A method, system and device for numerical simulation of complex flow problems in aircraft engines

Abstract

The present application discloses a method, system, and apparatus for numerically simulating complex flow problems in aircraft engines, relating to the field of aircraft engines. The method comprises: determining a computational region for the complex flow problem in an aircraft engine and meshing the computational region; initializing the fluid physical parameters of each mesh based on the complex flow problem in the aircraft engine; detecting each mesh using the TVB-MINMOD algorithm to determine meshes containing complex flow features and meshes not containing complex flow features; reconstructing the initial fluid physical parameters using the MR-WENO reconstruction method and the linear upwind reconstruction method for the meshes containing complex flow features and the meshes not containing complex flow features, respectively, and determining the fluid physical parameters within the computational region based on the reconstruction polynomials. The present application can effectively address the limitations of traditional methods in that parameters depend on specific problems, while maintaining shock wave capture accuracy and improving computational efficiency.

Description

Technical Field

[0001] The present application relates to the field of aero-engines, and in particular to a method, system, and apparatus for numerically simulating complex flow problems in aero-engines. Background Art

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

[0003] The purpose of this application is to provide a method, system and equipment for numerical simulation of complex flow problems in aircraft engines, which can solve the dilemma faced by traditional single numerical methods that it is difficult to strike a balance between accuracy and efficiency.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In the first aspect, the present application provides a numerical simulation method for complex flow problems of aircraft engines, including: determining a calculation area for the complex flow problem of an aircraft engine, and gridding the calculation area; initializing the fluid physical parameters of each grid according to the complex flow problem of the aircraft engine; the fluid physical parameters include simulated flow field pressure, simulated flow field velocity in the horizontal direction, simulated flow field velocity in the vertical direction and simulated flow field total energy; using the TVB-MINMOD algorithm to detect each grid, and determine the grid containing complex flow characteristics and the grid not containing complex flow characteristics; for the grid containing complex flow characteristics, the MR-WENO reconstruction method is used to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and the fluid physical parameters in the calculation area are determined based on the reconstruction polynomial; for the grid not containing complex flow characteristics, the linear upwind reconstruction method is used to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and the fluid physical parameters in the calculation area are determined based on the reconstruction polynomial.

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

[0007] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned numerical simulation method for complex flow problems of aircraft engines.

[0008] According to the specific embodiments provided in this application, the present application has the following technical effects: This application provides a method, system, and equipment for numerical simulation of complex flow problems in aircraft engines. Through the TVB (Total Variation Bounded)-MINMOD intelligent detection mechanism, it achieves accurate identification of problem cells (i.e., grids containing complex flow characteristics). Different reconstruction methods are used for grids in different situations. Among them, the MR-WENO reconstruction method inherits the advantages of multi-resolution analysis and provides high-resolution solutions in discontinuous areas; while the linear upwind reconstruction method ensures computational efficiency in smooth areas. This application effectively addresses the limitation of traditional methods in that parameters depend on specific problems, while maintaining shock wave capture accuracy while also improving computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0011] Figure 2 A flow chart of a method for numerically simulating complex flow problems in an aero-engine provided in one embodiment of the present application.

[0012] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0014] In the field of aerospace, efficient numerical simulation plays a key role in complex engineering problems such as shock wave-turbulence interaction simulation and multi-scale flow analysis of energy equipment. In response to this demand, this application proposes a numerical simulation method for complex flow problems of aero-engines. The core of this method is an adaptive discrimination mechanism constructed using an improved MINMOD function, which can accurately identify and automatically mark grids containing complex flow features 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. It not only inherits the advantages of traditional methods in computational accuracy and numerical stability, but also achieves significant improvements in computational efficiency and robustness, providing strong support for the numerical simulation of complex flow simulation of aero-engines, rocket engine combustion instability analysis, and aircraft aeroelastic coupling.

[0015] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0016] The numerical simulation method for complex flow problems in aircraft engines provided in the embodiments of the present application can be applied to 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 up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the complex flow problem of the aircraft engine to the server 104. After the server 104 receives the complex flow problem of the aircraft engine, the server 104 determines the calculation area of ​​the complex flow problem of the aircraft engine; initializes the fluid physical parameters of each grid according to the complex flow problem of the aircraft engine; uses the TVB-MINMOD algorithm to detect each grid, and uses the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters for the grid containing complex flow features; uses the linear upwind reconstruction method to reconstruct the initial fluid physical parameters for the grid that does not contain complex flow features; and determines the fluid physical parameters in the calculation area based on the reconstruction 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 complex flow problems of aircraft engines can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform numerical simulation on the complex flow problems of aircraft engines to obtain the fluid physical parameters in the calculation area, or the server 104 can obtain the complex flow problems of aircraft engines from the data storage system and perform numerical simulation on the complex flow problems of aircraft engines.

