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Article
Experimental and Theoretical Investigation of Polymeric Drag Reducing Agent in Turbulent Pipe Flow

Author: Izzat Niazzi Slaiman
Journal: Tikrit Journal of Engineering Sciences مجلة تكريت للعلوم الهندسية ISSN: 1813162X 23127589 Year: 2016 Volume: 23 Issue: 2 Pages: 46-53
Publisher: Tikrit University جامعة تكريت

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Abstract

In the present work, the drag reduction effectiveness of water soluble Carboxyl methyl cellulose (CMC) was studied as a function of polymer concentration and flow rate. Drag reduction results were assessed by measuring pressure drop over a one meter test section from the selected pipe. The effect of additives concentration was investigated over a range of 0 – 85 wppm, the solvent (water) flow conditions that were studied included higher flow rates. The experimental work was performed in a constructed re–circulating closed loop system. Maximum drag reduction percent (MDR%) of 17.3 % was obtained by using 85 wppm of CMC. The friction factor was calculated from experimental data with an acceptable average absolute percent Deviation. Correlation equation for fanning friction factor was suggested as a function of Re. The drag reduction results have been correlated based on an modification of a theoretical model available in the literature. The functional form of the model requires knowledge of the velocity profile, ratio of mixing length, friction factor, and the additive concentration as dependent variables.


Article
PREDICTION OF HEAT TRANSFER CHARACTERISTICS FOR FORCED CONVECTION PIPE FLOW USING ARTIFICIAL NEURAL NETWORKS

Authors: Khalid B. Saleem --- Imad A. Kheioon --- Hussien S. Sultan
Journal: KUFA JOURNAL OF ENGINEERING مجلة الكوفة الهندسية ISSN: 25230018 Year: 2019 Volume: 10 Issue: 3 Pages: 73-89
Publisher: University of Kufa جامعة الكوفة

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Abstract

This paper investigates the ability of utilizing the artificial neural network (ANN) in calculating the forced convection characteristics coefficients from internal flow of air inside a pipe subjected to constant heat flux. The heat transfer characteristics such as Nusselt number (Nu), Stanton number (St) and friction factor (f) which are calculated using the empirical correlations have high deviation from that obtained from the experiments. So, the ANN method is proposed for predicting these characteristics coefficients more close to the experimental results. The training and testing data for optimizing the ANN structure are based on the experimental data obtained from the experiments performed on a forced convection apparatus. Three training algorithms for the training of the ANN were used and the presented ANN is implemented by using such MATLAB program. For the preferable ANN structure acquired in the current work, an acceptable mean square error was achieved for the training and test data, using the Trainlm algorithm. The results reveal that the estimated results are very close to the experimental data. Also, a new Graphical User Interface (GUI) is implemented for the application of ANN in the calculation of the attempted heat transfer parameters.

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