Aero-engine part configuration design optimization method and system
By using aero-engine component configuration design optimization methods, combined with topology and parameter optimization, rapid iterative design is achieved, solving the problems of insufficient optimization range and accuracy in existing technologies. This enables rapid and efficient generation of design configurations, reducing development costs and time.
Patent Information
- Application Number
- CN202411225177.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-02
- Publication Date
- 2026-03-10
AI Technical Summary
Existing commercial optimization software has poor optimization scope and effect in the design of aero-engine components. The topology optimization results cannot be directly applied in engineering. The parameter optimization results are limited by the initial model. Furthermore, the two-dimensional/three-dimensional simulation calculation time is long and the calculation accuracy of the surrogate model is low, which cannot meet the requirements of large-scale optimization in terms of computation time and result accuracy.
An optimization method for the configuration design of aero-engine components is adopted, including determining engineering design requirements, defining an initial optimization space, performing topology optimization calculations, smoothing high-order surface fitting, reconstructing engineering geometric entities, performing feature recognition and parameterization, achieving convergence determination through multidisciplinary parameter optimization, and improving computational accuracy by utilizing a supercomputer load balancing parallel optimization strategy.
It enables rapid iterative design of aero-engine component configurations, reduces development costs, accelerates development speed, improves computational and simulation accuracy, and obtains the final design configuration.
Smart Images
Figure CN121637648A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of aero-engine component configuration design optimization technology, specifically to an aero-engine component configuration design optimization method and system. Background Technology
[0002] Multidisciplinary design optimization is a method that fully explores and utilizes the coordination mechanism of interactions between various disciplines in an engineering system to design subsystems and achieve overall system optimization.
[0003] The design of complex and critical equipment, such as aero-engines, is a typical multidisciplinary and multi-objective design engineering project. Multidisciplinary optimization can take into account the coupling relationship between various disciplines and find the design scheme with the best comprehensive performance that cannot be obtained by previous methods. Currently, multidisciplinary topology design optimization of aero-engines relies entirely on commercial software with limited functions and cannot be independently controlled.
[0004] Currently, commercial optimization software only has topology optimization or parameter optimization functions. Moreover, the results of topology optimization cannot be directly applied in engineering, and the application scope of parameter optimization results is limited by the initial model. The two-dimensional / three-dimensional simulation calculation models called by multidisciplinary optimization are often limited by their long calculation time, and can only use surrogate models for equivalence. However, for nonlinear calculations / simulations that are common in engineering problems, surrogate models have low calculation accuracy. None of these three methods can meet the requirements of large-scale optimization for computation time and result accuracy.
[0005] Based on this, the inventors of this application propose a method and system for optimizing the configuration design of aero-engine components, in order to solve the above-mentioned technical problems. Summary of the Invention
[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of existing commercial optimization software in terms of its poor optimization range and effect, and to provide a method and system for optimizing the configuration design of aero-engine components.
[0007] The present invention solves the above-mentioned technical problems through the following technical solution:
[0008] This invention provides a method for optimizing the configuration design of aero-engine components, characterized by comprising:
[0009] Step 1: Determine the engineering design requirements, and define the initial optimization space based on the engineering design requirements as input;
[0010] Step 2: Determine the target requirement as the optimization objective, establish a topology optimization model and perform topology optimization calculations to obtain the initial optimized configuration;
[0011] Step 3: The outer contour of the initial optimized configuration is smoothed by high-order surface fitting, and the topology optimization result of the smoothing process is reconstructed to generate an engineered geometric entity formed by smooth transition and connection of regular geometric entities;
[0012] Step 4: Perform feature recognition on the engineered geometric entity, define feature control parameters, and achieve parameterization;
[0013] Step 5: Determine the convergence of the parameterization results. If converged, output the component configuration that meets the requirements; if not converged, return to perform secondary topology optimization.
