Journal of the Korean Society of Systems Engineering (시스템엔지니어링학술지)

The Korean Society of Systems Engineering (한국시스템엔지니어링학회)
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A Systems Engineering Approach to Predict the Success Window of FLEX Strategy under Extended SBO Using Artificial Intelligence

  • Alketbi, Salama Obaid (Nuclear Engineering Department, KEPCO International Nuclear Graduate School) ;
  • Diab, Aya (Nuclear Engineering Department, KEPCO International Nuclear Graduate School)
  • Received : 2020.10.13
  • Accepted : 2020.11.24
  • Published : 2020.12.31
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Abstract

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

Keywords

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