A mechanism-driven reinforcement learning framework for shape optimization of airfoils

• URL: http://arxiv.org/abs/2403.04329v2
• Date: Mon, 27 May 2024 02:21:28 GMT
• Title: A mechanism-driven reinforcement learning framework for shape optimization of airfoils
• Authors: Jingfeng Wang, Guanghui Hu,
• Abstract summary: A novel mechanism-driven reinforcement learning framework is proposed for airfoil shape optimization. An efficient solver for steady equations is employed in the reinforcement learning method. A neural network architecture is designed based on an attention mechanism to make the learning process more sensitive to the minor change of the airfoil geometry.
• Score: 0.32885740436059047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a novel mechanism-driven reinforcement learning framework is proposed for airfoil shape optimization. To validate the framework, a reward function is designed and analyzed, from which the equivalence between the maximizing the cumulative reward and achieving the optimization objectives is guaranteed theoretically. To establish a quality exploration, and to obtain an accurate reward from the environment, an efficient solver for steady Euler equations is employed in the reinforcement learning method. The solver utilizes the B\'ezier curve to describe the shape of the airfoil, and a Newton-geometric multigrid method for the solution. In particular, a dual-weighted residual-based h-adaptive method is used for efficient calculation of target functional. To effectively streamline the airfoil shape during the deformation process, we introduce the Laplacian smoothing, and propose a B\'ezier fitting strategy, which not only remits mesh tangling but also guarantees a precise manipulation of the geometry. In addition, a neural network architecture is designed based on an attention mechanism to make the learning process more sensitive to the minor change of the airfoil geometry. Numerical experiments demonstrate that our framework can handle the optimization problem with hundreds of design variables. It is worth mentioning that, prior to this work, there are limited works combining such high-fidelity partial differential equatons framework with advanced reinforcement learning algorithms for design problems with such high dimensionality.

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