Nuclear Reactor

Nuclear reactor research currently focuses on improving safety, efficiency, and economic operation through advanced modeling and data analysis. This involves developing sophisticated digital twins, employing machine learning algorithms like deep neural networks and convolutional neural networks for real-time prediction and uncertainty quantification of core parameters (e.g., power distribution, fuel burnup), and optimizing sensor placement for improved data acquisition. These advancements enhance reactor monitoring, control, and design, leading to safer and more cost-effective nuclear power generation.

Papers

November 13, 2024

A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants
Siwei Li, Jiayan Fang, Yichun Wua, Wei Wang, Chengxin Li, Jiangwen Chen
Fault Detection Adverse Condition Useful Life Nuclear Reactor Fault Prognosis

November 12, 2024

Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network
Jiangwen Chen, Siwei Li, Guo Jiang, Cheng Dongzhen, Lin Hua, Wang Wei
DH Research Multi Granularity Fault Diagnosis First Attempt Nuclear Reactor Nuclear Engineering

November 11, 2024

October 17, 2024

Virtual Sensing for Real-Time Degradation Monitoring of Nuclear Systems: Leveraging DeepONet for Enhanced Sensing Coverage for Digital Twin-Enabling Technology
Raisa Bentay Hossain, Farid Ahmed, Kazuma Kobayashi, Seid Koric, Diab Abueidda, Syed Bahauddin Alam
Digital Twin VI DeepONet Nuclear Reactor Virtual Sensor Sensory Coverage

October 11, 2024

iFANnpp: Nuclear Power Plant Digital Twin for Robots and Autonomous Intelligence
Youndo Do, Marc Zebrowitz, Jackson Stahl, Fan Zhang
Non Humanoid Robot Robotics Domain Digital Twin Legal Autonomy Robot Behavior Cognitive Intelligence W Net Nuclear Reactor Reactor Geometry

September 25, 2024

AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors
Anirudh Tunga, Jordan Heim, Michael Mueterthies, Thomas Gruenwald, Jonathan Nistor
Artificial Intelligence Online Calibration Nuclear Reactor

June 27, 2024

Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
Aidan Furlong, Farah Alsafadi, Scott Palmtag, Andrew Godfrey, Xu Wu
Convolutional Neural Network Uncertainty Quantification Data Driven Model Prediction Nuclear Reactor Prototypical Calibration

June 24, 2024

Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing
Ibrahim Aliyu, Awwal M. Arigi, Tai-Won Um, Jinsul Kim
Network Programming Digital Twin Computation Offloading Digital Twinning Nuclear Reactor Human Action Multiaccess Edge Computing

May 28, 2024

Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property
Yu Chen, Edoardo Patelli, Zhen Yang, Adolphus Lye
Native Robustness Robust Learning Robust Prediction Material Property Scarce Data Life Prediction Nuclear Reactor Trustworthy Prediction

May 24, 2024

Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis, Georgios Gakis, Ioannis G. Aviziotis, Martin Kathrein, Gabriele Pozzetti, Christoph Czettl, Stéphane P. A. Bordas, Andreas G. Boudouvis
Unsupervised Learning Diffusion Explainer Surrogate Modeling Sensitivity Analysis Spatial Pattern Nuclear Reactor Process Insight Distinct Functional Regime

December 7, 2023

Efficient Parallel Reinforcement Learning Framework using the Reactor Model
Jacky Kwok, Marten Lohstroh, Edward A. Lee
Reinforcement Learning Multi Agent Reinforcement Learning Fast Simulation Nuclear Reactor Parallel Reinforcement Learning

November 7, 2023

Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation
Chenwei Tang, Wenqiang Zhou, Dong Wang, Caiyang Yu, Zhenan He, Jizhe Zhou, Shudong Huang, Yi Gao, Jianming Chen, Wentao Feng, Jiancheng Lv
Deep Learning General Analysis Data Centric Smart Manufacturing Finite Sample Nuclear Reactor Industrial Scenario Cohort Intelligence

August 15, 2023

Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions
Kazuma Kobayashi, Syed Bahauddin Alam
Digital Twin Surrogate Modeling Deep Operator Network VI DeepONet Nuclear Reactor Nuclear Engineering

June 23, 2023

Constrained optimization of sensor placement for nuclear digital twins
Niharika Karnik, Mohammad G. Abdo, Carlos E. Estrada Perez, Jun Soo Yoo, Joshua J. Cogliati, Richard S. Skifton, Pattrick Calderoni, Steven L. Brunton, Krithika Manohar
Digital Twin Constrained Optimization Sensor Placement Nuclear Reactor

April 20, 2023

Prediction of the evolution of the nuclear reactor core parameters using artificial neural network
Krzysztof Palmi, Wojciech Kubinski, Piotr Darnowski
Neural Network Human Prediction Specie Evolution Nuclear Reactor Reactor Geometry Reactor Optimization

March 15, 2023

On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem
Yu Yang, Helin Gong, Qihong Yang, Yangtao Deng, Qiaolin He, Shiquan Zhang
Loss Function Physic Informed Neural Network Uncertainty Estimation Nuclear Reactor Noise Prior Eigenvalue Problem Neutron Diffusion

November 23, 2022

Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction
James Daniell, Kazuma Kobayashi, Susmita Naskar, Dinesh Kumar, Souvik Chakraborty, Ayodeji Alajo, Ethan Taber, Joseph Graham, Syed Alam
Digital Twin Physic Informed Deep Learning Nuclear Reactor Reactor Geometry

November 16, 2022

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks
Lesego E. Moloko, Pavel M. Bokov, Xu Wu, Kostadin N. Ivanov
Neural Network Human Prediction Uncertainty Quantification Nuclear Reactor Experimental Verification Nuclear Engineering

May 23, 2022

Advanced Transient Diagnostic with Ensemble Digital Twin Modeling
Edward Chen, Linyu Lin, Nam T. Dinh
Digital Twin Advanced Transient Diagnostic Nuclear Reactor

Nuclear Reactor

Papers

A Fuzzy Reinforcement LSTM-based Long-term Prediction Model for Fault Conditions in Nuclear Power Plants

Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network

Predicting BWR Criticality with Data-Driven Machine Learning Model

Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm

Virtual Sensing for Real-Time Degradation Monitoring of Nuclear Systems: Leveraging DeepONet for Enhanced Sensing Coverage for Digital Twin-Enabling Technology

iFANnpp: Nuclear Power Plant Digital Twin for Robots and Autonomous Intelligence

AI Enabled Neutron Flux Measurement and Virtual Calibration in Boiling Water Reactors

Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks

Digital Twinning of a Pressurized Water Reactor Startup Operation and Partial Computational Offloading in In-network Computing-Assisted Multiaccess Edge Computing

Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property

Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis

Efficient Parallel Reinforcement Learning Framework using the Reactor Model

Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation

Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions

Constrained optimization of sensor placement for nuclear digital twins

Prediction of the evolution of the nuclear reactor core parameters using artificial neural network

On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem

Physics-Informed Multi-Stage Deep Learning Framework Development for Digital Twin-Centred State-Based Reactor Power Prediction

Prediction and Uncertainty Quantification of SAFARI-1 Axial Neutron Flux Profiles with Neural Networks

Advanced Transient Diagnostic with Ensemble Digital Twin Modeling