Publications (11)
Predictive Capability Maturity Quantification using Bayesian Network
Linyu Lin, Nam Dinh
In nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a con…
Uncertainty Quantification and Software Risk Analysis for Digital Twins in the Nearly Autonomous Management and Control Systems: A Review
Linyu Lin, Han Bao, Nam Dinh
A nearly autonomous management and control (NAMAC) system is designed to furnish recommendations to operators for achieving particular goals based on NAMAC's knowledge base. As a c…
Development and Assessment of a Nearly Autonomous Management and Control System for Advanced Reactors
Linyu Lin, Paridhi Athe, Pascal Rouxelin +5
This paper develops a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. The development process of NAMAC is characterized by a three layer-layer archit…
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…
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…
In-Context System Identification for Nonlinear Dynamics Using Large Language Models
Linyu Lin
Sparse Identification of Nonlinear Dynamics (SINDy) is a powerful method for discovering parsimonious governing equations from data, but it often requires expert tuning of candidat…
Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation
Han Bao, Nam Dinh, Linyu Lin +3
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond th…
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…
Advanced Transient Diagnostic with Ensemble Digital Twin Modeling
Edward Chen, Linyu Lin, Nam T. Dinh
The use of machine learning (ML) model as digital-twins for reduced-order-modeling (ROM) in lieu of system codes has grown traction over the past few years. However, due to the com…
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…
Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control System
Linyu Lin, Paridhi Athe, Pascal Rouxelin +4
The Nearly Autonomous Management and Control System (NAMAC) is a comprehensive control system that assists plant operations by furnishing control recommendations to operators in a…