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European Projects
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Efficient CFD Multi-physics programming research (E-CFD-GPU)
Efficient CFD Multi-physics programming research
(E-CFD-GPU)
Date du début: 1 févr. 2011,
Date de fin: 31 juil. 2013
PROJET
TERMINÉ
"The current evolution in the aeronautical field towards high-fidelity simulations, including multi-physics and more reliable modeling of turbulence and transition, calls for a new approach of the complete CFD-multi-physics simulation chain, with a drastic reduction of its turnaround time.This requires revising the whole CAE chain, from pre-processing (CAD handling and mesh generation), to very fast basic CFD algorithms and to efficient, full parallel post-processing, in order to achieve a reduction of the global turn-around time by several orders of magnitude.On a shorter term, of 24 months of the current CfP project, the following objectives can be ensured, based on very recent developments performed at NUMECA Int.:• A gain of one order of magnitude at the pre-processing level, covering automatic CAD cleaning, wrapping and parallel unstructured grid generation for arbitrary complex configurations with the software system HEXPRESS™/Hybrid.• A gain of one order of magnitude, due to a novel convergence acceleration algorithm, allowing calculations with CFL=1000 and convergence of steady state RANS simulations, in 50 multigrid cycles.The present proposal has as objective to respond to the CfP topic by• extending these capabilities to the GRA-LNC configurations• extending the convergence acceleration methodology to simulations with laminar-turbulent transition, and to unsteady flows• providing guidelines for a next generation software environment for industrial aerodynamics simulation, in response to task 2 of the CfP,• porting of the CFD code and the convergence acceleration algorithms to GPU’s, with an expected additional gain of 1 to 2 orders of magnitude.One could therefore expect, combining the above mentioned efforts that within the framework of the project duration, a gain of 3-to 4 orders of magnitude will be achieved, in global CPU performance and turn-around time, for steady state RANS simulations in a first step."
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