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Aerodynamic Shape Optimization Methods on Multiprocessor Platforms

Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE,volume 67)

Abstract

Abstract: An overview of modern optimization methods, including Evolutionary Al-gorithms (EAs) and gradient{based optimization methods adapted for Cluster and Grid Computing is presented. The basic tool is a Hierarchical Distributed Metamodel{Assisted EA supporting Multilevel Evaluation, Multilevel Search and Multilevel Parameterization. In this framework, the adjoint method computes the first and second derivatives of the objective function with respect to the design variables, for use in aerodynamic shape optimization. Such a multi{component, hierarchical and distributed scheme requires particular attention when Cluster or Grid Computing is used and a much more delicate parallelization compared to that of conventional EAs.

Keywords

  • Grid Resource
  • Adjoint Method
  • Total Pressure Loss
  • Wall Clock Time
  • Parallel Genetic Algorithm

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Giannakoglou, K. et al. (2009). Aerodynamic Shape Optimization Methods on Multiprocessor Platforms. In: Parallel Computational Fluid Dynamics 2007. Lecture Notes in Computational Science and Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92744-0_6

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