scholarly journals Prediction of flow boiling heat transfer coefficient in horizontal channels varying from conventional to small-diameter scales by genetic neural network

2019 ◽  
Vol 51 (8) ◽  
pp. 1897-1904
Author(s):  
Jing Zhang ◽  
Yichao Ma ◽  
Mingjun Wang ◽  
Dalin Zhang ◽  
Suizheng Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Small Diameter ◽  
Flow Boiling Heat Transfer ◽  
Boiling Heat Transfer Coefficient ◽  
Genetic Neural Network

scholarly journals Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Meiling Liang ◽  
Xiaohui Zhang ◽  
Rong Zhao ◽  
Xulin Wen ◽  
Shan Qing ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Network Model ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Average Deviation ◽  
Flow Boiling Heat Transfer ◽  
Boiling Heat Transfer Coefficient

An optimal prediction model for flow boiling heat transfer of refrigerant mixture R245fa inside horizontal smooth tubes is proposed based on the GRNN neural network. The main factors strongly affecting flow boiling such as mass flux rate (G), heat flux (q), quality of vapor-liquid mixture (x), evaporation temperature (Tev), and tube inner diameter (D) are used as the inputs of the model and the flow boiling heat transfer coefficient (h) as the output. Neural network model is used to optimize the prediction of flow boiling heat transfer coefficient of R245fa in horizontal light pipe through training and learning. The prediction results are in good agreement with the experimental results. For the network model of heat transfer, the average deviation is 7.59%, the absolute average deviation is 4.89%, and the root mean square deviation is 10.51%. The optimized prediction accuracy of flow boiling heat transfer coefficient is significantly improved compared with four frequently used conventional correlations. The simulation results reveal that the modeling method based on R245fa neural network is feasible to calculate the flow boiling heat transfer coefficient, and it may provide some guidelines for the optimization design of tube evaporators for R245fa.

Artificial intelligence approach for the prediction of heat transfer coefficient in boiling over tube bundles

2013 ◽  
Vol 228 (10) ◽  
pp. 1680-1688 ◽  
Author(s):  
Abhilas Swain ◽  
Mihir Kumar Das
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Inference System ◽  
Flow Boiling Heat Transfer ◽  
Tube Bundles ◽  
Boiling Heat Transfer Coefficient

The determination of heat transfer coefficient plays an important role in optimal designing of heat transfer equipments as it directly affects the heat surface area and thereby the weight and cost of the equipment. Thus, prediction of heat transfer coefficient with minimum error reduces the exhaustive experimental work. Therefore, the prime objective of the present work is the application of computational intelligence methods for improving the prediction accuracy of heat transfer coefficient in flow boiling over tube bundles. The adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are applied to predict the flow boiling heat transfer coefficient as output, taking pressure, pitch of the bundle, heat flux, mass flux and vapour quality as input. The performance of different derivatives of neural network such as multilayer perceptron, general regression neural network and radial basis network (RBF) is studied with varying the parameter. The ANFIS is tested with different types and number of membership functions for the prediction of flow boiling heat transfer coefficient. These methods are found to be better for predicting the flow boiling heat transfer coefficient than conventional correlations.

Experimental Study of Flow Boiling Heat Transfer Coefficient of FC-72 Over Micro-Pin-Finned Surfaces

2010 ◽  
Author(s):  
Y. F. Xue ◽  
M. Z. Yuan ◽  
J. J. Wei
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Fluid Velocity ◽  
Nucleate Boiling ◽  
Flow Boiling Heat Transfer ◽  
Nucleate Boiling Heat Transfer ◽  
Boiling Heat Transfer Coefficient

Experiments of flow boiling heat transfer coefficient of FC-72 were carried out over simulated silicon chip of 10×10×0.5 mm3 for electronic cooling. Four kinds of micro-pin-fins with the dimensions of 30×60, 30×120, 50×60, 50×120 μm2 (thickness, t × height, h) respectively, were fabricated on the chip surfaces by the dry etching technique to enhance boiling heat transfer. A smooth chip was also tested for comparison. The experiments were conducted at three different fluid velocities (0.5, 1 and 2m/s) and three different liquid subcoolings (15, 25 and 35K). All micro-pin-finned surfaces show a considerable heat transfer enhancement compared to the smooth surface. Both the forced convection and nucleate boiling heat transfer contribute to the total heat transfer performance. The contribution of each factor to the total heat transfer has been clearly presented in the flow boiling heat transfer coefficient curves. In a lower heat flux region, the heat transfer coefficient increases greatly with increasing fluid velocity, but increases slightly with increasing heat flux, indicating that the single-phase forced convection dominates the heat transfer process. With further increasing heat flux to the onset of nucleate boiling, the heat transfer coefficient increases remarkably. For a given liquid subcooling, the curves of flow boiling heat transfer coefficient at fluid velocities of 0.5 and 1 m/s almost follow one line for each surface, showing insensitivity of nucleate boiling heat transfer to fluid velocity. However, at the largest fluid velocity of 2 m/s, the slope of the flow boiling heat transfer coefficient curves for micro-pin-finned surfaces becomes smaller, indicating that the forced convection also plays an important role besides the nucleate boiling heat transfer. The curves of the flow boiling heat transfer coefficient can be used to determine the boiling incipience at different fluid velocities, which provides a basis for the suitable fluid velocity selection in designing highly efficient cooling scheme for electronic devices.

Flow Boiling Heat Transfer of R-22 in Small-Diameter Horizontal Round Tubes

2003 ◽  
Author(s):  
K. S. Park ◽  
W. H. Choo ◽  
K. H. Bang
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Mass Flux ◽  
Boiling Heat Transfer ◽  
Flow Boiling ◽  
Vapor Quality ◽  
Flow Boiling Heat Transfer ◽  
Boiling Heat Transfer Coefficient

The flow boiling heat transfer coefficient of R-22 in small hydraulic diameter tubes has been experimentally studied. Both brass and aluminum round tubes of 1.66 mm inside diameter are used for the test section. The ranges of the major experimental parameters are 300∼600 kg/m2s of refrigerant mass flux, 10∼20 kW/m2 of the wall heat flux, 0.0∼0.9 of the inlet vapor quality. The experimental result showed that the flow boiling heat transfer coefficient in this small tubes are in the range of 2∼4 kW/m2K and it varies only by heat flux, independent of mass flux and vapor quality. It is also observed that the heat transfer coefficients in the aluminum tube are up to 50% higher than in the brass tube.

Sign in / Sign up

Export Citation Format

Share Document