Jean C. Ragusa
· Professor, Nuclear EngineeringVerifiedTexas A&M University · Nuclear Engineering
Active 1996–2026
About
Jean C. Ragusa is a professor of Nuclear Engineering specializing in Scientific Computations with applications to Nuclear Science and Engineering. He is a member of the computational physics and applied data sciences group in the department of nuclear engineering at Texas A&M University. Professor Ragusa is passionate about modeling and simulations in areas such as Radiation Transport, Reactor Physics, Multiphysics Simulations, and Scientific Machine Learning, including Model Order Reduction, Surrogate Models, Uncertainty Quantification, Data Assimilation, and Machine Learning techniques like Physics-Informed Neural Networks (PiNNs). He is dedicated to educating students in modern computational methods relevant to nuclear engineering and beyond, preparing graduate students who are highly sought after by National Laboratories and industry. Additionally, he mentors undergraduate students and encourages involvement in scientific computing and research opportunities, including summer internships at national labs.
Research topics
- Computer Science
Selected publications
Mendeley Data · 2026-01-08
datasetOpen accessSenior authorOpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates (SN) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy. OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures. Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence. OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.
A full-resolution multiphysics model of the Molten Salt Reactor Experiment
Annals of Nuclear Energy · 2026-04-06
articleOpen accessThe Molten Salt Reactor Experiment (MSRE), operated at Oak Ridge National Laboratory in the 1960s, was a pioneering effort to demonstrate MSR technology and remains a cornerstone for MSR research. This study presents a full-resolution multiphysics model of the MSRE, resolving the entire reactor vessel and internal structures without porous-media or axisymmetric approximations. The workflow couples Monte Carlo neutron–photon transport (Serpent 2) with conjugate heat transfer and turbulent flow simulation (GeN-Foam within the foamForNuclear platform) on unstructured meshes, iterated to convergence with temperature and density feedback. Predicted effective multiplication factor and power fractions show good agreement with benchmark data, and velocity profiles in the volute, annulus, and core passages are compatible with experimental measurements and previous simulations within known uncertainties in geometry and operating conditions. Where design parameters are uncertain, the model demonstrates the ability to infer missing data from experimental observations, highlighting the potential for high-fidelity simulation techniques to support reactor start-up procedures and digital-twin applications. The model reproduces major experimental flow trends in the lower plenum while revealing three-dimensional structures that axisymmetric surrogates cannot capture. Some discrepancies remain, largely attributable to input uncertainties and limitations of RANS models under strong adverse pressure gradients, suggesting future extensions to LES turbulence modeling. • Developed a full-resolution multiphysics model of the MSRE. • Implemented cell-by-cell two-way coupling of Serpent 2 with GeN-Foam. • The model reproduces key experimental flow trends. • The model reveals three-dimensional flow structures not captured by simplified studies. • Results underscore the importance of resolving geometric details for MSR design and analysis.
Nuclear Science and Engineering · 2026-04-10
articleA full-resolution multiphysics model of the Molten Salt Reactor Experiment
SSRN Electronic Journal · 2026-01-01
preprintOpen accessMendeley Data · 2026-01-08
datasetOpen accessSenior authorOpenSn is an open-source, massively parallel deterministic radiation transport code for solving the discrete-ordinates (SN) form of the Boltzmann transport equation on unstructured, arbitrary polyhedral meshes. It supports high-fidelity simulations involving steady-state, eigenvalue, and adjoint problems for neutral particles (e.g., neutrons, photons, multi-particles), using the multigroup approximation in energy. OpenSn combines angular discretization via discrete ordinates with a discontinuous Galerkin finite element method (DGFEM) in space, enabling accurate resolution of transport physics on arbitrary polyhedral cells, included locally refined spatial grids. It includes multiple angular quadrature types, including locally refined angular quadratures. Written in modern C++ with a Python API, OpenSn runs efficiently on platforms ranging from laptops to supercomputers. The transport sweep algorithm is implemented using a task-based, directed-acyclic-graph (DAG) approach for each angle and supports asynchronous parallelism across thousands of MPI ranks. Group-set aggregation improves compute intensity, and synthetic acceleration techniques (e.g., diffusion synthetic acceleration, second-moment method) enhance solver convergence. OpenSn has been verified on reactor physics problems and demonstrated excellent weak and strong scaling performance on more than 32,768 processes, making it a versatile and robust platform for large-scale transport simulations in complex geometries.
