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Data-driven modeling for unsteady aerodynamics and aeroelasticity

机译：不稳定空气动力学和空气弹性的数据驱动建模

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摘要

Aerodynamic modeling plays an important role in multiphysics and design problems, in addition to experiment and numerical simulation, due to its low-dimensional representation of unsteady aerodynamics. However, in the traditional study of aerodynamics, developing aerodynamic and flow models relies on classical theoretical (potential flow) and empirical investigation, which limits the accuracy and extensibility. Recently, with significant progress in high-fidelity computational fluid dynamic simulation and advanced experimental techniques, very large and diverse fluid data becomes available. This rapid growth of data leads to the development of datadriven aerodynamic and flow modeling. Through advanced mathematical methods from control theory, data science and machine learning, a lot of data-driven aerodynamic models have been proposed. These models are not only more accurate than theoretical models, but also require very low computational cost compared with numerical simulation. At the same time, they help to gain physical insights on flow mechanism, and have shown great potential in engineering applications like flow control, aeroelasticity and optimization. In this review paper, we introduce three typical data-driven methods, including system identification, feature extraction and data fusion. In particular, main approaches to improve the performance of data-driven models in accuracy, stability and generalization capability are reported. The efficacy of data-driven methods in modeling unsteady aerodynamics is described by several benchmark cases in fluid mechanics and aeroelasticity. Finally, future development and potential applications in related areas are concluded.
机译：空气动力学建模在多体验和设计问题中起着重要作用，除了实验和数值模拟，由于其不稳定空气动力学的低维表示。然而，在对空气动力学的传统研究中，开发空气动力学和流动模型依赖于古典理论（潜在流量）和经验研究，这限制了准确性和可扩展性。最近，在高保真计算流体动态仿真和先进的实验技术方面，具有非常大而多样化的流体数据。这种数据的快速增长导致了数据发挥的空气动力学和流动建模的发展。通过从控制理论，数据科学和机器学习的先进数学方法，已经提出了许多数据驱动的空气动力学模型。这些模型不仅比理论模型更准确，而且与数值模拟相比也需要非常低的计算成本。与此同时，他们有助于获得流动机制的物理见解，并在流量控制，空气弹性和优化等工程应用中表现出巨大的潜力。在本文中，我们介绍了三种典型的数据驱动方法，包括系统识别，特征提取和数据融合。特别地，报道了提高数据驱动模型的准确性，稳定性和泛化能力的主要方法。在流体力学和空气弹性中，几种基准案例描述了数据驱动方法在建模非定常空气动力学中的功效。最后，结束了未来的发展和相关领域的潜在应用。

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