Ahmed M. A. Sattar

Neuro-Fuzzy GMDH Approach to Predict Longitudinal Dispersion in Water Networks

Longitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactors. The essential characteristics of longitudinal dispersion in pipes can be described by the longitudinal dispersion coefficient. This paper presents the application of the adaptive Neuro fuzzy group method of data handling to develop new empirical formulae for the prediction of longitudinal dispersion coefficients in pipe flow using 233 experimental case studies of dispersion coefficient with a Re range of 900 to 500,000 spanning laminar, transitional and turbulent pipe flow. The NF-GMDH network was improved using particle swarm optimization based evolutionary algorithm. The group method data handling is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Reynolds number, the average velocity, the pipe friction coefficient and the pipe diameter. GMDH holds advantage in the case of small data samples due to the optimal choice of the model complexity with automatic adaptation to an unknown level of the data uncertainties. Sensitivity analysis is performed on the developed model and the weight and importance of each control variable is presented. The results indicate that the proposed relations are simpler than previous numerical solutions and can effectively evaluate the longitudinal dispersion coefficients in pipe flow.

Gene Expression Models for the Prediction of Longitudinal Dispersion Coefficients in Transitional and Turbulent Pipe Flow

AbstractLongitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactors. The essential characteristics of longitudinal dispersion in pipes can be described by the longitudinal dispersion coefficient. This paper presents the application of evolutionary gene expression programming (GEP) to develop new empirical formulas for the prediction of longitudinal dispersion coefficients in pipe flow using 220 experimental case studies of the dispersion coefficient with a R range of 2,000–500,000 spanning transitional and turbulent pipe flow. Gene expression programming is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Reynolds number, the average velocity, the pipe friction coefficient, and the pipe diameter. Four GEP models are developed, and the weight and importance of each contro...

Urban stormwater thermal gene expression models for protection of sensitive receiving streams

Thermal impact of typical high-density residential, industrial, and commercial land uses is a major concern for the health of aquatic life in urban watersheds, especially in smaller, cold and cool-water streams. This is the first study of its kind that provides simple easy-to-use equations, developed using gene expression programming (GEP), that can guide the assessment and design of urban stormwater management systems to protect thermally sensitive receiving streams. We developed three GEP models using data collected during three years 2009-2011 from four urban catchments; the first GEP model predicts event mean temperature at the inlet of the pond; the second model predicts the stormwater temperature at the outlet of the pond; and the third model predicts the temperature of the stormwater after flowing through a cooling trench and before discharging to the receiving stream. The new models have high correlation coefficients of 0.90-0.94 and low prediction uncertainty of less than 4% of the median value of the predicted runoff temperatures. Sensitivity analysis shows that climatic factors have the highest influence on the thermal enrichment followed by the catchment characteristics and the key design variables of the stormwater pond and the cooling trench. The general method presented here is easily transferable to other regions of the world (but not necessarily the exact equations developed here); also through sensitivity and parametric analysis we gained insight on the key factors and their relative importance in modelling thermal enrichment of urban stromwater runoff.

A probabilistic projection of the transient flow equations with random system parameters and internal boundary conditions

ABSTRACT This paper presents a novel probabilistic approach based on the polynomial chaos expansion that can model the uncertainty propagation from the beginning of a waterhammer simulation and not as an afterthought. Uncertainties are considered in pipe diameter, friction coefficient, and wave speed, as well as internal boundary conditions of leaks and blockages. The polynomial chaos expansion solver results are in an excellent agreement with those calculated by using a model employing the traditional method of characteristics. The probabilistic polynomial chaos approach has the advantage of being robust and more efficient than other non-intrusive methods such as Monte Carlo simulation, which requires thousands of iterations for sharp solutions. The polynomial chaos approach is further extended to solve for randomness in frequency domain using the transfer matrix method with results of comparable accuracy. With further developments, this probabilistic approach can be integrated within existing network modelling software for practical hydraulic engineering problems.

Extreme learning machine model for water network management

A novel failure rate prediction model is developed by the extreme learning machine (ELM) to provide key information needed for optimum ongoing maintenance/rehabilitation of a water network, meaning the estimated times for the next failures of individual pipes within the network. The developed ELM model is trained using more than 9500 instances of pipe failure in the Greater Toronto Area, Canada from 1920 to 2005 with pipe attributes as inputs, including pipe length, diameter, material, and previously recorded failures. The models show recent, extensive usage of pipe coating with cement mortar and cathodic protection has significantly increased their lifespan. The predictive model includes the pipe protection method as pipe attributes and can reflect in its predictions, the effect of different pipe protection methods on the expected time to the next pipe failure. The developed ELM has a superior prediction accuracy relative to other available machine learning algorithms such as feed-forward artificial neural network that is trained by backpropagation, support vector regression, and non-linear regression. The utility of the models provides useful inputs when planning and budgeting for watermain inspection, maintenance, and rehabilitation.

