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Title: Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms

Abstract

This report documents work performed using ALCC computing resources granted under a proposal submitted in February 2016, with the resource allocation period spanning the period July 2016 through June 2017. The award allocation was 10.7 million processor-hours at the National Energy Research Scientific Computing Center. The simulations performed were in support of two projects: the Atmosphere to Electrons (A2e) project, supported by the DOE EERE office; and the Exascale Computing Project (ECP), supported by the DOE Office of Science. The project team for both efforts consists of staff scientists and postdocs from Sandia National Laboratories and the National Renewable Energy Laboratory. At the heart of these projects is the open-source computational-fluid-dynamics (CFD) code, Nalu. Nalu solves the low-Mach-number Navier-Stokes equations using an unstructured- grid discretization. Nalu leverages the open-source Trilinos solver library and the Sierra Toolkit (STK) for parallelization and I/O. This report documents baseline computational performance of the Nalu code on problems of direct relevance to the wind plant physics application - namely, Large Eddy Simulation (LES) of an atmospheric boundary layer (ABL) flow and wall-modeled LES of a flow past a static wind turbine rotor blade. Parallel performance of Nalu and its constituent solver routines residing in themore » Trilinos library has been assessed previously under various campaigns. However, both Nalu and Trilinos have been, and remain, in active development and resources have not been available previously to rigorously track code performance over time. With the initiation of the ECP, it is important to establish and document baseline code performance on the problems of interest. This will allow the project team to identify and target any deficiencies in performance, as well as highlight any performance bottlenecks as we exercise the code on a greater variety of platforms and at larger scales. The current study is rather modest in scale, examining performance on problem sizes of O(100 million) elements and core counts up to 8k cores. This will be expanded as more computational resources become available to the projects.« less

Authors:
 [1];  [2];  [2];  [1];  [2];  [1];  [3];  [3];  [2];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  3. Univ. of Texas, Austin, TX (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1374695
Report Number(s):
SAND-2017-8267R
655936
DOE Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY

Citation Formats

Barone, Matthew F., Ananthan, Shreyas, Churchfield, Matt, Domino, Stefan P., Henry de Frahan, Marc, Knaus, Robert C., Melvin, Jeremy, Moser, Robert, Sprague, Michael, and Thomas, Stephen. Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms. United States: N. p., 2017. Web. doi:10.2172/1374695.
Barone, Matthew F., Ananthan, Shreyas, Churchfield, Matt, Domino, Stefan P., Henry de Frahan, Marc, Knaus, Robert C., Melvin, Jeremy, Moser, Robert, Sprague, Michael, & Thomas, Stephen. Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms. United States. doi:10.2172/1374695.
Barone, Matthew F., Ananthan, Shreyas, Churchfield, Matt, Domino, Stefan P., Henry de Frahan, Marc, Knaus, Robert C., Melvin, Jeremy, Moser, Robert, Sprague, Michael, and Thomas, Stephen. Tue . "Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms". United States. doi:10.2172/1374695. https://www.osti.gov/servlets/purl/1374695.
@article{osti_1374695,
title = {Final Report for ALCC Allocation: Predictive Simulation of Complex Flow in Wind Farms},
author = {Barone, Matthew F. and Ananthan, Shreyas and Churchfield, Matt and Domino, Stefan P. and Henry de Frahan, Marc and Knaus, Robert C. and Melvin, Jeremy and Moser, Robert and Sprague, Michael and Thomas, Stephen},
abstractNote = {This report documents work performed using ALCC computing resources granted under a proposal submitted in February 2016, with the resource allocation period spanning the period July 2016 through June 2017. The award allocation was 10.7 million processor-hours at the National Energy Research Scientific Computing Center. The simulations performed were in support of two projects: the Atmosphere to Electrons (A2e) project, supported by the DOE EERE office; and the Exascale Computing Project (ECP), supported by the DOE Office of Science. The project team for both efforts consists of staff scientists and postdocs from Sandia National Laboratories and the National Renewable Energy Laboratory. At the heart of these projects is the open-source computational-fluid-dynamics (CFD) code, Nalu. Nalu solves the low-Mach-number Navier-Stokes equations using an unstructured- grid discretization. Nalu leverages the open-source Trilinos solver library and the Sierra Toolkit (STK) for parallelization and I/O. This report documents baseline computational performance of the Nalu code on problems of direct relevance to the wind plant physics application - namely, Large Eddy Simulation (LES) of an atmospheric boundary layer (ABL) flow and wall-modeled LES of a flow past a static wind turbine rotor blade. Parallel performance of Nalu and its constituent solver routines residing in the Trilinos library has been assessed previously under various campaigns. However, both Nalu and Trilinos have been, and remain, in active development and resources have not been available previously to rigorously track code performance over time. With the initiation of the ECP, it is important to establish and document baseline code performance on the problems of interest. This will allow the project team to identify and target any deficiencies in performance, as well as highlight any performance bottlenecks as we exercise the code on a greater variety of platforms and at larger scales. The current study is rather modest in scale, examining performance on problem sizes of O(100 million) elements and core counts up to 8k cores. This will be expanded as more computational resources become available to the projects.},
doi = {10.2172/1374695},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 01 00:00:00 EDT 2017},
month = {Tue Aug 01 00:00:00 EDT 2017}
}

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