Open Access
Aerodynamic shape optimization using the adjoint method
Ernani Volpe,Luis C.C. Santos,Carlos Augusto Constantino +2 more
- 01 Jan 2007
174
About: The article was published on 01 Jan 2007. and is currently open access.
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Citations
Closed-Loop Turbulence Control: Progress and Challenges
Steven L. Brunton,Bernd R. Noack +1 more
Abstract: Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift increase, mixing enhancement, and noise reduction. Current and future applications have epic proportion: cars, trucks, trains, airplanes, wind turbines, medical devices, combustion, chemical reactors, just to name a few. Methods to adaptively adjust open-loop parameters are continually improving toward shorter response times. However, control design for in-time response is challenged by strong nonlinearity, high-dimensionality, and time-delays. Recent advances in the field of model identification and system reduction, coupled with advances in control theory (robust, adaptive, and nonlinear) are driving significant progress in adaptive and in-time closed-loop control of fluid turbulence. In this review, we provide an overview of critical theoretical developments, highlighted by compelling experimental success stories. We also point to challenging open problems and propose potentially disruptive technologies of machine learning and compressive sensing.
656
Numerical sensitivity analysis for aerodynamic optimization: A survey of approaches
Jacques Peter,Richard P. Dwight +1 more
TL;DR: The historical development of these approaches are examined, the theoretical background of each major method and the associated numerical techniques required to make them practical in an engineering setting are described, and what is considered to be the state-of-the-art in these methods are described.
289
Discrete Adjoint-Based Approach for Optimization Problems on Three-Dimensional Unstructured Meshes
TL;DR: A comprehensive strategy for developing and implementing discrete adjoint methods for aerodynamic shape optimization problems is presented, and the adjoint of the complete optimization problem, including flow equations and mesh motion equations is constructed in a modular and verifiable fashion.
178
Direct shape optimization through deep reinforcement learning
TL;DR: In this article, an artificial neural network trained through DRL is able to generate optimal shapes on its own, without any prior knowledge and in a constrained time, and the optimization process itself is agnostic to details of the use case, and thus their work paves the way to new generic shape optimization strategies both in fluid mechanics, and more generally in any domain where a relevant reward function can be defined.
149
Rapid airfoil design optimization via neural networks-based parameterization and surrogate modeling
TL;DR: In this article, a B-spline-based generative adversarial network (GAN) is used to filter out unrealistic airfoils for a reduced design space that contains all relevant airfoil shapes.
140
References
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