| Summary: | 碩士 === 逢甲大學 === 化學工程學所 === 93 === This thesis considers the dynamic simulation, optimal design and control of CPU heat sink processes. A finite element method is used to solve the govern equations of the heat transfer process between the CPU and plate fin sink. In order to improve the efficiency of heat dissipation, a real code genetic algorithm along with the Lagrange method is applied for the optimal design of the plate-fin. For considering simultaneously the heat transfer and fluid friction, the objective function to be minimized is the entropy generation rate. Several practical design cases have been completed successfully using the proposed optimization scheme. With the optimal designed heat sink process, a direct adaptive model-based control scheme is proposed to regulate the CPU temperature. An output-bounded single neuron controller is utilized for the learning control of the process by merely observing the process output error, even though the process dynamics is characterized by partial differential equations. Extensive simulation results reveal that the proposed model-based control strategy is more superior to existing methodologies, such as on-off and PI controls. From the significant results presented in this thesis, it is believed that the proposed optimal design and control strategies can substantially enhance the effectiveness and performance of CPU heat sink processes.
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