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Inboard monolith
Inboard monolith











inboard monolith

and Maute K., “ A Schur–Newton–Krylov Solver for Steady-State Aeroelastic Analysis and Design Sensitivity Analysis,” Computer Methods in Applied Mechanics and Engineering, Vol. 195, Nos. 17–18, 2006, pp. 2050–2069. H., “ A Comparative Evaluation of Genetic and Gradient-Based Algorithms Applied to Aerodynamic Optimization,” Revue Européenne de Mécanique Numérique, Vol. 17, Nos. 1–2, March 2008, pp. 103–126. doi: JSCOEB 0885-7474 Crossref Google Scholar Jameson A., “ Aerodynamic Design via Control Theory,” Journal of Scientific Computing, Vol. 3, No. 3, 1988, pp. 233–260. doi: JFLSA7 0022-1120 Crossref Google Scholar Pironneau O., “ On Optimum Design in Fluid Mechanics,” Journal of Fluid Mechanics, Vol. 64, No. 1, 1974, pp. 97–110. doi: SMOTB4 1615-1488 Crossref Google Scholar W., “ High-Fidelity Aerostructural Optimization with Integrated Geometry Parameterization and Mesh Movement,” Structural and Multidisciplinary Optimization, Vol. 55, No. 4, April 2017, pp. 1217–1235. A., “ Aerostructural Optimization of the Common Research Model Configuration,” 15th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA Paper 2014-3274, June 2014. R., “ A Parallel Aerostructural Optimization Framework for Aircraft Design Studies,” Structural and Multidisciplinary Optimization, Vol. 50, No. 6, 2014, pp. 1079–1101. doi: AIAJAH 0001-1452 Link Google Scholar

inboard monolith

A., “ Scalable Parallel Approach for High-Fidelity Steady-State Aeroelastic Analysis and Adjoint Derivative Computations,” AIAA Journal, Vol. 52, No. 5, 2014, pp. 935–951. J., “ A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design,” Optimization and Engineering, Vol. 6, No. 1, 2005, pp. 33–62. J., “ High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet,” Journal of Aircraft, Vol. 41, No. 3, 2004, pp. 523–530. and Farhat C., “ Sensitivity Analysis and Design Optimization of Three-Dimensional Non-Linear Aeroelastic Systems by the Adjoint Method,” International Journal for Numerical Methods in Engineering, Vol. 56, No. 6, 2003, pp. 911–933. and Farhat C., “ Coupled Analytical Sensitivity Analysis and Optimization of Three-Dimensional Nonlinear Aeroelastic Systems,” AIAA Journal, Vol. 39, No. 11, 2001, pp. 2051–2061. C., “ A Coupled Aero-Structural Optimization Method for Complete Aircraft Configurations,” 37th Aerospace Sciences Meeting and Exhibit, AIAA Paper 1999-0187, 1999. Robustness of the monolithic solution method is shown via its reduced sensitivity to the choice of problem-dependent solution parameters as well as its ability to converge when the partitioned method fails. This advantage increases with increasing wing flexibility. In most cases, the monolithic solution algorithm requires 20–70% less computing time than its partitioned counterpart. The monolithic solution method is applied to problems with varying degrees of fluid–structure coupling as well as a wing-span optimization study. These include appropriate strategies for scaling and matrix–vector product evaluations as well as block Jacobi and block Gauss–Seidel preconditioning techniques that preserve the modularity between subproblems. Several aspects of the monolithic solution method have been investigated. A Newton–Krylov method is used for the aerostructural analysis, and a preconditioned Krylov subspace method is used for the coupled adjoint solution. The objective of the present paper is to investigate ways to maximize the efficiency of a monolithic solution method and further quantify its benefits in the context of aerostructural optimization. An efficient and robust solution algorithm for the aerostructural analysis and coupled adjoint problems is crucial to the success of high-fidelity aerostructural optimization.













Inboard monolith