This study aims to simulate and assess the hydraulic characteristics and residual chlorine in the water supply network of a selected area in Al-Najaf City using WaterGEMS software. Field and laboratory work were conducted to measure the pressure heads and velocities, and water was sampled from different sites in the network and then tested to estimate chlorine residual. Records and field measurements were utilized to validate WaterGEMS software. Good agreement was obtained between the observed and predicted values of pressure with RMSE range between 0.09–0.17 and 0.08–0.09 for chlorine residual. The results of the analysis of water distribution systems (WDS) during maximum demand hours showed that the pumps unit capability cannot cover the high water demand during that time and resulted in a loss of pressure values, which were ranged between 0.2 and 2.1 bar. Moreover, the simulated results of the residual chlorine levels were within the permissible limits of 0.4–0.7 ppm, in different locations in the network. Providing good quality and adequate water supply is an important component for human life development. Modeling WDS is an efficient method of gaining a true understanding of the functioning of the network and determining the factors and conditions affecting the performance of the network.
the study covered theoretical concering parial molal volume the applicability of jones-dole equation
ولاء طارق حميد, Mustansiriyah Journal of Sports Science, 2021
The present work included study of the effects of weather conditions such as solar radiation and ambient temperature on solar panels (monocrystalline 30 Watts) via proposed mathematical model, MATLAB_Simulation was used by scripts file to create a special code to solve the mathematical model , The latter is single –diode model (Five parameter) ,Where the effect of ambient temperature and solar radiation on the output of the solar panel was studied, the Newton Raphson method was used to find the output current of the solar panel and plot P-V ,I-V curves, the performance of the PV was determined at Standard Test Condition (STC) (1000W/m2)and a comparison between theoretical and experimental results were done .The best efficiency
... Show MoreIn the literature, several correlations have been proposed for bubble size prediction in bubble columns. However these correlations fail to predict bubble diameter over a wide range of conditions. Based on a data bank of around 230 measurements collected from the open literature, a correlation for bubble sizes in the homogenous region in bubble columns was derived using Artificial Neural Network (ANN) modeling. The bubble diameter was found to be a function of six parameters: gas velocity, column diameter, diameter of orifice, liquid density, liquid viscosity and liquid surface tension. Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 7.3 % and correlation coefficient of 92.2%. A
... Show MoreThis study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur
... Show MoreNovel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect
... Show MoreAmong many problems that reduced the performance of the network, especially Wide Area Network, congestion is one of these, which is caused when traffic request reaches or exceeds the available capacity of a route, resulting in blocking and less throughput per unit time. Congestion management attributes try to manage such cases. The work presented in this paper deals with an important issue that is the Quality of Service (QoS) techniques. QoS is the combination effect on service level, which locates the user's degree of contentment of the service. In this paper, packet schedulers (FIFO, WFQ, CQ and PQ) were implemented and evaluated under different applications with different priorities. The results show that WFQ scheduler gives acceptable r
... Show MoreA Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.