Skip Navigation LinksFY24_DECP_Awards

​​​​​​​​​​​​​​​​​​​​​​​FY 2024 Distinguish​ed Early Career Awards

DOE is awarding more than $3.1 million through its Distinguished Early Career Program to support four distinguished early career faculty in five states. This program invests in the innovative research and education programs of outstanding early career university faculty poised to pave new lines of inquiry and advance mission critical research directions in nuclear energy.

A complete list of the projects with their associated abstracts is available below. ​

​FY 2024 DECP Awards ​ ​​ ​ ​
​Distinguished Faculty
​Estimated Funding
​Project Description
Establishing the Predictive Credibility of Data-Driven
Scientific Machine Learning for Nuclear Applications.​

Dr. Xu Wu​

North Carolina State University​

The 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.s.

Integrating Thermal Hydraulic Simulation and Experimentation with Data-Driven Methods to Support Molten Salt Reactors Development​

Dr. Yang Liu​

Texas A&M University


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.

Development of Computational Methods for Neutron Noise Analysis: Improving Modeling and Understanding of Perturbations in Light Water Reactors
Dr. Hunter Belanger​​

Rensselaer Polytechnic Institute

Vibrational 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 needed  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 transform  understanding of these perturbations through advancing current Monte Carlo methods to simulate these phenomena.​

Lagrangian Particle Tracking Methods for Multiscale Graphite Dust Transport in Pebble Bed Reactors​

Dr. April Novak​

University of Illinois at Urbana-Champaign

Graphite 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.  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.​