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Efficient CFD Multi-physics programming research

Project

E-CFD-GPU - Efficient CFD Multi-physics programming research


Funding origin:
European
European Union
STRIA Roadmaps:
Vehicle design and manufacturing (VDM)
Vehicle design and manufacturing
Transport mode:
Airborne
Airbone
Transport sectors:
Passenger transport
Passenger transport
Freight transport
Freight transport
Duration:
Start date: 01/02/2011,
End date: 01/07/2013

Status: Finished
Funding details:
Total cost:
€150 000
EU Contribution:
€112 500

Overview

Objectives:

The current evolution in the aeronautical field towards high-fidelity simulations, including multi-physics and more reliable modelling 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 were 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 had an 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 therefore expected, combining the above mentioned efforts that within the framework of the project duration, a gain of 3-to 4 orders of magnitude would be achieved, in global CPU performance and turn-around time, for steady state RANS simulations in a first step.

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