This article uses coupled Eulerian–Lagrangian finite element algorithm to conduct a three-dimensional thermomechanical study to capture the shape and characteristics of defect type generated while achieving the dissimilar friction stir welding of aluminium alloys. The volume-of-fluid method is used to model the Eulerian region and predict the localised formation of process defects. Three different tool shapes are utilised to achieve the dissimilar friction stir welding joining between AA 2024-T3 on the advancing side and AA 6061-T6 on the retreating side. Process parameter effects such as rotational tool speed, traverse tool speed and tool tilt angle are also investigated. The finite element model results are validated by comparing with the results of a previous experimental study. The results showed the augmentation of the traverse welding speed from 40 to 80 mm/min is a key factor in causing process imperfections such as void and tunnel defects. The lower tilt angle value of 1° resulted in long tunnel defects when high rotational speeds are applied. Also, the combination of high rotational and low transverse speeds promotes the production of a free-defect friction stir welding joint.
We demonstrate the results of a mathematical model for investigation the nonlinear Stimulated Brillouin Scattering (SBS), which can be employed to achieve high optical amplifier. The SBS is created by interaction between the incident We demonstrate the results of a mathematical model for investigation the nonlinear Stimulated Brillouin Scattering (SBS), which can be employed to achieve high optical amplifier. The SBS is created by interaction between the incident light and the acoustic vibration fiber. The design criteria and the amplification characteristic of the Brillouin amplifier is demonstrated and discussed for fiber Brillouin amplifier using different pump power with different fiber length. The results show, high Brillouin gain can
... Show Morein this paper, we study and investigate a simple donor-acceptor model for charge transfer formation using a quantum transition theory. The transfer parameters which enhanced the charge transfer and the rate of the charge transfer have been calculated. Then, we study the net charge transfer through interface of Cu/F8 contact devices and evaluate all transfer coefficients. The charge transfer rate of transfer processes is found to be dominated in the low orientation free energy and increased a little in decreased potential at interface comparison to the high potential at interface. The increased transition energy results in increasing the orientation of Cu to F8. The transfer in the system was more active when the system has large driving for
... Show MoreThe aim of study To purify GPCR from a local strain of S. cerevisiae using Ion exchange and gel filtration chromatography techniques , by packing materials for columns which will be chosen of low cost comparing to the already used in published researches, which depend on the costly affinity chromatography and other expensive methods of purification. Local strain of S. cerevisiae chosen for extraction and purification of G-protein coupled receptor (GPCR) .The strains were obtained from biology department in Al- Mosul University, Iraq. The isolated colony was activated on Yeast Extract Pepton Dextrose Broth (YEPDB) and incubated at 30 C˚ for 24 h .Loop fully of the yeast culture was transferred to (10ml) of yeast extract peptone glucose
... Show MoreClimate change is one of the global issues that is receiving wide attention due to its clear impact on all living organisms. This is essential for Iraq since it was classified as the fifth most vulnerable country to climate change. One of the manifestations of these changes in Iraq is the increasing frequency and severity of dust storms. In this study, the Normalized Difference Dust Index (NDDI) spectral index for Moderate Resolution Imaging Spectroradiometer (MODIS) sensor bands was used to measure and track the dust storm that occurred on May 16, 2022, as well as to test the validity of one of the daily products of this sensor, MOD11A1, to measure surface temperature and emissivity before and after the storm. It was found that the MOD0
... Show MoreAgent technology has a widespread usage in most of computerized systems. In this paper agent technology has been applied to monitor wear test for an aluminium silicon alloy which is used in automotive parts and gears of light loads. In addition to wear test monitoring، porosity effect on
wear resistance has been investigated. To get a controlled amount of porosity, the specimens have
been made by powder metallurgy process with various pressures (100, 200 and 600) MPa. The aim of
this investigation is a proactive step to avoid the failure occurrence by the porosity.
A dry wear tests have been achieved by subjecting three reciprocated loads (1000, 1500 and 2000)g
for three periods (10, 45 and 90)min. The weight difference a
This study assessed the effect of co-substitution of strontium (Sr) and magnesium (Mg) ions into the hydroxyapatite (HA) coating which was deposited on Ti–6Al–4V dental alloys by an electrochemical deposition process. The deposited layers were examined using energy-dispersive X-ray spectroscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, atomic force microscopy and X-ray diffraction. The corrosion behavior of Ti–6Al–4V alloys in an artificial saliva environment was studied through potentiodynamic polarization technique and electrochemical impedance spectroscopy. The results indicated that the substituted Sr and Mg ions in HA improved the HA coating, where the protection efficiency percentage (PE%) for Ti
... Show MoreAfter the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings
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