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The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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Nuclear Science and Engineering
Fusion Science and Technology
Study indicates pilot facility could significantly reduce waste volumes
Waste disposal start-up Deep Isolation and fusion tech company SHINE Technologies have announced the completion of a collaborative study assessing the costs of disposing of radioactive byproducts from a pilot spent nuclear fuel recycling facility.
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.