182 citations · 442 across the 13 of their papers we have counts for
13 papers
Analysis of Stakeholder Involvement in Nuclear Power Plant Cost Overruns and Implications for Contract Structuring
Christopher Forsyth, Levi M. Larsen, Ryan Spangler +6
Existing evidence on nuclear power plant construction suggests that individual stakeholders involved in project delivery are often not responsible for all of the cost overruns in t…
Cross-disciplinary Reactor-to-Repository Framework for Evaluating Spent Nuclear Fuel from Advanced Reactors
Haruko M. Wainwright, Chloe Christiaen, Milos Atz +4
This study presents a cross-disciplinary reactor-to-repository framework to compare different advanced reactors with respect to their spent nuclear fuel (SNF). The framework consis…
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…
Development of a Two-Level ML Spatial-temporal Framework for Industrial Thermal Striping Applications
Yu-Jou Wang, Emilio Baglietto, Koroush Shirvan
A data-driven framework for spatial-temporal prediction is proposed for reducing the computational cost of industrial thermal striping applications. The framework aims to efficient…
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…
Machine learning-assisted surrogate construction for full-core fuel performance analysis
Yifeng Che, Joseph Yurko, Koroush Shirvan
Accurately predicting the behavior of a nuclear reactor requires multiphysics simulation of coupled neutronics, thermal-hydraulics and fuel thermo-mechanics. The fuel thermo-mechan…
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…
On the prediction of critical heat flux using a physics-informed machine learning-aided framework
Xingang Zhao, Koroush Shirvan, Robert K. Salko +1
The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the…
Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification
Xu Wu, Koroush Shirvan, Tomasz Kozlowski
Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the process to quantify the uncertainties of random input parameters based on experimental data. The introducti…
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE
Xu Wu, Tomasz Kozlowski, Hadi Meidani +1
Inverse Uncertainty Quantification (UQ) is a process to quantify the uncertainties in random input parameters while achieving consistency between code simulations and physical obse…
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 1: Theory
Xu Wu, Tomasz Kozlowski, Hadi Meidani +1
In nuclear reactor system design and safety analysis, the Best Estimate plus Uncertainty (BEPU) methodology requires that computer model output uncertainties must be quantified in…