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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 Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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Nuclear new build procurement considerations
It may seem counterintuitive, but the best time to enhance the ability to support operations and maintenance for a new plant is before construction starts. This is one of many lessons learned by the currently operating nuclear fleet. As construction and startup of many nuclear facilities was completed, it quickly became evident that the ability to efficiently support operations and maintenance was limited. Most of the information necessary to establish and manage procurement of spare and replacement items, maintenance, and configuration of the facilities was unavailable and had to be gathered on a case-by-case, “on-demand” basis. Absence of necessary information and the associated challenges resulted in the need for staff augmentation and multiyear-long projects to develop equipment bills of material and maintenance programs and to perform technical evaluations for the huge quantities of spare and replacement items being requested.
Yang Liu, Nam Dinh
Nuclear Science and Engineering | Volume 193 | Number 1 | January-February 2019 | Pages 81-99
Technical Paper – Selected papers from NURETH 2017 | doi.org/10.1080/00295639.2018.1512790
Articles are hosted by Taylor and Francis Online.
Two-fluid model-based multiphase computational fluid dynamics (MCFD) has been considered one of the most promising tools to investigate a two-phase flow and boiling system for engineering purposes. The MCFD solver requires closure relations to make the conservation equations solvable. The wall boiling closure relations, for example, provide predictions on wall superheat and heat partitioning. The accuracy of these closure relations significantly influences the predictive capability of the solver. In this paper, a study of validation and uncertainty quantification (VUQ) for the wall boiling closure relations in the MCFD solver is performed. The work has three purposes: (1) to identify influential parameters to the quantities of interest (QoIs) of the boiling system through sensitivity analysis (SA), (2) to evaluate the parameter uncertainty through Bayesian inference with the support of multiple data sets, and (3) to quantitatively measure the agreement between solver predictions and data sets. The widely used Kurul-Podowski wall boiling closure relation is studied in this paper. Several statistical methods are used, including the Morris Screening method for global SA, Markov Chain Monte Carlo for inverse Bayesian inference, and confidence interval as the validation metric. The VUQ results indicate that the current empirical correlations-based wall boiling closure relations achieved satisfactory agreement on wall superheat predictions. However, the closure relations also demonstrate intrinsic inconsistency and fail to give consistently accurate predictions for all QoIs over the well-developed nucleate boiling regime.