[0014] According to an embodiment of the present invention, step 1 includes:
[0015] Step 11: Taking the engineering design requirements as input, decompose the engineering design requirements into one or more of the following: size requirements, weight requirements, load requirements, performance requirements, installation requirements, and economic requirements.
[0016] Step 12: Define the initial optimization space based on the decomposed requirements.
[0017] According to an embodiment of the present invention, in step 2,
[0018] After establishing the topology optimization model, a suitable topology optimization algorithm is selected to perform topology optimization calculations to obtain the initial optimized configuration.
[0019] According to one embodiment of the present invention, the topology algorithm is one of the variable density method, the level set method, and the intelligent optimization algorithm.
[0020] According to one embodiment of the present invention, in step 3,
[0021] By employing typical geometric feature fitting and feature transition algorithms from computer graphics, the topology optimization results of the smoothing process are reconstructed to generate engineered geometric entities formed by smooth transitions and connections of regular geometric entities.
[0022] According to one embodiment of the present invention, in step 4,
[0023] An intelligent geometric parameterization modeling method based on the mechanical characteristics of typical geometric structures is adopted to build multiple types of geometric data and a parameterization model based on deep learning for rapid implementation. Feature recognition is performed on the engineered geometric entity, feature control parameters are defined, and parameterization is realized.
[0024] According to an embodiment of the present invention, step 5 includes:
[0025] Step 51: Automatically divide the parameterized geometric solid model into a solid element finite element model;
[0026] Step 52: Perform multidisciplinary parameter optimization on the parameterized model;
[0027] Step 53: Determine the convergence of the parameterization results.
[0028] According to an embodiment of the present invention, in step 51,
[0029] The parameterized geometric entity model is automatically partitioned using one of the methods of sweeping or mapping.
[0030] According to one embodiment of the present invention, in step 52,
[0031] By employing a supercomputer load balancing parallel optimization strategy, computing resources are adaptively allocated based on computational load, enabling the integration of one-dimensional, two-dimensional, and three-dimensional computation and simulation in multidisciplinary parameter optimization, thereby improving computational and simulation accuracy.
[0032] According to one embodiment of the present invention, in step 5,
[0033] If the requirements are met, the optimized results are output and compared and verified with the multidisciplinary system physical model.
[0034] This invention also provides an aero-engine component configuration design optimization system, implemented using the aero-engine component configuration design optimization method described above, the system comprising:
[0035] Determine the module, determine the engineering design requirements, and define the initial optimization space based on the engineering design requirements as input;
[0036] The initialization module defines the target requirement as the optimization objective, establishes a topology optimization model, performs topology optimization calculations, and obtains the initial optimized configuration.
[0037] The processing module uses a high-order surface fitting method to smooth the outer contour of the initial optimized configuration, and reconstructs the topology optimization result of the smoothing process to generate an engineered geometric entity formed by the smooth transition and connection of regular geometric entities.
[0038] The parameterization module performs feature recognition on the engineered geometric entity, defines feature control parameters, and realizes parameterization.
[0039] The convergence module performs convergence determination on the parameterization results. If convergence is achieved, it outputs the component configuration that meets the requirements; otherwise, it returns to perform secondary topology optimization.
[0040] The positive and progressive effects of this invention are as follows:
[0041] The present invention provides a method for optimizing the configuration design of aero-engine components. By jointly optimizing multidisciplinary topology and parameters, it enables rapid iteration and achieves automatic iteration of topology optimization and parameter optimization within the same system, obtaining the final design configuration from scratch, thereby accelerating the development speed and reducing the development cost. Attached Figure Description
[0042] The above and other features, properties and advantages of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings and embodiments, wherein:
[0043] Figure 1 This is a flowchart of the method for optimizing the configuration design of aero-engine components according to the present invention;
[0044] Figure 2 This is a functional flowchart of the aero-engine component configuration design optimization method of the present invention;
[0045] Figure 3 This is a functional diagram of the aero-engine component configuration design optimization system of the present invention. Detailed Implementation
[0046] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.
[0047] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.
