SBIR/STTR Research Award

Coupled Radiation-Thermal Degradation of Polymeric Systems in Nuclear Plants: Data-driven Tools to Predict Damage Accumulation & Early Failures

Award identifier a942c6f80748c49dc0c0c8cfb101722ee55688a63d882e76842f218ef12a746aAwarding agency Department of Energy

Award amount

$200,000

Program and phase

SBIR · Phase I

Award year

2021

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
NORDISCH LLC
Proposal award date
2021-06-28
Contract end date
2022-03-27
UEI
LN81MFHZAJN1
Solicitation number
DE-FOA-0002360
Topic code
39d

Research abstract

Life-time prediction and reliability of materials performance under combined radiation-thermal environments is of significant interest in nuclear industry. By developing a modular platform for systematic coupling of different damage models, the project aim to develop customizable physics-based predictive tools to describe damage accumulation and eventually damage capacity in elastomers that are concurrently exposed to a combination of mechanical and environmental stressors in high radiation dose. In our proposed framework, our goal is to predict the static fatigue through accumulation of the mechanical damages and environmental damages induced by concurrent thermal and radiation aging. In view of the complexity of defining mapping space for such coupled damage mechanisms, no model exists that can even consider two of those phenomena concurrently and that's the main advantage of our proposed technology. Using the NetP modular platform developed by MSU, this project provides software to describe and couples the effects of decay due to coupled thermal-radiation loads on elastomers. Deriving the environmental-mechanical damage as the static fatigue, we will bridge our model to a failure model to calculate the remaining damage capacity of the matrix in terms of cyclic fatigue variables, such as time-to-failure or cycles to failure. The coupling of the NetP platform and failure model will be carried out in our Nord2 framework software which will be eventually released into the MOOSE multi-physics solver developed by DOE. The objective is to develop two predictive tools to describe damage accumulation and failure in elastomer systems exposed to coupled thermal-radiation aging. To show the feasibility of such platform, we implement models of the mechanical, thermal and radiation damages into the Netp platform in Phase I, couple the platform to a failure model to develop the Nord2 framework, and validate it against experiment published by Sandia National lab. The proposed hybrid framework is a more rigorous, history-dependent, extensible based on location and environment, implementable based on continuum mechanics approach and portends a transformational change in damage predictive approaches. In view of the modular design of the proposed platform, other essential damage behaviors such as diffusion limited oxidation can be later integrated into this platform, and used concurrently in simulation of the elastomer behavior under thermomechanical boundary conditions. Model used in the platform can be updated/modified or replaced for any compound, and only onetime training is needed once the platform is assembled.

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 (2021). Reported award amounts total $339,550. Reported amounts are not federal obligations.

Phases: Phase I

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.