Research on Heat Transfer Performance Prediction of Heat Exchanger Using Improved Particle Swarm Optimization Algorithm

With the increase of running time, the heat exchanger will appear dirt and even blocked, affecting the performance of the equipment.In this paper, an adaptive particle cognitive domain method is proposed.In the particle position updating method, the particle moves to the current best position with the calculated best position as the center, the cognitive direction of the particle is determined as the direction, and the linear inertial descending weight is used to realize the particle optimization.Using three different particle swarm optimization algorithms, namely fixed weight particle swarm optimization, linear descending weight particle swarm optimization and step mass particle swarm optimization, the heat transfer performance prediction algorithm of heat exchanger is designed according to the basic calculation principle of heat transfer performance.Based on the imported data of the cold and hot side, the heat exchange performance of the heat exchanger is theoretically calculated by using the calculation software, and compared with the heat exchange performance data calculated according to the field measured data, the operation state of the heat exchanger can be qualitatively analyzed, and the theoretical basis for the intervention and maintenance of the heat exchanger can be provided.