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The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
2021 ANS Virtual Annual Meeting
June 14–16, 2021
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Federal subsidies for nuclear plants?
The Biden administration has indicated to lawmakers that it supports federal subsidies for struggling nuclear power plants, Reuters reported this morning, citing sources familiar with the discussions.
The subsidies would be in the form of production tax credits, according to the report, and would likely become part of the president’s $2.3 trillion infrastructure plan.
Yeni Li, Hany S. Abdel-Khalik, Acacia J. Brunett, Elise Jennings, Travis Mui, Rui Hu
Nuclear Science and Engineering | Volume 195 | Number 5 | May 2021 | Pages 520-537
Technical Paper | dx.doi.org/10.1080/00295639.2020.1840238
Articles are hosted by Taylor and Francis Online.
The System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non–light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network–based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.