Optimal Boundary Design of Radiant Enclosures Using Micro-Genetic Algorithm (Effects of Refractory Properties and Aspect Ratio of Enclosure on Heaters Setting)

Abstract

This study presents an optimization methodology for finding the heaters setting that produce a desired heat flux and temperature distribution over a region of the enclosure surface, called the design surface. Radiation element method by ray emission model (REM 2 ) was used to calculate the radiative heat flux on the design surface, which enable us to handle reflecting surfaces. Micro-genetic algorithm was used to minimize an objective function, which was expressed by the sum of square error between estimated and desired heat fluxes on the design surface. The novel features of this methodology were demonstrated by finding the optimal heaters setting of a three dimensional enclosure for three aspect ratio. The effects of refractory properties have been studied as well.

A Parametric Study of Optimal Number and Location of Radiant Heaters in Enclosures with Participating Media

“…This occurs because two conditions are speci ed on some parts of the system being designed (the design portion), and the unknown conditions on other parts of the system are to be determined. These types of problem are called inverse problems, and regularization [1][2][3] and optimization [4][5][6][7][8][9][10][11][12] methods are used to solve them.…”

“…In an optimization method, an objective function is de ned in such a way that its minimum corresponds to the optimal design outcome. The optimization techniques can be classi ed as gradient-based methods [4][5][6] and heuristic methods [7][8][9][10][11][12][13][14]. The gradient-based methods are most often used if objective functions are continuously di erentiable functions with few local extrema.…”

“…When design variables are discrete or combinatorial, heuristic methods are the only methods that can be used. Genetic Algorithms (GAs) [7][8][9][10][11] are the most popular heuristic methods and have gained a permanent position as an optimization algorithm for solving many di cult optimization problems.…”

Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem

“…From previous studies, they have been implemented in mostly inverse gas radiation analyses concerned with the determination of the unknown radiation properties of a gas medium, boundary conditions and temperature profiles from given radiation measurements [6][7][8][9][10][11]. For surface radiation problems containing transparent media, there are some works concerning the application of GA and PSO algorithms to the optimal geometry design of radiant enclosures inversely [12][13][14][15][16]. Hosseini Sarvari [12] applied the micro-genetic algorithm (mGA) to optimize the geometry of an enclosure which contains a transparent medium, and Safavinejad et al [13,14] used mGA for determining the optimal number and location of heaters over boundary surfaces in irregular 2-D transparent media.…”

“…For surface radiation problems containing transparent media, there are some works concerning the application of GA and PSO algorithms to the optimal geometry design of radiant enclosures inversely [12][13][14][15][16]. Hosseini Sarvari [12] applied the micro-genetic algorithm (mGA) to optimize the geometry of an enclosure which contains a transparent medium, and Safavinejad et al [13,14] used mGA for determining the optimal number and location of heaters over boundary surfaces in irregular 2-D transparent media. Also, Chopade et al [15] estimated heat flux distributions on a 3-D design object using mGA, while Farahmand et al [16] studied the shape optimization of a two-dimensional radiative enclosure with diffuse-gray surfaces by the PSO algorithm and compared with mGA results from Sarvari [12] briefly.…”