[0017] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0018] In an exemplary embodiment, Figure 2 As shown, a numerical simulation method for complex flow problems in aircraft engines is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps S1 to S5.

[0019] S1: Determine the computational domain for complex flow problems in aircraft engines and perform meshing on the computational domain.

[0020] In a specific embodiment, the simulation interval of the complex flow problem of the aircraft engine is determined to be ,in, is the left position of the calculation area, is the right side position of the calculation area, is the lower side position of the calculation area, is the upper position of the calculation area.

[0021] In addition, the calculation end time T_end must be given according to the results required for the complex flow problems of aircraft engines.

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

[0023] In a specific embodiment, the calculation parameters (ie, fluid physical parameters) are initialized based on an actual simulated complex flow problem of an aerospace engine.

[0024]

[0025] in, and Represents the simulated flow field in the horizontal direction and vertical direction speed, E represents the total energy, represents the pressure of the simulated flow field, is the inflow Mach number, represents the angle of attack, Indicates the density of the simulated flow field and gas medium parameters , is the internal energy of the gas in the simulated flow field.

[0026] S3: Use the TVB-MINMOD algorithm to detect each grid and determine the grids that contain complex flow features and the grids that do not contain complex flow features.

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

[0028] S31: Constructing a bivariate quartic polynomial on the fourth template using density information of the target unit in the flow field.

[0029] According to the fourth template Construct a quadratic polynomial in two variables ;in, is the target unit, - is the first layer neighboring unit of the target unit, - is the second layer neighboring unit of the target unit, - is the third-layer adjacent unit of the target unit, is the basis function The corresponding coefficients are, is the basis function, the label i corresponds to the degree of variable x, and j corresponds to the degree of variable y, which is used to distinguish different basis functions and coefficients.

[0030] The above reconstruction polynomial satisfies the following least squares conditions:

[0031]

[0032] in, is the set of serial numbers of adjacent units at each layer of the target unit, is the density mean on the target unit, For unit Density mean on ; basis function , Represents the area of ​​the target unit. The coordinates of the three vertices of the target unit can be expressed as , ;but , ; 、 They are the minimum values ​​of the horizontal coordinates and the minimum values ​​of the vertical coordinates of the three vertices of the target unit respectively.

[0033] S32: Calculate the second-order partial derivatives of a bivariate quartic polynomial.

[0034]

[0035]

[0036]

[0037] S33: Calculate the maximum value of all components of the second-order partial derivative over the rectangular domain of the target unit, and determine the maximum absolute value of the second-order partial derivative based on the maximum value of all components.

[0038] Consider including target units The minimum enclosing rectangle (i.e. rectangular domain) of is ,in, , , , In the rectangular domain Calculate the second-order partial derivatives on Since the maximum value of each term is non-negative, the second-order partial derivative can be estimated by simply adding the maximum values ​​of these terms. In the rectangular domain The maximum absolute value on A similar method can be used to obtain the second-order partial derivative and In the rectangular domain The maximum absolute value on and ,in, is the second derivative polynomial with respect to x. The maximum value of the item, is the first mixed second-order derivative polynomial The maximum value of the item, The second derivative polynomial of y is The maximum value of an item indicates the maximum value of its constituent items. .

[0039] S34: Determine a MINMOD function corrected by the TVB algorithm based on the maximum absolute value of the second-order partial derivative, and calculate a correction term.

[0040] Defining the target unit The midpoints of the three sides are , ; Adjacent units The center of gravity is , . Assume that the system of equations Solution Non-negative, calculate the MINMOD function modified by TVB:

[0041]

[0042] in, , , .

[0043] The return value of the MINMOD function modified by TVB. 、 is an intermediate variable. and When the signs of are the same, ,when and When the signs of are different, . is the midpoint coordinate of the Lth side of the target unit, is a quadratic polynomial in two variables The difference between the value at and the density mean on the target unit, is the density weight of the target triangle and the adjacent cells. M is the parameter of the TVB algorithm. represents the target unit, is the absolute maximum of the second-order partial derivative with respect to x, is the absolute maximum of the second-order partial derivative with respect to the mixture, is the absolute maximum of the second-order partial derivative with respect to y.

