Skip Navigation LinksFY23_DECP_Awards

​​​​​​​​​​​​​​​​​FY 2023 Distinguish​ed Early Career Awards

DOE is awarding more than $3.1 million through its Distinguished Early Career Program to support five 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 2023 DECP Awards ​ ​​ ​ ​
​Distinguished Faculty
​Estimated Funding
​Project Description
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. Diaconeasa

North Carolina State University​

The 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.

Advanced Surface Modification Strategies for Reliability Enhancement of Accident Tolerant Fuel Cladding in Nuclear Reactors​

Dr. Sougata Roy

Iowa State University


Proposed 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. 

An Optimization and Control Hub for Advanced Reactors: A Step Toward Modernizing Nuclear Engineering Education​

Dr. Majdi Radaideh

University of Michigan

This 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.

Using Machine Learning to Understand the Transient Critical Heat Flux and Post-CHF Heat Transfer​

Dr. Juliana Pacheco Duarte​

University of Wisconsin

To 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.

Improving resistance of ferritic-martensitic steels to environmental degradation in advanced nuclear reactors​

Xing Wang​

Pennsylvania State University

This 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.​