Determination of the Optimum Operating Conditions and Geometrical Dimensions of the Plate Fin Heat Sinks Using Teaching-Learning-Based-Optimization Algorithm

Electronic devices must be effectively cooled for long-term reliability and safe operation. It is essential to determine operating conditions and optimum dimensions of cooling devices in terms of device weight, space, cost, and sound limits. Plate fin heat sinks (PFHSs) are frequently used for cooling electronic devices. Optimum thermal designs of PFHSs are explored in this study using a teaching-learning-based-optimization (TLBO) algorithm where entropy generation (⁠$S˙gen$⁠) minimization, profit factor (⁠$J$⁠) maximization, base plate temperature excess (⁠$θb$⁠) minimization, total mass (⁠$mass)$ minimization, and total volume (⁠$volume$⁠) minimization are the objective functions of the constrained single-objective optimization problems. For further investigations of the entropy generation minimization method, three different optimization problems are also studied: minimization of thermal resistance (⁠$Rth$⁠), minimization of pressure drop (⁠$ΔP$⁠), and minimization of pumping power (⁠$W˙pump$⁠). Each optimization problem is subjected to a constraint, namely, temperature excess of base plate temperature (⁠$θb$⁠) should be lower than 10 K. Four optimization variables are considered which are the number of plate fins (N), freestream velocity (⁠$Vf$⁠), the thickness of the fin (⁠$tfin$⁠), and height of the fin (⁠$Hfin$⁠). Optimum configurations belonging to the different optimization problems are compared, and the effect of each optimization variable on the objective functions is discussed in detail. It is found that one can obtain optimum operating conditions and geometrical dimensions of the PFHSs according to the design objective, i.e., minimum mass requirement, space limitation, minimum base plate requirement, etc. As a result, the optimum designs of the studied cases are different which are superior to each other in terms of design targets.