The present research focusses on a comparison of experimental and numerical approaches for flow over fixed artificial rigid grass bed channels. Various flow parameters like longitudinal velocity, depth-averaged velocity (DAV), boundary shear stress (BSS) and secondary current are analysed and compared with seven numerical models: standard, realizable and renormalization group (RNG) kε models and standard, shear stress transport (SST), generalized kω (GEKO) and Baseline (BSL) kω models. To evaluate the strength of the seven applied models, the error analysis has been performed. It is found that the RNG kε and SST kω models provided better results for both the DAV and BSS prediction, but the RNG kε model is found to be the most suitable for predicting the DAV and the SST kω model for BSS as compared to the other models. For the longitudinal velocity profiles, both the RNG kε and SST kω models are found to provide good agreement with experimental results at the centre of the channel, whereas the SST kω model is more accurate near the wall. Overall, the SST kω model has predicted the results with good accuracy for all the flow parameters considered in the present study.

  • A comparative study of all the seven sub-models pertaining to the kω and kε groups was performed.

  • DAV and BSS profiles are presented using the discussed turbulence models, CES and compared with the experimental results.

  • Statistical error analysis is performed.

  • The RNG kε model estimated the depth-averaged velocity more accurately, whereas the SST kω model is found to be more accurate in predicting the boundary shear stress.

Hydraulic structures in open channels necessitate a thorough understanding of flow characteristics to ensure proper design. Parameters such as mean flow pattern, depth-averaged velocity (DAV) distribution, secondary flow properties, boundary shear stress (BSS), turbulent characteristics and conveyance capacity play a crucial role during the design process. In the study of turbulent flows in open channels, hydraulic engineers often rely on experimental and numerical investigations to understand the various flow components (Guo & Julien 2003; Yang et al. 2004; Sahu et al. 2014; Devi & Khatua (2016); Khuntia et al. (2018a,b); Tajnesaie et al. 2020; Khuntia et al. (2021); Devi et al. (2021); Qasim et al. 2022). Various research has been carried out on the flow and turbulent structure and it influences the riverine habitat (Bornette & Puijalon 2011), water quality (Dosskey et al. 2010), soil erosion and stability (El Bouanani et al. 2022; Ikhsan et al. 2022) and river planform disorderness (Nandi et al. 2022a).

With the advancement of computational techniques, numerical methods have become valuable tools for solving complex hydraulic engineering problems. Even with sophisticated turbulence models, accurately predicting turbulent structures and BSS in free surface flows remains a difficult task (Shnipov 1989; Yang et al. 2004; Yang & Lim 2005; Guo & Julien 2008). Additionally, incorporating vegetation adds complexity to the study of flow-vegetation interaction in open channels. Vegetated flow, which encompasses both physical and biological aspects, has emerged as a significant research area in the field of hydrodynamics (Nepf 1999; Ghani et al. 2019; Nandi et al. 2022b; Wang et al. 2022). In the existing literature, the focus has primarily been on vegetation as roughness elements that result in a decrease in velocity, affecting turbulence parameters (Ree 1958; Chen 1976; Vargas-Luna et al. 2018; Lera et al. 2019). However, it is crucial to recognize that vegetation has a more profound impact on the flow structure due to the generation of coherent structures and redistributes Reynolds shear stress components.

Various approaches have been developed to simulate turbulent flows due to the easiness in numerical modelling. Direct Numerical Simulations (DNS) are comprehensive but computationally intensive models that account for all scales of motion. On the other hand, Reynolds-averaged Navier–Stokes (RANS) models, which assume isotropic turbulence, have become industry standards due to their simplicity. RANS models based on two equation models, such as the kε and kω models, have been widely used. Reynolds stress models (RSMs) avoid the isotropic assumption and provide a solution for the transport of individual stress terms. Large Eddy Simulation (LES) models simulate most scales by modelling small eddies using sub-grid scale models. Although RSM and LES models tend to yield better results than two equation models, they are typically employed in cases where the application of two equation models is limited, such as in flow separation zones and mixing layers. Previous research by Sofialidis & Prinos (1998) comparing experimental results with a non-linear kε turbulence model of low-Reynolds type revealed that the model could not accurately predict the strength of secondary currents observed in experiments. Shiono & Knight (1988) developed the popular analytical model known as the ‘Lateral Distribution Method’, which calculates DAV distributions and later modified it to account for the effects of secondary currents, resulting in the ‘Modified Lateral Distribution Method’. Other researchers, such as Yang et al. (2004), have employed empirical models for secondary flow terms and parabolic distributions for eddy viscosity to simplify RANS equations. However, these methods have encountered difficulties in accurately predicting velocity in rough bed channels (Bonakdari et al. 2008; Guo & Julien 2008; Absi 2011; Lassabatere et al. 2012). Kang & Choi (2006) conducted a comparative study between RSMs and experimental data, demonstrating good agreement between computed and measured results.

