Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

With their development, machine learning models can be used instead of computational
fluid dynamics simulations to predict flow fields in aerodynamic optimization. However, it is …

Transonic buffet is a flow instability phenomenon that arises from the interaction between the
shock wave and the separated boundary layer. This flow phenomenon is considered to be …

Off-design constraints are essential in practical aerodynamic shape optimization. Physics-
based data-driven modeling has shown to be a feasible way to formulate generalizable off …

One strategy for predicting the state of nonlinear dynamical systems (typically of high
dimensionality) is global linearization, such as utilizing the Koopman analysis model to …

Aerodynamic shape optimization based on computational fluid dynamics still has a huge
demand for improvement in the optimization effect and efficiency when optimizing the …

Aerodynamic rules and knowledge are often obtained through theoretical research and
experiments, which have contributed greatly to aircraft design. For example, Korn's equation …

Transonic shock buffet is a significant self-excited shock oscillations and aerodynamic
instability phenomenon induced by shock-boundary layer interaction, which limits the flight …

Effective geometric parameterization is crucial in aerodynamic shape optimization for
enabling flexible surface deformation while maximizing design space coverage. This paper …

This study proposes a fast yet reliable optimization framework for the aerodynamic design of
transonic aircraft wings. Combining Computational Fluid Dynamics (CFD) and Machine …