Numerical simulation method, system and equipment for complex flow problem of aero-engine

Abstract

The invention discloses a numerical simulation method, system and equipment for the complex flow problem of an aero-engine, and relates to the field of aero-engines, the method comprises the following steps: determining a calculation area of the complex flow problem of the aero-engine, and carrying out grid division on the calculation area; fluid physical parameters of all grids are initialized according to the complex flow problem of the aero-engine; a TVB-MINMOD algorithm is adopted to detect the grids, and the grids containing the complex flow characteristics and the grids not containing the complex flow characteristics are determined; and reconstructing the initial fluid physical parameters of the grids containing the complex flow characteristics and the grids not containing the complex flow characteristics by adopting an MR-WENO reconstruction mode and a linear windward reconstruction mode, and determining the fluid physical parameters in the calculation area based on a reconstructed polynomial. According to the method, the limitation that parameters of a traditional method depend on specific problems can be effectively solved, and the calculation efficiency is improved while the shock wave capture precision is kept.

Description

Technical Field

[0001] This application relates to the field of aeroengines, and particularly to a numerical simulation method, system and device for complex flow problems of aeroengines. Background Art

[0002] The high-precision numerical simulation of complex flow problems has always been the core challenge in the research of computational fluid dynamics, especially in engineering applications involving strong discontinuity phenomena such as shock waves and contact discontinuities. Traditional single numerical methods often face the dilemma of being difficult to balance accuracy and efficiency. Although the high-order WENO (Weighted Essentially Non - Oscillatory) scheme can accurately capture flow details, its huge computational cost limits the scale of practical engineering applications; while the linear upwind scheme is computationally efficient, but it is difficult to maintain numerical stability at discontinuities, resulting in oscillations in the numerical solution and affecting the accuracy of numerical simulation. This contradiction is particularly prominent in the high-precision and high-performance computing problems of complex flow problems in the aviation field. Summary of the Invention

[0003] The purpose of this application is to provide a numerical simulation method, system and device for complex flow problems of aeroengines, which can solve the dilemma that traditional single numerical methods often face of being difficult to balance accuracy and efficiency.

[0004] To achieve the above purpose, the present application provides the following solutions.

[0005] In the first aspect, the present application provides a numerical simulation method for complex flow problems of aeroengines, including: determining the computational domain of the complex flow problem of the aeroengine and performing grid division on the computational domain; initializing the fluid physical parameters of each grid according to the complex flow problem of the aeroengine; the fluid physical parameters include the simulated flow field pressure, the velocity of the simulated flow field in the horizontal direction, the velocity of the simulated flow field in the vertical direction, and the total energy of the simulated flow field; using the TVB-MINMOD algorithm to detect each grid to determine the grids containing complex flow characteristics and the grids not containing complex flow characteristics; for the grids containing complex flow characteristics, using the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and determining the fluid physical parameters in the computational domain based on the reconstruction polynomial; for the grids not containing complex flow characteristics, using the linear upwind reconstruction method to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and determining the fluid physical parameters in the computational domain based on the reconstruction polynomial.

[0006] Second aspect, the present application provides a numerical simulation system for complex flow problems of an aeroengine, including: a calculation area determination and division module, configured to determine a calculation area of the complex flow problem of the aeroengine and perform grid division on the calculation area; an initialization module, configured to initialize fluid physical parameters of each grid according to the complex flow problem of the aeroengine; the fluid physical parameters include simulated flow field pressure, velocity of the simulated flow field in the horizontal direction, velocity of the simulated flow field in the vertical direction, and total energy of the simulated flow field; a detection module, configured to detect each grid by using the TVB-MINMOD algorithm to determine grids containing complex flow characteristics and grids not containing complex flow characteristics; a first reconstruction and calculation module, configured to, for grids containing complex flow characteristics, reconstruct the initial fluid physical parameters by using the MR-WENO reconstruction method to obtain a reconstruction polynomial, and determine the fluid physical parameters in the calculation area based on the reconstruction polynomial; a second reconstruction and calculation module, configured to, for grids not containing complex flow characteristics, reconstruct the initial fluid physical parameters by using the linear upwind reconstruction method to obtain a reconstruction polynomial, and determine the fluid physical parameters in the calculation area based on the reconstruction polynomial.

