Journal of the Korean Society of Systems Engineering (시스템엔지니어링학술지)

The Korean Society of Systems Engineering (한국시스템엔지니어링학회)
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A SE Approach to Predict the Peak Cladding Temperature using Artificial Neural Network

  • ALAtawneh, Osama Sharif (Nuclear Engineering Department, KEPCO International Nuclear Graduate School) ;
  • Diab, Aya (Nuclear Engineering Department, KEPCO International Nuclear Graduate School)
  • Received : 2020.10.05
  • Accepted : 2020.12.16
  • Published : 2020.12.31
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Abstract

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

Keywords

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