Further application of hybrid solution to another form of Boussinesq equations and comparisons

Recently, a new hybrid scheme is introduced for the solution of the Boussinesq equations. In this study, the hybrid scheme is used to solve another form of the Boussinesq equations. The hybrid solution is composed of finite-volume and finite difference method. The finite-volume method is applied to conservative part of the governing equations, whereas the higher order Boussinesq terms are discretized using the finite-difference scheme. Fourth-order accuracy is provided in both time and space. The solution is then applied to several test cases, which are taken from the previous studies. The results of this study are compared with experimental and theoretical results as well as those of the previous ones. The comparisons indicate that the Boussinesq equations solved here and in the previous study produce quite similar results. Copyright © 2006 John Wiley & Sons, Ltd

3D model for prediction of flow profiles around bridges

Computational fluid dynamics models have become well established as tools for simulating free surface flow over a wide range of structures. This study is an assessment and comparison of the performance of a commercially available three-dimensional numerical software, which solves the Reynolds-averaged Navier-Stokes equations, to predict the free surface profiles from up-to downstream of four different bridge types with and without piers in a compound channel. The model results were compared with the available experimental data. Comparisons indicate that the model provides a reasonably good description of free surface profiles under both gradually and rapidly varied flow conditions in the bridge vicinity, respectively. © 2010 International Association for Hydro-Environment Engineering and Research

Theoretical and numerical investigations on solitary wave solutions of Gardner equation

This paper formulates new hyperbolic functions ansatze to construct exact solitary wave solutions of the Gardner (combined KdV-mKdV) equation and a finite element approach for the numerical solutions. A novel class of exact solitary wave solutions is derived. The conditions on the physical parameters for the existence of the obtained structures are also presented. Accuracy of the proposed numerical scheme is assessed in terms of L2 and L? error norms. Numerical experiments demonstrate the accuracy and robustness of the method which can be further used for solving other nonlinear problems. © 2018, Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature

Prediction of backwater level of bridge constriction using an artificial neural network

Bridge constriction in channels usually increases the water level well above the normal depth and may result in overflow on the surrounding floodplain. In this paper, the experimental backwater level at which the maximum afflux value was observed due to bridge constriction was investigated. An artificial neural network (ANN) was used to predict the backwater level based on Manning's roughness coefficient of the main channel (nmc) and of the floodplain (nfp), bridge width (b) and flow discharge (Q). A multi-layer perceptron (MLP) ANN was used to predict the backwater level using these parameters. Multiple linear (MLR) regression and multiple non-linear regression (MNLR) were used as benchmarks for comparison of ANN results. It is concluded that an ANN can very accurately predict the backwater level. The developed ANN model was then used to conduct a parametric study to investigate the influence of nmc, nfp, b and Q on the backwater level due to a bridge constriction without piers. It is concluded that nmc and Q have a more profound effect on the backwater level than does nfp, while b has very little effect on the backwater level within this range of parameters. Other observations and conclusions are also drawn

Bridge afflux estimation using artificial intelligence systems

Most of the methods developed for the prediction of bridge afflux are generally based on either energy or momentum equations. Recent studies have shown that the energy method, which is one of the four bridge subroutines within the commonly used program HEC-RAS for computing water surface profiles along rivers, produced more accurate results than three other methods (momentum, WSPRO and Yarnell's methods) when applied to bridge afflux data obtained from experiments conducted in a two-stage channel. This work developed three artificial intelligence models (the radial basis neural network, the multi-layer perceptron and the adaptive neurofuzzy inference system) as alternatives to the energy method. Multiple linear and multiple non-linear regression models were also used in the analysis. The results showed that the performance of the adaptive neuro fuzzy inference system in predicting bridge afflux was superior to the other models

Extending the canopy flow model for natural, highly flexible macrophyte canopies

Flow structures above vegetation canopies have received much attention within terrestrial and aquatic literature. This research has led to a good process understanding of mean and turbulent canopy flow structure. However, much of this research has focused on rigid or semi-rigid vegetation with relatively simple morphology. Aquatic macrophytes differ from this form, exhibiting more complex morphologies, predominantly horizontal posture in the flow and a different force balance. While some recent studies have investigated such canopies, there is still the need to examine the relevance and applicability of general canopy layer theory to these types of vegetation. Here, we report on a range of numerical experiments, using both semi-rigid and highly flexible canopies. The results for the semi-rigid canopies support existing canopy layer theory. However, for the highly flexible vegetation, the flow pattern is much more complex and suggests that a new canopy model may be required

Hydrological characteristics of vegetated river flows: a laboratory flume study

Laboratory flume experiments were undertaken to measure the vertical profiles of mean flow velocity for three different flow discharges and four different stem densities of Hydrilla verticillata. The data were used to calculate three parameters, namely Manning's roughness coefficient, the Reynolds number and the Froude number. In addition, empirical equations were obtained for the vertical distribution of measured flow velocity within the transitional zone and above the plant canopy. The results show that: (a) the vertical distribution of measured flow velocity exhibits three zone profiles; (b) Manning's roughness coefficient decreases with increasing depth-averaged flow velocity; (c) the relationship between Manning's roughness coefficient and the depth-averaged flow velocity is within the smooth left inverse curve; (d) Manning's roughness coefficient significantly changes with increasing density of Hydrilla; (e) the Froude number is independent of the density of Hydrilla; and (f) both the Reynolds number and the Froude number increase with increasing depth-averaged flow velocity. Des expériences de laboratoire en canal ont été menées pour mesurer les profils verticaux des vitesses d’écoulement moyennes pour trois débits différents et quatre densités de tiges d’Hydrilla verticillata différentes. Les données ont été utilisées pour calculer trois paramètres : le coefficient de rugosité de Manning, le nombre de Reynolds et le nombre de Froude. En outre, des équations empiriques ont été obtenues pour les distributions verticales des vitesses d’écoulement mesurées dans la zone de transition et au-dessus du couvert végétal. Les résultats montrent que: (a) les distributions verticales des vitesses d’écoulement mesurées présentent des profils à trois zones; (b) le coefficient de rugosité de Manning diminue avec l'augmentation de la vitesse d’écoulement moyenne; (c) la relation entre le coefficient de rugosité de Manning et la vitesse d’écoulement moyenne se situe à l'intérieur de la courbe inverse gauche lissée, (d) le coefficient de rugosité de Manning change de manière significative avec la densité croissante d’Hydrilla; (e) le nombre de Froude est indépendant de la densité d’Hydrilla, et (f) le nombre de Reynolds et le nombre de Froude augmentent avec la vitesse d’écoulement moyenne