Journal ArticleDOI
Efficient Mesh Generation and Deformation for Aerodynamic Shape Optimization
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In this article, a mesh generation and deformation are critical elements in gradient-based aerodynamic shape optimization (ASO), and they may contain bad-quality cells that degrade the quality of the resulting mesh.Abstract:
Mesh generation and deformation are critical elements in gradient-based aerodynamic shape optimization (ASO). Improperly generated or deformed meshes may contain bad-quality cells that degrade the ...read more
Citations
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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.
Journal ArticleDOI
Machine Learning in Aerodynamic Shape Optimization
TL;DR: In this article , the authors review the applications of ML in aerodynamic shape optimization (ASO) and provide a perspective on the state-of-the-art and future directions.
Journal ArticleDOI
Adjoint-based aerodynamic shape optimization including transition to turbulence effects
TL;DR: In this paper, a smooth version of the amplification factor transport (AFT) model, called AFT-S, was used to perform gradient-based aerodynamic shape optimization (ASO) of airfoils in subsonic and transonic flow conditions.
Journal ArticleDOI
Aerodynamic design optimization: Challenges and perspectives
TL;DR: Agarwal et al. as mentioned in this paper reviewed recent developments for each of these components and present open-source tools available for aerodynamic shape optimization and discussed some of the issues encountered, including comparing Euler and RANS results and design space multimodality.
Journal Article
Physical Design using Differentiable Learned Simulators
Kelsey R. Allen,Tatiana Lopez-Guevara,Kimberly L. Stachenfeld,A. Sánchez-González,Peter W. Battaglia,Jessica B. Hamrick,Tobias Pfaff +6 more
TL;DR: This work explores a simple, fast, and robust approach to inverse design which combines learned forward simulators based on graph neural networks with gradient-based design optimization, and suggests that despite some remaining challenges, machine learning-based simulators are maturing to the point where they can support general-purpose design optimization across a variety of domains.
References
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Journal ArticleDOI
Multidimensional binary search trees used for associative searching
TL;DR: The multidimensional binary search tree (or k-d tree) as a data structure for storage of information to be retrieved by associative searches is developed and it is shown to be quite efficient in its storage requirements.
Journal ArticleDOI
A tensorial approach to computational continuum mechanics using object-oriented techniques
TL;DR: The implementation of various types of turbulence modeling in a FOAM computational-fluid-dynamics code is discussed, and calculations performed on a standard test case, that of flow around a square prism, are presented.
Journal ArticleDOI
Aerodynamic design via control theory
TL;DR: The purpose of the last three sections is to demonstrate by representative examples that control theory can be used to formulate computationally feasible procedures for aerodynamic design, Provided, therefore, that one can afford the cost of a moderate number of flow solutions.
Proceedings ArticleDOI
Development of a Common Research Model for Applied CFD Validation Studies
TL;DR: In this paper, a transonic supercritical wing design is developed with aerodynamic characteristics that are well behaved and of high performance for configurations with and without the nacelle/pylon group.
Journal ArticleDOI
The complex-step derivative approximation
TL;DR: Improvements to the basic method are suggested that further increase its accuracy and robustness and unveil the connection to algorithmic differentiation theory.