3 citations · 9 across the 9 of their papers we have counts for
10 papers
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…
Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis
Paul Seurin, Dean Price
Heat-pipe microreactors (HPMRs) are compact and transportable nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where access is limited…
Techno-economic optimization of a heat-pipe microreactor, part I: theory and cost optimization
Paul Seurin, Dean Price, Luis Nunez
Microreactors, particularly heat-pipe microreactors (HPMRs), are compact, transportable, self-regulated power systems well-suited for access-challenged remote areas where costly fo…
Adaptive Control for a Physics-Informed Model of a Thermal Energy Distribution System: Qualitative Analysis
Paul Seurin, Auradha Annaswamy, Linyu Lin
Integrated energy systems (IES) are complex heterogeneous architectures that typically encompass power sources, hydrogen electrolyzers, energy storage, and heat exchangers. This in…
Surpassing legacy approaches to PWR core reload optimization with single-objective Reinforcement learning
Paul Seurin, Koroush Shirvan
Optimizing the fuel cycle cost through the optimization of nuclear reactor core loading patterns involves multiple objectives and constraints, leading to a vast number of candidate…
Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization
Paul Seurin, Koroush Shirvan
A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in t…
Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization
Paul Seurin, Koroush Shirvan
The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraint…
H2-Golden-Retriever: Methodology and Tool for an Evidence-Based Hydrogen Research Grantsmanship
Paul Seurin, Olusola Olabanjo, Joseph Wiggins +4
Hydrogen is poised to play a major role in decarbonizing the economy. The need to discover, develop, and understand low-cost, high-performance, durable materials that can help maxi…
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…