2133700
Project Grant
Overview
Grant Description
Sbir Phase I: Utilizing Reinforcement Learning to Optimize Ocean Wave Energy Capture -The Broader Impact of This Small Business Innovation Research (SBIR) Phase I Project Seeks to Facilitate the Blue Economy's Continued Transition to a Big-Data Paradigm.
Currently, there is no cost-effective power solution for off-grid, small-scale, energy capture applications at sea. The project deliverables may benefit the commercial ocean sector as well as the federal government and local municipalities by enabling cheaper and more reliable power at sea. This enabling technology may contribute to the ability for planners and decision-makers to anticipate and adapt to changing marine conditions, which will ultimately reduce costs and increase reliability for taxpayers.
Additionally, to achieve its commercial objectives, the participating small business is committed to sustainability in its growth plan and aims to reduce carbon emission by working with local vendors and locally-sourced, recyclable materials. The small business will also continue its existing partnerships with local technical training/trade schools and workforce development programs to mentor underserved students and create jobs.
This Small Business Innovation Research (SBIR) Phase I Project Seeks to Leverage Advanced Artificial Intelligence for Optimizing Power Output. The project seeks to demonstrate the application of advanced machine learning techniques to improve the efficiency and energy capture, and reduce the intermittency, of renewable ocean-based power generation. The project enables adaptability by using an advanced control model methodology which adjusts the device hardware based on ambient environmental conditions for optimized performance.
Due to the deployment environment, this project will capture training data under a laboratory setting, train the control model offline, and apply it in the field by leveraging edge computing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Currently, there is no cost-effective power solution for off-grid, small-scale, energy capture applications at sea. The project deliverables may benefit the commercial ocean sector as well as the federal government and local municipalities by enabling cheaper and more reliable power at sea. This enabling technology may contribute to the ability for planners and decision-makers to anticipate and adapt to changing marine conditions, which will ultimately reduce costs and increase reliability for taxpayers.
Additionally, to achieve its commercial objectives, the participating small business is committed to sustainability in its growth plan and aims to reduce carbon emission by working with local vendors and locally-sourced, recyclable materials. The small business will also continue its existing partnerships with local technical training/trade schools and workforce development programs to mentor underserved students and create jobs.
This Small Business Innovation Research (SBIR) Phase I Project Seeks to Leverage Advanced Artificial Intelligence for Optimizing Power Output. The project seeks to demonstrate the application of advanced machine learning techniques to improve the efficiency and energy capture, and reduce the intermittency, of renewable ocean-based power generation. The project enables adaptability by using an advanced control model methodology which adjusts the device hardware based on ambient environmental conditions for optimized performance.
Due to the deployment environment, this project will capture training data under a laboratory setting, train the control model offline, and apply it in the field by leveraging edge computing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
San Diego,
California
92117-2775
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Ocean Motion Technologies was awarded
Project Grant 2133700
worth $255,558
from National Science Foundation in August 2022 with work to be completed primarily in San Diego California United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I:Utilizing Reinforcement Learning to Optimize Ocean Wave Energy Capture
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project seeks to facilitate the blue economy’s continued transition to a big-data paradigm. Currently, there is no cost-effective power solution for off-grid, small-scale, energy capture applications at sea. The project deliverables may benefit the commercial ocean sector as well as the Federal government and local municipalities by enabling cheaper and more reliable power at sea. This enabling technology may contribute to the ability for planners and decision-makers to anticipate and adapt to changing marine conditions, which will ultimately reduce costs and increase reliability for taxpayers. Additionally, to achieve its commercial objectives, the participating small business is committed to sustainability in its growth plan and aims to reduce carbon emission by working with local vendors and locally-sourced, recyclable materials. The small business will also continue its existing partnerships with local technical training/trade schools and workforce development programs to mentor underserved students and create jobs.This Small Business Innovation Research (SBIR) Phase I project seeks to leverage advanced artificial intelligence for optimizing power output. The project seeks to demonstrate the application of advanced machine learning techniques to improve the efficiency and energy capture, and reduce the intermittency, of renewable ocean-based power generation. The project enables adaptability by using an advanced control model methodology which adjusts the device hardware based on ambient environmental conditions for optimized performance. Due to the deployment environment, this project will capture training data under a laboratory setting, train the control model offline, and apply it in the field by leveraging edge computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 21-562
Status
(Complete)
Last Modified 8/18/22
Period of Performance
8/1/22
Start Date
7/31/23
End Date
Funding Split
$255.6K
Federal Obligation
$0.0
Non-Federal Obligation
$255.6K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2133700
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
FCRSDH7CFUT1
Awardee CAGE
88AN4
Performance District
52
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
Representative
Juan Vargas
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $255,558 | 100% |
Modified: 8/18/22