This paper presents a numerical optimization approach applicable to heat transfer design. A novel design procedure for the prediction of heat transfer inside a pressure vessel of an ocean current turbine using the finite element method of heat transfer analysis, artificial neural networks and genetic algorithms is presented. Numerical heat transfer analysis was done using commercial software ANSYS for two-dimensional heat transfer in simplified domains. Computation was confined to heat conduction. The ANSYS simulations results were then used for training and approximating the unknown functional behavior of heat transfer by using artificial neural networks (ANN). The trained ANN serves as the nonlinear objective function of the optimization procedure. Genetic algorithms (GA) were finally employed as the optimization tool. The optimum results obtained from the GA were verified against both ANSYS and ANN results. Both the ANN and GA were implemented in MATLAB environment. The overall methodology application was in effect validated by the results satisfactory for a specific ocean current turbine application.
The 20KW experimental ocean current research turbine developed by the Southeast National Marine Renewable Energy Center (SNMREC) at Florida Atlantic University (FAU) is designed to generate electricity from passing flow by a three-blade rotor connected to an induction electric motor/generator by a shaft supported by needle bearings and planetary gear reduction box. Ocean turbines are a new technology in the alternative energy community and the SNMREC is assisting commercial developers to test components and subsystems with this turbine (Driscoll et al., 2008). One of the important parts of the ocean turbine is the pressure vessel inside which the electric motor along with other electric components and sensors for controlling and monitoring purposes are installed. When the electric motor runs at full load, it generates a significant amount of energy in the form of heat.
