Equipment simulation design method and system based on adaptive line search grid fairing

Abstract

The invention relates to an equipment simulation design method and system based on adaptive line search grid fairing. The method comprises the steps of obtaining an aircraft grid file; performing ascending sorting on tetrahedral mesh units lower than a quality threshold in the aircraft mesh file, and performing local topological optimization on a sorting result to obtain an optimized mesh unit set; selecting a star-shaped structural domain comprising a unique vertex from the optimization grid unit set as a local optimization domain, and constructing a vertex optimization model by taking the maximization of the worst unit quality in the local optimization domain as a target; and solving the vertex optimization model to obtain an optimal moving direction and an optimal step length in the optimal moving direction, updating the vertex position to obtain a smoothed grid, optimizing the aircraft grid file according to the smoothed grid, and carrying out simulation design according to the optimized aircraft grid file. By adopting the method, the cycle of aircraft simulation design can be shortened.

Description

Technical Field

[0001] The present application relates to the field of simulation technology, and in particular to an equipment simulation design method and system based on adaptive line search grid smoothing. Background Art

[0002] With the rapid development of computer technology, computational fluid dynamics (CFD) numerical simulation technology has become an important means of performance analysis in the design process of equipment such as aircraft aerodynamic shape design, automobile engine thermal management, and ship navigation resistance analysis. Among them, every link of aircraft design is inseparable from the support of CFD technology. After the aircraft shape design is completed, the calculation domain is generally constructed according to the designed geometric model, and then a grid file for CFD numerical simulation software calculation is generated. Then the finite volume method or finite element method is used to realize the multi-physics field performance evaluation and iterative optimization of the design scheme. Among them, a core pre-processing step of CFD numerical simulation is to further improve the unit quality of the initial grid corresponding to the aircraft model through the grid vertex smoothing technology. The grid smoothing algorithm can effectively improve the unit orthogonality and size gradient characteristics of the initial grid by adjusting the spatial distribution of the nodes. This optimization can not only significantly improve the convergence speed of the solution of the Navier-Stokes equation, but also improve the calculation accuracy of the flow field parameters. CFD numerical simulation solves the control equations through discretization, completes the numerical solution of key physical quantities such as velocity field, temperature field, pressure field, etc. in each grid unit, and then derives the core indicators that characterize the performance of the aircraft, including but not limited to the lift-to-drag ratio characteristics of the aircraft, the heat conduction efficiency of the combustion chamber of the automobile engine, and the turbulent drag coefficient of the ship. Therefore, the grid smoothing technology not only affects the stability of the numerical calculation, but is also directly related to the accuracy of the prediction of the aerodynamic and thermodynamic performance of the aircraft design.

[0003] The mesh to be optimized is usually divided into structured mesh and unstructured mesh according to the topological connection mode of mesh nodes. Among them, the unstructured mesh is suitable for the boundary and local optimization of complex geometric bodies because the connection relationship of mesh nodes is randomly distributed and the local controllability of mesh units is good. It is widely used in various fields of numerical solution and is the main research target of current mesh smoothing and optimization technology.

[0004] The optimization of aircraft simulation-driven design solutions is highly dependent on the accuracy of CFD calculation results, and the generation of high-quality meshes by CFD pre-processing is the key prerequisite for quickly obtaining high-precision simulation results. For the complex geometric areas of some aircraft models, it is often difficult to directly generate high-quality mesh units by relying solely on conventional mesh generation methods (such as Delaunay triangulation, front propulsion method, etc.). Therefore, after the mesh is generated, it is particularly important to use mesh smoothing methods to optimize the local vertex positions.

[0005] In recent years, Laplacian smoothing algorithm has been widely used in tetrahedral mesh optimization. The algorithm defines the Laplacian operator for each node to determine its adjustment direction, and moves the node at a certain speed along this direction to achieve mesh optimization. However, for meshes with complex geometric shapes, the time complexity of the Laplacian smoothing algorithm is high, and the improvement of the tetrahedral mesh quality may not be guaranteed during the optimization process, and may even cause the mesh quality to deteriorate, thereby affecting the calculation accuracy and convergence of CFD simulation, and then affecting the simulation results of the aircraft design scheme. Another smoothing method is optimization-based mesh smoothing, which improves the mesh quality by maximizing the objective function of the mesh quality around the vertex. Compared with Laplacian smoothing, this method not only solves the problem of illegal unit generation, but also significantly improves the mesh quality. However, the traditional optimization-based smoothing algorithm has a high computational complexity in the process of searching for the direction solution, and is sensitive to the initial step size, and is prone to fall into the local optimum, resulting in too long optimization time and limited optimization effect. These problems not only affect the computational efficiency and convergence of CFD simulation, but also extend the cycle of aircraft simulation-driven design. Summary of the invention

[0006] Based on this, it is necessary to provide an equipment simulation design method and system based on adaptive line search grid smoothing to address the above technical problems.

