14 citations · 21 across the 18 of their papers we have counts for
19 papers
Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks
Nataly R. Panczyk, Athanasios Stamatopoulos, Josef Svoboda +1
This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmiss…
High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications
Logan A. Burnett, Hyungjun Kim, Hsien-Cheng Chou +5
This work presents a high-fidelity computational fluid dynamics (CFD) and data-driven modeling framework for assembly-level flow characterization in a four-loop pressurized water r…
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
Umme Mahbuba Nabila, Paul Seurin, Linyu Lin +1
Real-time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty-aware, yet remain data and computation…
MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
Omer F. Erdem, Dean Price, Paul Seurin +1
Multiobjective optimization remains challenging for many scientific and engineering problems due to the need to balance convergence, diversity, and computational efficiency across…
Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors
Meredith Eaheart, Majdi I. Radaideh
High-fidelity computational fluid dynamics (CFD) simulations are widely used to analyze nuclear reactor transients, but are computationally expensive when exploring large parameter…
Tensorized Discontinuous Isogeometric Analysis Method for the 2-D Time-Independent Linearized Boltzmann Transport Equation
Patrick A. Myers, Joseph A. Bogdan, Majdi I. Radaideh +1
We present the novel Tensorized Discontinuous Isogeometric Analysis (TDIGA) method applied to the discontinuous Galerkin (DG) time-independent 2-D linearized Boltzmann transport eq…
Variational Digital Twins
Logan A. Burnett, Umme Mahbuba Nabila, Majdi I. Radaideh
While digital twins (DT) hold promise for providing real-time insights into complex energy assets, much of the current literature either does not offer a clear framework for inform…
On the Impact of Monte Carlo Statistical Uncertainty on Surrogate-based Design Optimization
Omer F. Erdem, David P. Broughton, Josef Svoboda +2
In multi-objective design tasks, the computational cost increases rapidly when high-fidelity simulations are used to evaluate objective functions. Surrogate models help mitigate th…
Nuclear Microreactor Control with Deep Reinforcement Learning
Leo Tunkle, Kamal Abdulraheem, Linyu Lin +1
The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are oper…
Multi-objective Combinatorial Methodology for Nuclear Reactor Site Assessment: A Case Study for the United States
Omer Erdem, Kevin Daley, Gabrielle Hoelzle +1
As clean energy demand grows to meet sustainability and net-zero goals, nuclear energy emerges as a reliable option. However, high capital costs remain a challenge for nuclear powe…
Multistep Criticality Search and Power Shaping in Microreactors with Reinforcement Learning
Majdi I. Radaideh, Leo Tunkle, Dean Price +3
Reducing operation and maintenance costs is a key objective for advanced reactors in general and microreactors in particular. To achieve this reduction, developing robust autonomou…
A Comparative Analysis of Text-to-Image Generative AI Models in Scientific Contexts: A Case Study on Nuclear Power
Veda Joynt, Jacob Cooper, Naman Bhargava +5
In this work, we propose and assess the potential of generative artificial intelligence (AI) to generate public engagement around potential clean energy sources. Such an applicatio…
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions
Steven Goldenberg, Malachi Schram, Kishansingh Rajput +7
Providing accurate uncertainty estimations is essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaus…
Multi-module based CVAE to predict HVCM faults in the SNS accelerator
Yasir Alanazi, Malachi Schram, Kishansingh Rajput +7
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulat…
Fault Prognosis in Particle Accelerator Power Electronics Using Ensemble Learning
Majdi I. Radaideh, Chris Pappas, Mark Wezensky +2
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its pow…
Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
Majdi I. Radaideh, Hoang Tran, Lianshan Lin +6
The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the merc…
Bayesian Inverse Uncertainty Quantification of the Physical Model Parameters for the Spallation Neutron Source First Target Station
Majdi I. Radaideh, Lianshan Lin, Hao Jiang +1
The reliability of the mercury spallation target is mission-critical for the neutron science program of the spallation neutron source at the Oak Ridge National Laboratory. We prese…
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation
Pouria Mehrabi, Hamid D. Taghirad
Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in unknown environments. A novel -based method is proposed for the ground segmentation task in r…
Improving Intelligence of Evolutionary Algorithms Using Experience Share and Replay
Majdi I. Radaideh, Koroush Shirvan
We propose PESA, a novel approach combining Particle Swarm Optimisation (PSO), Evolution Strategy (ES), and Simulated Annealing (SA) in a hybrid Algorithm, inspired from reinforcem…