Moisture-carryover performance optimization using physics-constrained machine learning-Reference-Cited by-同舟云学术

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Moisture-carryover performance optimization using physics-constrained machine learning

Published:2021-05 Issue: Volume:135 Page:103691
ISSN:0149-1970
Container-title:Progress in Nuclear Energy
language:en
Short-container-title:Progress in Nuclear Energy

Author:

Wang Haoyu,Gruenwald J. Thomas,Tusar James,Vilim Richard

Funder

Office of Science

Office of Nuclear Energy

U.S. Department of Energy

Argonne National Laboratory

Publisher

Elsevier BV

Subject

Waste Management and Disposal,Energy Engineering and Power Technology,Safety, Risk, Reliability and Quality,Nuclear Energy and Engineering

Reference30 articles.

1. Scoping Study of Moisture Carryover in Boiling Water Reactors #1021185,2010

2. First principles simulation of events during mid-loop operation of PWRs;Espinosa;Simulation,1994

3. Modeling steam dryers;Fadda,2010

4. Measurement of steam generator or reactor vessel moisture carryover using a non-radioactive chemical tracer;Fournier,2009

5. Neural networks and the bias/variance dilemma;Geman;Neural Comput.,1992

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1. Physics-informed few-shot learning for wind pressure prediction of low-rise buildings;Advanced Engineering Informatics;2023-04

2. Preventing Reverse Engineering of Critical Industrial Data with DIOD;Nuclear Technology;2022-09-07

3. Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks;Nuclear Engineering and Design;2022-04

4. A physics-based parametric regression approach for feedwater pump system diagnosis;Annals of Nuclear Energy;2022-02

5. Optimization in nuclear systems;Nuclear Engineering Mathematical Modeling and Simulation;2022

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