[0007] An equipment simulation design method based on adaptive line search grid smoothing, the method comprising: Acquire a preset aircraft geometry model, and generate a corresponding aircraft mesh file according to the aircraft geometry model; the unit type of the mesh in the aircraft mesh file is a tetrahedral mesh; According to the tetrahedron distortion quality evaluation criterion, the tetrahedron mesh units in the aircraft mesh file that are below the quality threshold are sorted in ascending order, and local topology optimization is performed on the sorted results to obtain an optimized mesh unit set; Selecting a star-shaped structure domain including a unique vertex in the set of optimized grid cells as a local optimization domain, and constructing a vertex optimization model with the goal of maximizing the worst cell quality in the local optimization domain; The optimal moving direction satisfying the vertex optimization model is calculated by using a preset weighted average method based on fixed surface distance, the vertex optimization model is solved by using a preset adaptive line search method based on quadratic interpolation to obtain an optimal step length in the optimal moving direction, the vertex position is updated according to the optimal moving direction and the optimal step length to obtain a smoothed mesh, and the aircraft mesh file is optimized according to the smoothed mesh to obtain an optimized aircraft mesh file; By calculating the optimized aircraft grid file using CFD numerical simulation software, a numerical result of viscous flow near the boundary layer in the aircraft shape is obtained; The physical phenomena existing in the current aircraft shape near the boundary layer region are analyzed according to the numerical results, and when the aircraft shape reaches the required aerodynamic performance index, the current aircraft geometry is output.

[0008] In one of the embodiments, it also includes: obtaining sampling units according to units whose unit quality in the local optimization domain is lower than a sampling threshold, using a finite difference method based on a fixed surface distance to calculate the gradient of the sampling unit in each direction, and performing weighted averaging on the gradients in each direction to obtain the optimal moving direction.

[0009] In one of the embodiments, it also includes: obtaining the optimal step length of the first sequence in the current ascending initial step length sequence and the initial step length sequence, if the optimal step length of the first sequence is less than the maximum step length of the initial step length sequence, performing quadratic difference fitting according to the elements in the initial step length sequence to obtain a fitting function, searching for the optimal step length in the optimal moving direction in the fitting function to obtain a first candidate step length; obtaining the optimal step length of the second sequence in the current ascending step length sequence and the step length sequence, when the grid units corresponding to adjacent steps in the step sequence are different, solving the intersection using the local behavior of the unit quality evaluation function approximated by the first-order Taylor expansion to obtain the second candidate optimal step length; respectively obtaining the optimal step length of each candidate; Select a first interval and a second interval consisting of an optimal step length and adjacent step lengths, determine the optimal step length according to the size relationship of the difference between the endpoints of the first interval and the second interval, perform boundary processing on the interval where the optimal step length is located, and update the step length sequence according to the optimal step length corresponding to each candidate optimal step length; the larger value endpoint of the first interval is the candidate optimal step length, and the smaller value endpoint is the adjacent step length smaller than the optimal candidate step length; the larger value endpoint of the second interval is the adjacent step length larger than the optimal candidate step length, and the smaller value endpoint is the candidate optimal step length; use the updated step length sequence as the initial step length sequence for the next round of iteration, iteratively update the optimal step length, and stop the iteration when the iteration stop condition is met, and output the current optimal step length.

[0010] In one embodiment, it further includes: using a finite difference method based on a fixed surface distance to calculate the gradient of the sampling unit in each direction: ; in, is the unit quality assessment function, are vertex coordinates, is the disturbance value, , Vertex Distance from current Units The distance of the fixed surface, is the disturbance value control coefficient.

[0011] In one embodiment, the method further includes: if the optimal step length of the first sequence is equal to the maximum step length of the initial step length sequence, then extrapolating the initial step length sequence.

[0012] In one embodiment, it also includes: obtaining multiple sampling points from the initial step sequence, taking the step corresponding to the sampling point as the horizontal coordinate, and the unit mass corresponding to the step and the current optimal moving direction as the vertical coordinate, to obtain multiple discrete points, and performing quadratic difference fitting based on the multiple discrete points to obtain a fitting function.

[0013] In one embodiment, it also includes: when the coefficient of the quadratic term in the fitting function is greater than or equal to 0, solving the extreme point of the fitting function in the step length interval to obtain the first candidate optimal step length, or, when the coefficient of the quadratic term in the fitting function is less than 0, using the dichotomy method to solve to obtain the first candidate optimal step length.

[0014] In one embodiment, the method further includes: when the optimal step length of the second sequence is equal to the maximum step length of the step length sequence, extrapolating the step length sequence.

[0015] In one embodiment, it also includes: obtaining a first difference value according to the endpoint difference of the first interval, and obtaining a second difference value according to the endpoint difference of the second interval; if the first difference value is greater than the second difference value, determining the optimal step length according to the midpoint step length of the first interval; if the second difference value is greater than the first difference, determining the optimal step length according to the midpoint step length of the second interval.