[0044] Calculate correction terms .

[0045]

[0046]

[0047]

[0048] in, The target triangle and its adjacent cells The density weighted is the weighting coefficient, For unit The mean density on .

[0049] S35: Detecting the grids containing complex flow features and the grids not containing complex flow features based on the correction item. , the grid is determined to be a grid containing complex flow features, otherwise it is determined to be a grid without complex flow features.

[0050] Specifically, the grids are divided into two categories in this way: .in, Representing meshes containing complex flow features such as shock waves or contact discontinuities, Represents a mesh that does not contain complex flow features.

[0051] S4: For grids containing complex flow features, the MR-WENO reconstruction method is used to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and the fluid physical parameters in the calculation area are determined based on the reconstruction polynomial.

[0052] Specifically, for a grid containing complex flow characteristics, the geometric information of the target unit in the flow field and the internal fluid information are used to construct multiple polynomials on multiple templates; based on the multiple polynomials, the initial fluid physical parameters are reconstructed to obtain reconstructed polynomials.

[0053] A zero-order polynomial is constructed on the first template, a linear polynomial is constructed on the second template, a quadratic polynomial and a cubic polynomial are constructed on the third template, and a quartic polynomial is constructed on the fourth template.

[0054] After constructing multiple polynomials on multiple templates, the method further includes: calculating a smoothing factor on the target unit; and measuring the smoothness of each polynomial based on the smoothing factor.

[0055] S5: For grids that do not contain complex flow features, the linear upwind reconstruction method is used to reconstruct the initial fluid physical parameters to obtain a reconstruction polynomial, and the fluid physical parameters in the calculation area are determined based on the reconstruction polynomial.

[0056] The specific process of reconstructing the polynomial in steps S4 and S5 is as follows:

[0057] (1) For users The marked grid is reconstructed using the MR-WENO reconstruction method to reconstruct the initial fluid physical parameters.

[0058] 1) Based on a series of nested central space templates, related reconstruction polynomials of unequal degrees are obtained.

[0059] Based on the first template Construct a zero-degree polynomial , used for numerical approximation with first-order accuracy. Based on the second template Construct a first-degree polynomial , used for numerical approximation with second-order accuracy. Based on the third template Construct a quadratic polynomial , for numerical approximation of third-order accuracy; if the third template Contains at least ten non-overlapping triangular elements, on which a cubic polynomial can be constructed , used for numerical approximation with fourth-order accuracy. Based on the fourth template Construct a quartic polynomial , for numerical approximation to fifth-order accuracy.

[0060] These reconstruction polynomials satisfy the following least squares conditions:

[0061]

[0062] in, is the vector composed of the mean values ​​of the fluid physical parameters on the target unit, For unit The vector composed of the mean values ​​of the fluid physical parameters on is the set of labels used when computing the k-th reconstruction polynomial.

[0063] 2) Give an equivalent expression of the above reconstruction polynomial:

[0064]

[0065] Among them, the linear weight are any positive numbers that sum to one, is the kth equivalent expression linear weights, is the kth equivalent expression A linear weight. , when k=2,3, , when k=4,5, .

[0066] 3) Calculate the smoothing factor of the polynomial , which represents the measure of the smoothness of the polynomial.

[0067]

[0068] Among them, the intermediate variable , , Express The number of partial derivatives with respect to x is, Express The number of times the partial derivative is taken about y, To find the partial derivative of a vector whose degree is equal to its magnitude, For x Secondary partial derivatives, To find out about y Secondary partial derivative.

[0069] for , construct three polynomials:

[0070]

[0071] It meets the following conditions:

[0072]

[0073] here It is a unit The center of gravity, . Indicates that the first polynomial is in the unit The value at the center of gravity, Display unit The vector composed of the mean values ​​of physical quantities on Display unit The mean vector on .

[0074] Depend on , and obtain the relevant smoothing index .

[0075] make:

[0076]

[0077] in, Both represent given initial values. is a polynomial Smoothing indicator. is a small positive number that prevents the denominator from becoming zero. So:

[0078] .

[0079] 4) Calculate nonlinear weights based on smoothing factors .

[0080]

[0081] in, . represents the k-th reconstruction polynomial nonlinear weights, represents the kth reconstruction polynomial linear weights, Indicates the number of nonlinear weights required to calculate reconstruction polynomials of different precisions.

[0082] 5) The final MR-WENO reconstruction polynomial is .

