

[0025] The extracted and processed variables are grouped into a time snapshot matrix according to the output time step, and the matrix form is as follows: ; In the formula v 1, v 2...... v NThese are variables at different time steps, such as the pressure field distribution, velocity distribution, or vorticity distribution of turbomachinery. After forming a time snapshot matrix, it is split and the previous values ​​are taken from each time step. N -1 moment and after N The snapshot matrix at time -1 is shown in the following formula.

[0026] ; ; In aero-engines, we assume that data from a certain snapshot can be linearly mapped from a previous snapshot. In this case, we can... See as By performing a linear mapping, it can be described by the following formula: .

[0027] A is a mapping matrix of the entire sequence, with a rank of [value missing]. q The square formation, q Let be the number of meshes in the turbomachinery model. Since the number of meshes in a turbomachinery model is very large, directly calculating the mapping matrix would consume a significant amount of computational resources; therefore, the matrix needs to be reduced in order.

[0028] Step (4): Reduce the order of the mapping matrix. The specific implementation method is as follows: For each column vector in the time snapshot sequence, when the physical time interval between two consecutive snapshots is sufficiently small and there are enough time snapshots in the sequence, the data of any time snapshot can be regarded as a linear combination of the remaining time snapshots, as shown in the following formula. Where a i The coefficients representing snapshot data from different times. r It is the residual vector.

[0029] ; The current mapping matrix is ​​A, which is a rank of q The square formation, q The number of grid cells is the mapping matrix. For large grid models, the data volume of the mapping matrix will be enormous. To address this problem, we combine the above equation with the mapping formula, transforming the mapping matrix A, which should be solved, into solving the adjoint matrix S, as shown in the following equation: ; In the formula, the S matrix is ​​a (N-1)×(N-1) square matrix. For aero-engine models, the number of grids is often much larger than the number of output time snapshots. Therefore, this formula can reduce the computational resource requirements. The expression for the S matrix is ​​as follows: ; At this point, matrix A is transformed into a rank of... NThe accompanying matrix S of -1, since N is the number of time snapshots, which is usually much smaller than the number of grids, the computational load can be reduced by this step.

[0030] Step (5), singular value decomposition is performed on , and the specific steps are as follows: The singular value decomposition (SVD) method is used to reduce the dimension of the preprocessed flow field snapshot matrix. First, the space-time matrix is decomposed to obtain the formula shown below, where Σ is a diagonal matrix composed of singular values, and the non-zero elements are arranged in descending order; U and V are unitary matrices containing left and right singular vectors, respectively, and satisfy UU*=J*J=I, I is the unit matrix. In the formula, U and J* are both unitary matrices, where the rank of U is q, and the rank of J* is q. Σ is a diagonal matrix with rank P, and it should be noted that the size of P is originally the same as the rank of .

[0031] ; Step (6), the Σ matrix is truncated, and the specific steps are as follows: At this time, the matrices U and J and Σ still have the problem of large storage data volume. Based on this, the matrices are truncated, and only the first F order elements of the three matrices are taken. For large grid models, there are usually F << q It is not difficult to see that through the above truncation processing, the data is effectively compressed.

[0032] Specifically, since the number of grids of an aero-engine q is large, the q×q matrix U still needs to be stored during the calculation process, which leads to the inevitable occupation of a large amount of memory in the singular value decomposition process. Since the matrices Σ, U and J* are obtained by singular value decomposition, it can be considered that the flow characteristics of the flow field in the time sequence can be represented by Σ, U and J*, and the diagonal elements of the matrix Σ are arranged in descending order, and the larger the value of the diagonal element, the higher the contribution of the corresponding vectors in the matrices U and J* to the flow characteristics of the flow field. Therefore, the order of Σ is reduced, that is, only the first F order elements of the matrix Σ and the corresponding U and J* data are selected to approximate them. Since the aero-engine turbomachinery flow field has nonlinear characteristics, the number of broken orders F should ensure that the selected elements account for more than 99% of the total number of elements in the matrix Σ. The matrix Σ before and after truncation is shown below. At the same time, the matrices U and J* are truncated to the first F order data, respectively, to obtain q × F and F × N -1 matrices. For a typical turbomachinery model, the number of grids qUsually in the order of hundreds of millions, and after the method is processed, F The value is generally controlled in the order of hundreds. Although this truncation process introduces a certain numerical error, based on the flow characteristics of the aero-engine turbomachinery, the first-order modal has been able to fully capture the key aerodynamic characteristics including blade passing frequency, wheel vibration modal, etc. F

[0033] .