[0016] An equipment simulation design system based on adaptive line search grid smoothing, the system comprising: A mesh file generation module, used to obtain a preset aircraft geometry model, and generate a corresponding aircraft mesh file according to the aircraft geometry model; the unit type of the mesh in the aircraft mesh file is a tetrahedral mesh; A mesh preprocessing module, used for sorting tetrahedral mesh units in the aircraft mesh file that are below a quality threshold in ascending order according to a tetrahedral distortion quality evaluation criterion, and performing local topological optimization on the sorting results to obtain an optimized mesh unit set; A model building module, used for selecting a star-shaped structure domain including a unique vertex in the set of optimized grid cells as a local optimization domain, and building a vertex optimization model with the goal of maximizing the worst cell quality in the local optimization domain; A vertex smoothing module, used for calculating an optimal moving direction satisfying a vertex optimization model by using a preset weighted average method based on fixed surface distance, solving the vertex optimization model by using a preset adaptive line search method based on quadratic interpolation to obtain an optimal step length in the optimal moving direction, updating the vertex position according to the optimal moving direction and the optimal step length to obtain a smoothed mesh, optimizing an aircraft mesh file according to the smoothed mesh, and obtaining an optimized aircraft mesh file; A numerical simulation module, used to calculate the optimized aircraft grid file by using CFD numerical simulation software to obtain numerical results of viscous flow near the boundary layer in the aircraft shape; The result output module is used to analyze the physical phenomena existing in the current aircraft shape near the boundary layer area according to the numerical results, and output the current aircraft geometry when the aircraft shape reaches the required aerodynamic performance index.

[0017] The above-mentioned equipment simulation design method and system based on adaptive line search grid smoothing can provide a basis for subsequent optimization by obtaining the aircraft geometry model to generate a tetrahedral grid file, and can improve the quality of the grid structure by sorting the low-quality grid units in ascending order and optimizing the local topology according to the tetrahedral distortion quality evaluation criteria, and can improve the quality of the grid structure, select the star-shaped structure domain to construct the vertex optimization model, and maximize the worst unit quality as the goal, which can improve the local grid quality in a targeted manner, and use the weighted average method based on the fixed surface distance to calculate the optimal moving direction, and solve the optimal step length based on the quadratic interpolation adaptive line search method, which can reduce the computational complexity, avoid sensitivity to the initial step length and fall into the local optimal problem, improve the solution accuracy and robustness, update the vertex position smoothing grid according to the optimal direction and step length, optimize the aircraft grid file, accelerate the convergence of CFD simulation, improve the calculation accuracy, use CFD numerical simulation software to calculate and analyze the results, and output the aircraft geometry according to the indicators, which can form a full-process simulation-driven design scheme, and improve the design efficiency and reliability. The embodiment of the present invention can improve the grid optimization speed and accuracy, thereby shortening the cycle of aircraft simulation design. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is an application scenario diagram of an equipment simulation design method based on adaptive line search grid smoothing in one embodiment; Figure 2 A schematic diagram of a grid optimization process in an embodiment; Figure 3 A schematic diagram of a flow chart of a grid smoothing algorithm based on adaptive line search in one embodiment; Figure 4 In one embodiment, the final direction is calculated by sampling the unit gradient component Schematic diagram; Figure 5 It is a structural block diagram of an equipment simulation design system based on adaptive line search grid smoothing in one embodiment. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] In one embodiment, Figure 1 As shown, a method for equipment simulation design based on adaptive line search grid smoothing is provided, comprising the following steps: Step 102: Obtain a preset aircraft geometry model, and generate a corresponding aircraft mesh file according to the aircraft geometry model.

[0021] The aircraft geometry model is designed using CAD software and imported into the mesh generation software to generate the corresponding aircraft mesh file. The unit type of the mesh in the aircraft mesh file is tetrahedral mesh. Figure 2 The mesh optimization process diagram shown in the figure starts with inputting the initial mesh, followed by mesh preprocessing, topology optimization, and adaptive line search smoothing algorithm processing based on optimization, and then determines whether the current worst unit quality is greater than or equal to the worst quality threshold. q If the condition is satisfied, the optimized grid is output and the process ends; if it is not satisfied, the algorithm iteration number is further judged to see whether it reaches the upper limit. n , if it is reached, the optimized mesh is output to end the process, if it is not reached, it returns to continue. In the early stage, the foundation is laid through preprocessing and topology optimization to improve the mesh quality. The adaptive line search smoothing algorithm is targeted for smoothing, improving the quality and avoiding illegal units. The iterative judgment mechanism avoids invalid cycles and controls the calculation cost. Overall, it can effectively improve the quality of the worst unit, reduce the calculation complexity, speed up convergence, have both high precision and strong robustness, speed up the convergence of CFD simulation, and improve the calculation accuracy.

[0022] Step 104 , sorting the tetrahedral mesh units in the aircraft mesh file that are below the quality threshold in ascending order according to the tetrahedral distortion quality evaluation criterion, and performing local topology optimization on the sorting results to obtain an optimized mesh unit set.

[0023] According to the tetrahedral distortion quality evaluation criteria The tetrahedral mesh elements in the initial mesh that are below the threshold are sorted in ascending order, and the elements with poor quality are optimized first, ensuring that subsequent optimization can efficiently focus on the elements that have the greatest impact on the overall mesh quality. The sorted optimized element set is , where the tetrahedron distortion quality evaluation criterion formula is defined as follows: ; in, Represents the volume of the current tetrahedral unit and is the radius of the circumscribed sphere of the current unit, The value range is between 0 and 1. When it is 1, it indicates an ideal tetrahedral unit.