[0083] (2) For users The marked smooth unit uses an efficient linear upwind reconstruction method, and its reconstruction polynomial is .

[0084] The specific calculation process of the fluid physical parameters in step S4 and step S5 is as follows:

[0085] The semi-discrete terms of the space are calculated based on the reconstruction polynomials obtained in steps S4 and S5:

[0086]

[0087] in, is the spatial difference of the space vector U, The flux vectors representing the fluxes f and g, Target unit The border, represents the unit external normal vector of the target cell. The line integral will be discretized by the two-point Gaussian quadrature formula on each edge. For example, and For an edge with as its endpoint, its two Gaussian quadrature nodes are:

[0088]

[0089] The corresponding weight is Based on this formula, it is discretized into:

[0090]

[0091] here, represents the external normal vector on the cell boundary, represents the curve integral of the flux vector F on the cell boundary; Represents a Gaussian node The corresponding Gaussian weight, t represents the time variable, represents the external normal vector of the Lth edge, Represents the flux vector at the Gaussian node The value at . represents the length of the Lth edge, Indicates the Lth edge Gaussian nodes, , calculated by Lax-Friedrichs numerical flux, specifically:

[0092]

[0093] in, Take The upper bound of the eigenvalues ​​of the Jacobian matrix in the direction, and is a space vector The high-order reconstruction values ​​approximated inside and outside different Gaussian points of the target unit, e.g. for:

[0094] .

[0095] for Can be obtained similarly.

[0096] According to the spatial discrete terms discretized in the previous step, the third-order Runge-Kutta method is used for time discretization. The specific formula is:

[0097]

[0098] in, is the time step. and represents the mean vector of the physical quantity in the middle time layer, Indicates the previous time layer The mean vector of the physical quantity on , Indicates the next time layer The mean vector of the physical quantity on , Represents the corresponding vector spatial discretization.

[0099] Repeat the above steps until the calculation is terminated, completing the numerical calculation process. Based on the calculation results, the numerical simulation results of the complex flow problem of the aircraft engine are obtained. Post-processing obtains the density distribution, pressure distribution, and energy distribution of the calculation area, which are used to analyze and guide complex flow-related problems.

[0100] The numerical simulation method for complex flow problems in aerospace engines proposed in this application demonstrates several advantages over existing methods. This method utilizes an innovative TVB-MINMOD function to design a discontinuity detection mechanism, resulting in a simple and efficient algorithm implementation and accurate identification of problematic cells. Compared to traditional methods, this mechanism achieves rapid detection by applying the TVB-MINMOD function return value to the minimum enclosing rectangular domain of triangular cells, significantly reducing the computational effort. In particular, the established universal TVB parameter M calculation framework effectively overcomes the limitation of traditional methods in which parameter M depends on specific numerical problems.

[0101] In terms of numerical characteristics, this application fully retains the advantages of the MR-WENO reconstruction method in terms of shock wave capture, numerical oscillation suppression, etc. by applying the MR-WENO reconstruction method to the detected problem units, while ensuring high-precision calculations and good convergence on compact numerical templates. What is more worth mentioning is that this application adopts a hybrid reconstruction strategy: MR-WENO reconstruction is used for a small number of problem units, while efficient linear upwind reconstruction is used for smooth units that occupy the majority of the computational domain. This hybrid reconstruction strategy avoids unnecessary time-consuming operations such as calculating smoothing factors and nonlinear weights, greatly improving computational efficiency while ensuring computational accuracy, enabling this method to demonstrate significant performance advantages in actual engineering applications.

[0102] The innovative value of this application lies not only in its algorithmic approach but also in its breakthrough engineering practicality. In typical applications such as aerospace engine combustion chamber simulation and supersonic vehicle external flow field analysis, this application significantly improves computational efficiency compared to traditional methods while maintaining shock wave capture accuracy. Its successful implementation provides a new approach for numerical simulation of complex flow problems that balances accuracy and efficiency, and also lays an important foundation for the subsequent development of intelligent adaptive numerical methods.

[0103] Based on the same inventive concept, embodiments of the present application also provide a system for numerically simulating complex flow problems in aircraft engines. The implementation solutions provided by this system are similar to those described in the aforementioned methods. Therefore, the specific limitations of one or more embodiments of the system for numerically simulating complex flow problems in aircraft engines provided below can be found in the aforementioned limitations of the method for numerically simulating complex flow problems in aircraft engines, and will not be further elaborated here.

