جستجوی مقالات مرتبط با کلیدواژه « Longitudinal Dispersion Coefficient » در نشریات گروه « فنی و مهندسی »

The understanding of the mechanisms underlying the diffusion, advection, and mixing of pollutants constitutes a crucial aspect in monitoring water resources quality, particularly in the context of river systems. The longitudinal dispersion coefficient holds significant importance in predicting and illustration of pollutants in river systems. The current study investigated the advection and dispersion of pollution in meandering rivers experimentally. A series of experiments were undertaken to quantify the tracer concentration within a laboratory flume. The meandering flume with 8 bends was used to consider variation of tracer concentration for different discharges and solid and sedimentary beds. Utilizing the collected experimental data and employing the routing process, the longitudinal dispersion coefficient was determined across various segments of meandering rivers. Comparison of results for both solid and sedimentary bed indicates that as the flow discharge increases, the tracer's transfer speed increases while the transfer time decreases. Consequently, the difference between the peak concentration of tracer on the hydrographs (Cr) of input and output sections in the range of 21 to 100 percent decreases. Furthermore, by changing the bed from solid to sedimentary, Cr and the longitudinal mixing coefficient (DL) increased noticeably. In the mentioned conditions, the lowest values of Cr and DL parameters were obtained with 100 and 85% increase in value, respectively. Additionally, the results revealed a positive correlation between the mixing parameters and the length of the traversed tracer along the bends. The analysis of the data indicated that as the Reynolds number increased, the longitudinal mixing coefficient increased in both solid and sedimentary bed.

In many flowthrough patterns from body of rockfill dams or long rock drains with free water surface, modeling of advection and dispersion of pollutants is one of most important issues. In this research with application of sodium chloride as tracer and EC sensors installed inside the rockfill media, experimental breakthrough curves were extracted. Then with analytical solution of unsteady advection- dispersion equation and none linear optimization least square technique the analytical breakthrough curves at the positions of sensors were also determined. The data acquisition instrument consisted of EC sensors, data logger and application software. Two types of rocks with diameters of 1.1 and 1.8 centimeters, two completely flowthrough discharges of 0.26 and 0.37 liters per second and 5 injection mass of sodium chloride 5, 10, 15, 25 and 50 grams were the variables of laboratory experiments. Finally it was observed that against normal distribution of mass which is special and symmetric case of equation, the observed BC curves were asymmetric in raising and falling limbs. They had longer tail in falling limb because of transient storage of tracer inside the rock media and gradually transport towards the downstream. It was concluded that the classical AD equation is suitable for predicting the general trend of BC curves. In many cases it was observed that the peak point in the analytical solution is lower than the experimental one. Average magnitudes of RMSE, CRM, MAE and ME the statistical parameters for simulated concentration data were 1.35, 0.000608, 52.9ppm and 648.8 ppm respectively which showed a good agreement between experimental and analytical data series.

Vegetation can be a significant factor for improving water quality and reducing the concentration of pollutants. In this study, the longitudinal dispersion coefficient and variations of the tracer concentration have been studied using Potassium permanganate (KMnO4) as a non-decayable tracer. The experiments were conducted in a straight rectangular compound cross section with 8 m length, 0.25 m width, and 0.6 m height. Two rows of metal cylinders were used over the floodplain for simulating rigid vegetation. Digital image processing technique with imaging from tracer cloud in Matlab software was used for measuring tracer concentration in three sections downstream of the injection. The results showed that shear flow between vegetation and floodplain wall was invigorated in the presence of two rows of vegetation. Consequently, this shear flow had a significant impact on longitudinal dispersion coefficient. Accordingly, two rows of trees increase the longitudinal dispersion coefficient up to 39.2% in specific relative depth (0.56) compared to non-vegetated case and causes reduction in the concentration of contaminants downstream of the injection section. The results of non dimensional longitudinal dispersion coefficient obtained in this study compared with the empirical relationship proposed by different researchers and the accuracy of each method for estimating the longitudinal dispersion coefficient of waterways combined with vegetation over the floodplain were also investigated. The results showed that the models of Elder (1959) and Deng et al. (2002) predict the longitudinal dispersion coefficient with two rows of vegetation over flood plain with higher accuracy.

Over the course of recent decades, agricultural and industrial activities have led to a considerable pollution in rivers. To control the river pollution level, the waste water discharge should be regulated. Pollutions have been dispersed in longitudinal, lateral, and vertical direction. The longitudinal dispersion of pollutants in river is important. In this study, a comprehensive equation was proposed for longitudinal dispersion coefficient using dimensional analysis. Dimensional and statistical analysis indicated that longitudinal dispersion coefficient depends on roughness coefficient and the ratio of width to depth of river. Sensitivity analysis indicated that roughness coefficient had the highest effect on longitudinal dispersion coefficient. The proposed methodology is a new approach to estimate dispersion coefficient in streams and can be implemented into mathematical models of pollutant transfer.