Using Gene Expression Programming to Determine the Impact of Minerals on Erosion Resistance of Selected Cohesive Egyptian Soils

Cohesive sediment soils are encountered throughout Egypt at many locations, posing various physical and chemical characteristics in beds of lakes, estuaries and flash flood flows. The entire delta region is made up of clayey soil formed from various consecutive Nile floods before construction of the High Dam. Thus, it is very important to determine the erosional stability of such cohesive soils as a function of sediment chemical properties and mineral content. In the current research, 48 samples are collected from various locations throughout Egypt. All samples are subject to physical tests for grain size distribution, and X-ray diffraction analysis for mineral contents. Laboratory experiments are carried out on these samples for finding the difference in terms of erosion characteristics caused by different sediment composition among all samples. Assuming other properties of cohesive soils constant, the gene expression programming (GEP) algorithms are applied to relate the clay mineral content to experimental critical shear stress. Results show an excellent potentiality for the GEP for being applied on finding relations between complex parameters with nonlinear relationships with respect to soil erosion.

Predicting morphological changes DS New Naga-Hammadi Barrage for extreme Nile flood flows: A Monte Carlo analysis.

While construction of the Aswan High Dam (AHD) has stopped concurrent flooding events, River Nile is still subject to low intensity flood waves resulting from controlled release of water from the dam reservoir. Analysis of flow released from New Naga-Hammadi Barrage, which is located at 3460 km downstream AHD indicated an increase in magnitude of flood released from the barrage in the past 10 years. A 2D numerical mobile bed model is utilized to investigate the possible morphological changes in the downstream of Naga-Hammadi Barrage from possible higher flood releases. Monte Carlo simulation analyses (MCS) is applied to the deterministic results of the 2D model to account for and assess the uncertainty of sediment parameters and formulations in addition to sacristy of field measurements. Results showed that the predicted volume of erosion yielded the highest uncertainty and variation from deterministic run, while navigation velocity yielded the least uncertainty. Furthermore, the error budget method is used to rank various sediment parameters for their contribution in the total prediction uncertainty. It is found that the suspended sediment contributed to output uncertainty more than other sediment parameters followed by bed load with 10% less order of magnitude.

Explicit Solution for the Specific Flow Depths in Partially Filled Pipes

AbstractThis paper presents an explicit solution for the specific flow depths in partially filled pipes of circular cross-sectional area. Four depths encounter in most classical free-surface flow p...

Soil Aquifer Treatment System Design Equation for Organic Micropollutant Removal

Rapid population growth and mass migration from rural to urban centers have contributed to a new era of water sacristy, and a significant drop in per capita freshwater availability, resulting in the reuse of wastewater emerging as a viable alternative. The reuse of wastewater after treatment using the soil aquifer treatment (SAT) has recently gained popularity due to low operating/maintenance cost of the method. However, the presence of organic micropollutants (OMPs) may present a health risk if the SAT is not adequately designed to ensure required attenuation of the OMPs. An important aspect of the design of the SAT system is the large degree of natural variability in the OMP concentrations/loads in the wastewater and the uncertainty associated with the current methods for calculation of the removal efficiency of the SAT for the OMPs. This study presents a novel model for more accurate prediction of the removal efficiency of the SAT system for the OMPs and the fate of the OMPs trapped within the vadose zone. A large data set is compiled covering a broad range of aquifer conditions, and the SAT system parameters, including hydraulic loading rate and dry/wet ratio. This study suggests that removal of OMPs in SAT systems is most affected by biodegradation rate and soil saturated hydraulic conductivity, in addition to dry to wet ratio. This conclusion is reached by the application of the developed prediction model using data sets from the case study SAT systems in Egypt.

Three dimensional modeling of free surface flow and sediment transport with bed deformation using automatic mesh motion

This study presents the development of a 3D numerical code for flow-sediment interactions with associated bed changes in free surface flows. To capture the water-air interface, a novel volume of fluid (VOF) formulation is implemented using the ghost fluid method with one-side extrapolation for dynamic pressure. Equations for fluid flow and free surface motion are decoupled due to separation of time scales. Accordingly, time step size can be increased by a factor of 3-orders of magnitude and still preserve numerical stability and computational efficiency. A novel finite area method (FAM) is utilized to discretize Exner equation on irregular bed surface providing a 3D finite volume-like discretization on curved surfaces. The evolution of the water-sediment interface is captured by a novel vertex-based unstructured mesh dynamic motion solve using Laplace operator with variable diffusivity. The code is implemented in foam-extend and tested against two classical experiments. Good results are obtained with correct trends and lower absolute error compared to previous mobile bed models. This shows that the developed code has a good potential of being applied to mobile-bed hydraulic real problems.