空气动力学
翼型
计算机科学
插值(计算机图形学)
深度学习
人工神经网络
跨音速
不确定度量化
航空航天工程
人工智能
机器学习
工程类
运动(物理)
作者
Christian Sabater,Philipp Stürmer,Philipp Bekemeyer
出处
期刊:AIAA Journal
[American Institute of Aeronautics and Astronautics]
日期:2022-06-03
卷期号:60 (9): 5249-5261
被引量:36
摘要
The numerical analysis of aerodynamic components based on the Reynolds–averaged Navier–Stokes equations has become critical for the design of transport aircraft but still entails large computational cost. Simulating a multitude of different flow conditions with high-fidelity methods as required for loads analysis or aerodynamic shape optimization is still prohibitive. Within the past few years, the application of machine learning methods has been proposed as a potential way to overcome these shortcomings. This is leading toward a new data-driven paradigm for the modeling of physical problems. The objective of this paper is the development of a deep-learning methodology for the prediction of aircraft surface pressure distributions and the rigorous comparison with existing state-of-the-art nonintrusive reduced-order models. Bayesian optimization techniques are employed to efficiently determine optimal hyperparameters for all deep neural networks. Three data-driven methods which are Gaussian processes, proper orthogonal decomposition combined with an interpolation technique, and deep learning are investigated. The results are compared for a two-dimensional airfoil case and the NASA Common Research Model transport aircraft as a relevant three-dimensional case. Results show that all methods are able to properly predict the surface pressure distribution at subsonic conditions. In transonic flow, when shock waves and separation lead to nonlinearities, deep-learning methods outperform the others by also capturing the shock wave location and strength accurately.
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