With advancements in computational technology, Computational Fluid Dynamics (CFD) techniques have improved the simulation of fluid behaviours in various channel types, rivers and hydraulic structures (Nguyen et al. 2014). Numerical simulations offer cost and time savings while providing detailed information on the flow field with adequate precision (Thanh & Ling-Ling 2015). Sahu et al. (2014) utilized LES and ANFIS models to model velocity distribution and composite friction factor in an asymmetric compound channel, demonstrating the potential for accurate predictions with fewer computational resources using ANFIS. Two equation models, such as the kε and kω models, have been developed based on the Boussinesq approximation (Qu 2005). The Reynolds stress turbulence model (RSM) has proven effective in capturing three-dimensional flow characteristics and turbulence properties (Asif et al. 2020). Singh & Khatua (2021) and Rezaei & Amiri (2018) predicted the DAV and the BSS distribution in their experimental channels using the standard k–ε model. Tajnesaie et al. (2020) employed four turbulence models to simulate experimental data, comparing secondary flow cells, free-water surface in channels, DAV and shear stress distribution.

However, limited literature exists on the comparative study of various turbulence models for rough open channel flow with grass beds. Therefore, there is a need to analyse the efficiency of different turbulence models to determine their applicability in such conditions. This paper presents an attempt to simulate the experimental results of longitudinal velocity profiles, DAV, BSS and secondary current vectors using seven sub-models from the kω and kε groups of turbulence models designed for a grass bed open channel. Overall, this paper contributes to the advancement of knowledge in the field of hydraulic engineering by providing valuable insights into the selection and application of turbulence models in rough open channel flow with grass beds.

The structure of this article begins with an introduction section that provides essential background information, literature reviews, establishes the research context and outlines the objectives and novelty of the present study. The subsequent sections describe the experimental set-up, theoretical analysis, modelling approach, analysis of results and conclude with a summary section.

Experimental results in a recirculating, tilting, rectangular flume of 0.6 × 0.6 m and 12 m long in the Hydraulic Engineering Laboratory of Civil Engineering Department, National Institute of Technology Rourkela have been used for the study. The testing section of the channel has walls of glass, while the rest of the walls and bed are mild steel and by fixing artificial dense rigid grass (15 mm height) made of plastic along the channel bed, the bottom of the flume is transformed into a rough bed. Figure 1 illustrates a schematic representation of the test set-up (Khuntia et al. 2018a,b, 2021). The photo of bed roughness material is given in Figure 2. A longitudinal slope (S0) of about 0.0012 was fixed and maintained throughout the experiment.
Figure 1

A schematic representation of the test set-up, National Institute of Technology, Rourkela (NITR) (Khuntia et al. 2018a,b, 2021).

Figure 1

A schematic representation of the test set-up, National Institute of Technology, Rourkela (NITR) (Khuntia et al. 2018a,b, 2021).

Close modal
Figure 2

Photo of rough bed with ADV and point gauge (right panel: enlarged view of roughness material).

Figure 2

Photo of rough bed with ADV and point gauge (right panel: enlarged view of roughness material).

Close modal

Uniformity of the flow is maintained by installing a tail gate at the downstream end of the flume. To attain the required depth of flow in a steady uniform case, the tailgate was fixed at a specific height. A flow depth of 11 cm is fixed by adjusting the inlet valve. A SonTek Micro 16-MHz acoustic Doppler velocimeter (ADV) is used to measure three-dimensional velocities. The maximum sampling rate of the instrument was adopted, that is 50 Hz. ADV's remote sampling volume is located 5 cm below or above the acoustic transmitter for minimized flow interference. The down looking probe of the instrument is only used to measure the velocities at lower portion of the channel. It is inaccessible for a down looking probe of ADV to measure the upper layer velocity up to 5 cm from the free surface. So, an up-looking probe of ADV is utilized to prevent this limitation by taking the measurements of the upper part. In the present study, one section at X = 7 m from the inlet is chosen to measure the flow parameters where the full boundary layer and secondary flow developments are supposed to be ensured. The experimental data of the same location are also compared with the numerical results. The lateral distribution of velocities was measured at X = 7 m from the entrance (see Figure 1). The velocities at the flume's boundary were then measured using a Preston tube with an outer diameter of 4.77 mm. Table 1 shows the geometry, roughness and hydraulic parameters of the experiment. A post-processing software called WinADV was used to filter the ADV data by presuming a correlation of 70% and an Sound to Noise Ratio (SNR) level of 15 dB.

