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Data-driven hierarchical collaborative optimization method with multi-fidelity modeling for aerodynamic optimization
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AI
DOI：10.1016/j.ast.2024.109206.png)
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Abstract
En 中文
The aerodynamic optimization of aircraft based on evolutionary algorithms generally requires thousands or even tens of thousands of iterations. Moreover, it faces challenges such as high -dimensional design variables and high computational costs, leading to low efficiency in algorithm search and poor optimization results. In order to enhance the efficiency of aerodynamic optimization, a data -driven collaborative optimization and multi -fidelity hierarchical modeling method is proposed. Firstly, a Clustering -Enhanced Teaching -LearningBased Optimization (CETLBO) algorithm is introduced to improve its exploration and exploitation capabilities. By employing a collaborative optimization strategy, a divide -and -conquer paradigm is utilized to efficiently solve high -dimensional, nonlinear, and complex engineering optimization problems. Secondly, a two -stage hierarchical aerodynamic optimization framework is designed to reduce computational costs by integrating data from different fidelity levels. Stage 1 adopts a data -driven approximate optimization method to provide prior knowledge for the high-fidelity optimization of stage 2. Finally, the proposed method is applied to the optimization design of supersonic wing shapes. Compared to traditional optimization design systems, it demonstrates high efficiency in high -dimensional aerodynamic optimization problems, yielding better optimization results.
Keywords:
Aerodynamic shape design
High-dimensional optimization
Cooperation and collaborative optimization
Data-driven optimization
Multi-fidelity hierarchical framework
Journal
IF:
5.8
Papers:
1.0W
Citations:
3.0W
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