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Transfer Optimization for Efficient Aerodynamic Shape Design
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AI
DOI：10.3390/aerospace13050400.png)
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Abstract
En 中文
Constructing rapid aerodynamic shape optimization frameworks based on high-fidelity reduced-order models (ROMs) has become a mainstream solution for alleviating the excessive computational cost of full-order simulation-based design, especially for complex configurations with high-dimensional design spaces. In this study, we propose the concept of transfer optimization, where a low-fidelity, decoupled auxiliary submodule is used to guide the high-fidelity optimization of the full complex system. Building upon our previously proposed reduced-order-model based framework for efficient aerodynamic shape design, a transfer optimization framework is further developed to improve the efficiency of aerodynamic shape design for complex multi-component configurations. A novel auxiliary submodule method is introduced to address the "curse of dimensionality" in sampling over high-dimensional design parameter spaces. By reducing system complexity, this method significantly lowers the cost of individual samples. Based on the transfer optimization assumption, perturbation-based sampling around the low-fidelity solution overcomes the limitations of traditional data augmentation approaches. Moreover, the auxiliary submodule optimization results are used to construct a physically meaningful initial configuration, further accelerating convergence. The framework is validated on two transonic aerodynamic optimization test cases using the three-dimensional undeflected Common Research Model (uCRM) wing-body-tail configuration (with a wing aspect ratio of 9), with and without horizontal tail deflection. Results show that the proposed framework achieves accuracy comparable to full-order optimization while reducing computational cost by up to 69.8%. Compared to traditional ROM-based frameworks, efficiency is further improved by 18.5% and 24.1% in Case 1 and Case 2, respectively.
Keywords:
transfer optimization
active manifold
transonic
undeflected Common Research Model (uCRM)
Journal
IF:
2.2
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844
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7.3K