Particle swarm optimization inverse tracing method for mine tunnel fire based on quasi-steady-state heat transfer mechanism

Abstract

Rapidly locating and assessing fires in coal mines is essential for effective response and minimizing damage. Traditional methods often struggle to swiftly and accurately pinpoint fire locations and statuses. Hence, this study introduces a particle swarm inverse tracing approach based on the quasi-steady-state heat transfer between high-temperature flue gases and mine tunnel walls. This method aims to remotely monitor smoke flow temperatures and gas concentrations to determine fire locations and combustion trends. Validation of the fire source tracing model was conducted through comprehensive tunnel fire experiments and numerical simulations. The particle swarm optimization inverse tracing method yielded a maximum 7.65 % error in fire location determination compared to actual measurements, while accurately matching the measured fire source heat release rate curve. These findings underscore the method’s high precision and reliability, particularly evident during the developmental and stable stages of fire burning.