PREDICTION OF POOL BOILING HEAT TRANSFER COEFFICIENTS OF REFRIGERANT R-141b ON NANOCOATED SURFACES USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Author(s):  
Sandipan Deb ◽  
Prasenjit Dey
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
Boiling Heat Transfer ◽  
Pool Boiling ◽  
Transfer Coefficients ◽  
Pool Boiling Heat Transfer ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

Application of Artificial Neural Network for the Heat Transfer Investigation Around a High-Pressure Gas Turbine Rotor Blade

2011 ◽  
Author(s):  
Ibrahim Eryilmaz ◽  
Sinan Inanli ◽  
Baris Gumusel ◽  
Suha Toprak ◽  
Cengiz Camci
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
High Pressure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Transfer Coefficients ◽  
Experimental Conditions ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

This paper presents the preliminary results of using artificial neural networks in the prediction of gas side convective heat transfer coefficients on a high pressure turbine blade. The artificial neural network approach which has three hidden layers was developed and trained by nine inputs and it generates one output. Input and output data were taken from an experimental research program performed at the von Karman Institute for Fluid Dynamics by Camci and Arts [5,6] and Camci [7]. Inlet total pressure, inlet total temperature, inlet turbulence intensity, inlet and exit Mach numbers, blade wall temperature, incidence angle, specific location of measurement and suction/pressure side specification of the blade were used as input parameters and calculated heat transfer coefficient around a rotor blade used as output. After the network is trained with experimental data, heat transfer coefficients are interpolated for similar experimental conditions and compared with both experimental measurements and CFD solutions. CFD analysis was carried out to validate the algorithm and to determine heat transfer coefficients for a closely related test case. Good agreement was obtained between CFD results and neural network predictions.

Nucleate Pool Boiling Heat Transfer Coefficients of Distilled Water (H2O) and R-134a/Oil Mixtures From Rib-Roughened Surfaces

1997 ◽  
Vol 119 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Shou-Shing Hsieh ◽  
Chun-Jen Weng
Keyword(s):  
Heat Transfer ◽  
Boiling Heat Transfer ◽  
Pool Boiling ◽  
Distilled Water ◽  
Transfer Coefficients ◽  
Pool Boiling Heat Transfer ◽  
Heat Transfer Coefficients ◽  
Oil Mixtures ◽  
R 134A ◽  
Rib Roughened

Measurements of pool-boiling heat transfer coefficients in distilled water and R-134a/oil mixtures with up to 10 percent (by weight) miscible EMKARATE RL refrigeration lubricant oil are extensively studied for a smooth tube and four rib-roughened tubes (rib pitch 39.4 mm, rib height 4 mm, rib width 15 mm, number of rib element 8, rib angle 30 deg–90 deg). Boiling data of pure refrigerants and oil mixtures, as well as the influences of heat flux level on heat transfer coefficient, are presented and discussed. A correlation is developed for predicting the heat transfer coefficient for both pure refrigerants and refrigerant-oil mixtures. Moreover, boiling visualizations were made to broaden our fundamental understanding of the pool boiling heat transfer mechanism for rib roughened surfaces with pure refrigerants and refrigerant-oil mixtures.

Nucleate Pool Boiling Heat Transfer of R134a and R134a-PVE Lubricant Mixtures on Smooth and Five Enhanced Tubes

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Wen-Tao Ji ◽  
Ding-Cai Zhang ◽  
Nan Feng ◽  
Jian-Fei Guo ◽  
Mitsuharu Numata ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Boiling Heat Transfer ◽  
Pool Boiling ◽  
Saturation Temperature ◽  
Transfer Coefficients ◽  
Pool Boiling Heat Transfer ◽  
Heat Transfer Coefficients ◽  
Enhanced Tubes ◽  
Mass Fractions

Pool boiling heat transfer coefficients of R134a with different lubricant mass fractions for one smooth tube and five enhanced tubes were tested at a saturation temperature of 6°C. The lubricant used was polyvinyl ether. The lubrication mass fractions were 0.25%, 0.5%, 1.0%, 2.0%, 3.0%, 5.0%, 7.0%, and 10.0%, respectively. Within the tested heat flux range, from 9000 W/m2 to 90,000 W/m2, the lubricant generally has a different influence on pool boiling heat transfer of these six tubes.

Effect of Nano-Particles on Pool Boiling Heat Transfer of Refrigerant 141b

2007 ◽  
Author(s):  
Da-Wei Liu ◽  
Chien-Yuh Yang
Keyword(s):  
Heat Transfer ◽  
Boiling Heat Transfer ◽  
Heat Transfer Performance ◽  
Pool Boiling ◽  
Nano Particles ◽  
Test Results ◽  
Tube Surface ◽  
Transfer Coefficients ◽  
Pool Boiling Heat Transfer ◽  
Heat Transfer Coefficients

Fluids with nano-sized particles have been proved that may effectively enhance the single-phase convective heat transfer performance. For pool boiling heat transfer, the published test results seems conflicted to each other. Some measured heat transfer coefficient decreased with increasing particle concentration but some showed no appreciable difference. This study provides an experimental investigation on pool boiling heat transfer performance of refrigerants R-141b with and without nano-sized Au particles on horizontal plain tubes. The test results show that the boiling heat transfer coefficients increase with increasing nano-particles concentration. At particles concentration of 1.0%, the heat transfer coefficient is more than twice higher than those without nano-particles. However, the heat transfer coefficients decreased for each test after every 5 days and finally close to those of R-141b without nano-particles. The SPM investigation shows that the test tube surface roughness decreased from 0.317 μm before boiling test to 0.162 μm after test. Further investigation by TEM and Dynamic Light Scattering particle analyzer shows that the nano-particles aggregated from 3 μm before test to 110 μm after test. This results show that the nano-sized Au particles are able to significantly increase pool boiling heat transfer of refrigerant R-141b on plain tube surface. The tube surface roughness and particle size changed after boiling test. Both of these effects degrade the boiling heat transfer coefficients.

Prediction of Nucleate Pool Boiling Heat Transfer Coefficients of Refrigerant-Oil Mixtures

1984 ◽  
Vol 106 (1) ◽  
pp. 184-190 ◽  
Author(s):  
M. K. Jensen ◽  
D. L. Jackman
Keyword(s):  
Heat Transfer ◽  
Boiling Heat Transfer ◽  
Pool Boiling ◽  
Nucleate Pool Boiling ◽  
Transfer Coefficients ◽  
Fluid Mixture ◽  
Pool Boiling Heat Transfer ◽  
Heat Transfer Coefficients ◽  
Oil Mixtures ◽  
Oil Concentration

This paper describes an experimental investigation to determine the mechanism governing nucleate pool boiling heat transfer in refrigerant-oil mixtures, the role diffusion plays in this process, and the influence of the fluid mixture properties. Boiling heat transfer data were taken in mixtures of up to 10 percent oil by weight in R-113. Thermophysical properties of the mixtures (density, viscosity, surface tension, specific heat, and contact angle) were measured. The decrease in heat transfer coefficient with increasing oil concentration is attributed to diffusion in an oil-enriched region surrounding the growing vapor bubbles. A correlation based on the postulated mechanism is presented which shows fair agreement with the experimental data from this study and with data obtained from the literature.

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