Study On The Radiative Transfer In The Furnace Combustion Based On The Improved Particle Swarm Optimization Algorithm

In the measurement of combustion flame temperature in the furnace hearth, the best temperature measurement point selection can effectively reflect the real situation of the temperature distribution in the furnace. At the same time, the location distribution of the detection device outside the furnace hearth also affects receiving the information of the combustion flame radiation energy. Because of this, it has a high theoretical significance and practical application value to determine both the information by selecting rational optimization program. This paper mainly focuses on the study in this area, the specific results are as follows:1. According to the principle and the important law of radiation heat transfer, the equation of combustion flame radiative transfer is derived and simplified, and then, a physical model is established to determine the optimal temperature measurement point and the optimal location distribution of the detection device outside the furnace hearth. According to the characteristics of the physical model, turning it into a mathematical model to find the optimal value of the optimization problem and considering the measurement error and model error to determine the conditions the optimization problem need to meet when the optimal value is found.2. Because the gradient of the optimization problem based on the model is not easy to calculate and the form of the objective function based on the model is not clear, the common classical optimization algorithms can’t solve the problem effectively, then choosing the particle swarm optimization algorithm which is more satisfied with the needs to solve the optimization problem. With the aid of MATLAB simulation software, a lot of simulation experiments are carried out to obtain the optimal value and the curve of the optimization process. After analyzing the data and graphics, it is found that the particle swarm algorithm is easy to fall into local optimal value in the process of solving the optimization problem of the model, which leads to the optimization result is not ideal. Therefore, an improved particle swarm optimization algorithm combine with the differential evolution algorithm is proposed to solve the optimization problem based on the model. After the equivalent number of simulation experiments, the optimization process and results are compared with the PSO algorithm, and it proves that the PSODE algorithm has better optimization effect to solve the optimization problem based on the model, and the problems encountered when the PSO algorithm to solve the optimization problem are also solved effectively. Finally, we get the optimal temperature measurement point information and the optimal location distribution information of the detection device.