The Experts below are selected from a list of 5832 Experts worldwide ranked by ideXlab platform
Sukhwan Jang - One of the best experts on this subject based on the ideXlab platform.
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determination of Equivalent Roughness for estimating flow resistance in stabled gravel bed river ii review of model applicability
Journal of Environmental Sciences-china, 2008Co-Authors: Sangwoo Park, Sinjae Lee, Sukhwan JangAbstract:In this study, we estimated the Equivalent Roughness using an estimation model, which considered grain distribution on the bed and the protrusion height of the grains. We also reviewed the appropriateness of the estimated Equivalent Roughness at the Goksung and Gurey station in the Seomjin River. To review the appropriateness of this model, we presented the water level-discharge relation curve applying the Equivalent Roughness to the flow model and compared and reviewed it to observed data. Also, we compared and reviewed the observed data by estimating the Manning coefficient , the Chezy coefficient , and the Darcy-Weisbach friction coefficient by the Equivalent Roughness. The calculation results of the RMSE showed within 5% error range in comparison with observed value. Therefore the estimated Equivalent Roughness values by the model could be proved appropriate.
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determination of Equivalent Roughness for estimating flow resistance in stabled gravel bed river i theory and development of the model
Journal of Environmental Sciences-china, 2008Co-Authors: Sangwoo Park, Sinjae Lee, Sukhwan JangAbstract:Flow resistance in a natural stream is caused by complex factors, such as the grains on the bed, vegetation, and bed-form, reach profile. Flow resistance in a generally stable gravel bed stream is due to protrudent grains from bed. Therefore, the flow resistance can be calculated by Equivalent Roughness in gravel bed stream, but estimation of Equivalent Roughness is difficult because nonuniform size and irregular arrangement of distributed grain on natural stream bed. In previous study, Equivalent Roughness is empirically estimated using characteristic grain size. However, application of empirical equation have uncertainty in stream that stream bed characteristic differs. In this study, we developed a model using an analytical method considering grain diameter distribution characteristics of grains on the bed and also taking into account flow resistance acting on each grain. Also, the model consider the protrusion height of grain.
Pietro Angeli - One of the best experts on this subject based on the ideXlab platform.
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Pressure drop and holdup predictions in horizontal oil-water flows for curved and wavy interfaces
CHEMICAL ENGINEERING RESEARCH & DESIGN, 2015Co-Authors: Pietro AngeliAbstract:In this work a modified two-fluid model was developed based on experimental observations of the interface configuration in stratified liquid–liquid flows. The experimental data were obtained in a horizontal 14 mmID acrylic pipe, for test oil and water superficial velocities ranging from 0.02 m/s to 0.51 m/s and from 0.05 m/s to 0.62 m/s, respectively. Using conductance probes, average interface heights were obtained at the pipe centre and close to the pipe wall, which revealed a concave interface shape in all cases studied. A correlation between the two heights was developed that was used in the two-fluid model. In addition, from the time series of the probe signal at the pipe centre, the average wave amplitude was calculated to be 0.0005 m and was used as an Equivalent Roughness in the interfacial shear stress model. Both the interface shape and Roughness were considered in the two-fluid model together with literature interfacial shear stress correlations. Results showed that the inclusion of both the interface curvature and the Equivalent Roughness in the two-fluid model improved its predictions of pressure drop and interface height over the range of studied superficial oil and water velocities. Compared to the two-fluid model with other interfacial shear stress correlations, the modified model performed better particularly for predicting pressure drop.
Sangwoo Park - One of the best experts on this subject based on the ideXlab platform.
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determination of Equivalent Roughness for estimating flow resistance in stabled gravel bed river i theory and development of the model
Journal of Environmental Sciences-china, 2008Co-Authors: Sangwoo Park, Sinjae Lee, Sukhwan JangAbstract:Flow resistance in a natural stream is caused by complex factors, such as the grains on the bed, vegetation, and bed-form, reach profile. Flow resistance in a generally stable gravel bed stream is due to protrudent grains from bed. Therefore, the flow resistance can be calculated by Equivalent Roughness in gravel bed stream, but estimation of Equivalent Roughness is difficult because nonuniform size and irregular arrangement of distributed grain on natural stream bed. In previous study, Equivalent Roughness is empirically estimated using characteristic grain size. However, application of empirical equation have uncertainty in stream that stream bed characteristic differs. In this study, we developed a model using an analytical method considering grain diameter distribution characteristics of grains on the bed and also taking into account flow resistance acting on each grain. Also, the model consider the protrusion height of grain.
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determination of Equivalent Roughness for estimating flow resistance in stabled gravel bed river ii review of model applicability
Journal of Environmental Sciences-china, 2008Co-Authors: Sangwoo Park, Sinjae Lee, Sukhwan JangAbstract:In this study, we estimated the Equivalent Roughness using an estimation model, which considered grain distribution on the bed and the protrusion height of the grains. We also reviewed the appropriateness of the estimated Equivalent Roughness at the Goksung and Gurey station in the Seomjin River. To review the appropriateness of this model, we presented the water level-discharge relation curve applying the Equivalent Roughness to the flow model and compared and reviewed it to observed data. Also, we compared and reviewed the observed data by estimating the Manning coefficient , the Chezy coefficient , and the Darcy-Weisbach friction coefficient by the Equivalent Roughness. The calculation results of the RMSE showed within 5% error range in comparison with observed value. Therefore the estimated Equivalent Roughness values by the model could be proved appropriate.
