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DECP Funded Projects

TitleDistinguished FacultyFaculty BioInstitutionEstimated FundingProject DescriptionAbstract
Establishing the Predictive Credibility of Data-DrivenDr. Xu WuBiographyNorth Carolina State University$625,000The objective is to establish and enhance the predictive credibility of data-driven Scientific Machine Learning (SciML) for nuclear applications through rigorous uncertainty quantification of SciML models to establish confidence, as well as deep generative learning to address the data scarcity issue. The project will augment the applications of SciML in nuclear energy (NE) scientific computing and prepare the students for transformative solutions across various DOE missions through six education and three leadership thrusts areas.DocumentFY2024
Integrating Thermal Hydraulic Simulation and Experimentation with Data-Driven Methods to Support Molten Salt Reactors DevelopmentDr. Yang LiuBiographyTexas A&M University$625,000 This proposal aims to develop a framework based on data-driven methods to integrate thermal hydraulics experimentation and simulation. The framework will result in a fast response multiscale data engine for efficient transient analysis for advanced nuclear reactors. The proposed framework will support molten salt reactor development through applications on heat exchanger optimization, transient analysis, and risk-informed analysis to demonstrate its applicability.DocumentFY2024
Development of Computational Methods for Neutron Noise Analysis: Improving Modeling and Understanding of Perturbations in Light Water ReactorsDr. Hunter BelangerBiographyRensselaer Polytechnic Institute$625,000Vibrational perturbations in nuclear reactors lead to perturbations in the static neutron flux. In order to identify the location and type of vibration, special computational tools are neededÊ to simulate their effects on the flux. Such simulations are often done in the frequency domain, and the solution is therefore a complex quantity. This project aims to transformÊ understanding of these perturbations through advancing current Monte Carlo methods to simulate these phenomena.DocumentFY2024
Lagrangian Particle Tracking Methods for Multiscale Graphite Dust Transport in Pebble Bed ReactorsDr. April NovakBiographyUniversity of Illinois at Urbana-Champaign$625,000Graphite dust poses a challenge to graphite-moderated reactors, acting as a mobile source term and impediment to heat transfer. This project will develop a new capability for Lagrangian particle tracking methods in MOOSE.Ê These numerical methods will be deployed to a multiscale full primary loop model of a pebble bed reactor using Pronghorn and SAM, in order to explore key questions regarding graphite dust transport physics, operations and maintenance strategies, and source term.DocumentFY2024
The Application of Dynamic PRA to Revolutionize the PRA Model Development during the Design, Licensing, and Maintenance Activities of Current LWRs and Advanced Reactors​​ Dr. Mihai A. DiaconeasaBiographyNorth Carolina State University​​$625,000The main objective of the proposed work is to demonstrate the way dynamic PRA insights can be used to rethink how the completeness problem of the PRA models can be addressed during the design, licensing, and maintenance activities of current LWRs and advanced reactors by including dynamics observed during actual operating events.DocumentFY2023
Advanced Surface Modification Strategies for Reliability Enhancement of Accident Tolerant Fuel Cladding in Nuclear Reactors​ Dr. Sougata RoyBiographyIowa State University​$624,989Proposed project will explore the capability of the multi-feeder laser powder DED and in-situ mechanical working-based hybrid DED technology developed at UND for surface modification of accident tolerant fuel claddings used in light water nuclear reactors. Post-fabrication detailed microstructural characterization and property evaluation, in particular hardness, tensile behavior and wear resistance characteristics of test samples, will be explored. DocumentFY2023
An Optimization and Control Hub for Advanced Reactors: A Step Toward Modernizing Nuclear Engineering Education​Dr. Majdi RadaidehBiographyUniversity of Michigan​$625,000This project aims to develop a foundational paradigm that provides innovative optimization and control solutions to support DOE-NE synergistic advanced reactor programs. The key aspect of this planned work is based on an established framework enhanced with these novel contributions: (1) diverse bio-inspired ensemble optimizer with surrogate modeling for reactor siting & core-reload optimization, (2) hybrid model predictive reinforcement learning controller for microreactors, and (3) modern integrated research and teaching plan.DocumentFY2023
