In the present work, steady, laminar natural convection in nonrectangular enclosures is analyzed numerically with and without fin. Vertical walls insulated while horizontal walls maintained isothermal at different temperature and the fin was placed on horizontal surface. The length of fin was equal (B/L=0.22, 0.44 and 0.66) and thickness of fin was constant. Various parameters are studied: Rayleigh number (from 104 to 107 ), Prandtl number (0.7), number of fin change from (1-3) and aspect ratio (H/L= 0.15 to 0.5). The problem is formulated in terms of the vorticity-stream function procedure. A numerical solution based on program in Fortran 90 with Tec plot program. The finite difference method is used. Streamlines and isotherms are presented for different values of parameters studied. A Nusselt number correlation is derived by using program (DGA v1.00) and mean Nusselt numbers on hot walls are also calculated at different cases. The results show the mean Nusselt numbers decreases with increasing aspect ratio (H/L).Also, predictions reveal a decrease in heat transfer in the presence of fins. The results of the calculations are compared with the previous works and it showed a good agreement.
In order to understand the effect of (length of pile / diameter of pile) ratio on the load carrying capacity and settlement reduction behavior of piled raft resting on loose sand, laboratory model tests were conducted on small-scale models. The parameters studied were the effect of pile length and the number of piles. The load settlement behavior obtained from the tests has been validated by using 3-D finite element in ABAQUS program, was adopted to understand the load carrying response of piled raft and settlement reduction. The results of experimental work show that the increase in (Lp/dp) ratio led to increase in load carrying capacity by piled raft from (19.75 to 29.35%), (14.18 to 28.87%) and (0 to 16.49%) , the maximum load carr
... Show MorePetroleum is one of the most important substances consumed by man at present times, a major energy source in this century, petroleum oils can cause environmental pollution during various stages of production, transportation, refining and use, petroleum hydrocarbons pollutions ranging from soil, ground water to marine environment, become an inevitable problem in the modern life, current study focused on bioremediation process of hydrocarbons contaminants that remaining in the bottom of gas cylinders and discharged to the soil. Twenty-four bacterial isolates were isolated from contaminated soils all of them gram negative bacteria, bacterial isolates screening to investigate the ability of biodegradation of hydrocarbons, these isolates inocula
... Show MoreMost studies on deep beams have been made with reinforced concrete deep beams, only a few studies investigate the response of prestressed deep beams, while, to the best of our knowledge, there is not a study that investigates the response of full scale (T-section) prestressed deep beams with large web openings. An experimental and numerical study was conducted in order to investigate the shear strength of ordinary reinforced and partially prestressed full scale (T-section) deep beams that contain large web openings in order to investigate the prestressing existence effects on the deep beam responses and to better understand the effects of prestressing locations and opening depth to beam depth ratio on the deep beam performance and b
... Show MoreIn information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare
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