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Baranwal reviews virtual STEM lessons for U.S. tribal communities
In a blog post to the Department of Energy’s website on November 23, Rita Baranwal, assistant secretary for the Office of Nuclear Energy, commended recent virtual lesson projects from the Office of Nuclear Energy and the Nuclear Energy Tribal Working Group to increase STEM opportunities for Native American tribes.
The spotlighted lesson discussed in the article focused on a 3D-printed clip that turns a smartphone or tablet into a microscope with the ability to magnify items by 100 times. The Office of Nuclear Energy shipped nearly 1,000 of these microscope clips to students across the country, many of them going to U.S. tribal communities.
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 | dx.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.