[0034] Step (7), calculate the mapping matrix A and the accompanying matrix S, the specific steps are as follows: After obtaining the reduced-order singular value decomposition result (reduced U, J* and Σ) of the time snapshot sequence, the expression of the mapping matrix A can be obtained by combining the mapping formula, as shown in the following formula: ; Multiply U* on the left and U on the right of the formula, and combine the singular value decomposition formula to obtain the expression of the accompanying matrix S, as follows: .

[0035] Step (8), calculate the mode of the variable of interest changing with time, the specific steps are as follows: After obtaining the reduced-order accompanying matrix S (the accompanying matrix S is constructed based on the reduced data, and its dimension is determined by the number of basis vectors after reduction r ), the dynamic characteristics of the hydrogen fuel flow field are obtained by eigenvalue decomposition, and the eigenvalues Lambda i and eigenvectors Kappa i of the accompanying matrix S are further calculated, as follows: ; Since the accompanying matrix S is reduced from the mapping matrix A, and Kappa i is the eigenvector of the accompanying matrix, then the dynamic mode corresponding to each eigenvector of the hydrogen fuel flow field can be calculated by the following formula: ; Step (9), calculate the modal coefficient amplitude and amplification rate, the specific steps are as follows: After obtaining each order DMD mode of the hydrogen fuel flow in the aero-engine, the influence degree needs to be quantitatively characterized. The modal coefficient amplitude b a is used as an evaluation index, and its calculation expression is shown in the following formula. The physical meaning of this parameter is that due to the transient characteristics of turbomachinery unsteady flow, the difference between each time step flow field and the initial state can be decomposed into linear superposition of different modes. Specifically, the modal coefficient amplitude​b a The value of the product of the inverse of the mode and the initial flow field snapshot directly reflects the contribution weight of the mode to the overall flow evolution.

[0036] As shown in the following formula: wherein, is the flow field variable snapshot of the first time step.

[0037] The frequency and amplification of each mode can be obtained by mapping the eigenvalues, wherein the real part g i represents the amplification of the eigenvalue corresponding to the flow field mode, and the imaginary part w i represents the characteristic frequency of the mode. Lambda i is the eigenvalue of the companion matrix S, and it should be noted that the amplification coefficient is a description of the change characteristics of the mode in the time sequence evolution process, and if g i > 0, it indicates that the mode is an unstable mode, which will gradually intensify with the passage of time; on the contrary, g i < 0 indicates that the mode is a stable mode, which gradually weakens with the passage of time until it is dissipated; g i = 0 indicates that the mode is a periodic mode. As shown in the following formula: , t is the change amount of the time step; Step (10), restoration of different time snapshot results: Based on the above processing procedure, the whole process of compression storage and dynamic mode decomposition of the unsteady flow field data of the hydrogen fuel flow of the aero-engine is completed. For the periodic and significant flow phenomena such as rotor-stator interference of the engine, after obtaining the modes and their amplification g i and characteristic frequency w i , the flow field dynamic reconstruction and prediction can be realized by the mode superposition method.

[0038] Given the modes of the time snapshot, the mode amplification and the initial time snapshot data, the variable data at different times can be restored by the following formula.

[0039] wherein, is the reconstructed time snapshot matrix, is the dynamic mode of the first i order, g i is thei magnification of the order, w i characteristic frequency of the order, i b i modal coefficient amplitude of the order, i t is a time step.

[0040] The reconstructed hydrogen fuel time snapshot matrix can be written as follows: .

[0041] In this embodiment, a computer device is provided, as shown in Figure 3 the memory 301, a processor 302, and a computer program stored in the memory and capable of running on the processor, and the processor implements the post-processing method of hydrogen fuel flow simulation CFD of any of the above when executing the computer program.

[0042] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0043] In this embodiment, a computer readable storage medium is provided, which stores a computer program for executing the post-processing method of hydrogen fuel flow simulation CFD of any of the above.

[0044] Specifically, the computer readable storage medium includes permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition herein, computer readable storage media does not include transitory computer readable media, such as modulated data signals and carriers.