Table 1

Experiment's geometry, hydraulic and roughness parameters

Sl No.Item descriptionParameters
Type of channel Straight 
Cross-section of channel Rectangular 
Base width (b) of channel 0.6 m 
Channel depth 0.6 m 
Flow depth (h0.11 m 
Length of flume 12 m 
Type of bed Dense rigid grass 
Flow condition Steady 
Manning's n of bed material 0.0304 
10 Bed slope (S00.0012 
11 Discharge 0.00957 m3/s 
12 Mean velocity 0.175 m/s 
Sl No.Item descriptionParameters
Type of channel Straight 
Cross-section of channel Rectangular 
Base width (b) of channel 0.6 m 
Channel depth 0.6 m 
Flow depth (h0.11 m 
Length of flume 12 m 
Type of bed Dense rigid grass 
Flow condition Steady 
Manning's n of bed material 0.0304 
10 Bed slope (S00.0012 
11 Discharge 0.00957 m3/s 
12 Mean velocity 0.175 m/s 

Governing equations

When using CFD for 3D numerical modelling, the solution of Navier–Stokes (N-S) equations is required, and these equations are based on the laws of mass and momentum conservation inside a flowing fluid. For an incompressible Newtonian fluid turbulent flow, the Navier–Stokes equation is as follows:

Continuity equation (the conservation of mass),
formula
(1)
Momentum equation (the conservation of momentum),
formula
(2)
where ui (i = 1, 2, 3) are the components of instantaneous velocity, p is the instantaneous pressure and τij (i, j = 1, 2, 3) are the viscous stress tensor components, is the fluid density. The rate of strain tensor can be correlated to the stress tensor for a Newtonian fluid as follows:
formula
(3)
where denotes the fluid's kinematic viscosity.
The components of pressure and instantaneous velocity can be represented as an average value (P, Ui) using the Reynolds decomposition method with a fluctuating random component (p′, ui′) in order to make up for turbulence. To get the RANS equations, Reynold's decomposition principle is used and the N-S equations (Equations (1) and (2)) are time averaged and given as follows:
formula
(4)
formula
(5)
Eddy viscosity is introduced to relate lateral velocity gradients and transport equations are solved for turbulent kinetic energy (k) and dissipation rate (ε).

To simulate the flow filed in a grass bed channel, different turbulent models have been utilized in the present study. Out of all the available models in the Fluent software, only the two equation models are applied and discussed. The two equation models discussed in this study operates under assumption of isotropic eddy viscosity, the kind of secondary currents generated by the combined effect of geometry and roughness can be sufficiently reproduced by boundary conditions used in this study.

Two-equation turbulence models

The kε model

The kε model is incorporated in most commercial CFD software since it is perhaps the most commonly used and popular turbulence model. The model was first introduced by Jones and Launder in 1972, and it was later improved by Launder & Spalding (1974). It makes use of a modelling equation for the turbulent kinetic energy k, as well as a transport equation for the viscous dissipation rate ε. The standard kε model equations are presented as:
formula
(6)
formula
(7)
formula
(8)
Pk denotes the turbulence kinetic energy production by mean shear, which is modelled using the equation below:
formula
(9)
where is the turbulent eddy viscosity (m2/s) and , and are model coefficients.

The limitation of the standard kε model is that the turbulent fluctuations are isotropic in nature. As a result, in turbulent fluid flows, the kε model provides erroneous predictions (Mohammadi & Pironneau 1994).

As the limitations of the standard kε model became acknowledged, changes to the model were developed to enhance its efficiency. The renormalization group (RNG) kε model and the realizable kε model are two of these enhanced kε models (Yakhot & Orszag 1986; Shih et al. 1995). The RNG kε model has been refined to accommodate for low-Reynolds number implications and fast strained flows. The realizable kε model contrasts from the standard kε model in that it includes a different turbulent viscosity formulation. In the present study, low-Reynolds number corrections are applied, and wall function is an approximate approach in which semi-empirical equations are used to estimate the flow characteristics near the boundaries. Applications of wall functions are incorporated by using the ‘standard wall functions’ approach in kε models.

The kω model

The standard kω model
The kω model is used to solve two transport equations, one for the turbulent kinetic energy (k) and one for the turbulent frequency (ω = ε/k). The eddy viscosity approach is then used to compute the stress tensor. Kolmogorov (1942) first presented the ω-equation, by using identical physical reasoning as well as dimensional considerations as those used in the ε-equation's derivation. Wilcox (1988) suggested a current version of the kω model, which assumes the subsequent forms (ANSYS Inc. 2013):
formula
(10)
formula
(11)
formula
(12)
where , , , and are known as closure coefficients.

It is worth noting that the kω model contains a low-Reynolds number allowance for near-wall turbulence, which eliminates the need for near-wall modelling, however, wall functions can be implemented when required. Limitations of the k–ε model such as low predictability in curved streamlines and extreme pressure gradients can be overcome by application of the kω model. Another advantage is that the epsilon equation and the omega equation are interchangeable as omega and epsilon are related. These advantages laid the stone for developing a more flexible k–ω shear stress transport (SST) model.

