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2021 ANS Virtual Annual Meeting
June 14–16, 2021
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Nuclear Science and Engineering
Fusion Science and Technology
The consequences of closure: The local cost of shutting down a nuclear power plant
When on May 7, 2013, the Kewaunee nuclear power plant in rural Wisconsin was shut down, it took with it more than 600 full-time jobs and more than $70 million in lost wages, not including temporary employment from refueling and maintenance outages. Taking into account indirect business-to-business activity, the total economic impact of the closure of the single-unit pressurized water reactor was estimated to be more than $630 million to the surrounding three-county area.
Chih-Wei Chang, Jun Fang, Nam T. Dinh
Nuclear Science and Engineering | Volume 194 | Number 8 | August-September 2020 | Pages 650-664
Technical Paper | dx.doi.org/10.1080/00295639.2020.1712928
Articles are hosted by Taylor and Francis Online.
Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-ε, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations.
The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. The presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.