[0007] Third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned numerical simulation method for complex flow problems of an aeroengine.

[0008] According to the specific embodiments provided by the present application, the present application has the following technical effects: The present application provides a numerical simulation method, system, and device for complex flow problems of an aeroengine, and realizes accurate identification of problem cells (i.e., grids containing complex flow characteristics) through a TVB (Total Variation Bounded)-MINMOD intelligent detection mechanism. Different reconstruction methods are adopted for grids in different situations. Among them, the MR-WENO reconstruction method inherits the advantages of multi-resolution analysis and provides a high-resolution solution in discontinuous regions; while the linear upwind reconstruction method ensures calculation efficiency in smooth regions. The present application effectively solves the limitation of the traditional method's parameter dependence on specific problems, and improves the calculation efficiency while maintaining the shock capture accuracy. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0010] Figure 1 This is an application environment diagram of a numerical simulation method for complex flow problems of an aeroengine in an embodiment of the present application.

[0011] Figure 2 This is a schematic flowchart of a numerical simulation method for complex flow problems of an aeroengine provided in an embodiment of the present application.

[0012] Figure 3 This is a schematic structural diagram of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0014] In complex engineering problems such as shock-turbulence interaction simulation in the aerospace field and multi-scale flow analysis of energy equipment, efficient numerical simulation plays a key role. To meet this need, the present application proposes a numerical simulation method for complex flow problems of an aeroengine. The core lies in adopting an adaptive discrimination mechanism constructed by an improved MINMOD function, which can accurately identify and automatically mark the grids containing complex flow characteristics such as strong shock waves or contact discontinuities. Based on this high-precision detection technology, the multi-resolution WENO reconstruction method with excellent oscillation suppression characteristics is further organically combined with the computationally efficient linear upwind reconstruction strategy, not only inheriting the advantages of traditional methods in terms of computational accuracy and numerical stability, but also achieving significant improvements in computational efficiency and robustness, providing strong support for numerical simulations of complex flow simulations of aeroengines, combustion instability analysis of rocket engines, and aeroelastic coupling of aircraft.

[0015] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0016] The numerical simulation method for complex flow problems of an aeroengine provided in the embodiments of the present application can be applied, for example, as Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the complex flow problem of the aero-engine to the server 104. After receiving the complex flow problem of the aero-engine, the server 104 determines the calculation area of the complex flow problem of the aero-engine; initializes the fluid physical parameters of each grid according to the complex flow problem of the aero-engine; uses the TVB-MINMOD algorithm to detect each grid, and for the grid containing complex flow characteristics, uses the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters; for the grid not containing complex flow characteristics, uses the linear upwind reconstruction method to reconstruct the initial fluid physical parameters; determines the fluid physical parameters in the calculation area based on the reconstructed polynomial. The server 104 can feedback the fluid physical parameters in the calculation area to the terminal 102. In addition, in some embodiments, the numerical simulation method for the complex flow problem of the aero-engine can also be implemented by the server 104 or the terminal 102 alone. For example, the terminal 102 can directly perform numerical simulation on the complex flow problem of the aero-engine to obtain the fluid physical parameters in the calculation area, or the server 104 can obtain the complex flow problem of the aero-engine from the data storage system and perform numerical simulation on the complex flow problem of the aero-engine.

[0017] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0018] In an exemplary embodiment, as Figure 2 shown, a numerical simulation method for the complex flow problem of an aero-engine is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in it as an example for illustration, it includes the following steps S1 to S5.

[0019] S1: Determine the calculation area of the complex flow problem of the aero-engine, and perform grid division on the calculation area.

[0020] In a specific embodiment, it is determined that the simulation interval of the complex flow problem of the aero-engine is , where is the left position of the calculation area, is the right position of the calculation area, is the lower position of the calculation area, is the upper position of the calculation area.

