Massive parallelization as principal technology for constrained optimization of aerodynamic shapes Massive parallelization is a crucial element of any successful optimization of aerodynamic shapes in engineering environment. This technology was implemented in the framework of a new efficient optimization tool (the code OPTIMAS -- OPTIMization of Aerodynamic Shapes). The optimization method employs genetic algorithms in combination with the reduced-order models method, based on linked local data bases obtained by full Navier-Stokes computations. The algorithm is built upon a multilevel embedded parallelization strategy, which efficiently makes use of computational power supplied by massively parallel processors. Applications (implemented on a 456-processors distributed memory cluster) include various three-dimensional optimizations in the presence of nonlinear constraints. The results demonstrate that the approach combines high accuracy of optimization with high parallel efficiency, and thus allows application of the method to practical aerodynamic design in the aircraft industry.

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  1. Viquerat, Jonathan; Rabault, Jean; Kuhnle, Alexander; Ghraieb, Hassan; Larcher, Aurélien; Hachem, Elie: Direct shape optimization through deep reinforcement learning (2021)
  2. Nguyen, Nhu Van; Tyan, Maxim; Lee, Jae-Woo: A modified variable complexity modeling for efficient multidisciplinary aircraft conceptual design (2015)
  3. Peigin, S.; Epstein, B.; Gali, S.: Parallel implementation of fictitious surfaces method for aerodynamic shape optimization (2010)
  4. Peigin, Sergey; Epstein, Boris: Multiconstrained aerodynamic design of business jet by CFD driven optimization tool (2008)
  5. Epstein, Boris; Peigin, Sergey: Accurate CFD driven optimization of lifting surfaces for wing-body configuration (2007)
  6. Epstein, B.; Peigin, S.: Optimization of 3D wings based on Navier-Stokes solutions and genetic algorithms (2006)
  7. Abdoulaev, Gassan S.; Ren, Kui; Hielscher, Andreas H.: Optical tomography as a PDE-constrained optimization problem (2005)
  8. Arora, Jasbir S.; Wang, Qian: Review of formulations for structural and mechanical system optimization (2005)
  9. Peigin, S.; Epstein, B.: Massive parallelization as principal technology for constrained optimization of aerodynamic shapes (2005)
  10. Epstein, B.; Peigin, S.: Application of WENO (weighted essentially non-oscillatory) approach to Navier-Stokes computations (2004)
  11. Peigin, S.; Epstein, B.: Robust handling of nonlinear constraints for GA optimization of aerodynamic shapes (2004)
  12. Peigin, S.; Epstein, B.: Embedded parallelization approach for optimization in aerodynamic design (2004)
  13. Peigin, S.; Epstein, B.; Gali, S.: Multilevel parallelization strategy for optimization of aerodynamic shapes (2004)
  14. Peigin, Sergey; Epstein, Boris: Robust optimization of 2D airfoils driven by full Navier--Stokes computations (2004)
  15. Peigin, S.; Epstein, B.; Rubin, T.; Séror, S.: Parallel high accuracy CFD code for complete aircraft viscous flow simulations (2003)
  16. Ghattas, Omar; Bark, Jai-Hyeong: Optimal control of two-and three-dimensional incompressible Navier-Stokes flows (1997)
  17. Jäger, H.; Sachs, E. W.: Global convergence of inexact reduced SQP methods (1997)
  18. Kupfer, F.-S.; Sachs, E. W.: Reduced SQP methods for nonlinear heat conduction control problems (1993)