by Daniel Abode and Samson Oyetunji
Original Research
Management of energy consumption from the demand side has been inefficient and this has inadvertently affected the efforts of energy management on the supply side. This paper describes how machine learning integrated with Internet of things can enhance energy consumption management on the consumer side. Sensor nodes were designed and installed to gather data including date, time, temperature, humidity, light intensity, human presence and state of the load point switches. The sensor nodes transferred the data collected to a google firebase cloud storage which stored the data. The data collected was used in MATLAB neural network toolbox to train a machine learning algorithm that can predict the states of the receptacles, the ambient condition and the energy consumption statistics. The results show 99.7% success and 0.3% failure in the prediction of the state of the receptacle, 98.9% success and 1.1% failure in the prediction of the light state. In addition, the performance of the trained network for solving the classification and the regression problems that were involved in the prediction of the states of the switches and receptacles, the ambient condition and energy consumption statistics were shown graphically. Graphical results were also developed to show the relationship between the energy consumption, time of the day, human presence, and temperature. Conclusion was drawn on how time of the day, human presence and temperature affected the energy consumption on the consumer side.
American Journal of Energy Research. 2017, 5(3), 103-113. DOI: 10.12691/ajer-5-3-5
Pub. Date: December 29, 2017
20231 Views3098 Downloads
by Ahmed Ketata and Zied Driss
Original Research
The turbocharger which consists essentially on a radial turbine and a centrifugal compressor is commonly embedded to internal combustion engines in order to enhance its performance. The use of a mixed flow turbine instead of a radial one leads to better aerodynamic efficiency. The present investigation shows our optimized numerical model under steady conditions in purpose to predict the overall performance for an automotive vanned mixed flow turbine. Using the CFX 17.0 package, numerical results are obtained by solving the Reynolds averaged Navier Stokes equations by means of a finite volume discretization method. The standard k-ε turbulence model is used to close these equations. Based on the numerical solutions, the turbine performance and the reaction degree are computed. Equally, the distribution of the turbine output torque and its blades loading as a function of the isentropic velocity ratio are plotted. The mesh choice is based on the solution independency. Our numerical results show a good agreement compared to the test data.
American Journal of Energy Research. 2017, 5(3), 97-102. DOI: 10.12691/ajer-5-3-4
Pub. Date: November 20, 2017
15191 Views3066 Downloads
by O. Moussa and Z. Driss
Original Research
A two-dimensional axis-symmetric numerical model was solved to investigate the effect of four turbulence models on combustion characteristics, such as the velocity, the pressure, the turbulent kinetic energy and the dissipation rate in a methane-air no-premixed flame. Based on the commercial CFD code Ansys fluent 17.0, different turbulence models including the standard k-ε model, the RNG k-ε model, the realizable k-ε model and the standard k-ω model were used to simulate the flow field in a simple burner. The eddy dissipation model with the global reaction schema was applied to model the turbulence reaction interaction in the flame region. A finite volume approach was used to solve the Navier-Stokes equations with the combustion model. Particularly, the effect of these turbulence models on the combustion characteristics was analyzed. The numerical predictions were validated by comparison with anterior experimental results. Moreover, the predicted axial and radial gradients of velocity in the standard k-ε are overall agreement with literature results.
American Journal of Energy Research. 2017, 5(3), 85-93. DOI: 10.12691/ajer-5-3-3
Pub. Date: November 18, 2017
14144 Views2607 Downloads
by Oluwatosin Dorcas Sanya
Original Research
Silicon Carbide (SiC) was integrated into an Organic Rankine cycle (ORC) design to enhance its ability to operate at high temperature. Low-grade heat dissipated from large data centres was used to run the modified ORC to generate electrical power. This allowed for a temperature increase of low-grade heat to a temperature capable of improving the power output and efficiency of the turbine used in the ORC. This research demonstrated that energy management can be applied in large centres, 65 MW power can be generated from a 260 MW data centre at a temperature of 150°C and mass flow rate of 476.19 kg/sec. Heat pumps were integrated into the ORC system to boost the temperature of heat rejected from the condenser and making it available for the cycle. Temperature values from 135°C - 270°C were used to optimise the best temperature to achieve a maximum power production in the ORC; 157.5°C showed a maximum output power of 65 MW. A 25% electrical power can be produced from low-grade heat dissipated by data centres by using modified ORC provided the inlet and outlet enthalpies are constant for all data centres.
American Journal of Energy Research. 2017, 5(3), 79-84. DOI: 10.12691/ajer-5-3-2
Pub. Date: November 17, 2017
8310 Views2666 Downloads3 Likes
by Ahmed Ahmedov and K. M. Ebrahimi
Original Research
This paper presents the force interaction between fluid flow and a rotating H-type Darrieus vertical axis wind turbine. The main goal of this study is to determine the wind rotor’s performance characteristics under turbulent wind: torque M = f (n), normal force FN = f (n), output power P = f (n) and the aerodynamic characteristics CM = f (λ), CN = f (λ), CP = f (λ). The flow passing through the turbine has a complex structure due to the rotation of the rotor. The constantly changing angular position of the turbine’s blades is leading to a variation in the blades angle of attack. This angle can vary from positive to negative values in just a single turbine revolution. The constant fluctuations of the angle of attack are the main factor which leads to the unsteady nature of the flow passing through the turbine. At low tip-speed ratios, the phenomena deep dynamic stall occurs which leads to intensive eddy generation. When the turbine is operating at higher tip-speed ratio the flow is mainly attached to the blades and the effect of the dynamic stall over the turbine performance is from weak to none. The Darrius turbine performance characteristics are obtained through a numerical investigation carried out for several tip-speed ratios. The used CFD technique is based upon the URANS approach for solving the Navier-Stokes equations in combination with the turbulence model k – ω SST. Also, a numerical sensitive study concerning some of the simulation parameters is carried out.
American Journal of Energy Research. 2017, 5(3), 63-78. DOI: 10.12691/ajer-5-3-1
Pub. Date: November 13, 2017
27649 Views3411 Downloads3 Likes