[0045] ​​Based on the same inventive concept, the application further provides a post-processing device for hydrogen fuel flow simulation CFD, as described in the following embodiments. Since the post-processing device for hydrogen fuel flow simulation CFD solves problems by the similar principle as the post-processing method for hydrogen fuel flow simulation CFD, the implementation of the post-processing device for hydrogen fuel flow simulation CFD can refer to the implementation of the post-processing method for hydrogen fuel flow simulation CFD, and the repeated parts will not be described here. The terms "unit" or "module" used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware is also possible and is conceived.

[0046] Figure 4 is a structural block diagram of the post-processing device for hydrogen fuel flow simulation CFD of the embodiments of the application, as shown in Figure 4 , which comprises a grid data generation module 401, a modal calculation module 402 and a flow field reconstruction and prediction module 403, which will be described below.

[0047] The grid data generation module 401 is configured to perform hydrogen fuel flow CFD simulation based on an aero-engine according to the set input variables, and generate unstructured grid data; The modal calculation module 402 is configured to convert the unstructured grid data into a plurality of simulation result files, sort the grid geometry information and numerical distribution data in the simulation result files according to the physical time, and generate a time snapshot matrix , calculate the adjoint matrix S through the time snapshot matrix , and calculate the modal corresponding to each eigenvector of the hydrogen fuel flow field through the adjoint matrix S ; The flow field reconstruction and prediction module 403 is configured to calculate the amplification factor and the modal coefficient amplitude g i and the modal coefficient amplitude b a based on the amplification factor g i and the modal coefficient amplitude b a , and perform dynamic reconstruction and prediction of the hydrogen fuel flow field by a modal superposition method.

[0048] In one embodiment, the modal calculation module comprises: A distribution data extraction unit configured to parse the unstructured grid data to extract grid topology and spatial distribution data of physical quantities, wherein the grid topology comprises node coordinates and element connection relationships, and the physical quantities comprise a velocity field, a pressure field, and a temperature field; A type separation unit configured to establish a physical quantity mapping relationship based on element numbers, and separate the physical quantities by type based on the physical quantity mapping relationship; A simulation result file generation unit configured to generate independent simulation result files in units of each type of the physical quantities based on the spatial distribution data, wherein the simulation result files comprise the grid geometry information and the numerical distribution data of the physical quantities.

[0049] In one embodiment, the modal calculation module further comprises: A time snapshot matrix construction unit configured to sort the grid geometry information and the numerical distribution data in the simulation result files according to physical time to generate a time snapshot matrix wherein, , v 1、 v 2...... v N are variables at different time steps, and the variables comprise velocity, pressure, temperature, and turbulence; A preceding and subsequent snapshot matrix construction unit configured to obtain a snapshot matrix at a time point N -1 and a snapshot matrix N at a time point ; An adjoint matrix construction unit configured to obtain a mapping matrix A of an overall sequence according to a mapping relationship between the snapshot matrix N at the time point -1 N and the snapshot matrix at the time point , perform dimension reduction on the mapping matrix A of the overall sequence, and calculate an adjoint matrix S.

[0050] In one embodiment, the adjoint matrix construction unit is configured to perform dimension reduction processing on the snapshot matrix N at the time point -1 by using a singular value decomposition method to generate a diagonal matrix Σ composed of singular values, truncate the diagonal matrix Σ to generate a truncated diagonal matrix Σ, and obtain the mapping matrix A by using a unitary matrix U containing left singular vectors, the truncated diagonal matrix Σ, and the snapshot matrix N at the time point -1 , wherein, J is the unitary matrix of the right singular vector, and U is the unitary matrix containing the left singular vector; through the mapping matrix A and the unitary matrix U, the reduced-order adjoint matrix S is obtained, where, U * Let be the conjugate transpose of U.

[0051] In one embodiment, the flow field reconstruction and prediction module includes: The eigenvalue vector unit is used to delve into the dynamic characteristics of the hydrogen fuel flow field through eigenvalue decomposition, and to solve for the eigenvalues ​​of the adjoint matrix S. Lambda i and eigenvector K i ; Modal calculation unit, used for post-processing N Snapshot matrix at time -1 The feature vector K i By combining the truncated diagonal matrix Σ, the modes corresponding to each eigenvector of the hydrogen fuel flow field are calculated. ,in, J is the unitary matrix of the right singular vector.