[0048] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values of the components and steps described in these embodiments do not limit the scope of this application. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following drawings denote similar items; therefore, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.
[0049] Please refer to Figure 1 and Figure 2 This invention proposes a method for optimizing the configuration design of aero-engine components, including:
[0050] S1. Determine the engineering design requirements and define the initial optimization space based on the engineering design requirements as input.
[0051] First, taking the engineering requirements as input, the engineering requirements are decomposed into size requirements, weight requirements, load requirements, performance requirements, installation requirements, economic requirements, etc., and then the initial optimization space is defined based on the decomposed requirements.
[0052] S2. Determine the target requirement as the optimization objective, establish a topology optimization model and perform topology optimization calculations to obtain the initial optimized configuration.
[0053] Specifically, a topology optimization model is established with key requirements as optimization objectives or constraints, and a suitable topology optimization algorithm is selected.
[0054] For example, methods such as variable density method, level set method, and intelligent optimization algorithm are used to perform topology optimization calculations and obtain the initial optimized configuration.
[0055] S3. The outer contour of the initial optimized configuration is smoothed by high-order surface fitting, and the topology optimization result of the smoothing process is reconstructed to generate an engineered geometric entity formed by the smooth transition and connection of regular geometric entities.
[0056] Specifically, a high-order surface fitting method is first used to smooth the relatively rough outer contour of the initial configuration obtained by topology optimization;
[0057] Then, using typical geometric feature fitting and feature transition algorithms from computer graphics, the topology optimization results of the smoothing process are reconstructed to generate an engineered geometric entity formed by the smooth transition connection of regular geometric entities.
[0058] S4. Perform feature recognition on engineered geometric entities, define feature control parameters, and achieve parameterization.
[0059] In other words, an intelligent geometric parametric modeling method based on the mechanical characteristics of typical geometric structures is adopted to build multiple types of geometric databases and a deep learning-based parametric model for rapid implementation. This method enables feature recognition of engineered geometric entities, definition of feature control parameters, and parametricization.
[0060] S5. Determine the convergence of the parameterization results. If converged, output the component configuration that meets the requirements; if not converged, return to perform secondary topology optimization.
[0061] That is, the parameterized geometric solid model is first automatically divided into a solid element finite element model by using methods such as sweeping and mapping, and then the parameterized model is optimized by multiple disciplines.
[0062] It should be noted that by employing a supercomputer load balancing parallel optimization strategy, computing resources are adaptively allocated based on computational load, enabling the integration of one-dimensional, two-dimensional, and three-dimensional computation and simulation in multidisciplinary parameter optimization, thereby improving computational and simulation accuracy. The optimization algorithm library includes not only classic optimization algorithms but also intelligent optimization algorithms.
[0063] Finally, the convergence of the parameter optimization results is determined. If convergence fails, a second topology optimization is performed. If the requirements are met, the optimization results are output and compared with the multidisciplinary system physical model for verification.
[0064] This invention enables automatic iteration of topology optimization and parameter optimization by performing topology result geometric reconstruction and parameterization of the reconstructed geometric model. This allows the final design configuration to be obtained from scratch, accelerating the development speed and reducing the development cost.
[0065] By employing a supercomputer load balancing parallel optimization strategy, computing resources are adaptively allocated based on computational load, enabling the integration of one-dimensional, two-dimensional, and three-dimensional computation and simulation in multidisciplinary parameter optimization, thereby significantly improving computational and simulation accuracy.
[0066] This invention also proposes an aero-engine component configuration design optimization system, implemented using the aforementioned aero-engine component configuration design optimization method. The system includes a determination module, an initialization module, a processing module, a parameterization module, and a convergence module.
[0067] The determination module is used to determine engineering design requirements and, using these requirements as input, defines the initial optimization space.
[0068] The initialization module is used to determine the target requirements as optimization objectives, establish a topology optimization model, perform topology optimization calculations, and obtain the initial optimized configuration.
