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Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
2021 Student Conference
April 8–10, 2021
North Carolina State University|Raleigh Marriott City Center
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ANS Board of Directors votes to retire outdated position statements
The American Nuclear Society’s Board of Directors on November 19 voted to retire several outdated position statements, as requested by the Public Policy Committee. Among them are Position Statements #37 and #63, dating from 2010, which have been retired for lacking policy recommendations and for being redundant, as other position statements exist with language that better articulates the Society’s stance on those topics.
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 | dx.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.