On Scott's new correlation of the Texas Midway

1926 ◽  
Vol s5-12 (71) ◽  
pp. 453-455
Author(s):  
J. A. Gardner
Keyword(s):  
New Correlation

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

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.

A new correlation for quench front velocity

1982 ◽  
Vol 25 (10) ◽  
pp. 1589-1593 ◽  
Author(s):  
E. Oliveri ◽  
F. Castiglia ◽  
S. Taibi ◽  
G. Vella
Keyword(s):  
Front Velocity ◽  
New Correlation

A new correlation functional based on a transcorrelated Hamiltonian

2003 ◽  
Vol 118 (6) ◽  
pp. 2464 ◽  
Author(s):  
Yutaka Imamura ◽  
Gustavo E. Scuseria
Keyword(s):  
New Correlation

A correlation to predict the performance characteristics of centrifugal pumps handling slurries

1997 ◽  
Vol 211 (2) ◽  
pp. 147-157 ◽  
Author(s):  
K A Kazim ◽  
B Maiti ◽  
P Chand
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Suspended Solids ◽  
Reduction Factor ◽  
Performance Characteristics ◽  
Centrifugal Pumps ◽  
Pump Performance ◽  
New Correlation ◽  
Effect Of Particle Size ◽  
The Individual

Centrifugal pumps are being used increasingly for transportation of slurries through pipelines. To design a slurry handling system it is essential to have a knowledge of the effects of suspended solids on the pump performance. A new correlation to predict the head reduction factor for centrifugal pumps handling solids has been developed. This correlation takes into account the individual effect of particle size, particle size distribution, specific gravity and concentration of solids on the centrifugal pump performance characteristics. The range of validity of the correlation has been verified by experiment and by using experimental data available from the literature. The present correlation shows better agreement with the experimental data than existing correlations.

Evaluation Analysis of Prediction Methods for Two-Phase Flow Pressure Drop in Mini-Channels

2008 ◽  
Author(s):  
Licheng Sun ◽  
Kaichiro Mishima
Keyword(s):  
Pressure Drop ◽  
Two Phase Flow ◽  
Phase Flow ◽  
Two Phase ◽  
Turbulent Region ◽  
Frictional Pressure Drop ◽  
New Correlation ◽  
Mini Channels ◽  
Flow Pressure ◽  
Better Than

2092 data of two-phase flow pressure drop were collected from 18 published papers of which the working fluids include R123, R134a, R22, R236ea, R245fa, R404a, R407C, R410a, R507, CO2, water and air. The hydraulic diameter ranges from 0.506 to 12mm; Relo from 10 to 37000, and Rego from 3 to 4×105. 11 correlations and models for calculating the two-phase frictional pressure drop were evaluated based upon these data. The results show that the accuracy of the Lockhart-Martinelli method, Mishima and Hibiki correlation, Zhang and Mishima correlation and Lee and Mudawar correalion in the laminar region is very close to each other, while the Muller-Steinhagen and Heck correlation is the best among the evaluated correlations in the turbulent region. A modified Chisholm correlation was proposed, which is better than all of the evaluated correlations in the turbulent region and its mean relative error is about 29%. For refrigerants only, the new correlation and Muller-Steinhagen and Heck correlation are very close to each other and give better agreement than the other evaluated correlations.

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