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

Many problems in real life can be seen as Expensive Optimization Problems (EOPs).
Compared with traditional optimization problems, the evaluation cost of candidate solutions …

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

Summary pyOptSparse is an optimization framework designed for constrained nonlinear
optimization of large sparse problems and provides a unified interface for various gradient …

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 …

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

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

Flutter is a dynamic aeroelastic instability driven by the interaction of inertial, elastic, and
aerodynamic forces. It is an undesirable phenomenon in aircraft because it causes divergent …

The favorable convergence rates of Newton–Krylov-based solution algorithms have
increased their popularity for computational fluid dynamics applications. Unfortunately, these …

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 …