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- W1676084798 abstract "This chapter describes recent achievements regarding the development of parallel multigrid (PMG) methods and parallel genetic algorithms (PGAs) in the framework of compressible flow simulation and optimization. Genetic algorithms (GAs) are search algorithms based on mechanisms simulating natural selection. The technique operates on a population of potential solutions represented by strings of binary digits (called chromosomes or individuals) which are submitted to several semi-stochastic operators (selection, crossover and mutation). The population evolves during the generations according to the fitness value of the individuals; then, when a stationary state is reached, the population has converged to an optimized solution. The multigrid strategy is adopted to gain efficiency in the solution of the two sub-systerns. In the present method, the coarse-grid approximation is based on the construction of macro-elements, more specifically macro control-volumes by “volume-agglomeration”. Starting from the finest mesh, a “greedy” coarsening algorithm is applied to generate automatically the coarse discretizations. Parallelism is introduced in the overall flow solver by using a strategy that combines mesh partitioning techniques and a message passing programming model." @default.
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- W1676084798 date "2000-01-01" @default.
- W1676084798 modified "2023-09-23" @default.
- W1676084798 title "Parallel multigrid solution and optimization in compressible flow simulation and design" @default.
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- W1676084798 doi "https://doi.org/10.1016/b978-044482851-4.50005-0" @default.
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