Optimization of regression model using principal component regression method in passive system reliability assessment

• Published in: Progress in nuclear energy (New series) Vol. 103; pp. 126 - 134
• Main Authors: Solanki, R.B., Kulkarni, Harshavardhan D., Singh, Suneet, Verma, A.K., Varde, P.V.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2018; Elsevier BV
• Subjects: Collinearity; Component reliability; Computer simulation; Failure; Free convection; Influence; Integrated power ratio; Nuclear engineering; Nuclear reactors; Nuclear safety; Parameters; Principal component regression (PCR); Probabilistic methods; Probabilistic safety analysis (PSA); Probability; Regression models; Reliability analysis; Reliability of passive systems; Response surface methodology; Statistical analysis; Studies; System reliability; Systems analysis; Principal component regression (PCR); Integrated power ratio; Probabilistic safety analysis (PSA); Reliability of passive systems
• ISSN: 0149-1970; 1878-4224
• DOI: 10.1016/j.pnucene.2017.11.012

The estimation of reliability of a passive system employed in innovative nuclear reactors has been a challenging assignment due to substantial involvement of natural convection and gravity in its function. This involvement warrants phenomenological failures to cast major influence on probabilistic safety assessment (PSA) of passive system. The phenomenological failures associated with passive systems cannot be evaluated directly by deterministic approach. Probabilistic method deploys thermal-hydraulic codes to evaluate availability of the system and generates simulated response surfaces comprising of large amount of data, which eventually consumes improbable computation time. Regression models can be used to generate large amount of data with limited number of thermal –hydraulic code runs. To make regression models more effective, it is necessary to evaluate effect of each input parameter that goes into them. Isolated condenser system (ICS) is one of the passive systems used in nuclear reactors. The performance of ICS is evaluated by measuring a parameter called integrated power ratio (IPR) with use of regression models. In present study, these regression models are optimized using principal component regression (PCR) method. PCR method eliminates multi-collinearity present within input parameters and thus retains only those parameters which significantly affect the output. Results obtained by PCR method are compared to the outcome of hypothesis testing. The exercise indicates that under given design conditions, at least one input parameter can be omitted from the regression model without any significant effect on IPR. This makes ICS more reliable due to reduction in its probability of failure owing to omission of a redundant input parameter. •Optimization using principal component regression for PSA.•The analysis is backed by hypothesis testing.•Elimination of variables which are not explanatory is done using regression model.•Quantification of effect of optimization on reliability of system.