We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.
Kuhn, Michael B., et al. "AMR-Wind: A Performance-Portable, High-Fidelity Flow Solver for Wind Farm Simulations." Wind Energy, vol. 28, no. 5, Mar. 2025. https://doi.org/10.1002/we.70010
Kuhn, Michael B., Henry de Frahan, Marc T., Mohan, Prakash, et al., "AMR-Wind: A Performance-Portable, High-Fidelity Flow Solver for Wind Farm Simulations," Wind Energy 28, no. 5 (2025), https://doi.org/10.1002/we.70010
@article{osti_2553927,
author = {Kuhn, Michael B. and Henry de Frahan, Marc T. and Mohan, Prakash and Deskos, Georgios and Churchfield, Matt and Cheung, Lawrence and Sharma, Ashesh and Almgren, Ann and Ananthan, Shreyas and Brazell, Michael J. and others},
title = {AMR-Wind: A Performance-Portable, High-Fidelity Flow Solver for Wind Farm Simulations},
annote = {We present AMR-Wind, a verified and validated high-fidelity computational-fluid-dynamics code for wind farm flows. AMR-Wind is a block-structured, adaptive-mesh, incompressible-flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high-performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR-Wind is spatially and temporally second-order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12-turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.},
doi = {10.1002/we.70010},
url = {https://www.osti.gov/biblio/2553927},
journal = {Wind Energy},
issn = {ISSN 1095-4244},
number = {5},
volume = {28},
place = {United States},
publisher = {Wiley},
year = {2025},
month = {03}}
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Sandia National Laboratories (SNL-CA), Livermore, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
Exascale Computing Project (ECP); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Biological and Environmental Research (BER)
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