Effects of Nanoparticles on Non-Darcy Mixed Convective Heat Transfer in Nanofluids over a Shrinking and Stretching Wedge
Hassan Saadi Abdelaal El-dawy,
Rama Subba Reddy Gorla
Issue:
Volume 8, Issue 4, August 2019
Pages:
70-74
Received:
12 June 2019
Accepted:
3 July 2019
Published:
5 September 2019
Abstract: In this work we studied the effect of nanoparticles on the velocity and heat transfer during the flow of nanofluid in Non-Darcy mixed convection, over a wedge, taking into account of shrinking and stretching of the surface. The governing partial differential equations are converted into ordinary differential equations by means of coordinate transformation. The transformed equations are solved by means of fourth order Runge Kutta method in conjunction with shooting method. The results for the velocity and temperature fields are presented graphically as well as in tabular form. This research is expected to be useful for studying the movement of oil, gas, and water through the oil reservoir or the gas field, in the migration of groundwater and in the purification and purification of water. The friction factor decreases as the nanoparticle concentration increases whereas the heat transfer rate (Nusselt number) increases with nanoparticle concentration. The friction factor and heat transfer rate increase as the suction parameter increases. The friction factor decreases as the wedge angle increases whereas the heat transfer rate (Nusselt number) increases with wedge angle.
Abstract: In this work we studied the effect of nanoparticles on the velocity and heat transfer during the flow of nanofluid in Non-Darcy mixed convection, over a wedge, taking into account of shrinking and stretching of the surface. The governing partial differential equations are converted into ordinary differential equations by means of coordinate transfo...
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Prediction of Academic Talent Capacity Based on Gradient Boosting Decision Tree
Shunshun Shi,
Mingzhou Chen,
Rui Feng,
Hua Zhang,
Shuai Zhang
Issue:
Volume 8, Issue 4, August 2019
Pages:
75-81
Received:
5 August 2019
Published:
27 September 2019
Abstract: Talent introduction is an important force of academic development in universities. As the core of talent introduction, prediction of academic talent capacity is an essential and valuable research. However, it is hard to apply traditional statistical methods to extract knowledge from the mass and multi-dimensional talent information. Data mining approaches as up-to-date and efficient technologies are good at analyzing information, extracting patterns or rules from a big dataset and then making a prediction based on the relationship among extracted information. In this study, a series of data mining approaches are employed to evaluate the academic capacity of talent and to analyze the correlation between features. The Principal Component Analysis and Random Forest are used to feature extraction for improving the accuracy of prediction. A classical classification model, Gradient Boosting Decision Tree, is used as the primary analytic model to prediction. In order to validate the effectiveness of the model, other five classification models are used to conduct a comparative experiment based on prediction accuracy values and the F-measure metric. Further, to investigate the contribution of some important features, we make a marginal utility analysis of important features which have a high correlation with academic talent capacity. The experiment results reveals the important features for academic capacity and the positive factors for the academic production of talents.
Abstract: Talent introduction is an important force of academic development in universities. As the core of talent introduction, prediction of academic talent capacity is an essential and valuable research. However, it is hard to apply traditional statistical methods to extract knowledge from the mass and multi-dimensional talent information. Data mining app...
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