[0069] The processing module is used to smooth the outer contour of the initial optimized configuration by high-order surface fitting, and reconstruct the topology optimization result of the smoothing process to generate an engineered geometric entity formed by the smooth transition and connection of regular geometric entities;
[0070] The parameterization module is used to perform feature recognition on engineered geometric entities, define feature control parameters, and achieve parameterization.
[0071] The convergence module is used to determine the convergence of the parameterization results. If convergence is achieved, the module outputs the component configuration that meets the requirements; otherwise, it returns to perform secondary topology optimization.
[0072] Reference Figure 3 The optimization system of this invention integrates a computational and simulation model library, a topology optimization module, a geometric reconstruction and reconstruction model parameterization module, a multidisciplinary parameter optimization module, and an optimization configuration verification and validation module.
[0073] Among them, the topology optimization module corresponds to the processing module mentioned above, the geometric reconstruction and reconstruction model parameterization module and the multidisciplinary parameter optimization module correspond to the parameterization module mentioned above, and the optimized configuration verification and validation module corresponds to the convergence module mentioned above.
[0074] It should be noted that the computation and simulation model library provides algorithmic support for the topology optimization module and the multidisciplinary parameter optimization module. The topology optimization post-processing submodule provides the topology optimization result model input to the topology optimization result geometric reconstruction submodule. The reconstructed geometric model mesh generation submodule provides the finite element model input to the multidisciplinary parameter optimization modeling submodule. The multidisciplinary parameter optimization post-processing submodule provides the optimized configuration to the optimized configuration processing submodule. The test specimens obtained from the optimized configuration processing submodule provide test specimens to the optimized configuration physical test verification submodule. The test data obtained from the optimized configuration physical test verification submodule provides input to the optimized configuration verification and validation module.
[0075] The topology optimization module, geometric reconstruction and reconstruction model parameterization module, and multidisciplinary parameter optimization module are iteratively executed.
[0076] The computational and simulation model library includes aerodynamic, heat transfer, combustion, and noise performance algorithm libraries, a two-dimensional strength calculation model library, a three-dimensional strength calculation model library, and a surrogate model library.
[0077] The topology optimization module includes a topology optimization modeling submodule, a topology optimization calculation submodule, and a topology optimization post-processing submodule. The upper-level submodule provides input to the lower-level submodule.
[0078] The geometric reconstruction and reconstruction model parameterization module includes a topology optimization result geometric reconstruction submodule, a reconstruction geometric model parameterization submodule, and a reconstruction geometric model mesh generation submodule. The upper-level submodule provides input to the lower-level submodule.
[0079] The multidisciplinary parameter optimization module includes a multidisciplinary parameter optimization modeling submodule, a multidisciplinary parameter optimization calculation submodule, and a multidisciplinary parameter optimization post-processing submodule. The upper-level submodule provides input for the lower-level submodule.
[0080] The optimization configuration verification and validation module includes a numerical simulation verification submodule for optimization results, an experimental evaluation submodule for optimization results, and an engineering design optimization scheme submodule. After being evaluated by the numerical simulation verification submodule for optimization results, the final optimized configuration is output by the engineering design optimization scheme submodule.
[0081] This invention enables rapid iteration through multidisciplinary topology and parameter joint design optimization. It achieves automatic iteration of topology optimization and parameter optimization within the same system, obtaining the final design configuration from scratch, thus accelerating development speed and reducing development costs.
[0082] Moreover, by integrating one-dimensional, two-dimensional, and three-dimensional design work in the optimization loop, the computational tools and simulation software used in actual engineering design can be directly integrated, supplemented by accurate surrogate models, to improve optimization accuracy and effectiveness.
[0083] In the description of the embodiments of this application, unless otherwise expressly specified and limited, the technical terms such as "installation", "connection", "joining", and "fixing" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can also refer to mechanical connections. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0084] This application uses specific terms to describe embodiments of the application. Terms such as "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of the application. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.
