An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM

Abstract Advances in computing power have enabled computational fluid dynamics (CFD) to become a crucial tool in aerodynamic design. To facilitate CFD-based design, the combination of gradient-based optimization and the adjoint method for computing derivatives can be used to optimize designs with respect to a large number of design variables. Open field operation and manipulation (OpenFOAM) is an open source CFD package that is becoming increasingly popular, but it currently lacks an efficient infrastructure for constrained design optimization. To address this problem, we develop an optimization framework that consists of an efficient discrete adjoint implementation for computing derivatives and a Python interface to multiple numerical optimization packages. Our adjoint optimization framework has the following salient features: (1) The adjoint computation is efficient, with a computational cost that is similar to that of the primal flow solver and scales up to 10 million cells and 1024 CPU cores. (2) The adjoint derivatives are fully consistent with those generated by the flow solver with an average error of less than 0.1%. (3) The adjoint framework can handle optimization problems with more than 100 design variables and various geometric and physical constraints such as volume, thickness, curvature, and lift constraints. (4) The framework includes additional modules that are essential for successful design optimization: a geometry-parametrization module, a mesh-deformation algorithm, and an interface to numerical optimizations. To demonstrate our design-optimization framework, we optimize the ramp shape of a simple bluff geometry and analyze the flow in detail. We achieve 9.4% drag reduction, which is validated by wind tunnel experiments. Furthermore, we apply the framework to solve two more complex aerodynamic-shape-optimization applications: an unmanned aerial vehicle, and a car. For these two cases, the drag is reduced by 5.6% and 12.1%, respectively, which demonstrates that the proposed optimization framework functions as desired. Given these validated improvements, the developed techniques have the potential to be a useful tool in a wide range of engineering design applications, such as aircraft, cars, ships, and turbomachinery.

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