SBIR/STTR Research Award

FeaturePrint: Computer Vision for Pebble Bed Reactors' Material, Control, and Accounting

Award identifier bccd484a819ca57f4080ec4589e4ab1d4839e1f10e4dce102ad5a111182fc503Awarding agency Department of Energy

Award amount

$195,828

Program and phase

SBIR · Phase I

Award year

2023

Explore This Research Award in CaptureIQ

Research the technology, evaluate the recipient and agency, identify related opportunities, and explore potential follow-on work with CaptureIQ.

Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
ALITHEON INC
Proposal award date
2023-07-10
Contract end date
2024-01-09
UEI
MMJFE4B5MQP1
Solicitation number
DE-FOA-0002903
Topic code
C56-40n

Research abstract

C56-40n-272712The limiting factor in previous imaging approaches to material, control, and accounting of pebbles going through pebble bed reactors was the ability to reliably identify hundreds of thousands of unique pebbles in a short time. Moreover, the secondary approach of identifying types of pebbles does support fresh fuel loading by distinguishing fuel pebbles from moderator pebbles, but still fails to identify individual pebbles as such. The company provides a unique solution for pebble bed reactors’ material, control, and accounting via its patented, optical-AI-based software system, which can uniquely characterize and identify pebbles from their initial insertion in the reactor vessel to their discharge, to ensure that those exiting are the same as those entering. This could potentially enable Continuity of Knowledge on a statistically significant percentage of individual pebbles traversing through the reactors. PHASE I - Objective 1: Test the company’s solution’s ability to remain valid from pebble insertion to extraction to reinsertion and so forth. Objective 2: Test different cameras to determine those optimal for pebble image capture. Objective 3: Identify appropriate settings and thresholds for the system to achieve the target of 99.85% accuracy. Objective 4: Consult with current utilities to ensure that design choices for implementation could be optimized in ways that would best fit current structures. Final Deliverable: Achieve successful authentication and identification levels at both entry and exit points in the nuclear reactor vessel. From a technical perspective, the use of computer vision software would significantly improve the accuracy and efficiency of pebble identification and tracking in their reactors. This information would be valuable for research and development in the field of nuclear energy, enabling further improvements in the technology. From an economic perspective, the use of computer vision software would increase the efficiency of pebble bed reactors’ operations, reducing the need for manual inspection and increasing the overall capacity of the reactors. From a social perspective, the use of computer vision software in pebble bed reactors would contribute to a safer and more reliable energy supply, reducing the risk of accidents and increasing the overall public confidence in nuclear power, which is a critical factor in its continued development.

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.

Research funding history

6 SBIR/STTR records share this UEI (2020–2025). Reported award amounts total $3,411,666. Reported amounts are not federal obligations.

Phases: Phase I, Phase II – Direct to Phase II

Agencies: Department of Defense, Department of Energy

Agency intelligence

Department of Energy

Back to SBIR/STTR Research Awards

Don't take our word for it. Ask AI about RFPCapture.

See what leading AI assistants have to say about RFPCapture and CaptureIQ.

Gemini opens without a prefilled question. The question is copied to your clipboard when the button works. You can also copy it here:

What is RFPCapture.com and its CaptureIQ platform from Smart Logic AI? How does it help federal contractors discover opportunities, evaluate bid/no-bid decisions, research government contracts, and prepare proposals? How does it compare with other federal capture platforms? Use independent sources where available.