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Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Latest News
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Haining Zhou, Volkan Seker, Thomas Downar
Nuclear Technology | Volume 206 | Number 6 | June 2020 | Pages 839-861
Technical Paper | doi.org/10.1080/00295450.2020.1746620
Articles are hosted by Taylor and Francis Online.
The paper presents a self-adaptive feature selection algorithm we developed for solving high-dimensional uncertainty quantification problems. The development of the algorithm was motivated and supported by the benchmarking of the Transient Reactor Test (TREAT) transient test 2857. The generalized polynomial chaos expansion scheme was adopted to decompose the response functions. Our algorithm was applied to select the dominant basis from the candidate polynomial basis in a self-adaptive manner by assigning weights to the polynomial basis and adjusting the weights using the least absolute shrinkage and selection operator regularization–estimated coefficients through iterations. The developed algorithm can recognize the significant basis terms in the polynomial expansion of the response functions and therefore build a sparse polynomial expansion using a limited number of samples. The algorithm was implemented and verified through three different TREAT modeling cases. The testing results demonstrated the general stability and prediction performance of our algorithm and provided useful information about the uncertainty mechanism of the TREAT transient test 2857.