The SST kω model

The SST k–ω model is a modified version of the standard k–ω model developed by Menter (2002). It combines the kω and kε turbulence models such that the kω is used for the boundary layer's inner region and swaps to the kε in free shear flow. A blending function, F1, activates the Wilcox model near the boundary and the kε model in the free stream. This guarantees that the correct model is applied throughout the flow field:

  • The kω model is ideal for simulating flow in the viscous sub-layer.

  • The kε model is suitable for forecasting flow behaviour in areas away from the wall.

These properties make the SST kω model more precise and dependable for a broader type of flow. The governing equations and auxiliary equations of the SST kω model are presented below:

  • Turbulent kinetic energy
    formula
    (13)
  • Specific dissipation rate
    formula
    (14)
  • F1 (Blending function)
    formula
    (15)
  • Kinematic eddy viscosity
    formula
    (16)
  • F2 (Second blending function)
    formula
    (17)
  • Pk (Production limiter)
    formula
    (18)

Generalized kω (GEKO) model

With the goal of turbulence model consolidation, ANSYS introduced a new turbulence model family called generalized k–ω (GEKO). GEKO is a two-equation model based on the k–ω model formulation, but it is adaptable to a wide range of flow scenarios.

The GEKO model's main feature is that it has several free parameters for tuning the model to various flow situations. The formulation's starting point is:
formula
(19)
formula
(20)
formula
(21)
formula
(22)
formula
(23)
formula
(24)
formula
(25)
formula
(26)
formula
(27)
formula
(28)
A computational domain creation, sub-regions for solving the equations (meshing) and modelling appropriate boundary conditions are three important requirements of numerical simulation of every flow domain. The ANSYS Fluent structure can be described as a five-cell system, which is presented in Figure 3.
Figure 3

Data flow through Fluent package.

Figure 3

Data flow through Fluent package.

Close modal

Geometry design

The ANSYS 2021 R1 design modeller has been used to create the computational domain's geometry. The whole channel is designed as a solid domain and the flow depth of 11 cm is designed as a fluid domain inside the solid domain. The length, depth and width of the channel are considered in X, Y and Z directions, respectively. The three-dimensional view of the channel is presented in Figure 4. Following the creation of both fluid and solid domains (Figure 4), the created geometry is assigned with appropriate domain names in order to accommodate the respective boundary conditions during the process known as setting up the physics module. The domain names assigned are Inlet, Outlet, Bed, Side Walls and free surface symmetry.
Figure 4

Three-dimensional view of the designed geometry showing both solid and fluid domains.

Figure 4

Three-dimensional view of the designed geometry showing both solid and fluid domains.

Close modal

Meshing the flow domain

The creation of a suitable computational grid is critical to the numerical solution's stability and convergence. Meshing is splitting the fluid domain into smaller sub-domains to apply the partial differential equations that dictate fluid flow to each sub-domains and solve it by one of the three techniques, i.e., finite volume, finite element or finite difference. The solutions obtained in each of the cells in the domain are then assembled to give an approximate solution of the entire fluid domain. ANSYS Fluent is based on the finite volume method. To get more accurate results, near the channel walls and bed, computational cell sizes are made finer. The cell sizes are increased above at the interior regions of the channel. The y+ value (i.e., a non-dimensional wall distance for a wall-bounded flow) was maintained between 30 < y + <300 value to distinguish different regions near the wall or in the viscous region. The cross-section of the channel after meshing is shown in Figure 5 and the mesh statistics are given in Table 2. Hirt & Nichols (1981) conducted a study which found that the volume of fluid (VOF) method is superior to other methods in terms of efficiency and flexibility when dealing with complex free boundary configurations. Therefore, the VOF method is recommended as the preferred approach for treating such situations.
Table 2

Mesh statistics

Elements Solid domain 16,000 
Fluid domain 2,764,960 
Nodes Solid domain 24,723 
Fluid domain 2,886,113 
Skewness Fluid domain Max 0.037 
Average 0.018 
Min 1.31 × 10−10 
Aspect ratio Fluid domain Max 15.97 
Average 6.625 
Min 
Orthogonal quality Fluid domain Max 
Average 0.999 
Min 0.998 
Elements Solid domain 16,000 
Fluid domain 2,764,960 
Nodes Solid domain 24,723 
Fluid domain 2,886,113 
Skewness Fluid domain Max 0.037 
Average 0.018 
Min 1.31 × 10−10 
Aspect ratio Fluid domain Max 15.97 
Average 6.625 
Min 
Orthogonal quality Fluid domain Max 
Average 0.999 
Min 0.998 
Figure 5

The cross-section of the channel after meshing.

Figure 5

The cross-section of the channel after meshing.

Close modal

Boundary conditions

To set the attributes of flow field surfaces and fully describe the flow domain's physical features, boundary conditions are necessary on all of the computational domain's boundaries. The effort of selecting a sophisticated turbulence model may fail if the boundary conditions are not applied properly (Bates et al. 2005).

