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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
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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Latest News
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.
Chen Wang, Xu Wu, Tomasz Kozlowski
Nuclear Science and Engineering | Volume 193 | Number 1 | January-February 2019 | Pages 100-114
Technical Paper – Selected papers from NURETH 2017 | doi.org/10.1080/00295639.2018.1499279
Articles are hosted by Taylor and Francis Online.
In the framework of Best Estimate Plus Uncertainty methodology, the uncertainties involved in model predictions must be quantified to prove that the investigated design is reasonable and acceptable. The uncertainties in predictions are usually calculated by propagating input uncertainties through the simulation model, which requires knowledge of the model or code input uncertainties, for example, the means, variances, distribution types, etc. However, in best-estimate system thermal-hydraulic codes such as TRACE, some parameters in empirical correlations may have large uncertainties that are unknown to code users, and their uncertainties are therefore simply ignored or described by expert opinion.
In this paper, the issue of missing uncertainty information for physical model parameters in the thermal-hydraulic code TRACE is addressed with inverse uncertainty quantification (IUQ), using the steady-state void fraction experimental data in the Organisation for Economic Co-operation and Development/Nuclear Energy Agency PSBT (Pressurized water reactor Sub-channel and Bundle Tests benchmark. The IUQ process is formulated through a Bayesian perspective, which can yield the posterior distributions of the uncertain inputs. A Gaussian process emulator is employed to significantly reduce the computational burden involved in sampling the posteriors using the Markov Chain Monte Carlo method. The posterior distributions are further used in forward uncertainty quantification and sensitivity analysis to quantify the influences of those parameters on the quantities of interest. The results demonstrate the effectiveness of the IUQ framework with a practical nuclear engineering example and show the necessity of quantifying and reducing uncertainty of physical model parameters in future work.