Multigrid Solution of the Discrete Adjoint for Optimization Problems on Unstructured Meshes

By Dimitri J. Mavriplis. 2006

67 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/420fcc22c5a410242b7d54ad3726b370b8ff0bb1

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In Computational Mechanics

Citations in the corpus

Numerical sensitivity analysis for aerodynamic optimization: A survey of approaches (2010, methodology): “This relation is useful in verifying the correctness of the adjoint FPI and discretization [118,98].”
Optimum Shape Design for Unsteady Flows with Time-Accurate Continuous and Discrete Adjoint Methods (2007, methodology): “…have applied these advanced design algorithms, particularly the adjoint method, to numerous problems, ranging from the design of two-dimensional airfoils to full aircraft configurations to decrease drag, increase range, and reduce sonic boom [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11].”
Adjoint-Based Sensitivity Formulation for Fully Coupled Unsteady Aeroelasticity Problems (2009, methodology): “Deformation of the mesh is achieved through the linear tension spring analogy [4,21] which approximates the mesh as a network of interconnected springs.”
Effect of Approximations of the Discrete Adjoint on Gradient-Based Optimization (2006, methodology): “For example, by treating the coefficient of artificial viscosity in the Jameson–Schmidt–Turkel (JST) scheme [10] as constant it is possible to reduce the construction of the adjoint residual to two sweeps over the faces of the grid [11].”
Effective adjoint approaches for computational fluid dynamics (2019, background): “Xu et al. [26] reported a Jacobian-trained Krylov-implicit Runge–Kutta method to stabilize the flow and adjoint solutions and implemented it in an open source solver STAMPS [27].”