  • (a)

    Inlet

As flow is assumed to be steady, at the inlet uniform velocity distribution is assumed. The inlet was kept as pressure inlet, by providing the free surface level as 0.11 m and the mean velocity as 0.175 m/s. To define turbulence parameters at inlet, turbulent kinetic energy and dissipation rate are given as follows:
formula
(29)
formula
(30)
where is the mean velocity and I is the turbulent intensity (which is assumed as 5% for low turbulent flows) and Cμ is the von Karman constant (0.09) and l is the hydraulic length scale which is approximated as hydraulic radius (R) for non-circular geometric sections (Rameshwaran & Naden 2003).
  • (b)

    Outlet

A pressure outlet has been applied gauge pressure at outlet is set to zero and the free surface level was kept at 0.11 m.

  • (c)

    Wall

For the channel walls, a no slip boundary condition has been adopted, which specifies that the velocity of all fluid particles near to the boundary will assume the velocity of the wall, i.e., zero.

  • (d)

    Rough walls

Roughness is characterized by using parameter equivalent sand grain roughness (ks). It is calculated by using the Colebrook-White equation, which is expressed as
formula
(31)

Rough walls are inputted with two boundary conditions related to motion (no slip) and nature of bed. In the present study, the value of equivalent sand grain roughness (ks) is considered as 0.015 m, i.e., the height of roughness (Khuntia 2020).

  • (e)

    Free surface

Free surface is treated as a free slip wall with zero gradients to turbulent kinetic energy and applied to simulation as a symmetry boundary condition.

Detailed experimental and numerical results are presented and discussed in this section. Results of longitudinal velocity profile plots, DAV plots, BSS plots and secondary current vector plots are presented from the discussed turbulent models and compared with experimental results.

Longitudinal velocity profiles

Longitudinal velocity profiles help in better understanding of velocity variation throughout the flow depth of a channel flow. In this section, the longitudinal velocity profiles are presented for all the turbulence models considered in the present study and the numerical results are compared with the experimental results. Three vertical sections at a plane 7 m from inlet are chosen to plot these profiles, (a) near the side wall (2 cm from the side wall), (b) at 15 cm from the side wall and (c) at the centre of the channel. Due to the symmetry of channel, only half of the channel is considered, and the plot is presented in Figure 6. The point velocities are made dimensionless by dividing the mean velocity (Um).
Figure 6

(a) Longitudinal velocity profiles at centre of the channel. (b) Longitudinal velocity profiles at 15 cm from the side wall. (c) Longitudinal velocity profiles at 2 cm from the side wall.

Figure 6

(a) Longitudinal velocity profiles at centre of the channel. (b) Longitudinal velocity profiles at 15 cm from the side wall. (c) Longitudinal velocity profiles at 2 cm from the side wall.

Close modal

From Figure 6(a), it is observed that both the RNG k–ε model and the SST k–ω model are predicting the accurate longitudinal velocity profiles at the centre of the channel as compared to other turbulence models. Because near the centre of the channel, which is far away from the boundary, is free from the wall effect. So, both the advanced models are found to perform well at the middle of the channel. Figure 6(a)–6(c) shows that the SST k–ω model is forecasting the better longitudinal vertical velocity profiles in all the vertical positions of the channel studied as compared to the other turbulence models. The SST k–ω model is a fusion of the standard k–ω model and the k–ε model. A blending function has been used here to combine both the models as one model. Near the wall, the blending function activates the standard k–ω model (which is well suited for simulating the flow near boundaries) and the k–ε model away from the boundaries. The RNG k–ε model also shows good agreement with experimental results at all vertical positions, but it shows less accuracy near the wall region.

Vector plots of secondary current

Secondary flow vectors are calculated by , where V is the mean lateral velocity and W is the mean vertical velocity. The results of Nezu & Nakagawa (1984) and Folorunso (2018) are presented in Figures 7 and 8, respectively. Nezu & Nakagawa (1984) performed experiments in a rectangular flume with sand ribbons as the bed material and Folorunso (2018) used square patches of gravel and grass in a staggered manner as roughness condition on the bed for EXPT1. The results of secondary current vectors and secondary flow distributions are depicted for the present experimental channel in Figures 9 and 10, respectively.
Figure 7

Secondary flow distributions in the water channel (Nezu & Nakagawa 1984).

Figure 7

Secondary flow distributions in the water channel (Nezu & Nakagawa 1984).

Close modal
Figure 8

Secondary flow pattern in CRS3 of EXPT1 (Folorunso 2018).

Figure 8

Secondary flow pattern in CRS3 of EXPT1 (Folorunso 2018).

Close modal
Figure 9

Secondary flow distributions of the experimental channel.

Figure 9

Secondary flow distributions of the experimental channel.

Close modal
Figure 10

(a) Secondary flow distributions of the RNG k–ε model. (b) Secondary flow distributions of the SST k–ω model. (c) Secondary flow distributions of the Std k–ε model. (d) Secondary flow distributions of the Std k–ω model. (e) Secondary flow distributions of the Realizable k–ε model. (f) Secondary flow distributions of the GEKO k–ω model. (g) Secondary flow distributions of the BSL k–ω model.

