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Data-based approach for wing shape design optimization

机译:基于数据的机翼形状设计优化方法

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Aircraft design is a trade-off among different objectives and constraints, so multiple design rounds are usually required. Aerodynamic shape optimization based on high-fidelity computational fluid dynamics is consequently expensive, especially for wing design in the transonic regime. To address the interactive demand in aircraft design, we present a data-based approach to real-time high-fidelity wing shape optimization. Accurate and fast data-based models are constructed to perform aerodynamic analyses in lieu of costly computational fluid dynamic simulations. For the versatility of the data-based models, 135,108 training data that cover wing samples of different aerodynamic shapes, flight speeds, and flight altitudes are used. The verification on 47,967 wings shows that mean relative errors of C-L, C-D, and C-M compared to computational fluid dynamic simulations are all within 0.4%. The models are further verified in multiple single-point, multi-point, and multi-objective wing design optimization problems. The optimized wings have similar shapes to those obtained by computational-fluid-dynamics-based optimization, and the differences in C-D are merely one similar to two counts. These results demonstrate the effectiveness of the data-based approach to fast and high-fidelity wing design. This work showcases a real-time high-fidelity optimization approach to high-dimensional nonlinear engineering problems using data-based models. (C) 2021 Elsevier Masson SAS. All rights reserved.
机译:飞机设计是不同的目标和约束之间的权衡,因此通常需要多个设计轮。基于高保真计算流体动力学的空气动力学形状优化是昂贵的,特别是对于跨音制度中的机翼设计。为了解决飞机设计中的互动需求,我们提出了一种基于数据的实时高保真翼形优化方法。构建了准确和快速的基于数据的模型,以便采用昂贵的计算流体动态模拟来进行空气动力学分析。对于基于数据的模型的多功能性,使用了覆盖不同空气动力学形状,飞行速度和飞行高度的机翼样本的135,108个训练数据。 47,967翼验证表明,与计算流体动态模拟相比,C-1,C-D和C-M的平均相对误差均在0.4%以内。在多个单点,多点和多目标机翼设计优化问题中进一步验证了模型。优化的翼具有与基于计算流体动力学的优化获得的那些类似的形状,并且C-D的差异仅仅是类似于两个计数的差异。这些结果证明了基于数据的快速和高保真翼设计的方法的有效性。这项工作展示了使用基于数据的模型来实现高维度非线性工程问题的实时高保真优化方法。 (c)2021 Elsevier Masson SAS。版权所有。

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