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Neural Networks-BasedAerodynamic DataModeling: AComprehensive Review Liwei HuJun ZhangYu Xiang2020 год

Neural Networks-Based Aerodynamic Data Modeling: A Comprehensive Review
review

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: This paper reviews studies on neural networks in aerodynamic data modeling. In this paper, we analyze the shortcomings of computational fluid dynamics (CFD) and traditional reduced-order models (ROMs). Subsequently, the history and fundamental methodologies of neural networks are introduced. Furthermore, we classify the neural networks based studies in aerodynamic data modeling and illustrate comparisons among them. These studies demonstrate that neural networks are effective approaches to aerodynamic data modeling. Finally, we identify three important trends for future studies in aerodynamic data modeling: a) the transformation method and physics informed models will be combined to solve high-dimensional partial differential equations; b) in the research area of steady aerodynamic response predictions, model-oriented studies and data-integration-oriented studies will become the future research directions, while in unsteady aerodynamic response predictions, radial basis function neural networks (RBFNNs) are the best tools for capturing the nonlinear characteristics of flow data, and convolutional neural networks (CNNs) are expected to replace long short-term memories (LSTMs) to capture the temporal characteristics of flow data; and c) in the field of steady or unsteady flow field reconstructions, the CNN-based conditional generative adversarial networks (cGANs) will be the best frameworks in which to discover the spatiotemporal distribution of flow field data.
Год издания: 2020
Авторы: Liwei Hu, Jun Zhang, Yu Xiang, Wenyong Wang
Издательство: Institute of Electrical and Electronics Engineers
Источник: IEEE Access
Ключевые слова: Model Reduction and Neural Networks, Fluid Dynamics and Turbulent Flows, Computational Fluid Dynamics and Aerodynamics
Другие ссылки: IEEE Access (PDF)
IEEE Access (HTML)
DOAJ (DOAJ: Directory of Open Access Journals) (HTML)