Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Citation - Scopus: 1Modeling Pollutant Transport in Overland Flow Over Non-Planar and Non-Homogenous Infiltrating Surfaces(Zhejiang University Press, 2013) He, Zhi-guo; Tayfur, Gökmen; Ran, Qihua; Weng, HaoxuanPollutant transport in overland flow over surfaces with spatially varying microtopography, roughness, and infiltration was investigated using the diffusion wave equation and transport rate-based equation. The finite volume method in space and an implicit backward difference scheme in time were employed in the numerical solution of the 2D governing equations. The developed model was first tested against an analytical solution and an experimental study involving overland flow and the associated pollutant transport, subsequently a series of numerical tests were carried out. Non-point source pollution was investigated under spatially varying microtopography, roughness, and infiltration. The simulation results showed that microtopography and roughness were the dominant factors causing significant spatial variations in solute concentration. When the spatially varying microtopography was replaced by a smooth surface, the result was an overestimation of the solute rate at the outlet of the upland. On the other hand, when the spatially varying roughness was replaced by the average roughness and spatially varying infiltration rate by the average infiltration rate, the pollutant discharge at the outlet of the upland was not significantly affected. The numerical results further showed that one cannot ignore the spatial variations of slope and roughness when investigating the local pollutant concentration distribution.Article Citation - WoS: 70Citation - Scopus: 80Modeling of Coal Bed Methane (cbm) Production and Co2 Sequestration in Coal Seams(Elsevier Ltd., 2009) Özdemir, EkremA mathematical model was developed to predict the coal bed methane (CBM) production and carbon dioxide (CO2) sequestration in a coal seam accounting for the coal seam properties. The model predictions showed that, for a CBM production and dewatering process, the pressure could be reduced from 15.17 MPa to 1.56 MPa and the gas saturation increased up to 50% in 30 years for a 5.4 × 105 m2 of coal formation. For the CO2 sequestration process, the model prediction showed that the CO2 injection rate was first reduced and then slightly recovered over 3 to 13 years of injection, which was also evidenced by the actual in seam data. The model predictions indicated that the sweeping of the water in front of the CO2 flood in the cleat porosity could be important on the loss of injectivity. Further model predictions suggested that the injection rate of CO2 could be about 11 × 103 m3 per day; the injected CO2 would reach the production well, which was separated from the injection well by 826 m, in about 30 years. During this period, about 160 × 106 m3 of CO2 could be stored within a 21.4 × 105 m2 of coal seam with a thickness of 3 m.Article Citation - WoS: 91Citation - Scopus: 122Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings(Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, SelcenA prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.Article Citation - WoS: 1Citation - Scopus: 2Kinetic Estimation of the Adsorbate Distribution on the Surface From Adsorbed Amounts(Elsevier Ltd., 2006) Polat, MehmetA phenomenological multilayer adsorption model for a well-dispersed, homogeneous, nonporous adsorbent and a molecular adsorbate is presented. The model provides explicit kinetic expressions associating the adsorbed amounts to the fraction of the surface occupied and reduces to the first- and second-order adsorption models for special cases. Parameters of the model are a pair of true rate constants related to the adsorbate-adsorbent and adsorbate-surface adsorbate affinities. A general graphical procedure and analytical equations for special cases are provided to estimate the rate constants from kinetic adsorption data. Data from the adsorption of sodium stearate onto α-alumina from water were used to test the model. The predicted values of the rate constants suggested that the stearate was distributed homogeneously on the alumina surface and essentially adsorbed as a monolayer before starting to form the second layer.