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Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Fusion Science and Technology
Latest News
PPPL study points to better fusion plasma control
The combination of two previously known methods for managing plasma conditions can result in enhanced control of plasma in a fusion reactor, according to a simulation performed by researchers at the Department of Energy’s Princeton Plasma Physics Laboratory.
Yue Jin, Stephen M. Bajorek, Fan-Bill Cheung
Nuclear Science and Engineering | Volume 197 | Number 5 | May 2023 | Pages 967-986
Technical Paper | doi.org/10.1080/00295639.2022.2087834
Articles are hosted by Taylor and Francis Online.
The accurate prediction of the fluid flow mass and the heat transfer process as well as the system response during reflood transients has long been a critical and challenging issue for reactor system safety analyses. Accurate characterization of the flow and energy transport can also significantly facilitate the various system/component design and optimization tasks. In the current study based on the U.S. Nuclear Regulatory Commission/Pennsylvania State University Rod Bundle Heat Transfer (RBHT) reflood experimental data, a comprehensive uncertainty analysis framework is developed using DAKOTA. The developed framework is used to perform an in-depth reflood model validation and verification for the subchannel analysis code COBRA-TF. In the meantime, the artificial intelligence (AI)–based machine learning (ML) model for rod cladding temperature prediction during reflood is also developed and evaluated using the current framework. Key input parametric effects for reflood thermal-hydraulic prediction include the system pressure, inlet liquid temperature/enthalpy, inlet mass flow rate, and average bundle power input. The figure of merit under consideration is the peak cladding temperature variations. It is found in the current study that, while further model improvement is needed, COBRA-TF can predict the correct parametric trends when compared with the RBHT data. On the other hand, it is challenging for the pure AI-based ML models to correctly reflect the parametric trends. Suggestions for future ML model development are provided in the end.