Computer Physics Communications · 2025-12-21 · 1 citations
articleOpen accessSenior authorHyper-Reduction Techniques for Efficient Simulation of Large-Scale Engineering Systems
Archives of Computational Methods in Engineering · 2025-07-25 · 2 citations
articleOpen accessSenior authorAbstract Reduced-order models (ROMs) offer compact representations of complex engineering systems governed by partial differential equations or high-dimensional ordinary differential equations enabling efficient simulations of otherwise computationally intensive problems. These models are typically constructed by projecting the high-dimensional governing equations onto reduced subspaces derived using techniques such as Singular Value Decomposition (SVD) or Proper Orthogonal Decomposition (POD). However, conventional ROMs struggle with nonlinear systems due to the high computational cost of repeatedly accessing high-dimensional solution spaces for nonlinear term evaluations. Hyper-reduction methods address this challenge by efficiently approximating nonlinear term evaluations, significantly improving ROM performance. They are also essential for solving large parametric linear problems that lack an efficient parameter-affine decomposition. This paper provides a comprehensive overview of hyper-reduction algorithms, emphasizing both their theoretical foundations and practical implementations in academic research and industry. With the rapid advancement of data-driven methods, reduced-order modeling has become indispensable for analyzing and simulating large-scale systems, including fluid dynamics, thermal processes, and structural mechanics. As the demand for efficient computational tools in science and engineering continues to grow, a detailed discussion of hyper-reduction techniques is both timely and valuable. The paper explores state-of-the-art hyper-reduction techniques, including discrete empirical interpolation methods (DEIM), energy-conserving sampling and weighting (ECSW), and emerging machine learning-based approaches. A nonlinear parametric heat conduction example is presented to illustrate the implementation of these methods. The analysis evaluates their strengths and weaknesses using standard metrics, providing insights into their practical utility. Finally, the paper concludes by discussing future research directions and potential applications of hyper-reduction, including its integration with real-time simulations and digital twin systems.
ArXiv.org · 2025-12-15
preprintOpen accessDeterministic solutions to the Sn transport equation can be computationally expensive to calculate. Reduced Order Models (ROMs) provide an efficient means of approximating the Full Order Model (FOM) solution. We propose a novel approach for constructing ROMs of the Sn radiation transport equation, Offline Maximizing Minimally Invasive (OMMI) Proper Orthogonal Decomposition (POD). POD uses snapshot data to build a reduced basis, which is then used to project the FOM. Minimally Invasive POD leverages the sweep infrastructure within deterministic Sn transport solvers to construct the reduced linear system, even though the FOM linear system is never directly assembled. OMMI-POD extends Minimally Invasive POD by performing transport sweeps offline, thereby maximizing the potential speedup. It achieves this by generating a library of reduced systems from a training set, which is then interpolated in the online stage to provide a rapid approximate solution to the Sn transport equation. The model's performance is evaluated on a multigroup 2-D test problem, demonstrating low error and a 1600-fold speedup over the full order model.
Graphite-Moderated Molten Salt Reactor Progression Problems
2025-01-01
article2025-01-01
articleSenior author
Frequent coauthors
- 27 shared
Jim E. Morel
Texas A&M University
- 26 shared
Mauricio Tano
- 12 shared
Marvin L. Adams
Texas A&M University
- 11 shared
Yaqi Wang
Idaho National Laboratory
- 10 shared
Jan I.C. Vermaak
Idaho National Laboratory
- 9 shared
Ramiro Freile
Idaho National Laboratory
- 9 shared
Péter German
- 9 shared
Zachary M. Prince
Education
- 2001
PhD, Physics Energetics
Institut Polytechnique de Grenoble
- 1996
MS, Nuclear Engineering
Texas A&M University
- 1996
Engineer Diploma, Nuclear Engineering
Phelma
Awards & honors
- Engineering Genesis Award for Multidisciplinary Research
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