[0024] Using local topology optimization to optimize the tetrahedral mesh The optimization was performed by using edge swapping, including traditional 2-3, 3-2 and 4-4 flipping and edge collapse, and performing combined recursive optimization. The optimized grid is recorded as Local topology optimization reconstructs the mesh topology connectivity through edge exchange and edge collapse operations, providing a better initial mesh structure configuration for smoothing optimization.

[0025] Step 106 , selecting a star-shaped structure domain including a unique vertex in the set of optimized mesh cells as a local optimization domain, and constructing a vertex optimization model with the goal of maximizing the worst cell quality in the local optimization domain.

[0026] Building a vertex optimization model includes: Assuming that Euclidean space Define the local optimization domain grid in ,in Each tetrahedral mesh element in the grid has an element quality evaluation function , obtained through the distortion quality evaluation function , local optimization domain grid (star structure domain grid) There is a unique vertex in ,and Quality It is defined as the minimum value of the quality of all units connected to the vertex, that is, the worst unit quality. Representation and Vertex For a set of adjacent tetrahedral units, the vertex quality analytical function formula is as follows: ; By moving the vertices Location Maximize Vertex Quality , that is, by maximizing the vertex quality analytical function , the local optimization domain grid can be calculated Midpoint The best location , the formula is as follows: ; in is a continuously differentiable function, and the composite function There will be discontinuous partial derivatives, so this problem can be regarded as a nonsmooth optimization problem, and the solution model is as follows: ; in, is the coordinate of the center vertex in the optimization domain, For each round of iteration Location The direction of movement, is the moving step length.

[0027] Step 108, using a preset weighted average method based on fixed surface distance to calculate the optimal moving direction that satisfies the vertex optimization model, using a preset quadratic interpolation-based adaptive line search method to solve the vertex optimization model to obtain the optimal step length in the optimal moving direction, updating the vertex position according to the optimal moving direction and the optimal step length to obtain a smoothed mesh, optimizing the aircraft mesh file according to the smoothed mesh, and obtaining an optimized aircraft mesh file.

[0028] Step 106 and step 108 are the grid smoothing algorithm based on adaptive line search proposed in this application. The algorithm flow is as follows: Figure 3 As shown, The mesh in is vertex smoothed, and the smoothed mesh is recorded as ,The algorithm includes building a vertex optimization model, using a weighted average method based on fixed ,face distance to calculate the search direction, using quadratic interpolation to fit the step ,interval, using adaptive line search to optimize the step size, and when the ,iteration stopping condition is met, the calculation of the step

[0029] This method uses a fixed surface distance weighted average to calculate the search direction. In terms of solving the step size, it has the advantages of low computational complexity and fast convergence speed. The proposed adaptive line search algorithm based on quadratic interpolation reduces the sensitivity of the initial step size, reduces the possibility of falling into the local optimum, improves the accuracy and robustness of the step size solution, and achieves a strong global optimization capability after combining local topology optimization techniques such as edge exchange and edge collapse.

[0030] Step 110, calculate the optimized aircraft grid file by using CFD numerical simulation software to obtain the numerical result of the viscous flow near the boundary layer in the aircraft shape.

[0031] Save the optimized mesh as a mesh file in cgns format. Use CFD software to read the aircraft mesh file obtained in the previous step.

[0032] Step 112, analyzing the physical phenomena existing in the current aircraft shape near the boundary layer region according to the numerical results, and outputting the current aircraft geometry when the aircraft shape reaches the required aerodynamic performance index.

[0033] Set initial conditions and boundary conditions according to requirements, and obtain the performance indicators of the aircraft design through corresponding processing functions. Evaluate the numerical simulation results of the aircraft design, and output the design if the numerical simulation indicators meet the design requirements. When the aircraft shape does not meet the required aerodynamic performance indicators, redesign the geometric shape of the aircraft.

[0034] In the above-mentioned equipment simulation design method based on adaptive line search grid smoothing, the tetrahedral grid file is generated by obtaining the aircraft geometric model, which can provide a basis for subsequent optimization. According to the tetrahedral distortion quality evaluation criterion, the low-quality grid units are sorted in ascending order and local topology optimization is performed, which can improve the quality of the grid structure. The vertex optimization model is constructed by selecting the star-shaped structure domain, with the goal of maximizing the worst unit quality, which can improve the local grid quality in a targeted manner. The weighted average method based on the fixed surface distance is used to calculate the optimal moving direction. The optimal step length is solved based on the quadratic interpolation adaptive line search method, which can reduce the computational complexity, avoid sensitivity to the initial step length and fall into the local optimal problem, improve the solution accuracy and robustness, update the vertex position smoothing grid according to the optimal direction and step length, optimize the aircraft grid file, accelerate the convergence of CFD simulation, improve the calculation accuracy, use CFD numerical simulation software to calculate and analyze the results, output the aircraft geometry according to the index, and form a full-process simulation-driven design scheme to improve the design efficiency and reliability. The embodiment of the present invention can improve the grid optimization speed and accuracy, thereby shortening the cycle of aircraft simulation design.

[0035] In one embodiment, the optimal moving direction that satisfies the vertex optimization model is calculated using a preset weighted average method based on a fixed surface distance, including: obtaining a sampling unit based on units whose unit quality in a local optimization domain is lower than a sampling threshold, using a finite difference method based on a fixed surface distance to calculate the gradient of the sampling unit in each direction, and performing weighted averaging on the gradients in each direction to obtain the optimal moving direction.