[0104] In an exemplary embodiment, a numerical simulation system for complex flow problems in an aero-engine is provided, including the following modules.

[0105] The calculation area determination and division module is used to determine the calculation area of ​​the complex flow problem of the aircraft engine and to perform grid division on the calculation area.

[0106] The initialization module is used to initialize the fluid physical parameters of each grid according to the complex flow problem of the aircraft engine; the fluid physical parameters include the simulated flow field pressure, the simulated flow field velocity in the horizontal direction, the simulated flow field velocity in the vertical direction and the simulated flow field total energy.

[0107] The detection module is used to detect each grid using the TVB-MINMOD algorithm to determine the grids containing complex flow features and the grids not containing complex flow features.

[0108] The first reconstruction and calculation module is used to reconstruct the initial fluid physical parameters of the grid containing complex flow characteristics 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.

[0109] The second reconstruction and calculation module is used to reconstruct the initial fluid physical parameters of the grid that does not contain complex flow features using a linear upwind reconstruction method to obtain a reconstruction polynomial, and determine the fluid physical parameters in the calculation area based on the reconstruction polynomial.

[0110] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-mentioned method embodiments. The computer device can be a server or a terminal, and its internal structure can be as shown in FIG. Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 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 an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a numerical simulation method for complex flow problems of an aero-engine is implemented.

[0111] Those skilled in the art will understand that Figure 3The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0112] 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 used 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 must comply with relevant regulations.

[0113] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the 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-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0114] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0115] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0116] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A numerical simulation method for complex flow problems in aircraft engines, characterized by: include: Determine the computational domain of the complex flow problem of the aero-engine and perform meshing on the computational domain; Initialize the fluid physical parameters of each grid based on the complex flow problem of the aero-engine; The fluid physical parameters include simulated flow field pressure, simulated flow field velocity in the horizontal direction, simulated flow field velocity in the vertical direction and simulated flow field total energy; The TVB-MINMOD algorithm is used to detect each grid to determine the grid containing complex flow features and the grid not containing complex flow features; specifically, the method comprises: constructing a binary quartic polynomial on a fourth template using the density information of the target unit in the flow field; calculating the second-order partial derivative of the binary quartic polynomial; calculating the maximum value of all components of the second-order partial derivative on the rectangular domain of the target unit, and determining the maximum absolute value of the second-order partial derivative based on the maximum value of all components; determining the MINMOD function corrected by the TVB algorithm based on the maximum absolute value of the second-order partial derivative, and calculating the correction term; detecting the grid containing complex flow features and the grid not containing complex flow features based on the correction term; detecting the grid containing complex flow features and the grid not containing complex flow features based on the correction term, specifically comprising: when When , the grid is determined to be a grid containing complex flow features, otherwise it is determined to be a grid without complex flow features; is the correction term, is the midpoint coordinate of the Lth side of the target unit, is a quadratic polynomial in two variables The difference between the value at and the density mean on the target unit; the expression of the MINMOD function corrected by the TVB algorithm is: ； ； ； ； in, is the return value of the MINMOD function corrected using the TVB algorithm. 、 is an intermediate variable, when and When the signs of are the same, ,when and When the signs of are different, , is the midpoint coordinate of the Lth side of the target unit, is a quadratic polynomial in two variables The difference between the value at and the density mean on the target unit, is the density weight of the target triangle and the adjacent cells, M is the parameter of the TVB algorithm, represents the target unit, is the absolute maximum of the second-order partial derivative with respect to x, is the absolute maximum of the second-order partial derivative with respect to the mixture, is the absolute maximum of the second-order partial derivative with respect to y; calculate the correction term : ； ； ； in, The target triangle and its adjacent cells The density weighted is a quadratic polynomial in two variables The value at is the density mean on the target unit, is the weighting coefficient, For unit The density mean on ; For grids containing complex flow characteristics, the MR-WENO reconstruction method is used to reconstruct the initial fluid physical parameters to obtain the reconstruction polynomials, and the fluid physical parameters in the calculation area are determined based on the reconstruction polynomials; For grids that do not contain complex flow features, the linear upwind reconstruction method is used to reconstruct the initial fluid physical parameters to obtain the reconstruction polynomial, and the fluid physical parameters in the calculation area are determined based on the reconstruction polynomial.

2. The numerical simulation method for complex flow problems in aircraft engines according to claim 1, characterized in that: For grids containing complex flow characteristics, the MR-WENO reconstruction method is used to reconstruct the initial fluid physical parameters, including: For grids containing complex flow features, multiple polynomials are constructed on multiple templates using the geometric information of the target cells in the flow field and the internal fluid information; The initial fluid physical parameters are reconstructed based on a plurality of polynomials to obtain a reconstructed polynomial.

