Advances in nuclear power system design and fault-based condition monitoring towards safety of nuclear-powered ships

Sidum Adumene*, Rabiul Islam*, Md Tanjin Amin, Samson Nitonye, Mohammad Yazdi, Kombo Theophilus Johnson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The increasing need for cleaner and sustainable shipping operations, especially in the Arctic waters, has informed the rising technological advancements in ship nuclear power machinery (SNPM) design, such as the generation IV (GenIV) small modular reactor (SMR). Nuclear technology has also provided a potential alternative to decarbonization in the maritime industry. The recent design advances present safety challenges that need to be comprehensively studied and understood. The current study shows a review of the advancements in nuclear plant design and fault-based analysis towards safety in maritime operations. A systematic approach is adopted to explore the design improvements, operational challenges, fault diagnostics methodologies, and risk assessment frameworks for SNPM. While exploring the current state of knowledge on system design and fault-based risk assessment, the study highlights key research opportunities to provide a robust risk management framework towards safer nuclear-powered shipping operations, such as digitalization, resilient system design, and the internet of things (IoT). This study finds that the state of knowledge is inexhaustive and proposes a preliminary hybrid framework, able to integrate the hierarchical hazard structure into a fault-based risk aggregation methodology for risk-informed decision making in nuclear-powered shipping operations.
Original languageEnglish
Article number111156
Pages (from-to)1-10
Number of pages10
JournalOcean Engineering
Volume251
DOIs
Publication statusPublished - 1 May 2022
Externally publishedYes

Keywords

  • Fault detection and diagnosis
  • Nuclear powered shipping operations
  • Small modular reactor
  • Risk management
  • Data-driven model
  • Trustworthiness

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