Using Machine Learning to Understand the Transient Critical Heat Flux and Post-CHF Heat Transfer​Dr. Juliana Pacheco Duarte​BiographyUniversity of Wisconsin​$625,000To answer crucial questions on transient critical heat flux and post-CHF heat transfer behavior, the project plans to conduct experiments using high-resolution distributed temperature sensors in a directly heated fuel rod simulator at prototypical LWR accident conditions and use machine learning methods to improve the predictability capabilities of relevant computational codes. Two educational developments are proposed to promote broader and more inclusive nuclear engineering learning.DocumentFY2023
Improving resistance of ferritic-martensitic steels to environmental degradation in advanced nuclear reactors​ Xing Wang​BiographyPennsylvania State University​$625,000This project aims to improve the resistance of ferritic-martensitic (FM) steels to environmental degradation in advanced reactors via two technical objectives: (i) design creep-tolerant FM steels with superior swelling resistance based on systematic understanding of the evolution of nanosized precipitates under extended irradiation and (ii) develop novel refractory high entropy alloy coatings to suppress the corrosion of FM steels in molten fluoride salts.​DocumentFY2023
Robot-assisted Online Monitoring, Online Maintenance, and Dynamic Risk Assessment for LWRs and Advanced ReactorsDr. Fan ZhangBiographyGeorgia Institute of Technology $625,000 A robot-assisted online monitoring, online maintenance, and dynamic risk assessment platform for LWRs and advanced reactors will be developed. This project will integrate a 3D nuclear power plant (NPP) digital twin with data from a pressurized water reactor (PWR) simulator to enable robotic navigation and manipulation research, and will be used to develop algorithms for autonomous fault detection, diagnosis, and risk assessment integrating robot assistance. Research will be demonstrated both virtually and in a real laboratory environment.DocumentFY2022
The application of advanced high resolution optical diagnostics to answer long standing questions and make new discoveries in boiling heat transfer in LWR conditionsDr. Matteo BucciBiographyMassachusetts Institute of Technology $625,000 This project will study boiling heat transfer and the boiling crisis in prototypical light water reactor (LWR) conditions. High-resolution optical diagnostics will be utilized to measure the time-dependent temperature, heat flux, and vapor phase distribution on the boiling surface, the vapor distribution and velocity in the flow. These measurements will allow finding an answer to long-standing dilemma related to the boiling of water and the boiling crisis in nuclear reactor conditions.DocumentFY2022
Physics-Informed Machine Learning to Accelerate Process Modeling in Additive Manufacturing of Structure Materials for Nuclear IndustriesDr. Amrita BasakBiographyPennsylvania State University $625,000 The goal of this research is directed toward developing scientific and formalized physics-informed data-driven techniques toward accelerating the generation of both forward and inverse scaling laws that can be transferred across material systems or manufacturing processes to understand the fundamental linkages between processing and the properties of interest in metal additive manufacturing (AM).DocumentFY2022
Developing Digital Twins from High Fidelity SimulationsDr. Brendan KochunasBiographyUniversity of Michigan $625,000 The objective of this project is to develop the fundamental methods​ and techniques that would leverage advanced modeling and simulation to create efficient and accurate hybrid or integrated (e.g. physics based + data driven + machine learning) models. The project will have 5 majors tasks: developing hybrid models, investigating efficient learning strategies, V&V, incorporating UQ, and developing a new flexible curriculum to teach these methods.​DocumentFY2022
ARISE: Advanced Reactors Integral and Separate Effects Tests – An Integrated Research and Education ProgramDr. Minghui ChenBiographyUniversity of New Mexico $625,000 The objective of this integrated research and educational program is to perform separate effects tests (SETs) and integral effects tests (IETs) using a versatile molten salt test facility to validate system codes in support of the deployment of molten salt reactor (MSR) and Fluoride salt-cooled, High-temperature Reactors (FHRs) technologies and the expanded use of nuclear energy worldwide, and to offer students from undergraduate to graduate, especially Native American, Hispanic and underrepresented minorities, various training and education opportunities of advanced reactors and hands-on molten salt experiments.DocumentFY2022

*Actual project funding will be established during the award negotiation phase.​

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Funded Projects

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NEUP – Nuclear Energy University Program