A deep learning method for estimating the boiling heat transfer coefficient of porous surfaces

Investigation of pool boiling of nanofluids using artificial neural networks and correlation development techniques

Boiling heat transfer coefficient is one of the most efficient factors on the amount of transferred heat by boiling flow. New nanofluids have been extensively utilized for enhancing the performance of boiling process. Despite many experimental investigations around the pool boiling heat transfer coefficient of nanofluid, the precise mathematical scheme for the evaluation of this factor is of scarce up to now. The purpose of this research is prediction of heat transfer coefficient of Al2O3–water nanofluids in a nucleate pool boiling at low heat fluxes. The apparatus has been built to study the heat transfer coefficient in a nucleate pool boiling. Al2O3 nanoparticles are scattered into the pure water, and stability treatments are performed for the nanofluids. In the numerical simulation, the Eulerian two-phase method is applied and empirical correlations are utilized to predict bubble parameters. Since the concentration of nanoparticles in the nanofluid is low, it is considered as a homogenous liquid. Finally, a predictive equation is proposed for the heat transfer coefficient of nanofluid by using the response surface methodology. The investigated variables have a distance from the center of boiling surface, applied heat flux, nucleation site density, frequency of bubble, and bubble departure diameter. Statistical parameters reveal that the accuracy of model is suitable. Also results of response surface method demonstrate that nucleation site density and bubble departure diameter have the most and least effect on the heat transfer coefficient, respectively.

Estimation of pool boiling heat transfer coefficient of alumina water-based nanofluids by various artificial intelligence (AI) approaches

The effect of sedimentation phenomenon of the additives silver nano particles on water pool boiling heat transfer coefficient: A comprehensive experimental study

Experimental Study on Pool Boiling Heat Transfer for R22, R407c, and R410a on a Horizontal Tube Bundle With Enhanced Tubes

Nucleate pool boiling heat transfer coefficient (HTCs) were measured with one nonazeotropic mixture of propane/isobutane and two azeotropic mixtures of HFC134a/isobutane and propane/HFC 134a. All data were taken at the liquid pool temperature of 7°C on a horizontal plain tube of 19.0 mm outside diameter with heat fluxes of 10kW/m2 to 80 kW/m2 with an interval of 10 kW/m2 in the decreasing order of heat flux. The measurements were made through electrical heating by a cartridge heater. The nonazeotropic mixture of propane/isobutane showed a reduction of HTCs as much as 41% from the ideal values. The azeotropic mixtures of HFC134a/isobutane and propane/HFC 134a showed a reduction of HTCs as much as 44% from the ideal values at compositions other than azeotropic compositions. At azeotropic compositions, however, the HTCs were even higher than the ideal values due to the increase in the vapor pressure. For all mixtures, the reduction in heat transfer was greater with larger gliding temperature difference. Stephan and Korner’s and Jung et al’s correlations predicted the HTCs of mixtures with a mean deviation of 11 %. The largest mean deviation occurred at the azeotropic compositions of HFC 134a/isobutane and propane/HFC 134a.

The effects of oxidation on pool boiling on an oxidized enhanced boiling tube (GEWA-SE) have been studied. Water and a refrigerant R-11 were boiled at subatmospheric pressure. Oxidation increases the pool boiling heat transfer coefficient for both fluids. The pool boiling heat transfer coefficient increased as much as a factor of 2.4 for water, and 1.5 for the refrigerant. For the R-11 refrigerant, a highly wetting fluid, the boiling heat transfer coefficient increases with increase in the level of oxidation. The increase in the heat transfer coefficient with increasing level of oxidation for water as the boiling fluid was slightly less than that recorded for R-11. This study includes contact angle measurement and its influence on oxidation. The contact angle of different levels of oxidation and of various surface grades of copper and aluminum in contact with water, and refrigerants were measured. Contact angles can be used as a rough guide or indicator of the level of surface oxidation or the degree of wettability of fluids on a surface. With the depth of the roughness below 0.5 μm, no surface roughness effect on contact angle were observed.

Surrogate models are efficient tools which have been successfully applied in structural reliability analysis, as an attempt to keep the computational costs acceptable. Among the surrogate models available in the literature, artificial neural networks (ANNs) have been attracting research interest for many years. However, the ANNs used in structural reliability analysis are usually the shallow ones, based on an architecture consisting of neurons organized in three layers, the so-called input, hidden, and output layers. On the other hand, with the advent of deep learning, ANNs with one input, one output, and several hidden layers, known as deep neural networks, have been increasingly applied in engineering and other areas. Considering that many recent publications have shown advantages of deep over shallow ANNs, the present paper aims at comparing these types of neural networks in the context of structural reliability. By applying shallow and deep ANNs in the solution of four benchmark structural reliability problems from the literature, employing Monte Carlo simulation (MCS) and adaptive experimental designs (EDs), it is shown that, although good results are obtained for both types of ANNs, deep ANNs usually outperform the shallow ones.

The objectives of this paper are to study the pool boiling heat transfer characteristics of twisted tubes in the flooded evaporator. The twisted tubes are processed from common circular evaporating tubes with an outer diameter of 15.88mm. The outer major axis diameter, minor axis diameter, wall thickness and length of the twisted tube are 19.50mm, 11.28mm, 1.09mm, and 3310mm, respectively. The outside tube pool boiling heat transfer coefficients, tube side Reynolds numbers, the wall superheat, the saturation temperature of refrigerant and the heat flux are considered as the key parameters. The results show that pool boiling heat transfer coefficient data increase with , and , respectively, and decrease as the wall superheat increases. It can be found in the case study that the overall heat transfer coefficient of twisted tube flooded evaporator (TFE) is about 1.15 times as high as the one of common flooded evaporator (FE) with a same heat capacity. It is proved that an application of the TFE in the water-cooled screw chiller can be feasible.

Effects of different magnetic fields on the boiling heat transfer coefficient of the NiO/deionized water nanofluid, an experimental investigation

Experimental evaluation of nucleate pool boiling heat transfer correlations for R245fa and R1233zd(E) in ORC applications

Comparing the heat transfer coefficient of copper sulfate and isopropanol solutions in the pool boiling process: Bubble dynamic and ultrasonic intensification

Adaptive fuzzy model identification to predict the heat transfer coefficient in pool boiling of distilled water

Experimental investigation in pool boiling heat transfer of ammonia/water mixture and heat transfer correlations

An experimental investigation of the effect of mechanical vibrations of a copper flat circular surface on the pool boiling heat transfer coefficient of water at atmospheric pressure are presented in this paper. A vibration exciter was used to vibrate this copper test surface vertically. Effect of frequency and amplitude of vibration on the boiling heat transfer coefficient was studied. An increase in the heat transfer coefficient was observed at low frequency and amplitudes, at higher amplitude and frequency heat transfer deteriorates. Heat transfer coefficient increases up to 26% with vibration intensity, represented by vibrational Reynolds number.