Heat transfer correlations for jet impingement boiling over micro-pin-finned surface

Heat transfer performance of submerged jet impingement boiling over staggered micro-pin-finned surfaces was investigated using air-dissolved FC-72. The dimension of the silicon chips is 10 x 10 x 0.5 mm(3) (length x width x thickness) on staggered micro-pin-fins with four dimensions of 30 x 30 x 60 mu m(3), 50 x 50 x 60 mu m(3), 30 x 30 x 120 mu m(3) and 50 x 50 x 120 mu m(3) (width x thickness x height, named S-PF30-60, S-PF50-60, S-PF30-120, and S-PF50-120) were fabricated by using the dry etching technique. The effects of micro-pin-fins, jet-to-target distance (H = 3, 6, and 9 mm), and jet Reynolds number (Re = 2853, 5707, and 8560) on jet impingement boiling heat transfer performance were explored. For comparison, experiments with jet impinging on a smooth surface were also conducted. The results showed that all micro-pin-finned surfaces show better heat transfer performance than that of a smooth surface. The largest Nusselt number is 1367, corresponding to a heat transfer coefficient of 26387 W.m(-2).K-1 with S-PF30-120 at Re = 8560, Hid = 2, and q = 151 W.cm(-2), which is approximately twice the largest Nusselt number of Chip S. In the single-phase heat-transfer-dominant region, the Nusselt number (Nu) is mainly influenced by several dimensionless numbers, including Reynolds number (Re), boiling number (Bo), the ratio of jet-to-target distance to jet diameter (H/d), the ratio of micro-pin finned surface area to smooth surface area A/As, and a dimensionless number corresponding to flow resistance D-h/L-h. Correlations to predict Nu in both single-phase heat-transfer-dominant region and two-phase heat-transfer-dominant region for smooth and micro-pin-finned surfaces were proposed. The results show that most data (96%) in the single-phase heat-transfer-dominant region and most data (96%) in the two-phase heat-transfer-dominant region were predicted within +/- 13% and +/- 15%, respectively. In addition, CHF correlations for smooth and micro-pin-finned surfaces were also proposed, and most data (95%) are predicted within +/- 20% for a smooth surface and all the data within +/- 5% for the micro-pin-finned surfaces. (C) 2018 Elsevier Ltd. All rights reserved.