[0036] In this embodiment, the weighted average method based on the fixed plane distance is used to calculate the search direction, which includes: In order to balance the optimization quality and optimization efficiency of the optimization grid, a method with lower time cost is used to solve the search direction , considering that optimizing the worst cell quality may deteriorate the vertex The quality of other adjacent units is taken into account, so only the gradients of units whose quality is lower than the sampling threshold are sampled and calculated, that is, , thereby focusing on the direction with the fastest quality improvement while taking other poorer units into consideration.

[0037] Search direction To solve the problem, we first use the finite difference method based on fixed surface distance to calculate the points that meet the threshold condition and are close to the point Adjacent tetrahedral elements The gradient of The definition is as follows: .

[0038] like Figure 4 The final direction is calculated by sampling the unit gradient components as shown Schematic diagram, For point Distance from current Units The distance of the fixed surface, is the disturbance value control coefficient, gradient The calculation formula is defined as follows: ; By the gradient component Weighted average to get the final moving direction , the formula is as follows: ; in is the weight factor of the gradient component, which is used to adjust each gradient component Final moving direction Contribution N is the total number of sampled gradients.

[0039] In one embodiment, using a preset quadratic interpolation-based adaptive line search method to solve the vertex optimization model to obtain the optimal step length in the optimal moving direction includes: obtaining the current ascending initial step length sequence and the optimal step length of the first sequence in the initial step length sequence, if the optimal step length of the first sequence is less than the maximum step length of the initial step length sequence, performing quadratic difference fitting according to the elements in the initial step length sequence to obtain a fitting function, searching the fitting function for the optimal step length in the optimal moving direction to obtain a first candidate step length; obtaining the current ascending step length sequence and the optimal step length of the second sequence in the step length sequence, when the grid cells corresponding to adjacent step lengths in the step sequence are different, using a first-order Taylor expansion to approximate the local behavior of the unit quality evaluation function to obtain the optimal step length; Solve the intersection point to obtain the second candidate optimal step length; obtain the first interval and the second interval composed of each candidate optimal step length and the adjacent step length respectively, determine the optimal step length according to the size relationship of the endpoint difference between the first interval and the second interval, perform boundary processing on the interval where the optimal step length is located, and update the step length sequence according to the optimal step length corresponding to each candidate optimal step length; the larger value endpoint of the first interval is the candidate optimal step length, and the smaller value endpoint is the adjacent step length smaller than the optimal candidate step length; the larger value endpoint of the second interval is the adjacent step length larger than the optimal candidate step length, and the smaller value endpoint is the candidate optimal step length; use the updated step length sequence as the initial step length sequence for the next round of iteration, iteratively update the optimal step length, and stop the iteration when the iteration stop condition is met, and output the current optimal step length.

[0040] In this embodiment, the quadratic interpolation-based adaptive line search method includes: using quadratic interpolation to fit the step size interval; using adaptive line search to optimize the step size; when the iteration stop condition is met, ending the step size calculation and updating the vertex position according to the existing search direction and step size.

[0041] The step length intervals for fitting using quadratic interpolation include: First, sort the initial step lengths in ascending order and calculate the current step length sequence size and maximize The optimal step size of the value ;judge Is it equal to If "yes", then extrapolate the current step interval; if "no", then judge Is it interval, when this condition is met, the objective function is set to , indicating that along the search direction The mesh quality evaluation function needs to be maximized To locate the optimal step size. Through the quadratic interpolation function Approximating local behavior, the formula is as follows: ; The coefficient , , Determined by three sampling points , in , and represents the initial sampling step size, Represents the search direction The mesh quality evaluation function value. season By solving At the extreme point of the step interval , which can be quickly approached The maximum value of When , we can solve it by bisection method ,The advantage of this method is that the interpolation function is constructed through ,multiple sampling points, which reduces the dependence on a single step length initial ,value, and the quadratic function extreme point analysis avoids multiple ,iterations of the initial step length, which accelerates the solution convergence ,process, and the search interval is extrapolated or inwards according to the position of the extreme ,points to get closer to the global optimal solution.

[0042] Adaptive line search optimization step size includes: First, optimize the step length interval. Sort the step length sequence in ascending order and evaluate each Corresponding Filter out the current optimal step length , if the optimal step length If the step size is at the boundary of the step size interval, the step size interval search range is extrapolated to avoid missing potential extreme value areas. and The corresponding worst quality unit and If they are different, it means that the objective function has an extreme point in this interval. At this time, the first-order Taylor expansion is used to approximate Solve the intersection point by local behavior , the solution formula is as follows: ; Through this approximation, the candidate positions of extreme points can be quickly located and the search interval can be narrowed.

