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 …

Aerodynamic optimization based on computational fluid dynamics (CFD) is a powerful
design approach because it significantly reduces the design time compared with the human …

The adjoint method is used for high-fidelity aerodynamic shape optimization and is an
efficient approach for computing the derivatives of a function of interest with respect to a …

Computational fluid dynamics through the solution of the Navier–Stokes equations with
turbulence models has become commonplace. However, simply solving these equations is …

Surrogate-based optimization has been used in aerodynamic shape optimization, but it has
been limited due to the curse of dimensionality. Although a large number of variables are …

Aerodynamic design optimization currently lacks robustness with respect to the starting
design and requires trial and error in the flow solver and optimization algorithm settings to …

The adjoint method is an efficient approach for computing derivatives that allow gradient-
based optimization to handle systems parameterized with a large number of design …

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 …

Natural laminar-flow wings are one of the most promising technologies for reducing fuel
burn and emissions for commercial aviation. However, there is a lack of tools for performing …