Demonstrate multi-turbine simulation with hybrid-structured / unstructured-moving-grid software stack running primarily on GPUs and propose improvements for successful KPP-2

Technical Report · Fri Sep 30 00:00:00 EDT 2022

DOI:https://doi.org/10.2172/1891592· OSTI ID:1891592

Bidadi, Shreyas ^[1]; Brazell, Michael ^[1]; Brunhart-Lupo, Nicholas ^[1]; Henry de Frahan, Marc T. ^[1]; Lee, Dong Hun ^[1]; Hu, Jonathan ^[1]; Melvin, Jeremy ^[1]; Mullowney, Paul ^[1]; Vijayakumar, Ganesh ^[1]; Moser, Robert D. ^[1]; Rood, Jon ^[1]; Sakievich, Philip ^[1]; Sharma, Ashesh ^[1]; Williams, Alan ^[1]; Sprague, Michael A. ^[1]

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1. Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, capturing the thin boundary layers, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources.

• Research Organization: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 1891592
• Report Number(s): SAND2022-13899R; 710736
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
17 WIND ENERGY