Numerical sensitivity analysis for aerodynamic optimization: A survey of approaches

By Jacques Peter, Richard P. Dwight. 2010

289 citations recorded by the source corpus.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4547a2fb4a742b946300122d14fb9b202461eb76

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Citations in the corpus

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, unknown): “…Borgonovo (2007) investigated sensitivity and uncertainty measures; Mishra et al. (2009) reviewed the global SA methods used in groundwater models; Peter and Dwight (2010) discussed numerical SA approaches for aerodynamic optimization; Perz et al. (2013) reviewed the global SA and UA methods…”
Buffet-Onset Constraint Formulation for Aerodynamic Shape Optimization (2017, methodology): “One of the major advancements in this field was the development of adjoint methods [4, 5, 6], which in conjunction with gradient-based optimization has enabled optimization with respect to large numbers of shape parameters, enabling the aerodynamic shape optimization of wings [7, 8, 9, 10, 11, 12, 1”
The Onera elsA CFD software: input from research and feedback from industry (2013, methodology): “elsA also includes an “aeroelasticity module” [16, 17] offering a framework for aeroelastic simulations and an “optimization module” for calculation of sensitivities by linearized equation solution or by adjoint solver techniques [18, 19].”
Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013, background): “will not discuss the continuous approach in this paper, but ample literature can be found on the subject [72–77], including discussions comparing the two approaches [27,78,79].”
Effective adjoint approaches for computational fluid dynamics (2019, methodology): “Five primary options are available for computing the derivatives in a discrete adjoint implementation [8, 148]: analytical methods, finite-difference methods, the complexstep method [31], AD [17], and symbolic differentiation.”
Multipoint Aerodynamic Shape Optimization Investigations of the Common Research Model Wing (2015, methodology): “Furthermore, it is necessary to perform multipoint optimizations to ensure good performance for a range of flight conditions.”