[0052] In one embodiment, the modal calculation module further includes: The coefficient amplitude calculation unit is used to calculate the mode corresponding to each eigenvector of the hydrogen fuel flow field. and the variables of the first time step v 1. Calculate the modal coefficient amplitude. b a ,in, ; Magnification calculation unit, used to calculate the magnification by the first i Characteristic frequencies of the first-order modes w i and the adjoint matrix S of the first i eigenvalues ​​of order Lambda i Calculate the first i Magnification of the order g i ,in, , t This represents the change in the time step. Reconstruction prediction unit, used for based on the first i Characteristic frequencies of the first-order modes w i The first i Magnification of the order g i and the modal coefficient amplitude b a The hydrogen fuel flow field is dynamically reconstructed and predicted using the modal superposition method.

[0053] In one embodiment, a reconstruction prediction unit is configured to wherein, is a reconstructed time snapshot matrix, e is a natural constant, r is a modal order used for reconstruction, is a dynamic modal of the i order, b i is a modal coefficient amplitude of the i order, t is a time step, w i is a characteristic frequency of the modal of the i order.

[0054] The embodiments of the present application achieve the following technical effects: The embodiments of the present application propose a modal decomposition method, which is a data compression method for the simulation results of the hydrogen fuel flow of an aero-engine. Considering the large amount of calculation required by the turbomachinery of the aero-engine, the method effectively reduces the data storage space occupation of the post-processing by reducing the output results at different times and storing them in the form of modes. The embodiments of the present application provide an effective method for extracting the flow characteristics of the hydrogen fuel of an aero-engine. In view of the periodic evolution of the flow field near the turbomachinery under the action of the blade rotation when the turbomachinery is working, the method can accurately extract the flow field characteristic parameters corresponding to different frequencies, including key indicators such as pressure distribution and turbulent kinetic energy distribution. Based on the accurate analysis of these characteristic parameters, the method can realize accurate positioning of the fault source, thereby providing reliable technical support for the fault diagnosis and maintenance of the turbomachinery. In addition, the embodiments of the present application also propose an efficient post-processing method for the simulation of the hydrogen fuel flow of an aero-engine. Through the calculation of the flow field mode, the modal coefficient amplitude, and the modal amplification rate, the flow field state at any time can be reconstructed and predicted by the modal superposition method in combination with the initial value of the flow field, thereby avoiding a large amount of repeated simulation work and improving the post-processing efficiency.

[0055] Obviously, those skilled in the art should understand that each module or each step of the above-mentioned embodiments of the present application can be realized by a general computing device, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0056] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical scope of the present application shall fall into the scope of the present application.

Claims

1. A post-processing method of hydrogen fuel flow simulation CFD, characterized by, The method comprises: performing a hydrogen fuel flow CFD simulation based on an aero-engine according to set input variables to generate unstructured grid data; Converting the unstructured grid data into a plurality of simulation result files, sorting the grid geometry information and numerical distribution data in the simulation result files according to physical time to generate a time snapshot matrix , calculating an adjoint matrix S through the time snapshot matrix , and calculating a mode corresponding to each eigenvector of the hydrogen fuel flow field through the adjoint matrix S ; a mode corresponding to each eigenvector of the hydrogen fuel flow field , the magnification is calculated g i and the modal coefficient amplitude b a , based on the magnification g i and the modal coefficient amplitude b a , the hydrogen fuel flow field is dynamically reconstructed and predicted by the modal superposition method.

2. The post-processing method of hydrogen fuel flow simulation CFD of claim 1, wherein, converting the unstructured grid data into a plurality of simulation result files, comprising: parsing the unstructured grid data to extract grid topology and spatial distribution data of physical quantities, wherein the grid topology comprises node coordinates and element connection relationships, and the physical quantities comprise velocity field, pressure field and temperature field; establishing a physical quantity mapping relationship based on element numbers, and separating the physical quantities by type based on the physical quantity mapping relationship; based on the spatial distribution data, generating independent simulation result files for each type of physical quantity, wherein the simulation result files comprise grid geometry information and numerical distribution data of the physical quantities.

3. The post-processing method of hydrogen fuel flow simulation CFD of claim 1, wherein, According to physical time, the grid geometry information and the numerical distribution data in the simulation result file are sorted to generate a time snapshot matrix , and the accompanying matrix S is calculated through the time snapshot matrix , including: According to physical time, the grid geometry information and numerical distribution data in the simulation result file are sorted to generate a time snapshot matrix wherein, , v 1、 v 2...... v N are variables of different time steps, the variables including velocity, pressure, temperature, and turbulence; through the time snapshot matrix before N -1 snapshot matrix of the time and after N -1 snapshot matrix of the time ; According to the mapping relationship of the snapshot matrixes of the two time points N -1 time point and the snapshot matrix of the other time point N -1 time point , a mapping matrix A of the whole sequence is obtained, and the mapping matrix A of the whole sequence is reduced in order to obtain a companion matrix S.

