Multipoint and Multi-Objective Aerodynamic Shape Optimization

By M. Nemec, David W. Zingg, T. Pulliam. 2002

296 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/81b300ef396927f6c6c094566f2215e717ac847b

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Citations in the corpus

Three-Dimensional Large-Scale Aerodynamic Shape Optimization Based on Shape Calculus (2013, background): “Although this approach is known to be applicable and well working, one is often forced into finite differencing for these terms [2,3].”
Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models (2013, methodology): “The method was then extended by Jameson to perform airfoil shape optimization [37], and since then it has been used to optimize airfoils suitable for multipoint operation [67] and to design laminar-flow airfoils [22].”
RANS-based aerodynamic shape optimization investigations of the common research modelwing (2014, background): “The majority of the aerodynamic shape optimization problems in the literature are solved with gradient-based optimizers [3, 4, 5, 6, 7].”
Automatic Differentiation Adjoint of the Reynolds-Averaged Navier-Stokes Equations with a Turbulence Model (2013, methodology): “The discrete adjoint approach is also widely used in aerodynamic shape optimization [6, 7, 8, 9, 10, 11, 12].”
Expected drag minimization for aerodynamic design optimization based on aircraft operational data (2017, background): “In some airfoil optimization problems, the Mach numbers in the multipoint formulation were varied [9, 34].”
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].”
An adaptive geometry parametrization for aerodynamic shape optimization (2014, methodology): “Since the Hessian approximation necessitates gradient evaluations, the gradients of objective functions and constraints are computed using the well established discrete adjoint method (Jameson 1988; Nemec et al 2004; Hicken and Zingg 2010a).”
Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates (2016, unknown): “The most popular multiobjective optimization methods include multiobjective evolutionary algorithms (MOEAs) [17–20] because these techniques are capable of yielding the entire representation of the Pareto front in a single algorithm run.”
Intuitive Class/Shape Function Parameterization for Airfoils (2014, methodology): “In recent years, CAD-based methods and free-form deformation methods are becoming increasingly popular [6,17–23].”
Machine Learning in Aerodynamic Shape Optimization (2022, background): “Most multiobjective applications have only considered low-dimensional design problems, such as airfoil shape design [94] and wing platform design [95], with a few exceptions [96, 97].”
Multimodality and Global Optimization in Aerodynamic Design (2013, methodology): “The key components of the aerodynamic-shape-optimization algorithms used are thoroughly described byNemec andZingg [1,17] for 2-D problems and Hicken and Zingg [18,19] for 3-D problems.”
OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization (2019, background): “, multiple CFD analyses that are run for the same geometry, but at different flow conditions [26, 77, 85].”
Parallel Newton-Krylov Solver for the Euler Equations Discretized Using Simultaneous-Approximation Terms (2008, methodology): “Our choice of grid type and discretization is based on experience with serial two- and three-dimensional flow solvers [3–5].”