Thermal Optimal Design for Fully-Confined Compact Heat Sinks by Using Various Optimization Methods

• Main Authors: JENN-TSONG HORNG, 洪振聰
• Other Authors: YING-HUEI HUNG
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/53847298969469968063

博士 === 國立清華大學 === 動力機械工程學系 === 96 === In the present study, a series of experimental investigations and theoretical analyses on the fluid flow friction and heat transfer behavior for fully-confined compact heat sinks in a ducted flow have been performed. From the experimental data and theoretical results, the distribution of local effective Nusselt number is symmetric along the spanwise direction; and the local effective Nusselt number decreases along the streamwise direction. The local effective Nusselt number is insignificantly affected by GrH, Hb/H or Fin/Base material; while, it increases with increasing ReD. Similar trends can be found for the effects of relavant influencing parameters on other heat transfer characteristics such as average effective Nusselt number, local and average external thermal conductance. New correlations for fluid flow friction and thermal performance, including the effective friction factor, average effective Nusselt number, average external thermal resistance and overall thermal resistance, in terms of relevant influencing parameters have been presented. Prior to the execution of thermal optimization, two numerical models such as the RSM with a quadratic explicit formula and the ANN with a third-order explicit formula are effectively employed to accurately fit the data of overall thermal resistance, pressure drop and mass. As compared with the actual experimental data and theoretical results, the maximum deviations of the predictions for overall thermal resistance, pressure drop and mass by using the RSM method with a quadratic explicit formula are 7.3%, 1.7%, and 6.3%; those by using the ANN method with a third-order formula are 3.2%, 3.8% and 5.2%, respectively. In addition, with an effective back-propagation training algorithm, a more accurate implicit correlation between the performance outputs and design variables has been obtained. As compared with the actual experimental data and theoretical results, the maximum deviations of the predictions by this implicit correlation for overall thermal resistance, pressure drop and mass are 2.6%, 2.8%, and 3.4%. Furthermore, four types of optimization technique such as RSM-SQP, ANN-GA, RSM-GA and ANN-SQP methods have been successfully employed for the optimal evaluation on the thermal performance of fully-confined compact heat sinks in a ducted flow under multi-constraints such as pressure drop, mass, and space limitations. Among these four optimization methods, the superiority of using either the RSM-SQP or ANN-GA method can be found as compared with using the ANN-SQP or RSM-GA method. As compared with the optimal results obtained by the ANN-GA method, a significant deviation evaluated by using the RSM-SQP method for Cases I and II is found because the optimal values of the design variables are located beyond the region of operability. In contrast, a satisfactory agreement is achieved by the RSM-SQP method for Cases III and IV because the optimal values of the design variables are located within the region of operability. In summary, the advantage of using the RSM-SQP method with a smaller number of test cases, say 77 instead of 320 in the ANN-GA method, can be significantly found in the saving of computation time for some design cases with multi-constraints in the region of operability, e.g. Cases III and IV. As for the advantage of using the ANN-GA method, although more test cases are needed for the ANN-GA method as compared to that for the RSM-SQP method, the ANN-GA method which has randomly uniform-distributed training patterns in the whole solving domain can be applied to the global region of interest, not just in the region of operability; a globally precise optimal solution can be achieved with the ANN-GA method for all the cases (i.e., Cases I through IV) explored in the present study.