Figure 10

(a) Secondary flow distributions of the RNG k–ε model. (b) Secondary flow distributions of the SST k–ω model. (c) Secondary flow distributions of the Std k–ε model. (d) Secondary flow distributions of the Std k–ω model. (e) Secondary flow distributions of the Realizable k–ε model. (f) Secondary flow distributions of the GEKO k–ω model. (g) Secondary flow distributions of the BSL k–ω model.

Close modal

From these plots, it is observed that a pair of secondary current vortices is formed as an outcome of channel cross-section and the grass bed roughness. These two pairs of vortices are acting in opposite directions and transporting low momentum fluid from side walls towards the quarter of the channel, where high momentum fluid is present. This confirms the findings form the experimental results of Figure 9. Similar phenomena also have been observed in Figures 7 and 8. However, magnitudes of high velocity are seen at some distance from free surface. Thus, it shows the formation of secondary currents resulting in velocity dip phenomenon. Near the side walls, the intensity is very low, and the secondary currents move upward due to resistance of the vegetated bed. The BSL k–ω model predicted the maximum magnitude of in the range of 0.000182 m/s and for the standard k–ε model, it is 0.000153 m/s. The difference in output by different models is due to their respective model constants and assumptions in modelling shear stress terms. Progress in understanding turbulence-driven secondary currents is limited in literature, 3D modelling approaches may help in a better understanding of this phenomenon.

Depth-averaged velocity

DAV distribution in an open channel is useful in understanding the flow characteristics of the channel. Additionally, DAV distribution can provide insights into the flow behaviour, such as the presence of eddies, turbulent spots and other features that affect the flow properties. The present research work rigorously evaluates the effectiveness of the k–ω and k–ε models in predicting DAV and BSS distribution by comprehensively applying all the sub-models associated with these models.

The depth-averaged velocities (Ud) are calculated using the equation:
formula
(32)
where U is the point velocity at a vertical line. Ud is calculated by integrating local point streamwise velocities (U) over a flow depth H. DAV distribution is a plot that is plotted by joining the obtained value of Ud along the lateral direction of a channel. Point velocities are measured at a vertical section at a plane 7 m (Test section considered during the experiment) from the inlet by using probe tool in ANSYS post-processer. The 11 cm flow depth is divided equally in 10 points and point velocity readings are measured at each point. Then the velocities are depth-averaged as shown in Equation (32). The comparison of depth-averaged velocities of experimental results, CES (Conveyance Estimation System) and all the discussed turbulence models has been presented in Figure 11. CES is a quasi-2D modelling software developed by Wallingford HR, which uses the Shiono-Knight method (SKM) after simplification of RANS equations. CES is largely used for predicting flows using 2D lateral distribution methods. In this study, along with ANSYS, CES is also used to distinguish the ability of 2D and 3D modelling methods in predicting flows. All the depth-averaged velocities are made dimensionless by dividing the mean velocity (Um).
Figure 11

Comparison of lateral depth-averaged velocity distribution for different numerical models vs. experimental results.

Figure 11

Comparison of lateral depth-averaged velocity distribution for different numerical models vs. experimental results.

Close modal

Boundary shear stress

Measurement of BSS in experimental channels helps to understand the information on the potential trend of erosion and deposition in natural channels. In this section, the experimental results of bed shear stress are compared with the numerical results as well as results from CES. The comparison of BSS of experimental results, CES and all the discussed turbulence models has been presented in Figure 12.
Figure 12

Comparison of boundary shear stress distribution for different numerical models vs. experimental results.

Figure 12

Comparison of boundary shear stress distribution for different numerical models vs. experimental results.

Close modal
The present study is compared with the similar work done by Tajnesaie et al. (2020), who used the experimental work of Tominaga et al. (1989) to validate their numerical results with the experimental results of DAV and BSS for a simple trapezoidal channel (with a base width of 0.2 m, a side wall angle of 44°, a flow depth of 0.0905 m and a mean velocity of 0.3733 m/s). The validation of DAV and BSS experimental results with numerical results of Tajnesaie et al. (2020) are presented in Figures 13 and 14, respectively.
Figure 13

Comparison of lateral depth-averaged velocity profiles in experimental and numerical studies of Tominaga & Nezu (1991) and Tajnesaie et al. (2020).

Figure 13

Comparison of lateral depth-averaged velocity profiles in experimental and numerical studies of Tominaga & Nezu (1991) and Tajnesaie et al. (2020).

Close modal
Figure 14

Comparison of boundary shear stress profiles in experimental and numerical studies of Tominaga & Nezu (1991) and Tajnesaie et al. (2020).

Figure 14

Comparison of boundary shear stress profiles in experimental and numerical studies of Tominaga & Nezu (1991) and Tajnesaie et al. (2020).

