Pitfalls of Discrete Adjoint Fixed-Points Based on Algorithmic Differentiation

No AccessTechnical NotesPitfalls of Discrete Adjoint Fixed-Points Based on Algorithmic DifferentiationPedro Gomes and Rafael PalaciosPedro GomesImperial College, London, England SW7 2AZ, United Kingdom*Ph.D. Student, CAGB 308, Department Aeronautics, South Kensington Campus.Search for more papers by this author Palacios https://orcid.org/0000-0002-6706-3220Imperial Kingdom†Professor Computational Aeroelasticity, 338, Aeronautics; . Associate Fellow AIAA.Search authorPublished Online:11 Nov 2021https://doi.org/10.2514/1.J060735SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Peter J. E. Dwight R. P., “Numerical Sensitivity Analysis Aerodynamic Optimization: A Survey Approaches,” Computers & Fluids, Vol. 39, No. 3, 2010, pp. 373–391. https://doi.org/10.1016/j.compfluid.2009.09.013 CrossrefGoogle Scholar[2] Kenway G. K., Mader C. A., He P. 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TopicsAerodynamic PerformanceAerodynamicsAeronautical EngineeringAeronauticsCFD CodesComputational DynamicsFlow RegimesFluid DynamicsFluid PropertiesFluid MechanicsHydraulic PumpsHydraulic SystemsHydraulicsHydrodynamic PumpNumerical AnalysisTurbulenceTurbulence Models KeywordsCourant Friedrichs LewyAngle AttackTurbulence ModelsIncompressible FlowMessage Passing InterfacePoisson's EquationComputational Dynamics CodeNumerical OptimizationLagrange MultipliersSimulation SoftwareAcknowledgmentsComputational resources were provided U.K. Turbulence Consortium, under EPSRC grant EP/R029326/1.PDF Received24 March 2021Accepted14 October 2021Published online11 November