SBIR/STTR Research Award

Innovative Reduced-Order Models for Subsurface Radionuclide Transport in Nuclear Waste Management

Award identifier ef989ae62525a8497dad54559fe06d0ef387f0e3a3d90af63000ab9772e09f5fAwarding agency Department of Energy

Award amount

$206,431

Program and phase

SBIR · Phase I

Award year

2022

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
ILLINOISROCSTAR LLC
Proposal award date
2022-06-27
Contract end date
2023-04-26
UEI
EYFJS7XN6ME3
Solicitation number
Not reported
Topic code
C54-37a

Research abstract

o provide for safe, secure long term storage of nuclear waste in the United States, one solution is to properly sequester the waste in subsurface geological formations. The Department of Energy has embarked on producing a simulation suite designed to help manage the risk of this by simulating the complex physical processes that occur over very long periods in underground storage. The physics and chemistry needed to adequately capture these processes can require very large investments in computational resources, and methods for reducing this burden are being sought. How This Problem is Being Addressed: Illinois Rocstar proposes to leverage state-of-the-art machine learning models and physics-based reduced order modeling capabilities to produce representations of subsurface reactive transport that will run quickly and produce accurate results for nuclear waste repository simulation. The proposed tool will both provide a framework within which users can produce their own new reduced-order models using these techniques, and will also provide for integration with the government repository simulation system under development. What Will be Done in Phase I: During Phase I, subsurface simulations of radionuclide transport in the subsurface will be modeled as a series of 1D, 2D, and finally 3D scenarios, building up consistently more capable reduced-order models. Techniques to be used are based on proper orthogonal decomposition, with a parameterized closure based on advanced machine learning models. The 2D and 3D simulations will use the government’s developing system for nuclear waste repository characterization so that the resulting models will be compatible and consistent with those advanced multiphysics modeling tools. A connection back to the government system will be made through production of a new reduced-order process model to be embedded in the reactive flow solver system. Commercial Applications and Other Benefits: Application of the system to be constructed will first be in the nuclear waste repository modeling and simulation area. However, the reactive transport reduced-order-models that will be generated should be able to be used in other subsurface modeling and simulation areas as well, such as to model geologic carbon storage sites, environmental restoration or accident sites, or possibly for oil and gas exploration. Ultimately applications to site characterization, site monitoring, accident analysis, and permitting applications are possible.

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

68 SBIR/STTR records share this UEI (2007–2022). Reported award amounts total $26,056,217. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: Department of Defense, Department of Energy, National Aeronautics and Space Administration

Agency intelligence

Department of Energy

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