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Proceedings Papers

PID Parameter Tuning Method for Steam Generator Level Control Based on Ant Colony Algorithm

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Yulong Wang, Xinyu Wei, PeiWei Sun
Proc. ASME. ICONE31, Volume 3: I&C, Digital Control, and Influence of Human Factors, V003T03A021, August 4–8, 2024
Publisher: American Society of Mechanical Engineers
Paper No: ICONE31-136009
https://doi.org/10.1115/ICONE31-136009
View Paper titled, PID Parameter Tuning Method for Steam Generator Level Control Based on Ant Colony Algorithm
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Topics: Algorithms, Artificial neural networks, Boilers, Control systems, Differential equations, Transfer functions, Calibration, Neural network models, Nuclear power stations, Optimization
Proceedings Papers

Anomaly Detection of Thermal System Using CAE-DDQN Model

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Tong Li, Jiahao Cheng, Bo Wang, Sichao Tan, Ruifeng Tian
Proc. ASME. ICONE31, Volume 11: Student Paper Competition, V011T15A040, August 4–8, 2024
Publisher: American Society of Mechanical Engineers
Paper No: ICONE31-134870
https://doi.org/10.1115/ICONE31-134870
View Paper titled, Anomaly Detection of Thermal System Using CAE-DDQN Model
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Topics: Computer-aided engineering, Deep reinforcement learning, Thermal systems, Time series, Safety, Thermal engineering, Machine learning, Neural network models, Nuclear power, Accidents
Proceedings Papers

Prediction of Sensor Data Accuracy in Thermal Experimental Benches Using GRU-GCN Neural Network Model

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Linjun Yang, Tong Li, Yongchao Liu, Bo Wang, Jiangkuan Li, Jiming Wen, Sichao Tan, Ruifeng Tian
Proc. ASME. ICONE31, Volume 6: Thermal-Hydraulics and Safety Analysis, V006T07A049, August 4–8, 2024
Publisher: American Society of Mechanical Engineers
Paper No: ICONE31-134843
https://doi.org/10.1115/ICONE31-134843
View Paper titled, Prediction of Sensor Data Accuracy in Thermal Experimental Benches Using GRU-GCN Neural Network Model
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Topics: Complex systems, Cycles, Deep learning, Energy efficiency, Neural network models, Nuclear power stations, Safety, Sensors, Time series
Proceedings Papers

Estimation of External Dose Volume Correction Factors Based on Neural Network

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Junjun Chen, Jingyuan Qu, Junjun Gong
Proc. ASME. ICONE26, Volume 7: Decontamination and Decommissioning, Radiation Protection, and Waste Management; Mitigation Strategies for Beyond Design Basis Events, V007T10A013, July 22–26, 2018
Publisher: American Society of Mechanical Engineers
Paper No: ICONE26-81616
https://doi.org/10.1115/ICONE26-81616
View Paper titled, Estimation of External Dose Volume Correction Factors Based on Neural Network
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Topics: Artificial neural networks, Control rooms, Neural network models, Nuclides, Shapes, Accidents, Design, Errors, Geometry, Radioactive substances
Proceedings Papers

Reconstruction of Core Overheating Damage Fraction Based on Neural Network Method

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Wenjing Li, Xiaoming Yang, Xinlu Yu
Proc. ASME. ICONE25, Volume 4: Nuclear Safety, Security, Non-Proliferation and Cyber Security; Risk Management, V004T06A043, July 2–6, 2017
Publisher: American Society of Mechanical Engineers
Paper No: ICONE25-67840
https://doi.org/10.1115/ICONE25-67840
View Paper titled, Reconstruction of Core Overheating Damage Fraction Based on Neural Network Method
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Topics: Artificial neural networks, Damage, Damage assessment, Neural network models, Nuclear power stations, Containment, Emergency response, Errors, Hydrogen, Pressure
Proceedings Papers

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

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Fabrice Fouet, Pierre Probst
Proc. ASME. ICONE21, Volume 4: Thermal Hydraulics, V004T09A055, July 29–August 2, 2013
Publisher: American Society of Mechanical Engineers
Paper No: ICONE21-15674
https://doi.org/10.1115/ICONE21-15674
View Paper titled, Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code
Open the PDF for Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code in another window
Topics: Neural network models, Nuclear power stations, Sensitivity analysis, Uncertainty, Safety, Artificial neural networks, Fuels, Acceptance criteria, Accidents, Cladding systems (Building)
Proceedings Papers

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

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Rose Mary G. P. Souza, Joa˜o M. L. Moreira
Proc. ASME. ICONE14, Volume 1: Plant Operations, Maintenance and Life Cycle; Component Reliability and Materials Issues; Codes, Standards, Licensing and Regulatory Issues; Fuel Cycle and High Level Waste Management, 285-294, July 17–20, 2006
Publisher: American Society of Mechanical Engineers
Paper No: ICONE14-89726
https://doi.org/10.1115/ICONE14-89726
View Paper titled, Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems
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Topics: Neural network models, Robustness, Artificial neural networks, Errors, Signals, Computer software, Reactor protection systems, Safety, Algorithms, Multilayer perceptron