Two-phase heat transfer microchannel system identification with Particle Swarm Optimization (PSO) approach

Abstract

The complex behavior of two-phase flow particularly in microchannels can be unpredictable. Experimental measurements are near impossible because of the unavailable compatible assessment equipment. Meanwhile, repeated experiments for reliability of outcomes are costly and involved much time and effort. Environmentally friendly propane is currently being considered as a replacement for hazardous coolants in available refrigeration and air-conditioning systems. This paper reports a system identification (SI) analysis of the collected experimental data of two-phase flow of refrigerant R290 in a microchannel test rig. An ARX model was chosen as the dynamic model, and the modeling of the input–output data was done using a new methodology based on particle swarm optimization (PSO) technique. Measured temperature difference across the microchannel test section and the mass flow rate were the input and output, respectively. The performance of the particle swarm optimization with discoverer (PSOd) was investigated and compared to the original PSO technique. The model was then validated by mean-squared error (MSE). Results demonstrate the advantages of discoverer in PSOd over its standard counterpart with a smaller MSE of 6.2629 × 10−11 and a faster convergence. The SI allows a better prediction of the mass flow rate before any further experiments to obtain the heat transfer coefficient are done. The model provides better management of design of experiments that involve the complex two-phase flow in a microchannel, consequently saving experimental time and cost.