Application of Particle Swarm Optimization (PSO) Algorithm in Determining Thermodynamics of Solid Combustibles

The thermodynamics of a solid are crucial in predicting thermal responses and fire behaviors, and they are commonly determined by inverse modeling and optimization algorithms at constant heat flux. However, in practical scenarios, the imposed heat flux frequently varies with time, and related thermodynamics determination methods are rarely reported. In this study, the particle swarm optimization (PSO) algorithm and a 1D numerical model were utilized to determine temperature-dependent thermal conductivity and specific heat of beech wood and polymethyl methacrylate (PMMA). Surface, 3 and 6 mm in-depth temperatures were measured in three sets of ignition tests where constant and time-dependent heat fluxes (HFs) were applied. In each set, PSO was implemented at individual HFs, and the average value was deemed as the final outcome. Reliability of the optimized thermodynamics was verified by comparing with the reported values in the literature and predicting the experimental measurements that were not employed during parameterization. The results showed that wood thermodynamics attained under constant and time-dependent HFs in agreement with previously reported ones. Similar optimization procedures were conducted for PMMA, and good agreement with literature values was found. Using the obtained thermodynamics of wood under constant HF, the numerical model successfully captured the surface temperature at time-dependent HFs. Meanwhile, comparisons using wood temperatures at constant HFs and PMMA temperatures at linear HFs also verified the feasibility of PSO.