Abstract: In this study, MHD free convection of a nanofluid filled trapezoidal cavity with a stationary adiabatic circular cylinder is numerically investigated. The bottom wall of the cavity is heated and the side walls are kept at constant temperature lower than that of the heater. Other walls of the enclosure and cylinder surface are assumed to be adiabatic. The governing equations are solved with a commercial solver using finite element method. The effects of the Grashof number, Hartmann number and solid volume fraction of the nanoparticle are numerically studied for both cylinder and no-cylinder configurations. It is observed that averaged heat transfer decreases with increasing Hartmann number, decreasing Grashof numbers and increasing inclination angles of the side walls. The presence of the cylinder deteriorates the averaged heat transfer and this is more pronounced at low Grashof, high Hartmann numbers and high values of inclination angles of side walls. Averaged heat transfer increases

• Publication Date: 26-Oct-2014
• DOI: 10.15224/978-1-63248-031-6-145
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Abstract: This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for self-adaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of convergence to the optimal solution and incorporates concept from neural network for self-adaptive of control parameters. It is shown to be statistically significantly superior to other Evolutionary Algorithms (EAs) on numerical benchmark problems and constrained real-parameter optimization.

• Publication Date: 26-Oct-2014
• DOI: 10.15224/978-1-63248-031-6-146
• Views: 0
• Downloads: 0