[0085] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any variations and modifications can be made by those skilled in the art without departing from the spirit and scope of the invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the invention, fall within the protection scope defined by the claims of the present invention.
Claims
1. A method of optimizing a configuration design of an aeroengine component, characterized in that, The method comprises the following steps: Step 1, determining engineering design requirements, and defining an initial optimization space as input of the engineering design requirements; Step 2, determining target requirements as optimization targets, establishing a topology optimization model and performing topology optimization calculation to obtain an initial optimization configuration; Step 3, smoothing the initial optimization configuration by using a high-order curved surface fitting method, and reconstructing the topology optimization result after smoothing to generate an engineering geometry entity formed by smooth transition connection of regular geometry entities; Step 4, identifying features of the engineering geometry entity, defining feature control parameters, and realizing parameterization; Step 5, performing convergence judgment on the parameterization result, and outputting a part configuration meeting the requirements if the convergence is met, or returning to perform secondary topology optimization if the convergence is not met.
2. The method of claim 1, wherein, The step 1 comprises: Step 11, inputting the engineering design requirements, and decomposing the engineering design requirements into one or more of size requirements, weight requirements, load requirements, performance requirements, installation requirements, and economic requirements; Step 12, defining the initial optimization space based on the decomposed requirements.
3. The method of claim 1, wherein, In the step 2, After establishing the topology optimization model, a suitable topology optimization algorithm is selected to perform topology optimization calculation to obtain the initial optimization configuration.
4. The method of claim 3, wherein, The topology optimization algorithm is one of a variable density method, a level set method, and an intelligent optimization algorithm.
5. The method of claim 1, wherein, In the step 3, The topology optimization result after smoothing is reconstructed to generate an engineering geometry entity formed by smooth transition connection of regular geometry entities by using a computer graphics typical geometric feature fitting and transition algorithm.
6. The method of claim 1, wherein, In the step 4, An intelligent geometric parameterization modeling method based on typical geometric structure mechanical characteristics is used to build a multi-class geometric data and a parameterization model based on deep learning to realize feature identification, definition of feature control parameters, and parameterization of the engineering geometry entity.
7. The method of claim 1, wherein, The step 5 comprises: Step 51, automatically dividing the parameterized geometry entity model into a volume element finite element model; Step 52, performing multidisciplinary parameter optimization on the parameterized model; Step 53, performing convergence judgment on the parameterization result.
8. The method of claim 7, wherein, In the step 51, The parameterized geometry entity model is automatically divided by using one of a sweeping method and a mapping method.
9. The method of claim 7, wherein, In the step 52, Through a supercomputer load balancing parallel optimization strategy, computing resources are adaptively allocated based on the calculation amount, one-dimensional, two-dimensional, and three-dimensional calculation and simulation integration are realized in multidisciplinary parameter optimization, and the calculation and simulation accuracy is improved.
10. The method of claim 7, wherein, In the step 5, If the requirements are met, the optimization result is output and compared with a multidisciplinary system physical model for checking.
11. An aeroengine component configuration design optimization system, comprising: The method is realized by using the aero-engine part configuration design optimization method in any one of claims 1-19, and the system comprises: A determination module for determining engineering design requirements, and defining an initial optimization space as input of the engineering design requirements; An initialization module for determining target requirements as optimization targets, establishing a topology optimization model and performing topology optimization calculation to obtain an initial optimization configuration; An initialization module for determining target requirements as optimization targets, establishing a topology optimization model and performing topology optimization calculation to obtain an initial optimization configuration; The processing module performs fairing processing on the initial optimized configuration outer contour in a high-order curved surface fitting manner, and reconstructs the topology optimization result after the fairing processing to generate an engineering geometric entity formed by smooth transition connection of regular geometric entities; The parameterization module performs feature recognition on the engineering geometric entity, defines feature control parameters, and realizes parameterization; The convergence module performs convergence judgment on the parameterization result. If the result converges, a part configuration meeting the requirements is output. If the result does not converge, secondary topology optimization is performed.