In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and introduced. Optimal results showed that the optimum viscosity and thermal conductivity occurs at maximum temperature.
Addition chloro acetyl isothiocyanate (C3H2ClNOS) with 3-Aminoaceto phenone (C8H9NO) to prepare a fresh Ligand [N-(3-acetyl phenyl carbamothioyl)-2-chloroacetamide](L). The ligand (L) behaves as bidentate coordinating through O and S donor with metal ions, the general formula of all complexes [M(L)2(Cl)2](M+2 = Manganese(II), Cobalt(II), Cadmium(II) and Mercury(II)). Compounds were investigation by Proton-1, Carbon -13 NMR spectra (ligand (L) only), Element Microanalysis for C, N, H, O, S, Fourier-transform infrared, UV visible, Conductance
A novel metal complexes Cu (II), Co (II), Cd (II), Ru (III) from azo ligand 5-((2-(1H-indol-2-yl)
ethyl) diazinyl)-2-aminophenol were synthesized by simple substitution of tryptamine with 2-aminophenol.
Structures of all the newly synthesized compounds were characterized by FT IR, UV-Vis, Mass spectroscopy
and elemental analysis. In addition measurements of magnetic moments, molar conductance and atomic
absorption. Then study their thermal stability by using TGA and DSC curves. The DCS curve was used to
calculate the thermodynamic parameters ΔH, ΔS and Δ G. Analytical information showed that all complexes
achieve a metal:ligand ratio of [1:1]. In all complex examinations, the Ligand performs as a tri
Phase change materials (PCMs) such as paraffin wax can be used to store or release large amount of energy at certain temperature at which their solid-liquid phase changes occurs. Paraffin wax that used in latent heat thermal energy storage (LHTES) has low thermal conductivity. In this study, the thermal conductivity of paraffin wax has been enhanced by adding different mass concentration (1wt.%, 3wt.%, 5wt.%) of (TiO2) nano-particles with about (10nm) diameter. It is found that the phase change temperature varies with adding (TiO2) nanoparticles in to the paraffin wax. The thermal conductivity of the composites is found to decrease with increasing temperature. The increase in thermal conductivity ha
... Show MoreThis paper presents a comparison study on thermal performance conic cut twist tape inserts in laminar flow of nanofluids through a constant heat fluxed tube. Three tape configurations, namely, quadrant cut twisted tape (QCT), parabolic half cut twisted tape (PCT), and triangular cut twisted (VCT) of twist ratio= 2.93 and cut depth= 0.5 cm were used with 1% and 2% volume concentration of SiO2/water and TiO
... Show MorePermeability is an essential parameter in reservoir characterization because it is determined hydrocarbon flow patterns and volume, for this reason, the need for accurate and inexpensive methods for predicting permeability is important. Predictive models of permeability become more attractive as a result.
A Mishrif reservoir in Iraq's southeast has been chosen, and the study is based on data from four wells that penetrate the Mishrif formation. This study discusses some methods for predicting permeability. The conventional method of developing a link between permeability and porosity is one of the strategies. The second technique uses flow units and a flow zone indicator (FZI) to predict the permeability of a rock mass u
... Show MoreA new ligand type (O2) [2,3-O-diacetyl-5,6-O-benzylidene L- ascorbic acid] [L] and its complexes of general formula [M(L)2(X)(Y)]Cln (where: M=CrIII ,X=Y=H2O, n=3; CoII, X = Y = 0, n= 2; NiII and CuII, X = Cl, Y = H2O, n= 1; ZnII, X = Y = H2O,n = 2) are reported. The ligand was prepared in two steps; first step involved the synthesis of [5,6-O-benzylidene-L-ascorbic acid] (A). In second step derivative-A was then reacted with acetyl chloride and anhydrous pyridine as a base to give the titled ligand. Metal complexes of the ligand with CrIII,CoII
... Show MoreIn this study new derivatives of O-[2-{''2-Substituted Aryl (''1,''3,''4 thia diazolyl) ['3,'4b]-'1,'2,'4- Triazolyl]-Ethyl]-p- chlorobenzald oxime (6-11) have been synthesized from the starting material p-chloro – E- benzaldoxime 1. Compound 2 was synthesized by the reaction of p-chloro – E- benzaldoxime with ethyl acrylate in basic medium. Refluxing compound 2 with hydrazine hydrate in ethanol absolute afforded 3. Derivative 4 was prepared by the reaction of 3 with carbon disulphide, treated of compound 4 with hydrazine hydrate gave 5. The derivatives (6-11) were prepared by the reaction of 5 with different substitutes of aromatic acids. The structures of these compounds were characterized from their melting points, infra
... Show MoreBearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that us
... Show MoreSurvival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re
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