American Journal of Energy Research

Current Issue» Volume 2, Number 4 (2014)

Article

Impact of Shape of Obstacle Roof on the Turbulent Flow in a Wind Tunnel

1Laboratory of Electro-Mechanic Systems (LASEM), National School of Engineers of Sfax (ENIS), Univrsity of Sfax, TUNISIA


American Journal of Energy Research. 2014, 2(4), 90-98
DOI: 10.12691/ajer-2-4-3
Copyright © 2014 Science and Education Publishing

Cite this paper:
Slah Driss, Zied Driss, Imen Kallel Kammoun. Impact of Shape of Obstacle Roof on the Turbulent Flow in a Wind Tunnel. American Journal of Energy Research. 2014; 2(4):90-98. doi: 10.12691/ajer-2-4-3.

Correspondence to: Zied  Driss, Laboratory of Electro-Mechanic Systems (LASEM), National School of Engineers of Sfax (ENIS), Univrsity of Sfax, TUNISIA. Email: zied.driss@enis.rnu.tn

Abstract

In this paper, we are interested in the impact of shape of obstacle roof on the turbulent flow in a wind tunnel. Particularly,arched, inclined, pitched and flat roofs obstacles are examined. A three-dimensional flow of a fluid is numerically analyzed using the Navier-Stokes equations in conjunction with the standard k-ε turbulence model. These equations were solved by a finite-volume discretization method. The comparison between our numerical and experimental results shows a good agreement and confirms the numerical method.

Keywords

References

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Article

Energy Consumption and Economic Growth Nexus: Empirical Evidence from Tunisia

1Faculty of Economics and Management, University of Sfax, Street of Airport, LP 1088, Sfax 3018, Tunisia


American Journal of Energy Research. 2014, 2(4), 81-89
DOI: 10.12691/ajer-2-4-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Kais Saidi, Sami Hammami. Energy Consumption and Economic Growth Nexus: Empirical Evidence from Tunisia. American Journal of Energy Research. 2014; 2(4):81-89. doi: 10.12691/ajer-2-4-2.

Correspondence to: Kais  Saidi, Faculty of Economics and Management, University of Sfax, Street of Airport, LP 1088, Sfax 3018, Tunisia. Email: forever_kais@yahoo.fr (K. Saidi); sami_hammami2005@yahoo.fr

Abstract

This article examines the two-way linkages between energy consumption and economic growth using data from Tunisia over the period 1974-2011. This research tests this interrelationship between variables using the Johansen cointegration technique. Our empirical results show that there exists bidirectional causal relationship between energy consumption and economic growth in the long-run. The study suggests that energy policies should recognize the differences in the nexus between energy consumption and economic growth in order to maintain sustainable economic growth in Tunisia.

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References

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Article

An Application of Artificial Neural Network for Predicting Engine Torque in a Biodiesel Engine

1Siirt University, Faculty of Engineering &Architecture, Department of Mechanical Engineering, Kezer Campus, Siirt / TURKEY


American Journal of Energy Research. 2014, 2(4), 74-80
DOI: 10.12691/ajer-2-4-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Bekir Cirak, Selman Demirtas. An Application of Artificial Neural Network for Predicting Engine Torque in a Biodiesel Engine. American Journal of Energy Research. 2014; 2(4):74-80. doi: 10.12691/ajer-2-4-1.

Correspondence to: Bekir  Cirak, Siirt University, Faculty of Engineering &Architecture, Department of Mechanical Engineering, Kezer Campus, Siirt / TURKEY. Email: bekircirak@mynet.com

Abstract

In this application study, an artificial neural network (ANN) model to predict the torque of a diesel engine. Using ANN performance of a diesel engine using biodiesel produced from canola and soybean oils through transesterification. To acquire data for training and testing of the proposed ANN. A four cylinder and four stroke test engine was fuelled with biodiesel and eurodiesel mixtured fuels with various percentages of biodiesel % amounts to half the CB with SB and operated at different loads engine speeds, coolant temperatures, biofuel mixtures and exhaust temperature. Levenberg Marquards algorithms for the engine was developed using some of the experimental data for training. As a nonlinear system has been accepted. The performance of the ANN was validated by comparing the prediction dataset with the experimental results. It was observed that the ANN model can predict the engine performance quite well with correlation coefficient R 0.98 for the engine torque respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0002 by the model.

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References

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