[0043] Screening for step length sequences. Fixed step length or proportional adjustment of step length can easily cause the algorithm to converge to a local solution too early. Through multi-step length screening, you can make fine adjustments with smaller step lengths when approaching the optimal solution to improve the accuracy of the solution. To screen the optimal step length obtained by the quadratic interpolation maximum and the first-order Taylor approximation in the local solution domain , first calculate Adjacent step length difference and , defined as follows: ; ; like This means that the objective function is in the interval The change is significant, and the interval search needs to be further refined, and the step size should be adjusted based on the trend. The midpoint step length is Otherwise, take Midpoint step length Finally, the step size interval is processed at the boundary, and the step size is avoided to be too large by introducing the convergence control parameter ε.

[0044] The iteration stopping conditions include: (1) The difference between the intervals where the optimal step length is located is less than the interval threshold ; (2) The number of step iterations is greater than the set number of iterations ; (3) The worst grid quality change between two iterations is less than the convergence threshold .

[0045] When one of the above three conditions is met, the step length calculation ends and the vertex position is updated according to the existing search direction and step length.

[0046] In one embodiment, using a finite difference method based on a fixed surface distance to calculate the gradient of the sampling unit in each direction includes: using a finite difference method based on a fixed surface distance to calculate the gradient of the sampling unit in each direction as follows: ; in, is the unit quality assessment function, are vertex coordinates, is the disturbance value, , Vertex Distance from current Units The distance of the fixed surface, is the disturbance value control coefficient.

[0047] In one embodiment, the method further comprises: if the optimal step length of the first sequence is equal to the maximum step length of the initial step length sequence, then extrapolating the initial step length sequence.

[0048] In one embodiment, performing quadratic difference fitting according to elements in the initial step sequence to obtain a fitting function includes: obtaining multiple sampling points from the initial step sequence, taking the step size corresponding to the sampling point as the horizontal coordinate, and the unit mass corresponding to the step size and the current optimal moving direction as the vertical coordinate to obtain multiple discrete points, and performing quadratic difference fitting according to the multiple discrete points to obtain a fitting function.

[0049] In one embodiment, searching for the optimal step length in the optimal moving direction in the fitting function to obtain the first candidate step length includes: when the coefficient of the quadratic term in the fitting function is greater than or equal to 0, solving the extreme point of the fitting function in the step length interval to obtain the first candidate optimal step length.

[0050] In one embodiment, searching for the optimal step length in the optimal moving direction in the fitting function to obtain the first candidate step length includes: when the coefficient of the quadratic term in the fitting function is less than 0, using a dichotomy method to solve and obtain the first candidate optimal step length.

[0051] In one embodiment, the method further comprises: when the optimal step length of the second sequence is equal to the maximum step length of the step length sequence, extrapolating the step length sequence.

[0052] In one embodiment, determining the optimal step length based on the size relationship between the endpoint differences of the first interval and the second interval includes: obtaining a first difference based on the endpoint difference of the first interval, and obtaining a second difference based on the endpoint difference of the second interval; if the first difference is greater than the second difference, determining the optimal step length based on the midpoint step length of the first interval; if the second difference is greater than the first difference, determining the optimal step length based on the midpoint step length of the second interval.

[0053] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0054] In one embodiment, Figure 5 , provides an equipment simulation design system based on adaptive line search grid smoothing, including: A mesh file generating module 502 is used to obtain a preset aircraft geometry model and generate a corresponding aircraft mesh file according to the aircraft geometry model; the unit type of the mesh in the aircraft mesh file is a tetrahedral mesh; The mesh preprocessing module 504 is used to sort the tetrahedral mesh units in the aircraft mesh file that are below the quality threshold in ascending order according to the tetrahedral distortion quality evaluation criterion, and perform local topology optimization on the sorting results to obtain an optimized mesh unit set; A model building module 506 is used to select a star-shaped structure domain including a unique vertex in the set of optimized grid cells as a local optimization domain, and to build a vertex optimization model with the goal of maximizing the worst cell quality in the local optimization domain; Vertex smoothing module 508, used to calculate the optimal moving direction that satisfies the vertex optimization model by using a preset weighted average method based on fixed surface distance, solve the vertex optimization model by using a preset adaptive line search method based on quadratic interpolation to obtain the optimal step length in the optimal moving direction, update the vertex position according to the optimal moving direction and the optimal step length, obtain a smoothed mesh, optimize the aircraft mesh file according to the smoothed mesh, and obtain an optimized aircraft mesh file; The numerical simulation module 510 is used to calculate the optimized aircraft grid file by using CFD numerical simulation software to obtain the numerical results of the viscous flow near the boundary layer in the aircraft shape; The result output module 512 is used to analyze the physical phenomena existing in the current aircraft shape near the boundary layer region according to the numerical results, and output the current aircraft geometry when the aircraft shape reaches the required aerodynamic performance index.

[0055] In one of the embodiments, it is also used to obtain sampling units based on units whose unit quality in the local optimization domain is lower than the sampling threshold, and the gradients of the sampling units in various directions are calculated using a finite difference method based on fixed surface distance, and the gradients in various directions are weighted averaged to obtain the optimal moving direction.

