SBIR-STTR Award

Developing a Novel AI/ML Approach for High-efficiency, High-fidelity Marine Wave Energy Characterization and Assessment for Powering the Blue Economy (PBE)
Award last edited on: 11/13/2023

Sponsored Program
STTR
Awarding Agency
DOE
Total Award Amount
$200,000
Award Phase
1
Solicitation Topic Code
C56-14a
Principal Investigator
Ruoying He

Company Information

Fathom Science LLC

514 Daniels Street
Raleigh, NC 27612
   (919) 260-1871
   info@fathomscience.com
   www.fathomscience.com

Research Institution

North Carolina State University

Phase I

Contract Number: DE-SC0023973
Start Date: 7/10/2023    Completed: 1/9/2024
Phase I year
2023
Phase I Amount
$200,000
Developing regional marine renewable energy resource characterizations and assessments is a significant technical challenge which requires state-of-the-art modeling, application of best modeling practices, accurate model skills, high-quality inputs, and high-performance computing resources. Further, resource characterization and assessment for Powering the Blue Economy markets is needed, especially in regions where these markets (coastal resiliency, ocean observations, and more) have been identified and are actively being developed. In this project, Fathom Science will develop a novel artificial intelligence/machine learning framework that can deliver high-efficiency, high-resolution, and high-fidelity wave forecasts to support Blue Economy activities in the coastal ocean of the U.S. East Coast, Gulf of Mexico, and Caribbean Sea. The development of this framework will include supervised machine learning of high-resolution output of a physics-based wave model and in situ wave observations. We expect this novel machine learning approach, once fully implemented, will require only a fraction (<1/1,000th) of the computation time and resources needed for wave forecasting using a conventional dynamical model to deliver accurate wave and wave energy forecasts. During Phase I, the Applicant will 1) develop the methodology for formulating this new framework, 2) process training data sets based on several decades of high-resolution dynamical ocean wave model reanalysis, and 3) carry out preliminary feasibility studies to validate machine learning forecasts of wave characteristics and resource assessment. Multiple iterations will likely be required to optimize the machine learning model to achieve the desired accuracy and process speeds, which will be refined in a Phase II project. The high-resolution physics-based wave modeling will leverage capabilities of the partner research institution and ongoing collaborations with a federal partner that performs research and development to improve performance, lower costs, and accelerate the deployment of wave energy technologies. The commercial applications of improved wave forecasts are in sectors such as ship routing, tourism, storm forecasting, environmental assessment, fishing, search and rescue, and those with offshore assets such as wind, oil, and gas. Improved wave energy assessment will benefit marine hydrokinetic energy development, a renewable energy resource. Engaging with potential end users will begin in Phase II, with committed end users involved in Phase III.

Phase II

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Start Date: 00/00/00    Completed: 00/00/00
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