Radial two-phase thermal conductivity and wall heat transfer coefficient of ceramic sponges – Experimental results and correlation

Author(s):  
M. Wallenstein ◽  
M. Kind ◽  
B. Dietrich
Author(s):  
S. V. Sridhar ◽  
R. Karuppasamy ◽  
G. D. Sivakumar

Abstract In this investigation, the performance of the shell and tube heat exchanger operated with tin nanoparticles-water (SnO2-W) and silver nanoparticles-water (Ag-W) nanofluids was experimentally analyzed. SnO2-W and Ag-W nanofluids were prepared without any surface medication of nanoparticles. The effects of volume concentrations of nanoparticles on thermal conductivity, viscosity, heat transfer coefficient, fiction factor, Nusselt number, and pressure drop were analyzed. The results showed that thermal conductivity of nanofluids increased by 29% and 39% while adding 0.1 wt% of SnO2 and Ag nanoparticles, respectively, due to the unique intrinsic property of the nanoparticles. Further, the convective heat transfer coefficient was enhanced because of improvement of thermal conductivity of the two phase mixture and friction factor increased due to the increases of viscosity and density of nanofluids. Moreover, Ag nanofluid showed superior pressure drop compared to SnO2 nanofluid owing to the improvement of thermophysical properties of nanofluid.


Author(s):  
Raphael Mandel ◽  
Amir Shooshtari ◽  
Serguei Dessiatoun ◽  
Michael Ohadi

Manifold microchannels utilize a system of manifolds to divide long microchannels into an array of parallel ones, resulting in reduced flow length and more localized liquid feeding. Reducing flow length is desirable because it enables the simultaneous enhancement of heat transfer rate and reduction of pressure drop. Furthermore, localized feeding reduces potential for localized dryout, increasing the operational heat flux. Because of the failure of the available conventional heat transfer correlations to predict the thermal performance of manifold microchannels operating in two phase mode, a “streamline” model was created. The heat transfer surface area was divided into parallel, non-interacting streamlines, and the quality, void fraction, film thickness, heat transfer coefficient, heat flux, and pressure drop was calculated sequentially along the streamline. The mass flow rate through each streamline was adjusted in order to obtain the specified pressure drop, and the value of this pressure drop was adjusted in order to obtain the desired microchannel mass flux. Finally, the average wall heat transfer coefficient was calculated, and temperature profile in the fin was adjusted to correspond with the analytical 1-D temperature distribution of a thin fin with an average wall heat transfer coefficient and specified base superheat. The average wall heat transfer coefficients predicted by the model was then compared to the available experimental data with sufficiently good agreement with a wide variety of geometries and working fluids at low mass fluxes.


2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.


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