A state-of-the-art review on performance measurement petri net models for safety critical systems of NPP. (January 2022)

• Record Type: Journal Article
• Authors: Jyotish, Nand Kumar; Singh, Lalit Kumar; Kumar, Chiranjeev
• Is Part Of: Annals of nuclear energy. Volume 165(2022)
• Publication Date: 2022-01
• Subjects: Performance Evaluation -- Petri nets -- Model performance evaluation -- Safety Critical System (SCS) -- Nuclear Power Plant (NPP); Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2021.108635
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: SCS of NPP needs to be validated for their performance for safety significance. The paper presents PN-based performance models & their suitability for SCS of NPP. We evaluate the model performance, its benefits, drawbacks, and performance metrics. We consider the papers which are published during the years 2009–2020. These models' performance can be used to build better systems. This survey helps identify acceptable methodologies and future research areas. Abstract: Considering the safety significance, safety critical systems (SCS) of nuclear power plant (NPP) needs to be validated for their performance. Authors are putting their continuous efforts to device new models for performance analysis of the dynamical systems. The results of any model's performance analysis are critical because they help in identifying any potential bottlenecks by predicting the performance parameters of a system when direct measurement is not possible. A model's efficiency can be assessed using specific performance parameters or metrics. Depending on the type of system to be built, these metrics can include throughput, waiting time, response time, reliability, system availability, usage rate, resource consumption, and so on. Petri nets, as an analytical model, assist in the performance assessment of any device model. This survey presents various Petri nets-based models for their suitability analysis for use in SCS of NPP. We evaluate the model performance, their benefits, drawbacks, and performance measuring parameters. We consider the papers which are published during the years 2009–2020. These models' performance can be used to build better systems–this survey paper aids in the identification of appropriate approaches and promising research areas for the future.