Multi-Fidelity simulation in the exascale era
With every generational shift in computing capability, we need to consider both what we can do with the new capabilities and how to do it. For the combustion community exascale computing offers the opportunity for a transformation increase in realism of near-first principles calculations (‘DNS’) in terms of complexity of the geometry and physics that can be included. However, even with this change the impact on real-world design considerations will still be dependent on the efficiency of transferring knowledge developed from the high fidelity simulations to engineering calculations that can be used directly for parameter space exploration and design optimization. Machine learning has the potential for accelerating this linkage. This presentation will explore some specific examples of how these three avenues: engineering simulation, exascale computing code development, and machine learning are being pursued at NREL to address combustion research challenges.