[0056] In one of the embodiments, it is also used to obtain the optimal step length of the first sequence in the current ascending initial step length sequence and the initial step length sequence; if the optimal step length of the first sequence is less than the maximum step length of the initial step length sequence, a quadratic difference fitting is performed according to the elements in the initial step length sequence to obtain a fitting function, and the optimal step length in the optimal moving direction is searched in the fitting function to obtain a first candidate step length; the optimal step length of the second sequence in the current ascending step length sequence and the step length sequence is obtained; when the grid units corresponding to adjacent step lengths in the step sequence are different, the local behavior of the unit quality evaluation function is approximated by a first-order Taylor expansion to solve the intersection point to obtain the second candidate optimal step length; and each candidate is obtained respectively. Select the first interval and the second interval consisting of the optimal step length and the adjacent step lengths, determine the optimal step length according to the size relationship of the endpoint difference between the first interval and the second interval, perform boundary processing on the interval where the optimal step length is located, and update the step length sequence according to the optimal step length corresponding to each candidate optimal step length; the larger value endpoint of the first interval is the candidate optimal step length, and the smaller value endpoint is the adjacent step length smaller than the optimal candidate step length; the larger value endpoint of the second interval is the adjacent step length larger than the optimal candidate step length, and the smaller value endpoint is the candidate optimal step length; use the updated step length sequence as the initial step length sequence for the next round of iteration, iteratively update the optimal step length, and stop the iteration when the iteration stop condition is met, and output the current optimal step length.

[0057] In one embodiment, the gradient of the sampling unit in each direction is calculated by using a finite difference method based on a fixed surface distance: ; in, is the unit quality assessment function, are vertex coordinates, is the disturbance value, , Vertex Distance from current Units The distance of the fixed surface, is the disturbance value control coefficient.

[0058] In one embodiment, if the optimal step length of the first sequence is equal to the maximum step length of the initial step length sequence, then the initial step length sequence is extrapolated.

[0059] In one embodiment, it is also used to obtain multiple sampling points from the initial step sequence, take the step corresponding to the sampling point as the horizontal coordinate, and the unit mass corresponding to the step and the current optimal moving direction as the vertical coordinate, to obtain multiple discrete points, and perform quadratic difference fitting based on the multiple discrete points to obtain a fitting function.

[0060] In one embodiment, it is also used to solve the extreme point of the fitting function in the step length interval to obtain the first candidate optimal step length when the coefficient of the quadratic term in the fitting function is greater than or equal to 0, or to use the dichotomy method to solve to obtain the first candidate optimal step length when the coefficient of the quadratic term in the fitting function is less than 0.

[0061] In one embodiment, it is also used to extrapolate the step length sequence when the optimal step length of the second sequence is equal to the maximum step length of the step length sequence.

[0062] In one embodiment, it is also used to obtain a first difference value based on the endpoint difference of the first interval, and to obtain a second difference value based on the endpoint difference of the second interval; if the first difference value is greater than the second difference value, the optimal step length is determined based on the midpoint step length of the first interval; if the second difference value is greater than the first difference, the optimal step length is determined based on the midpoint step length of the second interval.

[0063] For the specific definition of the equipment simulation design system based on adaptive line search grid smoothing, please refer to the definition of the equipment simulation design method based on adaptive line search grid smoothing in the above text, which will not be repeated here. Each module in the above-mentioned equipment simulation design system based on adaptive line search grid smoothing can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0064] The technical features of the above embodiments may 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.

[0065] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An equipment simulation design method based on adaptive line search grid smoothing, characterized in that: The method comprises: Acquire a preset aircraft geometry model, and generate a corresponding aircraft mesh file according to the aircraft geometry model; the unit type of the mesh in the aircraft mesh file is a tetrahedral mesh; According to the tetrahedron distortion quality evaluation criterion, the tetrahedron mesh units in the aircraft mesh file that are below the quality threshold are sorted in ascending order, and local topology optimization is performed on the sorted results to obtain an optimized mesh unit set; Selecting a star-shaped structure domain including a unique vertex in the set of optimized grid cells as a local optimization domain, and constructing a vertex optimization model with the goal of maximizing the worst cell quality in the local optimization domain; The optimal moving direction satisfying the vertex optimization model is calculated by using a preset weighted average method based on fixed surface distance, the vertex optimization model is solved by using a preset adaptive line search method based on quadratic interpolation to obtain an optimal step length in the optimal moving direction, the vertex position is updated according to the optimal moving direction and the optimal step length to obtain a smoothed mesh, and the aircraft mesh file is optimized according to the smoothed mesh to obtain an optimized aircraft mesh file; By calculating the optimized aircraft grid file using CFD numerical simulation software, a numerical result of viscous flow near the boundary layer in the aircraft shape is obtained; The physical phenomena existing in the current aircraft shape near the boundary layer region are analyzed according to the numerical results, and when the aircraft shape reaches the required aerodynamic performance index, the current aircraft geometry is output.

2. The method according to claim 1, characterized in that The optimal moving direction that satisfies the vertex optimization model is calculated by using a preset weighted average method based on fixed surface distance, including: The sampling unit is obtained according to the units whose unit quality is lower than the sampling threshold in the local optimization domain. The gradient of the sampling unit in each direction is calculated by the finite difference method based on the fixed surface distance. The optimal moving direction is obtained by weighted average of the gradients in each direction.

