Numerous researches are performed to take account of the heat transfer in the microchannel heat sink, due to its high efficiency and application in various fields of microelectronics. An attempt has been made to numerically investigate the effects of variation of cylindrical pin fin height, entrenched on bottom wall of microchannel. Three cases of microchannel heat sink are prepared: case 1: plain rectangular channel; case 2 and case 3 with decreasing pin fin height and with increasing pin fin height in the direction of flow, respectively. Also, diameter of pin fin is varied to obtain effects of any underlying flow feature on heat transfer augmentation. The analysis is performed for single-phase fluid deionized ultra-filtered water with temperature-dependent properties, for low Reynolds number range of 150–300. Higher Nusselt number is obtained for case 2, whereas lower pressure drop is obtained in case 3. The overall thermal performance of case 3 with increasing pin fin height outperforms the corresponding case 2 with decreasing pin fin height for the same pin fin diameter, due to the velocity distribution and reduced pressure drop in the downstream region in microchannel, which shows that the downstream region of microchannel heat sink has a significant impact in terms of the overall efficiency while establishing pressure drop as essential characteristics.

This paper numerically investigates heat transfer performance and characteristics in single-phase liquid cooling in microchannels with radial arrangement of fins having inherent symmetry to mitigate the problem of flow maldistribution. The effects of number of fins, channel depth and flow rate on Nusselt number and substrate temperature are obtained. The relative thermal performance and overall efficiency are compared with respect to two different base cases having minimum number of channels and minimum channel depth. For increasing values of flow rate and channel depth, contradicting effects in terms of temperature uniformity are obtained. By varying only number of channels, has no significance until it is coupled with channel depth, which suggests that aspect ratio of inlet cross-sectional area should be considered as a significant factor which is capable of generating secondary flows in single phase liquid flows. The overall efficiency of case with 70 number of channels, 200 µm depth and 15 ml/min is found to be highest.

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This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance.

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