Data-driven modeling for unsteady aerodynamics and aeroelasticity

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 data-driven 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.