3. The numerical simulation method for complex flow problems in aircraft engines according to claim 2, characterized in that: Construct multiple polynomials on multiple templates, including: A zero-degree polynomial is constructed on the first template, a linear polynomial is constructed on the second template, a quadratic polynomial and a cubic polynomial are constructed on the third template, and a quartic polynomial is constructed on the fourth template.

4. The numerical simulation method for complex flow problems in aircraft engines according to claim 2, characterized in that: For grids containing complex flow features, the geometric information of the target cells in the flow field and the internal fluid information are used to construct multiple polynomials on multiple templates. The following also applies: Calculating a smoothing factor on the target cell; The smoothness of each polynomial is measured based on the smoothness factor.

5. The numerical simulation method for complex flow problems in aircraft engines according to claim 1, characterized in that: For grids containing complex flow characteristics, the MR-WENO reconstruction method is used to reconstruct the initial fluid physical parameters and the resulting reconstruction polynomial is: ； For grids that do not contain complex flow features, the linear upwind reconstruction method is used to reconstruct the initial fluid physical parameters and the resulting reconstruction polynomial is: ； Among them, Q(x,y) is the reconstruction polynomial; The first used when constructing the reconstruction polynomial Q(x,y) l nonlinear weights, for The equivalent expression of The polynomials constructed on each template that satisfy the least squares condition, is a quartic polynomial constructed on the fourth template.

6. A numerical simulation system for complex flow problems in aircraft engines, characterized by: include: A calculation region determination and division module is used to determine the calculation region of the complex flow problem of the aircraft engine and to perform grid division on the calculation region; Initialization module, used to initialize the fluid physical parameters of each grid according to the complex flow problem of the aircraft engine; The fluid physical parameters include simulated flow field pressure, simulated flow field velocity in the horizontal direction, simulated flow field velocity in the vertical direction and simulated flow field total energy; A detection module is used to detect each grid using the TVB-MINMOD algorithm to determine the grids that contain complex flow features and the grids that do not contain complex flow features; Specifically, it includes: constructing a binary quartic polynomial on a fourth template using density information of a target unit in a flow field; calculating the second-order partial derivative of the binary quartic polynomial; calculating the maximum value of all components of the second-order partial derivative on a rectangular domain of the target unit, and determining the maximum absolute value of the second-order partial derivative based on the maximum value of all components; determining a MINMOD function corrected by a TVB algorithm based on the maximum absolute value of the second-order partial derivative, and calculating a correction term; detecting a grid containing complex flow features and a grid not containing complex flow features based on the correction term; detecting a grid containing complex flow features and a grid not containing complex flow features based on the correction term, specifically including: when When , the grid is determined to be a grid containing complex flow features, otherwise it is determined to be a grid without complex flow features; is the correction term, is the midpoint coordinate of the Lth side of the target unit, is a quadratic polynomial in two variables The difference between the value at and the density mean on the target unit; the expression of the MINMOD function corrected by the TVB algorithm is: ； ； ； ； in, is the return value of the MINMOD function corrected using the TVB algorithm. 、 is an intermediate variable, when and When the signs of are the same, ,when and When the signs of are different, , is the midpoint coordinate of the Lth side of the target unit, is a quadratic polynomial in two variables The difference between the value at and the density mean on the target unit, is the density weight of the target triangle and the adjacent cells, M is the parameter of the TVB algorithm, represents the target unit, is the absolute maximum of the second-order partial derivative with respect to x, is the absolute maximum of the second-order partial derivative with respect to the mixture, is the absolute maximum of the second-order partial derivative with respect to y; calculate the correction term : ； ； ； in, The target triangle and its adjacent cells The density weighted is a quadratic polynomial in two variables The value at is the weighting coefficient, For unit The density mean on , is the density mean on the target unit; The first reconstruction and calculation module is used to reconstruct the initial fluid physical parameters of the grid containing complex flow characteristics 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; The second reconstruction and calculation module is used to reconstruct the initial fluid physical parameters of the grid that does not contain complex flow features using a linear upwind reconstruction method to obtain a reconstruction polynomial, and determine the fluid physical parameters in the calculation area based on the reconstruction polynomial.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the numerical simulation method for complex flow problems of an aerospace engine according to any one of claims 1 to 5.

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