2321894
Cooperative Agreement
Overview
Grant Description
SBIR Phase II: Composing Digital-Twins from Disparate Data Sources - The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project relates to the creation of a digital twin, an interactive, 3-dimensional model of a real-world system, of complex industrial environments and assets.
This digital twin provides infrastructure necessary for the application of virtual reality training and augmented reality live-guided procedures in industrial workplaces, at scale. By making the full-scale roll-out of these technologies possible, the technology seeks to impact human health and safety, operational efficiencies, and environmental risk reduction for process operations facilities, such as oil refineries and chemical plants.
The long-term impacts of this technology may also enable automation and optimization, improving their efficiency, security, and safety. Such facilities are critical infrastructure and play a significant role in the national economy. The availability of this product may also enhance market opportunities for other businesses in the scanning, spatial computing, and training markets. The impact may be further broadened by adapting the process for digital twin production to new domains unrelated to the industrial market.
This Small Business Innovation Research (SBIR) Phase II project is advancing knowledge and understanding in both machine learning and spatial computing. This project focuses on a method for digitizing a complex, real-world system, in an efficient manner, sufficient to recreate the captured reality as an interactive digital twin.
The primary technical hurdle is the combining of different data sources, that describe aspects of a particular real-world system, into a single, complete description. The initial physical systems being modeled are industrial process operations, but the core methods could apply to other types of systems, including natural systems, such as a rainforest.
For industrial process operations, the goal is to encode the entire process operations facilities, at the component level, with sub-centimeter accuracy, at 10% of the current time and cost requirements. To achieve this, this project will combine physical scans with engineering documentation and relational probabilities. Once combined, the model will be used as the basis for a digital twin of the real-world system projected into spatial computed environments, such as virtual and augmented reality.
These techniques replace a tedious and limited static scan and intensive human labor workflow with rapid scans, computer vision, and a combination of procedural and trained algorithms.
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 not planned for this award.
This digital twin provides infrastructure necessary for the application of virtual reality training and augmented reality live-guided procedures in industrial workplaces, at scale. By making the full-scale roll-out of these technologies possible, the technology seeks to impact human health and safety, operational efficiencies, and environmental risk reduction for process operations facilities, such as oil refineries and chemical plants.
The long-term impacts of this technology may also enable automation and optimization, improving their efficiency, security, and safety. Such facilities are critical infrastructure and play a significant role in the national economy. The availability of this product may also enhance market opportunities for other businesses in the scanning, spatial computing, and training markets. The impact may be further broadened by adapting the process for digital twin production to new domains unrelated to the industrial market.
This Small Business Innovation Research (SBIR) Phase II project is advancing knowledge and understanding in both machine learning and spatial computing. This project focuses on a method for digitizing a complex, real-world system, in an efficient manner, sufficient to recreate the captured reality as an interactive digital twin.
The primary technical hurdle is the combining of different data sources, that describe aspects of a particular real-world system, into a single, complete description. The initial physical systems being modeled are industrial process operations, but the core methods could apply to other types of systems, including natural systems, such as a rainforest.
For industrial process operations, the goal is to encode the entire process operations facilities, at the component level, with sub-centimeter accuracy, at 10% of the current time and cost requirements. To achieve this, this project will combine physical scans with engineering documentation and relational probabilities. Once combined, the model will be used as the basis for a digital twin of the real-world system projected into spatial computed environments, such as virtual and augmented reality.
These techniques replace a tedious and limited static scan and intensive human labor workflow with rapid scans, computer vision, and a combination of procedural and trained algorithms.
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 not 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
Houston,
Texas
77059-5218
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased from $999,757 to $1,005,757.
Diamond Age Technology was awarded
Cooperative Agreement 2321894
worth $1,005,757
from National Science Foundation in October 2023 with work to be completed primarily in Houston Texas 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:Composing Digital-Twins from Disparate Data Sources
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project relates to the creation of a digital twin, an interactive, 3-dimensional model of a real-world system, of complex industrial environments and assets. This digital twin provides infrastructure necessary for the application of virtual reality training and augmented reality live-guided procedures in industrial workplaces, at scale. By making the full-scale roll-out of these technologies possible, the technology seeks to impact human health and safety, operational efficiencies, and environmental risk reduction for process operations facilities, such as oil refineries and chemical plants. The long-term impacts of this technology may also enable automation and optimization, improving their efficiency, security, and safety. Such facilities are critical infrastructure and play a significant role in the national economy. The availability of this product may also enhance market opportunities for other businesses in the scanning, spatial computing, and training markets. The impact may be further broadened by adapting the process for digital twin production to new domains unrelated to the industrial market._x000D_ _x000D_ This Small Business Innovation Research (SBIR) Phase II project is advancing knowledge and understanding in both machine learning and spatial computing. This project focuses on a method for digitizing a complex, real-world system, in an efficient manner, sufficient to recreate the captured reality as an interactive digital twin. The primary technical hurdle is the combining of different data sources, that describe aspects of a particular real-world system, into a single, complete description. The initial physical systems being modelled are industrial process operations, but the core methods could apply to other types of systems, including natural systems, such as a rainforest. For industrial process operations, the goal is to encode the entire process operations facilities, at the component level, with sub-centimeter accuracy, at 10% of the current time and cost requirements. To achieve this, this project will combine physical scans with engineering documentation and relational probabilities. Once combined, the model will be used as the basis for a digital twin of the real-world system projected into spatial computed environments, such as virtual and augmented reality. These techniques replace a tedious and limited static scan and intensive human labor workflow with rapid scans, computer vision, and a combination of procedural and trained algorithms._x000D_ _x000D_ 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
AV
Solicitation Number
NSF 23-516
Status
(Ongoing)
Last Modified 6/10/24
Period of Performance
10/1/23
Start Date
9/30/25
End Date
Funding Split
$1.0M
Federal Obligation
$0.0
Non-Federal Obligation
$1.0M
Total Obligated
Activity Timeline
Transaction History
Modifications to 2321894
Additional Detail
Award ID FAIN
2321894
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
NXLQL6MS1UQ4
Awardee CAGE
8CT62
Performance District
TX-36
Senators
John Cornyn
Ted Cruz
Ted Cruz
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) | $999,757 | 100% |
Modified: 6/10/24