[0021] In addition, according to the results required for the complex flow problems of aero-engines, the calculation termination time T_end needs to be given.

[0022] S2: Initialize the fluid physical parameters of each grid according to the complex flow problems of aero-engines; the fluid physical parameters include the simulated flow field pressure, the velocity of the simulated flow field in the horizontal direction, the velocity of the simulated flow field in the vertical direction, and the total energy of the simulated flow field.

[0023] In a specific embodiment, the calculation parameters (i.e., fluid physical parameters) are initialized according to the actual simulated complex flow problems of aero-engines.

[0024]

[0025] where and respectively represent the velocity of the simulated flow field in the horizontal direction and in the vertical direction , E represents the total energy, represents the pressure of the simulated flow field, represents the inflow Mach number, represents the angle of attack, represents the density of the simulated flow field, and the gas medium parameter , is the internal energy of the gas in the simulated flow field.

[0026] S3: Use the TVB-MINMOD algorithm to detect each grid to determine the grids containing complex flow characteristics and the grids not containing complex flow characteristics.

[0027] Step S3 specifically includes the following steps S31 - step S35.

[0028] S31: Construct a biquadratic polynomial on the fourth template using the density information of the target cell in the flow field.

[0029] According to the fourth template Construct a biquadratic polynomial ; where is the target cell, - are the first-layer adjacent cells of the target cell, - are the second-layer adjacent cells of the target cell, - is the third - layer adjacent unit of the target unit, is the basis function corresponding coefficient, is the basis function, where the label i corresponds to the degree of the variable x and j corresponds to the degree of the variable y, used to distinguish different basis functions and coefficients.

[0030] The above - reconstructed polynomial satisfies the following least - squares condition:

[0031] where, is the set of serial numbers of the adjacent units of each layer of the target unit, is the average density on the target unit, is the unit the average density on; the basis function , represents the area of the target unit, and the coordinates of the three vertices of the target unit can be expressed as , ; then , ; , are respectively the minimum value of the abscissas of the three vertices of the target unit and the minimum value of the ordinates.

[0032] S32: Calculate the second - order partial derivatives of the binary quartic polynomial.

[0033]

[0034]

[0035]

[0036] S33: Calculate the maximum value of all components of the second - order partial derivatives on the rectangular domain of the target unit, and determine the maximum absolute value of the second - order partial derivatives based on the maximum value of all components.

[0037] Consider the smallest circumscribed rectangle (i.e., the rectangular domain) containing the target unit as , where, , , , . Calculate the maximum value of all components of the second - order partial derivative on the rectangular domain . Since the maximum value of each term is non - negative, simply adding the maximum values of these components can estimate the maximum absolute value of the second - order partial derivative on the rectangular domain , that is, The second-order partial derivatives can be obtained by a similar method. and on the rectangular domain The maximum absolute value, that is, and , where is the maximum value of the -th term in the polynomial of the second-order derivative with respect to x, is the maximum value of the -th term in the mixed second-order derivative polynomial, is the maximum value of the -th term in the polynomial of the second-order derivative with respect to y, representing the maximum value of the constituent terms. .

[0038] S34: Determine the MINMOD function corrected by the TVB algorithm based on the maximum absolute value of the second-order partial derivatives, and calculate the correction term.

[0039] Define the midpoints of the three sides of the target cell as , ; The centroid of the adjacent cell is , . Assume that the solution of the system of equations is non-negative, and calculate the MINMOD function corrected by TVB:

[0040] where , , .

[0041] is the return value of the MINMOD function corrected by TVB, , are intermediate variables. When and have the same sign, , when and have different signs, . is the midpoint coordinate of the L-th side of the target cell, is the difference between the value of the binary quartic polynomial at and the density mean value on the target cell, is the density weighting of the target triangle and the adjacent cell. M is a parameter of the TVB algorithm, represents the target cell, is the absolute maximum value of the second-order partial derivative with respect to x, is the absolute maximum value of the mixed second-order partial derivative, is the absolute maximum value of the second-order partial derivative with respect to y.

[0042] Calculate the correction term .

