Quantitative Risk Assessment of a High Power Density Small Modular Reactor (SMR) Core using Uncertainty and Sensitivity Analyses


The use of uncertainty quantification and machine learning platforms in ensuring the robustness of small modular reactor (or popularly known as SMR) core design is rare. Most importantly, there have not been many studies in SMR core design that need significant attention in terms of uncertainty quantification to ensure thermal-hydraulics safety. The majority of the previous SMR core studies have been limited to low core power density (∼60-65 MW/m3) environment, whereas typical land-based light-water cooled power reactors are operated in ∼100 MW/m3. In this paper, we attempt to fill the major gap in the robustness of SMR design system by using advanced VVUQ (Verification, Validation, and Uncertainty Quantification) approaches. Therefore, this work addresses the uncertainty issue and quantifies the sensitivity for the 100 MW/m3 SMR core system. Non-intrusive polynomial chaos, an efficient, well-developed, and validated approach, is applied to a subchannel thermal-hydraulic SMR system to compute the effect of input uncertainties on the SMR core. The impact of input uncertainties for 10% variability is evaluated on the key thermal-hydraulic parameters in the hot channel for the SMR reactor core with 100 MW/m3. It has been observed that all the output system parameters and their uncertainties are within the prescribed core safety limits for the 100 MW/m3 SMR core, except for the pressure drop and surface heat flux. It is also noticed that these two parameters exhibit an approximately 20% probability of exceeding the limiting values. The sensitivity analysis concluded that the pressure drop and surface heat flux are highly sensitive to the inlet temperature and linear power profile, respectively.


Nuclear Engineering and Radiation Science

Keywords and Phrases

High Power Density; Quantitative Risk Assessment; Sensitivity Analyses; Small Modular Reactor (SMR); Uncertainty Quantification; Verification, Validation, And Uncertainty Quantification (VVUQ)

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Article - Journal

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Publication Date

15 Jul 2021