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The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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June 16–19, 2024
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College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
Nathan W. Porter, Vincent A. Mousseau, Maria N. Avramova
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1607-1617
Technical Paper | doi.org/10.1080/00295450.2018.1548221
Articles are hosted by Taylor and Francis Online.
This paper introduces a framework for model selection that includes parameter estimation, uncertainty propagation, and quantified validation. The framework is applied to single-phase turbulent friction modeling in CTF, which is a thermal-hydraulic code for nuclear engineering applications. The friction model is chosen because it is well understood and easy to separate from other physics, which allows focus to be on the model selection framework instead of on the particulars of the chosen model. Two different empirical models are compared: the McAdams Correlation and the Simplified McAdams Correlation. The parameter estimation is performed by calibrating each of the friction models to experimental data using the Delayed Rejection Adaptive Metropolis algorithm, which is a Markov Chain Monte Carlo method. State point uncertainties are also considered, which are determined based on measurement errors from the experiment. The input parameter distributions are propagated through CTF using a statistical method with samples. A variety of validation metrics is used to quantify which empirical model is more accurate. It is shown that model form uncertainty can be quantified using validation once all other sources of uncertainty—numerical, sampling, experimental, and parameter—have been quantitatively addressed. When multiple models are available, the one that has the smallest model form error can be selected. Though the framework is applied to a simple example here, the same process can quantify the model form uncertainty of more complicated physics, multiple models, and simulation tools in other fields. Therefore, this work is a demonstration of best practices for future assessments of model form uncertainty.