[0043]

[0044]

[0045]

[0046] wherein, is the density weighting on the target triangle and its adjacent elements and is the weighting coefficient, is the density mean value on the element .

[0047] S35: Detect grids containing complex flow features and grids not containing complex flow features based on the correction term. When , determine that the grid contains complex flow features, otherwise it does not contain complex flow features.

[0048] Specifically, the grids are classified into two categories in this way: . Wherein, represents a grid containing complex flow features such as shock waves or contact discontinuities, represents a grid not containing complex flow features.

[0049] S4: For grids containing complex flow features, use the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and determine the fluid physical parameters in the calculation region based on the reconstruction polynomial.

[0050] Specifically, for grids containing complex flow features, use the geometric information of the target element and the internal fluid information in the flow field to construct multiple polynomials on multiple templates; reconstruct the initial fluid physical parameters based on the multiple polynomials to obtain a reconstruction polynomial.

[0051] Among them, a zero-order polynomial is constructed on the first template, a first-order polynomial is constructed on the second template, a second-order polynomial and a third-order polynomial are constructed on the third template, and a fourth-order polynomial is constructed on the fourth template.

[0052] After constructing multiple polynomials on multiple templates, it further includes: calculating a smooth factor on the target element; measuring the smoothness of each polynomial based on the smooth factor.

[0053] S5: For the grids that do not contain complex flow characteristics, the initial fluid physical parameters are reconstructed by means of linear upwind reconstruction to obtain a reconstructed polynomial, and the fluid physical parameters within the computational domain are determined based on the reconstructed polynomial.

[0054] The specific processes of the reconstructed polynomials in steps S4 and S5 are as follows: (1) For the grids marked with , the initial fluid physical parameters are reconstructed by means of MR-WENO reconstruction.

[0055] 1) Based on a series of nested central space templates, the relevant reconstructed polynomials of different orders are obtained.

[0056] Based on the first template a zero-order polynomial is constructed for numerical approximation of first-order accuracy. Based on the second template a first-order polynomial is constructed for numerical approximation of second-order accuracy. Based on the third template a second-order polynomial is constructed for numerical approximation of third-order accuracy; if the third template contains at least ten non-overlapping triangular elements, a third-order polynomial can be constructed on it for numerical approximation of fourth-order accuracy. Based on the fourth template a fourth-order polynomial is constructed for numerical approximation of fifth-order accuracy.

[0057] These reconstructed polynomials satisfy the following least-squares conditions:

[0058] where, is the vector composed of the mean values of the fluid physical parameters on the target element, is the vector composed of the mean values of the fluid physical parameters on the element , is the label set used when calculating the k-th reconstructed polynomial.

[0059] 2) Give the equivalent expression forms of the above reconstructed polynomials:

[0060] where, the linear weights are positive numbers with an arbitrary sum of one, is the -th linear weight of the k-th equivalent expression, is the -th linear weight of the k-th equivalent expression. When \(k = 2, 3\), When \(k = 4, 5\), .

[0061] 3) Calculate the smoothness factor of the polynomial , which represents the quantity measuring the smoothness of the polynomial.

[0062]

[0063] Among them, the intermediate variable , , represents the order of partial derivative of with respect to \(x\), represents the order of partial derivative of with respect to \(y\), is to find the partial derivative whose order is equal to the modulus of the vector, is the -th order partial derivative with respect to \(x\), is the -th order partial derivative with respect to \(y\).

[0064] For , construct three polynomials:

[0065] which satisfy the following conditions:

[0066] Here is the centroid of the element , . represents the value of the first polynomial at the centroid of the element , represents the vector composed of the mean values of the physical quantities on the element , represents the mean value vector on the element .

[0067] From , the relevant smoothing index is obtained.

[0068] Let:

[0069] Among them, all represent the given initial values, is the smoothing index of the polynomial . is a small positive number, which can avoid the denominator from becoming zero. Then there is: .

[0070] 4) Calculate the non - linear weights based on the smoothness factor .

