Reducing the drag force has become one of the most important concerns in the automotive industry. This study concentrated on reducing drag through use of some external modifications of passive flow control, such as vortex generators, rear under body diffuser slices and a rear wing spoiler. The study was performed at inlet velocity (V=10,20,30,40 m/s) which correspond to an incompressible car model length Reynolds numbers (Re=2.62×105, 5.23×105, 7.85×105 and 10.46×105), respectively and we studied their effect on the drag force. We also present a theoretical study finite volume method (FVM) of solving Reynolds-averaged Navier-tokes equations (RANS) using a realizable k–epsilon (k-ε) turbulence model, conducted on a car, model KIA Pride, which is popular in Iraq and Iran. All computational analysis and modifications were carried out using the ANSYS Fluent 19 computational fluid dynamics (CFD) software and SOLIDWORKS 2018 modeller. The drag coefficient of the analysed car was found to be 0.34 and the results show that the drag can be reduced up to1.73% using vortex generators, up to 3.05% using a rear wing spoiler and up to 2.47% using rear under-body diffuser slices modifications, whereas it may be reduced up to 3.8% using all previous modifications together.
A computational investigation is carried out in the field of charged particle optics with the aid of the numerical analysis methods. The work is concerned with the design of symmetrical double pole piece magnetic lens. The axial magnetic flux density distribution is determined by using exponential model, from which the paraxial-ray equation is solved to obtain the trajectory of particles that satisfy the suggested exponential model. From the knowledge of the first and second derivatives of axial potential distribution, the optical properties such as the focal length and aberration coefficients (radial distortion coefficient and spiral distortion coefficient) are determined. Finally, the pole piece profiles capable of pr
... Show MoreThe convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog
... Show MoreIn this work , we applied the nuclear shell model by using Modified Surface Delta Interaction ( MSDI ) to study the nuclear structure for Ti42-44 nuclei from the calculation of the energy level values and its total angular momentum . After comperation with the experiment values which found to be rather in good agreement and determined the total angular momentum values of energy levels which are not assigned experimently , as soon as , we certify some values that were not certained experimently .
The different interactions between cometary tail and solar wind ions are studied in the present paper based on three-dimensional Lax explicit method. The model used in this research is based on the continuity equations describing the cometary tail-solar wind interactions. Three dimensional system was considered in this paper. Simulation of the physical system was achieved using computer code written using Matlab 7.0. The parameters studied here assumed Halley comet type and include the particle density , the particles velocity v, the magnetic field strength B, dynamic pressure p and internal energy E. The results of the present research showed that the interaction near the cometary nucleus is mainly affected by the new ions added to the
... Show MoreIn this paper we used frequentist and Bayesian approaches for the linear regression model to predict future observations for unemployment rates in Iraq. Parameters are estimated using the ordinary least squares method and for the Bayesian approach using the Markov Chain Monte Carlo (MCMC) method. Calculations are done using the R program. The analysis showed that the linear regression model using the Bayesian approach is better and can be used as an alternative to the frequentist approach. Two criteria, the root mean square error (RMSE) and the median absolute deviation (MAD) were used to compare the performance of the estimates. The results obtained showed that the unemployment rates will continue to increase in the next two decade
... Show MoreObjective: To generate a model that conceptualizes the phenomenon of health promotion and its related factors.
Methodology: A grounded theory methodology is used as qualitative method to explore the health promotion as
phenomenon of interest and its other related factors from the perspectives of specialists in this field. The study is
carried out from January 2002 through September 2004. A sample of (20) specialists in health sciences are
selected and interviewed as experts in the area of health promotion. The investigators conducted intensive and
structured interviews with the specialists to collect the data. These interviews were transcribed verbatim,
analyzed and interpreted.
Results: Findings of the study indicat
This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.