Lithium-ion batteries (LiBs) are a proven technology for energy storage systems, mobile electronics, power tools, aerospace, automotive and maritime applications. LiBs have attracted interest from academia and industry due to their high power and energy densities compared to other battery technologies. Despite the extensive usage of LiBs, there is a substantial fire risk associated with their use which is a concern, especially when utilised in electric vehicles, aeroplanes, and submarines. This review presents LiB hazards, techniques for mitigating risks, the suppression of LiB fires and identification of shortcomings for future improvement. Water is identified as an efficient cooling and suppressing agent and water mist is considered the most promising technique to extinguish LiB fires. In the initial stages, the present review covers some relevant information regarding the material constitution and configuration of the cell assemblies, and phenomenological evolution of the thermal runaway reactions, which in turn can potentially lead to flaming combustion of cells and battery assemblies. This is followed by short descriptions of various active fire control agents to suppress fires involving LiBs in general, and water as a superior extinguishing medium in particular. In the latter parts of the review, the phenomena associated with water mist suppression of LiB fires are comprehensively reviewed.

This paper presents a comprehensive overview of currently known applications of computing with words (CWW) in risk assessment. It is largely grouped into the following 5 categories: (1) fuzzy number based risk assessment; (2) fuzzy rule-based risk assessment; (3) fuzzy extension of typical probabilistic risk assessment; (4) ordinal linguistic approach for risk assessment; and (5) miscellaneous applications. In addition, the role of CWW within the broad area of risk assessment is briefly characterized.

This study describes a life cycle assessment (LCA) of a fourth generation (4G) nuclear power plant. A high temperature helium cooled reactor and gas turbine technology with modular helium reactor (GT-MHR) is used in this study as an example. This is currently one the safest design of a nuclear power plant. The study also takes into account impact of accidents and incidents (AI) which happened around the world at nuclear power generation facilities. The adopted method for the study is a hybrid LCA analysis. The analysis of each phase of the life cycle was done on the basis of process chain analysis (PCA). Where detailed data were not available, the Input/Output (I/O) databases was employed. The obtained results show that greenhouse gases (GHG) emissions and energy intensity per unit of electricity production are relatively low. In fact, these are even lower than emissions from a number of renewable energy sources. The results show considerably different greenhouse gases (GHG) emissions and energy intensity per unit of electricity production when effects of AI are taken into account.

A mathematical model, named FiresCone, was developed to simulate the pyrolysis and combustion processes of different types of combustible materials, which also took into account both gas and solid phases. In the present study, some non-charring and intumescent-protected polymer samples were investigated regarding their combustion behaviors in response to pre-determined external heat fluxes. The modeling results were validated against the experimental outcomes obtained from a cone calorimeter. The predicted mass loss rates of the samples were found to fit reasonably well with the experimental data collected under various levels of external irradiation. Both the experimental and modeling results showed that the peak mass loss rate of the non-charring polymer material occurred near the end of burning, whereas for the intumescent-protected polymer it happed shortly after the start of the experiment. “FiresCone” is expected to act as a practical tool for the investigation of fire behavior of combustible materials. It is also expected to model fire scenarios under complicated conditions.