[0071]

[0072] Among them, . represents the th non - linear weight used to calculate the k - th reconstructed polynomial, represents the th linear weight used to calculate the k - th reconstructed polynomial, represents the number of non - linear weights required to calculate the reconstructed polynomials with different precisions.

[0073] 5) The final MR - WENO reconstructed polynomial is .

[0074] (2) For the smooth cells marked with , adopt an efficient linear upwind reconstruction method, and its reconstructed polynomial is .

[0075] The specific calculation process of the fluid physical parameters in steps S4 and S5 is as follows: Calculate the semi - discrete terms of the space based on the reconstructed polynomials obtained in steps S4 and S5:

[0076] Among them, is the spatial difference of the spatial vector U, represents the flux vectors of fluxes f and g, is the boundary of the target cell , represents the unit outer normal vector of the target cell. The line integral will be discretized by the two - point Gaussian quadrature formula on each edge. For example, for an edge with and as endpoints, its two Gaussian quadrature nodes are:

[0077] The corresponding weights are . Based on this formula, it is discretized by numerical integration as:

[0078] Here, represents the outer normal vector on the cell boundary, represents the line integral of the flux vector F on the cell boundary; represents the Gaussian weight corresponding to the Gaussian node , t represents the time variable, Denotes the outer normal vector of the $L$-th edge. Denotes the value of the flux vector at the Gauss node . Denotes the length of the $L$-th edge. Denotes the -th Gauss node on the $L$-th edge. , which is obtained by calculating the Lax-Friedrichs numerical flux, specifically:

[0079] Among them, takes the upper bound of the eigenvalues of the Jacobian matrix in the direction. and are the high-order reconstruction values of the inner and outer approximations of the spatial vector at different Gauss points of the target element. For example, is: .

[0080] For , it can be obtained similarly.

[0081] According to the spatially discretized terms discretized in the previous step, the third-order Runge-Kutta method is used for time discretization. The specific formula is:

[0082] Among them, is the time step. and denote the mean vector of the physical quantities at the intermediate time level. denotes the mean vector of the physical quantities at the previous time level . denotes the mean vector of the physical quantities at the next time level . denotes the spatial discretization of the corresponding vector .

[0083] Repeat the above steps until the calculation terminates to complete the numerical calculation process. According to the calculation results, the numerical simulation results of the complex flow problem of the aero-engine are obtained, and the density distribution, pressure distribution, energy distribution, etc. of the calculation region are obtained through post-processing for analyzing and guiding problems related to complex flows.

[0084] The numerical simulation method for complex flow problems of aeroengines proposed in this application shows superiority in many aspects compared with existing methods. This method uses an innovative TVB-MINMOD function to design a discontinuity detection mechanism, which is not only simple and efficient in algorithm implementation, but also can accurately identify problem cells. Compared with traditional methods, this mechanism realizes rapid detection through the return value of the TVB-MINMOD function acting on the minimum circumscribed rectangle domain of triangular cells, and the computational amount is significantly reduced. In particular, the established general TVB parameter M calculation framework effectively overcomes the limitation of the parameter M in traditional methods depending on specific numerical problems.

[0085] In terms of numerical characteristics, this application applies the MR-WENO reconstruction method to the detected problem cells, completely retaining the advantages of this reconstruction method in shock capturing, numerical oscillation suppression, etc., while ensuring high-precision calculation and good convergence on a compact numerical template. More notably, this application adopts a hybrid reconstruction strategy: applying MR-WENO reconstruction to a small number of problem cells, while using efficient linear upwind reconstruction for the smooth cells that account for the main body of the computational domain. This hybrid reconstruction strategy avoids time-consuming operations such as unnecessary calculation of smooth factors and nonlinear weights, greatly improves the computational efficiency while ensuring the computational accuracy, and makes this method show significant performance advantages in practical engineering applications.

[0086] The innovative value of this application is not only reflected at the algorithm level, but more importantly, in the breakthrough of its engineering practicality. In typical applications such as the simulation of aeroengine combustion chambers and the external flow field analysis of supersonic aircraft, this application maintains the shock-capturing accuracy while significantly improving the computational efficiency compared with traditional methods. Its successful practice provides a new way that takes into account both accuracy and efficiency for the numerical simulation of complex flow problems, and also lays an important foundation for the development of subsequent intelligent adaptive numerical methods.

