This paper constructs a numerical simulation model for the fire and fire-fighting system of an all-electric vehicle ro-ro passenger ship to study the influence of fire characteristics and fire-fighting system layout parameters on the fire-extinguishing system. The simulation results show that the fire can spread to the upper deck within 52 s, and the smoke will fill the main deck within 57 s. The study found that the battery capacity has a super-linear relationship with the fire hazard, and the fire thermal spread radius of a 240 Ah battery can reach 3.5 m. The high-expansion foam system has a low applicability in quickly suppressing battery fires due to its response delay and limited cooling capacity for deep-seated fires; the fire-extinguishing efficiency of fine water mist has spatial dependence: 800 µm droplets achieve effective cooling in the core area of the fire source with stronger penetrating power, while 200 µm droplets show better environmental cooling ability in the surrounding area; at the same time, the large-angle nozzles with an angle of 80–120° have a wider coverage range and perform better in overall temperature control and smoke containment than small-angle nozzles. The study also verified the effectiveness of fire curtains in forming fire compartments through physical isolation, which can reduce the heat radiation range by approximately 3 m. This research provides an innovative solution for improving the fire safety level of transporting all-electric vehicles on ro-ro passenger ships.

Maritime safety is crucial as vessels underpin global trade, but engine room explosions threaten crew safety, the environment, and assets. With modern ship designs growing more complex, numerical simulation has become vital for analyzing and preventing such events. This study examines safety risks from alternative fuel explosions in ship engine rooms, using the Trinitrotoluene (TNT)-equivalent method. A finite element model of a double-layer cabin explosion is developed, and simulations using blastFOAM in OpenFOAM v9 analyze shock wave propagation and stress distribution. Four explosion locations and five scales were tested, revealing that explosion scale is the most influential factor on shock wave intensity and structural stress, followed by equipment layout, with location having the least—though still notable—impact. Near the control room, an initial explosion caused a peak overpressure of 2.4 × 10⁶ Pa. Increasing the charge mass from 10 kg to 50 kg raised overpressure to 3.9 × 10⁶ Pa, showing strong dependence of blast intensity on explosive mass. Equipment absorbs and reflects shock waves, amplifying localized stresses. The findings aid in optimizing engine room layouts and improving explosion resistance, particularly for alternative fuels like liquefied natural gas (LNG), enhancing maritime safety and sustainability.

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources.