Experimental investigations on nucleate pool boiling over micro-finned cylindrical surfaces

The nucleate pool boiling heat transfer over micro-finned cylindrical surfaces has application in the heat exchangers used in thermal power plants and chemical industries. The estimation of boiling heat transfer coefficient is an important parameter in the design of two-phase heat exchangers using micro-finned cylindrical surfaces. In the present work, related experimental investigations on four micro-finned cylindrical surfaces with different surface geometry using refrigerant R-141b at atmospheric pressure are conducted to determine the boiling heat transfer coefficient over micro-finned cylindrical surfaces. A correlation is developed by dimensional analysis wherein the effects of geometrical parameters, operating pressure and thermo-physical properties of fluids are taken into consideration and dimensional analysis conducted using Buckingham π-theorem. The correlation developed utilizes experimental data obtained over the present study as well as from previous studies by various researchers including experimental data for water over different micro-finned cylindrical surfaces at 1 bar by Mehta and Kandlikar, experimental data for R123 at 0.97 bar by Saidi et al. and experimental data for R134a over micro-finned cylindrical surface at 6.1 bar, 8.1 bar, 10.1 bar and 12.2 bar by Rocha et al. The heat flux ranging from 5 to 1100 kW/m2 are considered for the analysis. The data points have been compared with the proposed correlation and the absolute average deviation of the whole data set was obtained as 13.43% with root mean square deviation of 0.0273. All the predicted values were within ±15% of the experimental values of the boiling heat transfer coefficient.

Surface wettability effect of plain and plasma-sprayed copper-coated surfaces on CHF enhancement during saturated pool boiling of FC-72

In the current scenario, CHF study is essential for the safe operation of electronics equipment comprising a two-phase heat transfer process. Therefore, the present experimental investigation involves saturated pool boiling and CHF study of FC 72 over a plain stainless steel surface (SS) and microporous copper-coated SS surfaces under atmospheric conditions. Accordingly, three different plasma-sprayed copper-coated surfaces with coating thicknesses of 65 μm, 105 μm, and 145 μm prepared using micro copper particles of size 25–45 μm. The analysis of the results shows that with an increase in heat flux values, the boiling heat transfer coefficient increases over plain as well as coated surfaces. The plasma-spayed copper-coated surfaces with a coating thickness of 65 μm and 105 μm exhibit a higher boiling heat transfer coefficient as than the plain surface. On the other hand, a 145 μm thick coated surface resulted in a comparable boiling heat transfer coefficient with the plain SS surface. Among the three porous-coated surfaces, the boiling heat transfer coefficient decreases continuously from 65 μm to 145 μm of the coated surface. On the contrary, to the observed nucleate boiling behavior, all the porous-coated surfaces show a higher value of CHF than the plain surface, and the CHF value is found to increase continuously from 65 μm to 145 μm of the coated surfaces. The enhancement of CHF values was found to be 66.29%, 69.17%, and 77.75% for a coating thickness of 65 μm, 105 μm, and 145 μm, respectively, compared with the plain surface. The porous coating thickness of 65 μm shows a greater value of heat transfer coefficient than 105 μm and 145 μm whereas 145 μm exhibits a higher value of CHF as than 65 μm and 105 μm.

Numerical investigation of surface roughness effect on pool boiling heat transfer of Al2O3/water nanofluid

This study examined the effect of surface roughness on the pool boiling heat transfer coefficient of pure water and water-alumina nanofluid with 0.1% and 0.01% volume concentration using computational fluid dynamics on the surface of a stainless-steel cylinder. The effect of nanoparticles was checked by averaging the thermophysical properties in the equations of the flow field with boiling. Simulations were performed for initial surface roughnesses from 0.1 to 0.8 µm. Furthermore, the presence of nanoparticles would make their deposition on the heated surface and change the surface properties. Thus, once again simulations were performed for roughness with the same values but because of the deposition of nanoparticles. In doing so, two separate equations were used for the nucleation site density parameter. Ultimately, the results obtained from both types of roughness were compared. The results indicated that with an increase in the roughness, the boiling heat transfer coefficient increased. Further, at the same roughness, the boiling heat transfer rate of the deposited surface decreased for nanofluid of 0.01% vol and increased for nanofluid of 0.1% vol compared to the non-deposited surface. For pure water at 0.8 µm roughness, the sediment improved heat transfer but it reduced heat transfer for 0.4 µm and lower roughness.

Heat transfer enhancement at evaporation and boiling of liquid on capillary-porous surfaces created by 3D printing

The paper presents the results of experiments on measuring heat transfer in laminar-wave films of R114/R21 refrigerant mixture flowing down a vertical plate (70 × 80 mm). The experiments were carried out on the saturation line at a pressure of 2 bar. To enhance heat transfer, a porous copper coating with a flat layer thickness of 400 μm and a porosity of 45% was applied to the heat-transfer surface by 3D printing. The effectiveness of the application of the technique used for the heat transfer enhancement is demonstrated: an increase in the boiling heat transfer coefficient is obtained up to three times in comparison with the reference smooth surface, as well as two-fold increase in heat transfer coefficient in the evaporation regime. Based on the obtained experimental results and analysis of the research data of other authors, the geometric structure of promising multiscale porous enhancing coating is proposed for further research.

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.