Inverse Geometry Design Of Radiative Enclosures Using Swarm Intelligence Optimization Algorithms

Radiave heating devices are encountered in various industrial fields, such as industial boilers, infrared reflecting ovens, material processing and molding, and so on. The design of radiave enclosure has a direct impact on security issues. Inverse design technique is a new method in recent years, whose solving approach is to establish an appropriate objective function according to the design requirements at first, and then optimizing the objective function by some optimization methods, and achieve the purpose finally. Inverse design technique has the advantages of simple process, short design circle, good optimization results, etc., which get more and more attention and application.Most of the inverse geometry design of radiative enclosures are based on the derivation of the objective function, which have the advantages that fast convergence and good stability results. However, the solving process of the objective function gradient is very complicated. Swarm intelligence optimization algorithm starting from a solution randomly to search the next solution whose objective function value is smaller, keeping this search mode until find the optimal solution. This solving way is easier than the gradient methods and ease of programming. In addition, the swarm intelligence optimization algorithm have the advantages of parallelism, self-organization, and versatility, and get scholars’ special attention.In this dissertation, Swarm Intelligence optimization algorithms are used to solve the inverse geometry design of two-dimensional radiative heat transfer enclosure, and the main contents include the following aspects:(1) The Discrete Ordinate Method(DOM) in body-fitted coordinate system is used to solve the radiative heat transfer in irregularly medium. Comparing the retrieved results to the Finite Volume Method(FVM) to verify the reliability of the direct model. Based on the retrieved heat flux on boundary, establish objective function.(2) The main characteristics and classification of Swarm Intelligence optimization algorithm are reviewed, the solving thoughts of Group Search Optimizer(GSO), Artificial Fish Swarm Algorithm(AFSA), Ant Colony Algorithm(ACO) and Shuffled Frog Leaping Algorithm(SFLA) are introduced also. Moreover, the principle and solving process of Krill Herd(KH) and Particle Swarm Optimization(PSO) are dicussed, which provides the theoretical support for the geometry design.(3) KH, standard PSO, and Stochastic PSO(SPSO) are used to solve the inverse geometry design problems of radiative heat transfer enclosure respectively, and achieve the purpose of heat flux uniform distribution on pre-appointed design surface after geometry optimization. Meanwhile, the computing performance in terms of solving inverse design problems of these three methods is compared. In addition, the influences of the number of control point on design surface, the properties of media, and the blockness of boundary are analyzed.