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

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 …

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 …

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 …

Derivatives are key to numerous science, engineering, and machine learning applications.
While existing tools generate derivatives of programs in a single language, modern parallel …

Adjoint methods efficiently compute gradients for systems with many inputs and have been
widely used for large-scale gradient-based optimization in fluid mechanics. To ensure …

Natural laminar flow is one of the most promising ways to reduce the drag of future aircraft
configurations. However, there is a lack of efficient tools for performing shape optimization …

The adjoint method is an efficient approach for computing derivatives because its
computational cost is independent of the number of design variables. Using the derivatives …

A computational fluid dynamics code is differentiated using algorithmic differentiation (AD) in
both tangent and adjoint modes. The two novelties of the present approach are (1) the …

An automatic mesh generation method for optimal computational fluid dynamics (CFD)
analysis of a blade passage is developed using deep reinforcement learning (DRL). Unlike …