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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Nano Nuclear wins Air Force contract for Kronos MMR
New York City–based advanced nuclear technology developer Nano Nuclear Energy has been awarded a Direct-to-Phase II Small Business Innovation Research contract for its Kronos micro modular reactor (MMR) by AFWERX, the innovation and venture arm of the U.S. Air Force. The contract calls for AFWERX, with the 11th Civil Engineering Squadron, to explore the feasibility of deploying the Kronos MMR Energy System at Joint Base Anacostia-Bolling (JBAB) in Washington, D.C.
Mohamed H. Elhareef, Zeyun Wu
Nuclear Science and Engineering | Volume 197 | Number 4 | April 2023 | Pages 601-622
Technical Paper | doi.org/10.1080/00295639.2022.2123211
Articles are hosted by Taylor and Francis Online.
In this paper, the physics-informed neural network (PINN) method is investigated and applied to nuclear reactor physics calculations with neutron diffusion models. The reactor problems were introduced with both fixed-source and eigenvalue modes. For the fixed-source problem, the loosely coupled reactor model was solved with the forward PINN approach, and then, the model was used to optimize the neural network hyperparameters. For the k-eigenvalue problem, which is unique for reactor calculations, the forward PINN approach was modified to expand the capability of solving for both the fundamental eigenvalue and the associated eigenfunction. This was achieved by using a free learnable parameter to approximate the eigenvalue and a novel regularization technique to exclude null solutions from the PINN framework. Both single-energy-group and multiple-energy-group diffusion models were examined in the work to demonstrate the PINN capabilities of solving systems of coupled partial differential equations in reactor problems. A series of numerical examples was tested to demonstrate the viability of the PINN approach. The PINN solution was compared against the finite element method solution for the neutron flux and the power iteration solution for the k-eigenvalue. The error in the predicted flux ranged from 0.63% for simple fixed-source problems up to about 15% for the two-group k-eigenvalue problem. The deviations in the predicted k-eigenvalues from the power iteration solver ranged from 0.13% to 0.92%. These generally acceptable results preliminarily justified the feasibility of PINN applications in reactor problems. The advantageous application potentials as well as the observable computational deficits of the PINN approaches are discussed along with the pilot study.