2403902
Cooperative Agreement
Overview
Grant Description
SBIR Phase II: Detection of high-risk lightning strikes for wildland fire management
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project includes a notable reduction in the area burned by lightning-initiated wildfires.
Such wildfires are responsible for over 70% of the area burned in the environmental catastrophes in the Western United States.
Globally, wildfires are responsible for 6.45 gigatons of CO2 emissions annually (18% of total emissions).
This technology can identify a fire in seconds, unlike the present heat or smoke identification products that can take hours or days.
This would help in significantly reducing loss of life, habitats, property, and forests.
The reduction of wildfires would reduce large evacuations and smoke-related health conditions, thereby improving the health and welfare of the American public.
Additional benefits could come for businesses and homeowners from lower insurance rates due to the decreased risk of wildfire damage.
If such a technology is implemented in California alone, it has the potential to reduce economic losses by an estimated $84B-$112B per year.
The intellectual merit of this project lies in the ground-based characterization of extremely-low-frequency (ELF) lightning emissions through electrostatic field changes to identify long-continuing current (LCC) strikes, with a 95% target detection efficiency with 40 m accuracy.
Long-continuing currents are those that last for 40 ms or longer and are essentially responsible for excessive heating.
Wildfires start when a long-continuing current strikes the ground at a location where the environmental conditions are conducive for fire ignition.
The project will use machine learning algorithms to pinpoint high-risk lightning ignitions, by analyzing the environmental conditions at the LCC strike location.
While Phase I has successfully demonstrated the technical feasibility of the ELF-based detection of LCC on a relatively flat landscape, Phase II of the project will focus on research for the technology’s deployment in diverse fire-prone terrains, including hilly or mountainous landscapes, with vastly different topographical, connectivity, and forest conditions, with minimal loss of the lightning detection range.
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.
Subawards are planned for this award.
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project includes a notable reduction in the area burned by lightning-initiated wildfires.
Such wildfires are responsible for over 70% of the area burned in the environmental catastrophes in the Western United States.
Globally, wildfires are responsible for 6.45 gigatons of CO2 emissions annually (18% of total emissions).
This technology can identify a fire in seconds, unlike the present heat or smoke identification products that can take hours or days.
This would help in significantly reducing loss of life, habitats, property, and forests.
The reduction of wildfires would reduce large evacuations and smoke-related health conditions, thereby improving the health and welfare of the American public.
Additional benefits could come for businesses and homeowners from lower insurance rates due to the decreased risk of wildfire damage.
If such a technology is implemented in California alone, it has the potential to reduce economic losses by an estimated $84B-$112B per year.
The intellectual merit of this project lies in the ground-based characterization of extremely-low-frequency (ELF) lightning emissions through electrostatic field changes to identify long-continuing current (LCC) strikes, with a 95% target detection efficiency with 40 m accuracy.
Long-continuing currents are those that last for 40 ms or longer and are essentially responsible for excessive heating.
Wildfires start when a long-continuing current strikes the ground at a location where the environmental conditions are conducive for fire ignition.
The project will use machine learning algorithms to pinpoint high-risk lightning ignitions, by analyzing the environmental conditions at the LCC strike location.
While Phase I has successfully demonstrated the technical feasibility of the ELF-based detection of LCC on a relatively flat landscape, Phase II of the project will focus on research for the technology’s deployment in diverse fire-prone terrains, including hilly or mountainous landscapes, with vastly different topographical, connectivity, and forest conditions, with minimal loss of the lightning detection range.
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.
Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23516
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Gainesville,
Florida
32601-6279
United States
Geographic Scope
Single Zip Code
Helios Pompano was awarded
Cooperative Agreement 2403902
worth $955,736
from National Science Foundation in July 2024 with work to be completed primarily in Gainesville Florida United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Cooperative Agreement was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase II Programs (SBIR/STTR Phase II).
SBIR Details
Research Type
SBIR Phase II
Title
SBIR Phase II: Detection of High-Risk Lightning Strikes for Wildland Fire Management
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase II project includes a notable reduction in the area burned by lightning-initiated wildfires. Such wildfires are responsible for over 70% of the area burned in the environmental catastrophes in the western United States. Globally, wildfires are responsible for 6.45 Gigatons of CO2 emissions annually (18% of total emissions). This technology can identify a fire in seconds, unlike the present heat or smoke identification products that can take hours or days. This would help in significantly reducing loss of life, habitats, property, and forests. The reduction of wildfires would reduce large evacuations and smoke-related health conditions, thereby improving the health and welfare of the American public. Additional benefits could come for businesses and homeowners from lower insurance rates due to the decreased risk of wildfire damage. If such a technology is implemented in California alone, it has the potential to reduce economic losses by an estimated $84B-$112B per year.
The intellectual merit of this project lies in the ground-based characterization of Extremely-Low-Frequency (ELF) lightning emissions through electrostatic field changes to identify Long-Continuing Current (LCC) strikes, with a 95% target detection efficiency with 40 m accuracy. Long-continuing-currents are those that last for 40 ms or longer and are essentially responsible for excessive heating. Wildfires start when a long-continuing-current strikes the ground at a location where the environmental conditions are conducive for fire ignition. The project will use machine learning algorithms to pinpoint High-Risk-Lightning ignitions, by analyzing the environmental conditions at the LCC strike location. While Phase I has successfully demonstrated the technical feasibility of the ELF-based detection of LCC on a relatively flat landscape, Phase II of the project will focus on research for the technology’s deployment in diverse fire-prone terrains, including hilly or mountainous landscapes, with vastly different topographical, connectivity, and forest conditions, with minimal loss of the lightening detection range.
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
ET
Solicitation Number
NSF 23-516
Status
(Ongoing)
Last Modified 7/23/24
Period of Performance
7/15/24
Start Date
6/30/26
End Date
Funding Split
$955.7K
Federal Obligation
$0.0
Non-Federal Obligation
$955.7K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2403902
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
RNNJYSF1JBS8
Awardee CAGE
8U2J2
Performance District
FL-03
Senators
Marco Rubio
Rick Scott
Rick Scott
Modified: 7/23/24