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a study on Roughness coefficient estimations in gravel bed stream without water level discharge data
Journal of Korea Water Resources Association, 2006Co-Authors: Sinjae Lee, Sangwoo ParkAbstract:This study developed a model that could calculate Equivalent Roughness using shear stress acting on distributed grains in gravel bed stream. The estimated Equivalent Roughness by the model developed was used for estimation of water level and Roughness coefficient in the stream without water level-discharge data. The model was applied to the Gurey-Songjeong stage station section located in the Sumjin river mid-downstream. The Equivalent Roughness by the model developed in this study was estimated to be 0.194m at the Gurey stage station. Calculated water level which the estimated Equivalent Roughness was applied to the flow model was shown ewer of within 6% in comparison with observed water level. Also, Roughness coefficient was estimated using observed and calculated water level about each discharge scale by unsteady flow analysis. As a result, error of Roughness coefficient estimated by observed and calculated water level was shown error of and could consider variability of Roughness coefficient.
Michael S. Murillo - One of the best experts on this subject based on the ideXlab platform.
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Data-driven prediction of the Equivalent sand-grain height in rough-wall turbulent flows
Journal of Fluid Mechanics, 2021Co-Authors: Mostafa Aghaei Jouybari, Junlin Yuan, Giles Brereton, Michael S. MurilloAbstract:This paper investigates a long-standing question about the effect of surface Roughness on turbulent flow: What is the Equivalent Roughness sand-grain height for a given Roughness topography? Deep neural network (DNN) and Gaussian process regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse Equivalent sand-grain height with an average error of less than 10 % and a maximum error of less than 30 %, which appears to be significantly more accurate than existing prediction formulae. They also identified the surface porosity and the effective slope of Roughness in the spanwise direction as important factors in drag prediction.
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Data-driven prediction of the Equivalent sand-grain height in rough-wall turbulent flows.
arXiv: Fluid Dynamics, 2020Co-Authors: Mostafa Aghaei Jouybari, Junlin Yuan, Giles Brereton, Michael S. MurilloAbstract:This paper investigates a long-standing question about the effect of surface Roughness on turbulent flow: what is the Equivalent Roughness sand-grain height for a given Roughness topography? Deep Neural Network (DNN) and Gaussian Process Regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse Equivalent sand-grain height $k_s$ for turbulent flows over a wide variety of different rough surfaces. To this end, 45 surface geometries were generated and the flow over them simulated at $\hbox{Re}_\tau=1000$ using direct numerical simulations. These surface geometries differed significantly in moments of surface height fluctuations, effective slope, average inclination, porosity and degree of randomness. Thirty of these surfaces were considered fully-rough and they were supplemented with experimental data for fully-rough flows over 15 more surfaces available from previous studies. The DNN and GPR methods predicted $k_s$ with an average error of less than 10% and a maximum error of less than 30%, which appears to be significantly more accurate than existing prediction formulas. They also identified the surface porosity and the effective slope of Roughness in the spanwise direction as important factors in drag prediction.
Sinjae Lee - One of the best experts on this subject based on the ideXlab platform.
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determination of Equivalent Roughness for estimating flow resistance in stabled gravel bed river i theory and development of the model
Journal of Environmental Sciences-china, 2008Co-Authors: Sangwoo Park, Sinjae Lee, Sukhwan JangAbstract:Flow resistance in a natural stream is caused by complex factors, such as the grains on the bed, vegetation, and bed-form, reach profile. Flow resistance in a generally stable gravel bed stream is due to protrudent grains from bed. Therefore, the flow resistance can be calculated by Equivalent Roughness in gravel bed stream, but estimation of Equivalent Roughness is difficult because nonuniform size and irregular arrangement of distributed grain on natural stream bed. In previous study, Equivalent Roughness is empirically estimated using characteristic grain size. However, application of empirical equation have uncertainty in stream that stream bed characteristic differs. In this study, we developed a model using an analytical method considering grain diameter distribution characteristics of grains on the bed and also taking into account flow resistance acting on each grain. Also, the model consider the protrusion height of grain.
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determination of Equivalent Roughness for estimating flow resistance in stabled gravel bed river ii review of model applicability
Journal of Environmental Sciences-china, 2008Co-Authors: Sangwoo Park, Sinjae Lee, Sukhwan JangAbstract:In this study, we estimated the Equivalent Roughness using an estimation model, which considered grain distribution on the bed and the protrusion height of the grains. We also reviewed the appropriateness of the estimated Equivalent Roughness at the Goksung and Gurey station in the Seomjin River. To review the appropriateness of this model, we presented the water level-discharge relation curve applying the Equivalent Roughness to the flow model and compared and reviewed it to observed data. Also, we compared and reviewed the observed data by estimating the Manning coefficient , the Chezy coefficient , and the Darcy-Weisbach friction coefficient by the Equivalent Roughness. The calculation results of the RMSE showed within 5% error range in comparison with observed value. Therefore the estimated Equivalent Roughness values by the model could be proved appropriate.
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a study on Roughness coefficient estimations in gravel bed stream without water level discharge data
Journal of Korea Water Resources Association, 2006Co-Authors: Sinjae Lee, Sangwoo ParkAbstract:This study developed a model that could calculate Equivalent Roughness using shear stress acting on distributed grains in gravel bed stream. The estimated Equivalent Roughness by the model developed was used for estimation of water level and Roughness coefficient in the stream without water level-discharge data. The model was applied to the Gurey-Songjeong stage station section located in the Sumjin river mid-downstream. The Equivalent Roughness by the model developed in this study was estimated to be 0.194m at the Gurey stage station. Calculated water level which the estimated Equivalent Roughness was applied to the flow model was shown ewer of within 6% in comparison with observed water level. Also, Roughness coefficient was estimated using observed and calculated water level about each discharge scale by unsteady flow analysis. As a result, error of Roughness coefficient estimated by observed and calculated water level was shown error of and could consider variability of Roughness coefficient.