4. The post-processing method of hydrogen fuel flow simulation CFD of claim 3, wherein, According to the foregoing N snapshot matrix at one time point and the mapping relationship of the snapshot matrix at one time point N snapshot matrix at one time point , a mapping matrix A of the overall sequence is obtained, and the mapping matrix A of the overall sequence is reduced in order to obtain a companion matrix S. The singular value decomposition method is used to process the front N -1 snapshot matrix at a time The dimensionality is reduced to generate a diagonal matrix Σ composed of singular values, and the diagonal matrix Σ is truncated to generate a truncated diagonal matrix Σ; by a unitary matrix U comprising left singular vectors, the truncated diagonal matrix Σ and a post N - a snapshot matrix at time instant t , obtaining a mapping matrix A, wherein, J is a unitary matrix of right singular vectors and U is a unitary matrix comprising left singular vectors. By means of the mapping matrix A and the unitary matrix U, a reduced order adjoint matrix S is obtained, wherein , U * is the conjugate transpose matrix of U.

5. The post-processing method of hydrogen fuel flow simulation CFD of claim 1, wherein, By means of said companion matrix S, the mode corresponding to each eigenvector of the hydrogen fuel flow field is calculated comprising: solving the eigenvalues of the companion matrix S by eigenvalue decomposition of the dynamic characteristics of the hydrogen fuel flow field λ i and eigenvectors K i ; By post N -1 snapshot matrix at time , the feature vector K i and the truncated diagonal matrix Σ, the mode corresponding to each feature vector of the hydrogen fuel flow field is calculated where, , J is the unit matrix of the right singular vector.

6. The post-processing method of hydrogen fuel flow simulation CFD of claim 1, wherein, a mode corresponding to each feature vector of the hydrogen fuel flow field , the magnification g i and the modal coefficient amplitude b a , based on the magnification g i and the modal coefficient amplitude b a , the hydrogen fuel flow field is dynamically reconstructed and predicted by a modal superposition method, comprising: a mode corresponding to each eigenvector of the hydrogen fuel flow field and the variable of the first time step v 1, the mode coefficient amplitude is calculated b a wherein ; By the first i order modal characteristic frequency w i and the first i order eigenvalue of the companion matrix S λ i , the first i order amplification factor g i wherein , t is the change in time step based on the first i order modal characteristic frequency w i , the first i order amplification g i and the modal coefficient amplitude b a , the hydrogen fuel flow field is dynamically reconstructed and predicted by the modal superposition method.

7. The post-processing method of hydrogen fuel flow simulation CFD of claim 6, wherein, based on the first i order modal characteristic frequency w i , the first i order amplification g i and the modal coefficient amplitude b a , the hydrogen fuel flow field is dynamically reconstructed and predicted by a modal superposition method, comprising: ,in, It is the reconstructed time snapshot matrix. e It is a natural constant. r The modal order used for reconstruction, For the first i The dynamic modes of the first order, b i For the first i The modal coefficient amplitude of the first order, t For time step, w i For the first i The characteristic frequencies of the order mode.

8. A post-processing device of a hydrogen fuel flow simulation CFD, characterized by, The method comprises: a grid data generation module configured to perform a hydrogen fuel flow CFD simulation based on an aero-engine according to set input variables to generate unstructured grid data; A modal calculation module is configured to convert the unstructured grid data into a plurality of simulation result files, sort grid geometry information and numerical distribution data in the simulation result files according to physical time, and generate a time snapshot matrix An adjoint matrix S is calculated through the time snapshot matrix Each eigenvector of the hydrogen fuel flow field corresponds to a mode calculated through the adjoint matrix S ​ a flow field reconstruction and prediction module for reconstructing and predicting the hydrogen fuel flow field by modal superposition method based on the magnification factor g i and the modal coefficient amplitude b a g i and the modal coefficient amplitude b a and the modal coefficient amplitude​​ 9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, the processor executes the computer program to implement the post-processing method of the hydrogen fuel flow simulation CFD according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program for executing the post-processing method of the hydrogen fuel flow simulation CFD according to any one of claims 1 to 7.

Images