FLOWMAS: Floating Offshore Wind Modeling and Simulation

The Floating Offshore Wind Modeling and Simulation (FLOWMAS) Energy Earthshot Research Center (EERC) delivers the fundamental research necessary for a 70% levelized cost reduction of floating offshore wind by 2035.

Enabling Offshore Wind Breakthroughs

The FLOWMAS EERC supports the U.S. Department of Energy's (DOE) Floating Offshore Windshot initiative targeting the reduction of costs associated with floating offshore wind energy.

From a combination of high-fidelity modeling and high-performance computing (HPC) for floating offshore wind energy, researchers will create next-generation data-driven surrogate models and a new understanding of the floating offshore wind energy system, including how climate change will impact offshore wind resources, the physics of floating wind farm and turbine wake dynamics, and the loads and dynamics of floating wind turbines during operational and extreme events.

To enable offshore wind energy breakthroughs and to help achieve this energy goal, FLOWMAS researchers will:

• Create a suite of high-fidelity codes for floating offshore wind energy that incorporates the microscale (i.e., wind turbines, floating platforms, and mooring systems), mesoscale (i.e., regional weather dynamics), and global/climate scales, building on DOE investments in high-fidelity models for climate and land-based wind energy that can exploit exascale-class computing.
• Use results from high-fidelity simulations and ongoing DOE-supported field campaigns to create surrogate models that are computationally efficient and can explore many system conditions and for long time durations not accessible with computationally expensive high-fidelity models.
• Seek to understand the present and near-future ocean environment including extreme events to maximize the potential for offshore wind going forward.
• Leverage exascale-computing power to create a new understanding of the floating offshore wind energy system, including how climate change will impact offshore wind energy resources, the physics of floating wind farm and turbine wake dynamics, and the loads and dynamics of floating wind turbines during operational and extreme events.

The FLOWMAS EERC is a collaboration between the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Sandia National Laboratories (Sandia), Howard University, University of Minnesota, and University of California-Merced.

Open-Source Software Code Suites

FLOWMAS research is centered on three open-source software code suites that were created under DOE, including the Exascale Computing Project. Each code is “performance portable” and is designed to run on modern HPC systems built around GPUs. The FLOWMAS codes span the microscale (i.e., wind turbines, floating platforms, and mooring systems), mesoscale (i.e., regional weather dynamics), and global/climate scales.

Microscale Codes

ExaWind (GitHub) provides multi-fidelity modeling capability for wind turbines and wind farms.

Mesoscale Codes

Energy Research and Forecasting (GitHub) is a modern state-of-the-art, atmospheric energy flow prediction system.

Global/Climate Scale Codes

The Energy Exascale Earth System Model (GitHub) features repositories of code for investigating energy-relevant science optimized for DOE's advanced computers.

Research

News

NREL Will Lead Two $19M Research Centers to Spur Decarbonization Efforts as Part of DOE's Energy Earthshots, NREL (2023)

DOE Announces $264 Million for Basic Research in Support of Energy Earthshots™, DOE (2023)

Acknowledgments

This work is supported by the DOE Office of Science Advanced Scientific Computing Research and Biological and Environmental Research programs.

FLOWMAS is operated in close collaboration with the Floating Turbine High Fidelity Modeling and Simulation project and the OpenTurbine project funded by the DOE Office of Energy Efficiency and Renewable Energy's Wind Energy Technologies Office.

Contact

If you have any questions about FLOWMAS, contact: