Math. Comput. Appl., Volume 11, Issue 3 (December 2006) – 8 articles , Pages 163-235

A random flow generation (RFG) algorithm for a previously established large eddy simulation (LES) code is successfully incorporated into a finite element fluid flow solver to generate the required inflow/initial turbulence boundary conditions for the LES computations of viscous incompressible turbulent flow over a nominally twodimensional circular cylinder at Reynolds number of 140,000. The effect of generated turbulent inflow boundary conditions on the near wake flow and the shear layer and on the prediction of integral flow parameters is studied based on long time average results. No-slip velocity boundary function is used but wall effects are taken into consideration with a near wall modelling methodology based on van Driest Damping approach. The numerical results obtained from simulations are compared with each other and with the experimental data for different turbulent inflow boundary conditions to assess the functionality of the RFG algorithm for the present LES code and hence its influence on the vortex shedding mechanism and the resulting flow field predictions. Full article

In this study, the artificial neural networks have been used for the estimation of hourly ambient temperature in Denizli, Turkey. The model was trained and tested with four years (2002-2005) of hourly mean temperature values. The hourly temperature values for the years 2002-2004 were used in training phase, the values for the year 2005 were used to test the model. The architecture of the ANN model was the multi-layer feedforward architecture and has three layers. Inputs of the network were month, day, hour, and two hourly mean temperatures at the previous hours, and the output was the mean temperature at the hour specified in the input. In the model, Levenberg-Marquardt learning algorithm which is a variant of backpropagation was used. With the software developed in Matlab, an ANN was constructed, trained, and tested for a different number of neurons in its hidden layer. The best result was obtained for 27 neurons, where R2, RMSE and MAPE values were found to be 0.99999, 0.92024 and 0.20900% for training, and 0.9999, 0.91301 and 0.20907% for test. The results show that the artificial neural network is powerful an alternate method in temperature estimations. Full article

The fuel injection pressure is one of the significant operating parameters affects atomization of fuel and mixture formation; therefore, it determines the performance and emissions of a diesel engine. Increasing the fuel injection pressure decrease the particle diameter and caused the diesel fuel spray to vaporize quickly. However, with decreasing fuel particles their inertia will also decrease and for this reason fuel can not penetrate deeply into the combustion chamber. In this study, artificial neural-networks (ANNs) are used to determine the effects of injection pressure on smoke emissions and engine performance in a diesel engine. Experimental studies were used to obtain training and test data. Injection pressure was changed from 100bar to 300bar in experiment (standard injection pressure of test engine is 150bar). Injection pressure and engine speed have been used as the input layer; smoke emission, engine torque and specific fuel consumption have been used as the output layer. Two different training algorithms were studied. The best results were obtained from Levenberg-Marquardt (LM) and Scaled Conjugate gradient (SCG) algorithms with 11 neurons. However, The LM algorithm is faster than the SCG algorithm, and its error values are smaller than those of the SCGs. For the torque with LM algorithm, fraction of variance (R²) and mean absolute percentage error (MAPE) were found to be 0.9927 and 7.2108%, respectively. Similarly, for the specific fuel consumption (SFC), R² and MAPE were calculated as 0.9872 and 6.0261%, respectively. For the torque with SCG algorithm, R² and MAPE were found to be 0.9879 and 9.0026%, respectively. Similarly, for the specific fuel consumption (SFC), R² and MAPE were calculated as 0.9793 and 8.7974%, respectively. So, these ANN predicted results can be considered within acceptable limits and the results show good agreement between predicted and experimental values. Full article

This paper presents an optimal design method to optimize three-phase induction motor in manufacturing process. The optimally designed motor is compared with an existing motor having the same ratings. The Genetic Algorithm is used for optimization and three objective functions namely torque, efficiency, and cost are considered. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses and thermal limits. Computer simulation results are given to show the effectiveness of the proposed design process. Full article

This work constructs the membership functions of the system characteristics of a batch-arrival queuing system with multiple servers, in which the batch-arrival rate and customer service rate are all fuzzy numbers. The α-cut approach is used to transform a fuzzy queue into a family of conventional crisp queues in this context. By means of the membership functions of the system characteristics, a set of parametric nonlinear programs is developed to describe the family of crisp batch-arrival queues with multiple servers. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, the fuzzy batch-arrival queues with multiple servers are represented more accurately and the analytic results are more useful for system designers and practitioners. Full article

One of the leading causes for death after heart diseases and cancer in all over the world is still stroke. Most strokes happen because an artery that carries blood uphill from the heart to the head is clogged. Most of the time, as with heart attacks, the problem is atherosclerosis, hardening of the arteries, calcified buildup of fatty deposits on the vessel wall. The primary troublemaker is the carotid artery, one on each side of the neck, the main thoroughfare for blood to the brain. Only within the last twenty-five years, though, have researchers been able to put their finger on why the carotid is especially susceptible to atherosclerosis. In this study, the fluid dynamic simulations were done in a diseased carotid bifurcation under the steady flow conditions computationally. Reynolds numbers representing the steady flow were 300, 1020 and 1500 for diastolic, average and systolic peak flow represented by pulsatile flow waveform, respectively. In vivo geometry and boundary conditions were obtained from a patient who has stenosis located at external carotid artery (ECA) and internal carotid artery (ICA) of his common carotid artery (CCA). The location of critical flow fields such as low wall shear stress (WSS), stagnation regions and separation regions were detected near the highly stenosed region and at branching region. Full article

In this study, modelling of microhardness values by means of artificial neural networks of Al/SiCp metal matrix composite material couples with diffusion method and manufactured by powder metallurgy process, were obtained using a backpropagation neural network that uses gradient descent learning algorithm. After diffusion bonding and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. Then, the neural network was trained using the prepared training set. At the end of the training process, the test data were used to check the system accuracy. As a result the neural network was found successful in the prediction of modelling of microhardness values of Al/SiCp metal matrix composite material couples processed with diffusion method and behavior. Full article