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A Review of Surrogate Modeling Techniques for Aerodynamic Analysis and Optimization: Current Limitations and Future Challenges in Industry

2018; Springer Nature (Netherlands); Linguagem: Inglês

10.1007/978-3-319-89988-6_2

2543-0203

Raul Yondo, Kamil Bobrowski, E. Andrés, Eusebio Valero,

Model Reduction and Neural Networks

Recent progresses in aircraft aerodynamics have witnessed the introduction of surrogate-based approaches in design analysis and optimization as an alternative to address the challenges posed by the complex objective functions, constraints, the high-dimensionality of the aerodynamic design space, the computational burden, etc. The present review revisits the most popular sampling strategies used in conducting physical and simulation-based experiments in aircraft aerodynamics. Moreover, a comprehensive and state-of-the art survey on numerous surrogate modeling techniques for aerodynamic analysis and surrogate-based optimization (SBO) is presented, with an emphasis on models selection and validation, sensitivity analysis, infill criterion, constraints handling, etc. Closing remarks foster on the drawbacks and challenges linked with SBO aircraft aerodynamic industrial applications, despite its increased interest among the academic community.