3. The method according to claim 1, characterized in that The method of using a preset quadratic interpolation-based adaptive line search method to solve the vertex optimization model to obtain an optimal step length in an optimal moving direction includes: Obtain the current initial step length sequence sorted in ascending order and the optimal step length of the first sequence in the initial step length sequence; if the optimal step length of the first sequence is less than the maximum step length of the initial step length sequence, perform quadratic difference fitting according to the elements in the initial step length sequence to obtain a fitting function; search the optimal step length in the optimal moving direction in the fitting function to obtain a first candidate step length; Obtaining the current step length sequence sorted in ascending order and the second sequence optimal step length in the step length sequence, and when the grid units corresponding to adjacent step lengths in the step length sequence are different, solving the intersection point by using the first-order Taylor expansion to approximate the local behavior of the unit quality evaluation function to obtain the second candidate optimal step length; The first interval and the second interval formed by each candidate optimal step length and the adjacent step length are respectively obtained, the optimal step length is determined according to the size relationship of the endpoint difference between the first interval and the second interval, and the boundary processing is performed on the interval where the optimal step length is located, and the step length sequence is updated according to the optimal step length corresponding to each candidate optimal step length; the larger value endpoint of the first interval is the candidate optimal step length, and the smaller value endpoint is the adjacent step length smaller than the optimal candidate step length; the larger value endpoint of the second interval is the adjacent step length larger than the optimal candidate step length, and the smaller value endpoint is the candidate optimal step length; The updated step length sequence is used as the initial step length sequence for the next round of iteration, and the optimal step length is iteratively updated until the iteration stop condition is met, then the iteration is stopped and the current optimal step length is output.

4. The method according to claim 2, characterized in that: The gradients of the sampling unit in various directions calculated using the finite difference method based on fixed surface distance include: The finite difference method based on fixed surface distance is used to calculate the gradient of the sampling unit in each direction: ； in, is the unit quality assessment function, are vertex coordinates, is the disturbance value, , Vertex Distance from current Units The distance of the fixed surface, is the disturbance value control coefficient.

5. The method according to claim 3, characterized in that: The method further comprises: If the optimal step length of the first sequence is equal to the maximum step length of the initial step length sequence, the initial step length sequence is extrapolated.

6. The method according to claim 3, characterized in that According to the elements in the initial step sequence, a quadratic difference fitting is performed to obtain the fitting function including: A plurality of sampling points are obtained from the initial step sequence, the step corresponding to the sampling point is used as the horizontal coordinate, and the unit mass corresponding to the step and the current optimal moving direction is used as the vertical coordinate to obtain a plurality of discrete points, and a quadratic difference fitting is performed according to the plurality of discrete points to obtain a fitting function.

7. The method according to claim 3, characterized in that Searching for the optimal step length in the optimal moving direction in the fitting function to obtain the first candidate step length includes: When the coefficient of the quadratic term in the fitting function is greater than or equal to 0, the extreme point of the fitting function in the step length interval is solved to obtain the first candidate optimal step length, or, when the coefficient of the quadratic term in the fitting function is less than 0, the dichotomy method is used to solve and obtain the first candidate optimal step length.

8. The method according to claim 3, characterized in that The method further comprises: When the optimal step length of the second sequence is equal to the maximum step length of the step length sequence, the step length sequence is extrapolated.

9. The method according to claim 3, characterized in that: Determining the optimal step length according to the magnitude relationship between the endpoints of the first interval and the second interval comprises: A first difference is obtained according to the endpoint difference of the first interval, and a second difference is obtained according to the endpoint difference of the second interval; If the first difference is greater than the second difference, determining the optimal step length according to the midpoint step length of the first interval; If the second difference is greater than the first difference, the optimal step length is determined according to the midpoint step length of the second interval.

10. An equipment simulation design system based on adaptive line search grid smoothing, characterized in that: The system comprises: A mesh file generation module, used for acquiring a preset aircraft geometric model, and generating a corresponding aircraft mesh file according to the aircraft geometric model; the unit type of the mesh in the aircraft mesh file is a tetrahedral mesh; A mesh preprocessing module, used for sorting tetrahedral mesh units in the aircraft mesh file that are below a quality threshold in ascending order according to a tetrahedral distortion quality evaluation criterion, and performing local topological optimization on the sorting results to obtain an optimized mesh unit set; A model building module, used for selecting a star-shaped structure domain including a unique vertex in the set of optimized grid cells as a local optimization domain, and building a vertex optimization model with the goal of maximizing the worst cell quality in the local optimization domain; A vertex smoothing module, used for calculating an optimal moving direction satisfying a vertex optimization model by using a preset weighted average method based on fixed surface distance, solving the vertex optimization model by using a preset adaptive line search method based on quadratic interpolation to obtain an optimal step length in the optimal moving direction, updating the vertex position according to the optimal moving direction and the optimal step length to obtain a smoothed mesh, optimizing an aircraft mesh file according to the smoothed mesh, and obtaining an optimized aircraft mesh file; A numerical simulation module, used to calculate the optimized aircraft grid file by using CFD numerical simulation software to obtain numerical results of viscous flow near the boundary layer in the aircraft shape; The result output module is used to analyze the physical phenomena existing in the current aircraft shape near the boundary layer area according to the numerical results, and output the current aircraft geometry when the aircraft shape reaches the required aerodynamic performance index.

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