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

Antony Jameson pioneered CFD-based aerodynamic design optimization in the late 1980s.
In addition to developing the fundamental theory, Jameson implemented that theory in …

Mesh generation and deformation are critical elements in gradient-based aerodynamic
shape optimization (ASO). Improperly generated or deformed meshes may contain bad …

As a high-fidelity approach to transition prediction, the coupled Reynolds-averaged Navier–
Stokes (RANS) and linear stability theory (LST)-based e N method is widely used in …

Low-Reynolds-number high-lift airfoil design is critical to the performance of unmanned
aerial vehicles (UAV). However, since laminar-to-turbulent transition dominates the …

Geometric filtering based on deep-learning models has been shown to be effective to shrink
the design space and improve the efficiency of aerodynamic shape optimization. However …

Aircraft design is a trade-off among different objectives and constraints, so multiple design
rounds are usually required. Aerodynamic shape optimization based on high-fidelity …

Unmanned aerial vehicles (UAV) on Mars operate in a thin atmosphere of carbon dioxide,
where the flow Reynolds number and local sound velocity are significantly reduced …

The inclusion of transition to turbulence effects in aerodynamic shape optimization makes it
possible to use it as a tool for the design of airframes with laminar flow. Modified Reynolds …

Adjoint-free methods are required in aerodynamic shape design optimization if an adjoint
solver is unavailable. However, their performance is highly criticized in high-dimensional …