SBIR/STTR Research 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 identifier f5669d97b075e2b563563895c3a100ddb7021abb08f72084298397d42ae754d2Awarding agency Department of Energy

Award amount

$200,000

Program and phase

STTR · Phase I

Award year

2023

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
Fathom Science, Inc.
Proposal award date
2023-07-10
Contract end date
2024-01-09
UEI
LXXTKK3PFLN8
Solicitation number
Not reported
Topic code
C56-14a

Research abstract

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.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

2 SBIR/STTR records share this UEI (2023–2024). Reported award amounts total $339,325. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Defense, Department of Energy

Agency intelligence

Department of Energy

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