Close modal

Statistical error analysis

The study of quantification of error, or the uncertainty that may be associated with a model, is known as error analysis. Normally, error analysis is performed to predict the efficacy of a model with respect to the observed data. In the present study, the different statistical error analyses such as Mean Percentage Error (MPE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Nash–Sutcliffe model efficiency coefficient (NSE) used to check the performance of the models. For NSE, nearer to the magnitude 1 shows a greater match to the observed data. The errors are calculated using the formula given below:
formula
(33)
formula
(34)
formula
(35)
formula
(36)
formula
(37)
where is the experimental DAV and is the predicted DAV from various models. The MAPE errors produced from different models are presented in the error plot in the form of a bar chart in Figure 15. For the computation of errors in modelling the BSS, the above equation is used by replacing with , where being the BSS. The MAPE error plot resulted in comparing the experimental and numerically modelled BSS is presented in Figure 16. The statistical error analysis results in DAV and BSS are presented in Tables 3 and 4, respectively.
Table 3

Statistical error analysis of different models used for DAV

ModelMAPERMSEMPEMAENSE
RNG k–ε 2.254 0.004 − 0.108 0.297 0.909 
SST k–ω 2.663 0.005 0.193 0.357 0.886 
Std k–ε 2.972 0.005 1.014 0.413 0.878 
Std k–ω 3.045 0.005 1.442 0.431 0.877 
Realizable k–ε 3.693 0.006 2.529 0.539 0.832 
GEKO k–ω 3.694 0.006 2.884 0.546 0.827 
BSL k–ω 5.432 0.008 4.443 0.804 0.648 
CES 12.901 0.020 −13.791 1.896 −1.014 
ModelMAPERMSEMPEMAENSE
RNG k–ε 2.254 0.004 − 0.108 0.297 0.909 
SST k–ω 2.663 0.005 0.193 0.357 0.886 
Std k–ε 2.972 0.005 1.014 0.413 0.878 
Std k–ω 3.045 0.005 1.442 0.431 0.877 
Realizable k–ε 3.693 0.006 2.529 0.539 0.832 
GEKO k–ω 3.694 0.006 2.884 0.546 0.827 
BSL k–ω 5.432 0.008 4.443 0.804 0.648 
CES 12.901 0.020 −13.791 1.896 −1.014 
Table 4

Statistical error analysis of different models used for BSS

ModelMAPERMSEMPEMAENSE
SST k–ω 3.981 0.029 − 3.853 2.631 0.960 
RNG k–ε 5.210 0.036 −5.104 3.130 0.937 
Std k–ε 6.572 0.045 −6.740 4.294 0.902 
Realizable k–ε 10.447 0.066 −10.669 4.848 0.788 
GEKO k–ω 11.714 0.075 −12.111 7.071 0.726 
Std k–ω 12.594 0.085 −13.020 8.288 0.648 
BSL k–ω 13.574 0.087 −14.034 8.388 0.632 
CES 16.252 0.105 −16.433 8.986 0.470 
ModelMAPERMSEMPEMAENSE
SST k–ω 3.981 0.029 − 3.853 2.631 0.960 
RNG k–ε 5.210 0.036 −5.104 3.130 0.937 
Std k–ε 6.572 0.045 −6.740 4.294 0.902 
Realizable k–ε 10.447 0.066 −10.669 4.848 0.788 
GEKO k–ω 11.714 0.075 −12.111 7.071 0.726 
Std k–ω 12.594 0.085 −13.020 8.288 0.648 
BSL k–ω 13.574 0.087 −14.034 8.388 0.632 
CES 16.252 0.105 −16.433 8.986 0.470 
Figure 15

Bar chart showing errors resulted from different models while calculating depth-averaged velocity for a grass bed channel.

Figure 15

Bar chart showing errors resulted from different models while calculating depth-averaged velocity for a grass bed channel.

Close modal
Figure 16

Bar chart showing errors resulted from different models while calculating boundary shear stress for a grass bed channel.

Figure 16

Bar chart showing errors resulted from different models while calculating boundary shear stress for a grass bed channel.

