An improved Metal Solar Wall (MSW) with integrated thermal energy storage is presented in this research. The proposed MSW makes use of two, combined, enhanced heat transfer methods. One of the methods is characterized by filling the tested ducts with a commercially available copper Wired Inserts (WI), while the other one uses dimpled or sinusoidal shaped duct walls instead of plane walls. Ducts having square or semi-circular cross sectional areas are tested in this work.
A developed numerical model for simulating the transported thermal energy in MSW is solved by finite difference method. The model is described by system of three governing energy equations. An experimental test rig has been built and six new duct configurations have been fabricated and tested. Air is passed through the six ducts with Reynolds numbers from 1825 to 7300.
Six, new, correlations for Nusselt number and friction factor are developed to assess the benefits that are gained from using the WI and the dimpled and sine-wave duct walls. It is found that higher heat transfer rates are achieved using the Dimpled, semi–circular duct with Wired Inserts (DCWI). Also, it is found that Nusselt number and the pressure drop in the DCWI are respectively
(44.2% -100%) and (101.27% - 172.8%) greater than those of the flat duct with WI. The improvement in Nusselt number for flat duct with WI is found to be (1.4 – 2) times the values for flat duct with no WI. The results demonstrated that DCWI provides enhancements efficiency value that is higher than those obtained from other types of ducts. The developed MSW ducts have added to local knowledge a better understanding of the compound heat transfer enhancement.
(Cu1-x,Agx)2ZnSnSe4 alloys have been fabricated with different Ag content(x=0, 0.1, and 0.2) successfully from their elements. Thin films of these alloys have been deposited on coring glass substrate at room temperature by thermal evaporation technique under vacuum of 10-5Torr with thickness of 800nm and deposition rate of 0.53 nm/sec. Later, films have been annealed in vacuum at (373, and 473)K, for one hour. The crystal structure of fabricated alloys and as deposited thin films had been examined by XRD analysis, which confirms the formation of tetragonal phase in [112] direction, and no secondary phases are founded. The shifting of main polycrystalline peak (112) to lower Bragg’s angle as compared to Cu2ZnSnSe4 angle refers to incorpora
... Show MoreAlthough renewable energy systems have become an interesting global issue, it is not continuous either daily or seasonally. Latent heat energy storage (LHES) is one of the suitable solutions for this problem. LHES becomes a basic element in renewable energy systems. LHES compensate for the energy lack when these systems are at low production conditions. The present work considered a shell and tube LHES for numerical investigation of the tube rotation influence on the melting process. The simulation and calculations were carried out using ANSYS Fluent software. Paraffin wax represents the phase change material (PCM) in this work, while water was selected to be the heat transfer fluid (HTF). The calculations were carried o
... Show MoreClinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b
In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa
... Show MoreIn the present study, metal complexes of Mn(II), Ni(II), Co(II), Cu(II) and Hg(II) were synthesized using new Tetraazamacrocyclic Schiff Base (5E,8E,14E,17E)-6,8,15,17-tetramethyl-1,2,3,4,4a,7,9a,10,11,12,13,13a,16,18a-tetradecahydrodibenzo [b,i][1,4,8,11]tetraazacyclotetradecine (L) derived from 1,2-diamino cyclo hexane with the acetyl acetone. Compounds have been exanimated and confirmed by fourier-transform infrared (FT-IR), ultraviolet-visible (UV-visible), proton nuclear magnetic resonance (1HNMR), carbon nuclear magnetic resonance (13CNMR), microelemental analyses (CHN), thermal analysis (TG), conductivity and magnetic susceptibility. The propose geometry for all complexes [MLCl2] structures were octahedral. Therm
... Show MoreMetal-organic frameworks (MOFs) are a relatively new class of materials of unique porous structures and exceptional properties. Currently, more than 110,000 types of MOFs have been reported among the countless possibilities. In this study, we have synthesised a novel MOF using zirconium chloride as the metal source and 4,4'-dicarboxy-2,2'-biquinoline (bicinchoninic acid disodium salt) as the linker, which reacted in N,N-Dimethylformamide (DMF) solvent. Three preparation methods were employed to prepare five types of the MOF, and they were compared to optimize the synthesis conditions. The resulting MOFs, named Zr-BADS, were characterised using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), microscopy, and
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