http://yetl.yabesh.ir/yetl1/handle/yetl/19052 2026-08-26T13:11:26Z 2026-08-26T13:11:26Z Duan, Dingli Guan, Shuaibing Qiao, Yanfeng Wang, Jay http://yetl.yabesh.ir/yetl1/handle/yetl/4315424 2026-08-23T07:40:05Z 2026-01-01T00:00:00Z

Computational Investigation of the Cooling and Heating Effects in Vortex Tube Duan, Dingli; Guan, Shuaibing; Qiao, Yanfeng; Wang, Jay Abstract. Vortex tubes exhibit a unique temperature separation phenomenon, leading to their widespread application in refrigeration and heating. Using the exact flow and temperature field simulation, this study examined the cooling effect and heating effect under different cold mass fractions, inlet pressure, and diameter ratios. The cooling effect shows a trend of first increasing and then decreasing with the increase of the cold mass fractions, while the heating effect gradually increases. Actually, the cooling and heating effects are suppressed by backflow; it is difficult for the working fluid to flow out of the cold end, leading to severe backflow at the cold end at low cold mass fractions, and the reverse flow boundary exceeds the control valve, causing the backflow at the hot exit to be enhanced at high cold mass fractions. To get excellent cooling effect and heating effect of the vortex tube, the inlet pressure should be increased while reducing the diameter ratio of the vortex tube, and the cold mass fractions should be controlled within a reasonable range [cold mass fraction (CMF) = 0.3–0.7].

2026-01-01T00:00:00Z Patel, Sahil F. Diller, Thomas E. Ponder, Monica A. Li, Mohan http://yetl.yabesh.ir/yetl1/handle/yetl/4315423 2026-08-23T07:40:03Z 2026-01-01T00:00:00Z

Thermal Modeling of Vacuum-Assisted Steam Pasteurization for Improved Product Safety of Low Water Activity Foods Patel, Sahil F.; Diller, Thomas E.; Ponder, Monica A.; Li, Mohan Abstract. Vacuum-assisted steam pasteurization is a common method used to inactivate microorganisms on dried, ready to eat foods, including nuts, seeds, and spices. Thermal experiments were performed on five types of dried seeds and nuts that varied in size and shape, both individual products or contained within mesh bags of product. Based on heat transfer and temperature measurements, it was found that the steam penetrated extremely slowly with unpredictable behavior in bags when the system first pulled a vacuum followed by steam injection. Consequently, a small fan system was added to increase the steam velocity and flush out noncondensable gases (NCGs) trapped in the open spaces between surfaces. A conduction heat transfer model was developed based on single product experimentation. Computational fluid dynamic (CFD) models were developed for full bags of each product based on the steam penetration and the transient temperature response throughout the package. The model was validated at a total of three treatment temperatures, 60, 70, and 80 °C. The measured surface temperature of the product within the package was successfully predicted using the CFD model result as input to the conduction heat transfer model.

2026-01-01T00:00:00Z Ozdogan, Muhammet Namli, Lutfu Durmus, Aydin http://yetl.yabesh.ir/yetl1/handle/yetl/4315422 2026-08-23T07:40:01Z 2026-01-01T00:00:00Z

Effects of Channel Cross-Section Geometry on the Performance of Polymer Electrolyte Membrane Fuel Cells Ozdogan, Muhammet; Namli, Lutfu; Durmus, Aydin Abstract. In this study, the effects of the flow-field cross-section geometry on the polymer electrolyte membrane fuel cell's performance were investigated by using a three-dimensional numerical model. For this purpose, the fuel cells with rectangular, triangular, trapezoidal, and semi-elliptical channel cross-section geometries were modeled. When changing the channel geometry, one or more of the channel cross-sectional area sizes, channel height, channel width, flow collecting plate shoulder width, and cell width sizes changed. Therefore, the influence of the flow-channel cross-section geometries was examined for four cases. With the change in channel geometry, the cross-sectional areas are unequal in the first case. In the second case, by varying channel heights, the flow-field areas were equalized. To equalize the flow-field area sizes, in the third case, the channel and shoulder widths of the current-collector plate were set differently for each geometry. Lastly, in the fourth case, the channel and cell widths were treated differently. Among the four investigated cases, the influence of channel cross-sectional geometry was found to be most pronounced in Case-4. In Case-4, the cell voltage and power values obtained for varying channel-section geometries can be sorted as triangular > semi-elliptical > trapezoidal > rectangular.

2026-01-01T00:00:00Z Kinjo, Tomihiro Mochizuki, Takeshi Nakano, Hayato Enoki, Koji Sei, Yuichi http://yetl.yabesh.ir/yetl1/handle/yetl/4315420 2026-08-23T07:39:57Z 2026-01-01T00:00:00Z

Machine-Learning and Experimental Study on Predicting the Heat Transfer Coefficient in Vertical and Microgravity Flow Boiling Using Horizontal Flow Data Kinjo, Tomihiro; Mochizuki, Takeshi; Nakano, Hayato; Enoki, Koji; Sei, Yuichi Abstract. This study aims to develop a highly accurate prediction method for heat transfer characteristics across the entire range from low quality to the postdryout region, without dependence on refrigerant properties, flow conditions, gravity orientation, and gravity environment. The proposed novel machine learning approach was combined with experimental investigations. A new experimental facility was developed to measure the boiling heat transfer coefficient of the hydrofluoroolefin (HFO) refrigerant R1233zd(E) in upward flow from low quality to the postdryout region. Experiments were conducted under heat flux conditions of 3–9 kW m−2 and mass flux conditions of 30–90 kg m−2 s−1, providing new data in parameter ranges insufficiently reported in previous studies. The experimental results deviated from the trends predicted by existing correlations, and the effects of heat flux and mass flux on the onset of dryout were clarified. A comprehensive database was constructed by combining 1433 points obtained in this study with literature data, yielding 3289 points for upward flow. Additional databases were compiled: 467 points for downward flow and 222 points for microgravity. Although machine learning models typically require large datasets, their prediction accuracy deteriorates when the available data are limited. To address this issue, the proposed method performs pretraining using horizontal-flow data, which are closely related to the heat transfer characteristics of vertical upward/downward and microgravity flows, followed by fine-tuning with the target datasets. The resulting model accurately predicts heat transfer coefficients from low quality through the postdryout region without dependence on refrigerant properties, flow conditions, gravity orientation, and gravity environment.

2026-01-01T00:00:00Z