A COMPARISON OF THE CONTINUOUS AND DISCRETE ADJOINT APPROACH TO AUTOMATIC AERODYNAMIC OPTIMIZATION

By Siva K. Nadarajah, Antony Jameson. 2000

384 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/df3ccf3c7b027f648551a1ffcd6b240f1a7d415e

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In Applied Mathematics

In Computational Mechanics

Citations in the corpus

A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design (2005, background): “This fact has been demonstrated in comparisons between different approaches to solving the adjoint equations (Nadarajah and Jameson, 2000).”
Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013, background): “the continuous approach in this paper, but ample literature can be found on the subject [3, 27, 37, 38, 50, 87], including discussions comparing the two approaches [23, 65, 72].”
Review of options for structural design sensitivity analysis. Part 1: Linear systems (2005, background): “It has been demonstrated, that, in general, for the same discretization continuum and discrete methods may still be diﬀerent [69,70].”
A continuous adjoint formulation for the computation of topological and surface sensitivities of ducted flows (2008, background): “[16] for a comparison), and we will not dwell on that any further.”
Effective adjoint approaches for computational fluid dynamics (2019, background): “The fact that the PDEs are first linearized and then discretized means that the discretized form of these equations is guaranteed to result in a fully consistent gradient only at the limit of an infinitely fine mesh [129].”
Expected drag minimization for aerodynamic design optimization based on aircraft operational data (2017, background): “The first adjoint-based investigations focused on drag minimization at a single flight condition [2, 33, 37, 39].”
Universal Differential Equations for Scientific Machine Learning (2020, background): “This trade-oﬀ between discrete and continuous adjoint approaches has been demonstrated on some equations as a trade-oﬀ between stability and computational eﬃciency [50, 51, 52, 53, 54, 55, 56, 57, 58].”
Adjoint formulation and constraint handling for gradient-based optimization of compositional reservoir flow (2014, result): “We note that these finding are consistent with those reported in [18] and [41] for general Runge–Kutta time stepping methods, in [26], where the discrete and continuous adjoint approaches were applied to”
Issues in active flow control: theory, control, simulation, and experiment (2004, methodology): “, [121,122]) and both approaches have been used in the literature.”
Optimization and Computational Fluid Dynamics (2008, methodology): “In previous works [43, 44, 74], the optimization problem was simple (single objective) and the Simplex method [28, 63] has been used to speed-up the computational procedure.”