[0087] Based on the same inventive concept, the embodiment of this application also provides a numerical simulation system for complex flow problems of aeroengines. The implementation solution provided by this system for solving problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the numerical simulation system for complex flow problems of aeroengines provided below can refer to the limitations on the numerical simulation method for complex flow problems of aeroengines in the above text, and will not be elaborated here.

[0088] In an exemplary embodiment, a numerical simulation system for complex flow problems of aeroengines is provided, including the following modules.

[0089] A calculation region determination and division module, configured to determine the calculation region of the complex flow problem of the aeroengine and perform grid division on the calculation region.

[0090] An initialization module for initializing the fluid physical parameters of each grid according to the complex flow problems of aeroengines; the fluid physical parameters include the simulated flow field pressure, the velocity of the simulated flow field in the horizontal direction, the velocity of the simulated flow field in the vertical direction, and the total energy of the simulated flow field.

[0091] A detection module for detecting each grid by using the TVB-MINMOD algorithm to determine the grids containing complex flow characteristics and the grids not containing complex flow characteristics.

[0092] A first reconstruction and calculation module for reconstructing the initial fluid physical parameters of the grids containing complex flow characteristics by using the MR-WENO reconstruction method to obtain a reconstruction polynomial, and determining the fluid physical parameters in the calculation region based on the reconstruction polynomial.

[0093] A second reconstruction and calculation module for reconstructing the initial fluid physical parameters of the grids not containing complex flow characteristics by using the linear upwind reconstruction method to obtain a reconstruction polynomial, and determining the fluid physical parameters in the calculation region based on the reconstruction polynomial.

[0094] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a numerical simulation method for complex flow problems of aeroengines.

[0095] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0096] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0097] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0098] In each of the embodiments provided in this application, the database involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. In each of the embodiments provided in this application, the processor may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0099] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0100] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A numerical simulation method for complex flow problems of an aeroengine, characterized in that Including: Determine the computational domain of the complex flow problem of an aeroengine, and perform grid division on the computational domain; Initialize the fluid physical parameters of each grid according to the complex flow problem of the aeroengine; The fluid physical parameters include the simulated flow field pressure, the velocity of the simulated flow field in the horizontal direction, the velocity of the simulated flow field in the vertical direction, and the total energy of the simulated flow field; Use the TVB-MINMOD algorithm to detect each grid, and determine the grids containing complex flow characteristics and the grids not containing complex flow characteristics; For the grids containing complex flow characteristics, use the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters, obtain the reconstruction polynomial, and determine the fluid physical parameters in the computational domain based on the reconstruction polynomial; For the grids not containing complex flow characteristics, use the linear upwind reconstruction method to reconstruct the initial fluid physical parameters, obtain the reconstruction polynomial, and determine the fluid physical parameters in the computational domain based on the reconstruction polynomial.

2. The numerical simulation method for complex flow problems of an aero-engine according to claim 1, characterized in that, Using the TVB-MINMOD algorithm to detect each grid, and determining the grids containing complex flow characteristics and the grids not containing complex flow characteristics specifically includes: Construct a biquadratic polynomial on the fourth template using the density information of the target cell in the flow field; Calculate the second-order partial derivative of the biquadratic polynomial; Calculate the maximum value of all components of the second-order partial derivative on the rectangular domain of the target cell, and determine the maximum absolute value of the second-order partial derivative based on the maximum value of all components; Determine the MINMOD function corrected by the TVB algorithm based on the maximum absolute value of the second-order partial derivative, and calculate the correction term; Detect the grids containing complex flow characteristics and the grids not containing complex flow characteristics based on the correction term.

