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Simulation of flow pattern at rectangular lateral intake with different dike and submerged vane scenarios

Water Science and Engineering 2017年第3期10卷 246-255页

作者： Hojat Karami saeed farzin Mohammad Tavakol Sadrabadi Hasan Moazeni Faculty of Civil Engineering Semnan University School of Water Sciences Engineering Shahid Chamran University of Ahvaz

A comprehensive understanding of the sediment behavior at the entrance of diversion channels requires complete knowledge of threedimensional(3 D) flow behavior around such structures. Dikes and submerged vanes are typical structures used to control sediment entrainment in the diversion channel. In this study, a 3 D computational fluid dynamic(CFD) code was calibrated with experimental data and used to evaluate flow patterns, the diversion ratio of discharge, the strength of secondary flow, and dimensions of the vortex inside the channel in various dike and submerged vane installation scenarios. Results show that the diversion ratio of discharge in the diversion channel is dependent on the width of the flow separation plate in the main channel. A dike perpendicular to the flow with a narrowing ratio of 0.20 doubles the ratio of diverted discharge in addition to reducing suspended sediment input to the basin, compared with a no-dike situation, by creating the outer arch conditions. A further increase in the narrowing ratio decreases the diverted discharge. In addition, increasing the longitudinal distance between consecutive vanes(Ls) increases the velocity gradient between the vanes and leads to a more severe erosion of the bed, near the vanes.

关键词： Three-dimensional simulation Computational fluid dynamics Submerged vanes Dike Side and vortex flow

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Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

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Water Science and Engineering 2019年第1期12卷 45-54页

作者： Armin Azad Hojat Karami saeed farzin Sayed-Farhad Mousavi Ozgur Kisi Faculty of Civil Engineering Semnan UniversitySemnan 35131-19111Iran Faculty of Natural Sciences and Engineering Ilia State UniversityTbilisi 0162Georgia

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.

关键词： Water quality parameters ANFIS Evolutionary algorithm Particle swarm optimization Ant colony optimization for continuous domains

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