3. The numerical simulation method for complex flow problems of an aero-engine according to claim 2, wherein The expression of the MINMOD function corrected by the TVB algorithm is: ； ； ； ； Among them, is the return value of the MINMOD function corrected by the TVB algorithm, , are intermediate variables. When and have the same sign, . When and have different signs, . is the midpoint coordinate of the L-th side of the target element, is the difference between the value of the biquadratic polynomial at and the average density on the target element, is the density weighting of the target triangle and adjacent elements, M is a parameter of the TVB algorithm, represents the target element, is the absolute maximum value of the second-order partial derivative with respect to x, is the absolute maximum value of the mixed second-order partial derivative, is the absolute maximum value of the second-order partial derivative with respect to y.

4. The numerical simulation method for complex flow problems of an aero-engine according to claim 2, characterized in that Detecting the grids containing complex flow characteristics and the grids not containing complex flow characteristics based on the correction term specifically includes: When is satisfied, the grid is determined to be a grid containing complex flow features; otherwise, it is a grid not containing complex flow features. Among them, is a correction term, is the midpoint coordinate of the L-th side of the target element, is the difference between the value of the biquadratic polynomial at and the average density on the target element.

5. The numerical simulation method for complex flow problems of an aeroengine according to claim 1, wherein For the grids containing complex flow characteristics, using the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters specifically includes: For the grids containing complex flow characteristics, use the geometric information of the target cell in the flow field and the internal fluid information to construct multiple polynomials on multiple templates; Reconstruct the initial fluid physical parameters based on multiple polynomials to obtain the reconstruction polynomial.

6. The numerical simulation method for complex flow problems of an aero-engine according to claim 5, characterized in that, Constructing multiple polynomials on multiple templates specifically includes: Construct a zero-degree polynomial on the first template, a first-degree polynomial on the second template, a second-degree polynomial and a third-degree polynomial on the third template, and a fourth-degree polynomial on the fourth template.

7. The numerical simulation method for complex flow problems of an aeroengine according to claim 5, characterized in that After constructing multiple polynomials on multiple templates using the geometric information of the target cell in the flow field and the internal fluid information for the grids containing complex flow characteristics, it further includes: Calculate the smoothness factor on the target cell; Measure the smoothness of each polynomial based on the smoothness factor.

8. The numerical simulation method for complex flow problems of an aeroengine according to claim 1, wherein, The reconstruction polynomial obtained by using the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters for the grids containing complex flow characteristics is: ； For a grid that does not contain complex flow characteristics, the reconstructed polynomial obtained by reconstructing the initial fluid physical parameters using the linear upwind reconstruction method is as follows: ； Among them, Q(x, y) is the reconstruction polynomial; is the l th non-linear weight used when constructing the reconstruction polynomial Q(x, y), is the equivalent expression of, is the polynomial constructed on each template that satisfies the least squares condition, is the quartic polynomial constructed on the fourth template.

9. A numerical simulation system for complex flow problems of an aeroengine, characterized in that, Including: A calculation region determination and division module, configured to determine the calculation region of the complex flow problem of an aeroengine and perform grid division on the calculation region; An initialization module, configured to initialize the fluid physical parameters of each grid according to the complex flow problem of the aeroengine; The fluid physical parameters include the simulated flow field pressure, the velocity of the simulated flow field in the horizontal direction, the velocity of the simulated flow field in the vertical direction, and the total energy of the simulated flow field; A detection module, configured to detect each grid using the TVB-MINMOD algorithm to determine the grids containing complex flow characteristics and the grids not containing complex flow characteristics; A first reconstruction and calculation module, configured to, for the grids containing complex flow characteristics, reconstruct the initial fluid physical parameters using the MR-WENO reconstruction method to obtain a reconstructed polynomial, and determine the fluid physical parameters within the calculation region based on the reconstructed polynomial; A second reconstruction and calculation module, configured to, for the grids not containing complex flow characteristics, reconstruct the initial fluid physical parameters using the linear upwind reconstruction method to obtain a reconstructed polynomial, and determine the fluid physical parameters within the calculation region based on the reconstructed polynomial.

10. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the numerical simulation method for the complex flow problem of an aeroengine according to any one of claims 1-8.

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