Close modal

From Figures 11 and 15 and Table 3, it is clearly observed that the CES model overpredicts the flow because of the non-incorporation of secondary currents of grass bed in modelling the DAV. The error percentage in calculating the depth-averaged velocities is very high as compared to the other turbulent models. CES is unable to take the effect of grass bed and wall effect of turbulence though it is a quasi-2D model based on SKM through simplified RANS equations. The CES uses the calibrating constants which need to be modified suiting to this turbulent flow on a grass bed channel. However, the numerical 3D modelling tool is successful in matching the pattern of DAV distribution of experimental results. Among all the discussed models, the RNG k–ε model (MAPE = 2.254%, RMSE = 0.004, MPE = −0.108%, MAE = 0.297 and NSE = 0.909) and the SST kω model (MAPE = 2.663%, RMSE = 0.005, MPE = 0.193, MAE = 0.357 and NSE = 0.886) provided more accurate results. CES performed the least with MAPE = 12.901%, RMSE = 0.020, MPE = −13.791%, MAE = 1.896 and NSE = −1.014. The reason may be the accurate incorporation of secondary currents due to grass bed by the RNG k–ε model. The MSE of DAV prediction also confirms the efficiency of models. This may be the reason that the RNG k–ε model is a modified form of the standard k–ε model which accounts for the different scales of motion through changes to the production term. The RNG model takes the effects of smaller scale motion due to wall and dense grass bed. Among the turbulent models, the BSL k–ω model performed poor as this model is designed for turbulent flows over flat plates or pipes and generally holds good for isotropic and local equilibrium cases. For the present channel, both the wall and dense grass make unsuitable for better prediction of flow by this model. More precision in results may be achieved by further refining the mesh or by the usage of non-structural grids. From the k–ε and k–ω groups of turbulence models, Tajnesaie et al. (2020) used only the standard k–ε and SST k–ω models for numerical validation of experimental results of Tominaga & Nezu (1991) (T03 case). In Figure 13, it is observed that at the centre of the channel, the experimental depth-averaged profile is having a sudden depression. This may be due to the velocity dip phenomenon. It is also observed that both the models used by Tajnesaie et al. (2020) underpredict the experimental depth-averaged values which is noticed in the present study as well.

From Figures 12 and 16 and Table 4, it is further observed that the SST k–ω model predicted the best results of BSS with MAPE = 3.981%, RMSE = 0.029, MPE = −3.853%, MAE = 2.631 and NSE = 0.960. The CES software is unable to predict the BSS efficiently when compared to the experimental results providing with MAPE = 16.25%, RMSE = 0.105, MPE = −16.433%, MAE = 8.986 and NSE = 0.470. In the prediction of BSS, both the models (SST k–ω and RNG k–ε) also provided the best results with the minimum error value found from the SST k–ω model. Because this model is a modified version of the standard kω which combines both the k–ω and k–ε models such that the kω model is used for the boundary layer's inner region and swaps to the kε in the inner regions of flow, making the SST kω better in predicting BSS results as compared to other discussed turbulence models. From Figure 14, it is observed that the numerical BSS results are underpredicted at most of the channel except at the centre and near the wall. Due to the velocity dip phenomenon at the centre of the channel, the experimental BSS plot is seen to have a sudden depression at the centre. But the numerical models did not provide such type of results for the study of Tajnesaie et al. (2020). But, in the present study, all the applied turbulent models and CES over predicted the experimental results of BSS.

The present study aims at simulating the longitudinal velocity profiles, DAV, BSS and secondary flow patterns in a grass bed open channel by the experiments and by the standard turbulence models (two-equation) using ANSYS Fluent. The following conclusions are drawn:

  • The SST kω model demonstrates superior accuracy in forecasting longitudinal velocity profiles at all vertical positions within the grass bed channel when compared to the other seven simulated models. This improved performance may be attributed to the SST kω model's unique combination of the standard kω model and the kε model. The RNG kε model also shows good agreement with experimental results at all vertical positions, but it shows less accuracy near the wall region.

  • It is observed that a pair of secondary current vortices is formed as an outcome of channel cross-section and the grass bed roughness. These two pairs of vortices are acting in opposite directions and transporting low momentum fluid from side walls towards the quarter of the channel, where high momentum fluid is present (i.e., confirming the work of other researchers). Secondary current flow fields by all the models are found to provide similar patterns but of different magnitudes which may be due to the different wall effect and model coefficients utilized by the respective modelling approaches.

  • In the prediction of lateral distribution of DAV and BSS, both the SST kω and RNG kε models provided satisfactory results. The RNG k–ε model predicted the DAV with more accuracy as it can account the smaller scale motion due to the combined effect of wall and dense grass bed precisely as compared to other models. Similarly, the SST kω model predicted the BSS results more accurately than other models as it is a modified version of the standard kω which combines both the kω and kε models such that the kω model is used for the boundary layer's inner region and swaps to the kε in the inner regions of flow. This reason makes the SST kω model better in predicting BSS results compared to other discussed turbulence models.

  • The statistical error analysis shows that the RNG kε model demonstrates the best prediction for the DAV results compared to other models (MAPE = 2.254%, RMSE = 0.004, MPE = −0.108%, MAE = 0.297 and NSE = 0.909). For the case of BSS prediction, the SST kω model predicted the best (MAPE = 3.981%, RMSE = 0.029, MPE = −3.853%, MAE = 2.631 and NSE = 0.960). It is further observed that the 3D turbulence models provided better results than the quasi-2D model (CES software) in predicting both DAV and BSS. The performance of CES can be improved by incorporating appropriate values of the calibrating coefficients suitable for this grass bed channel condition.

  • As there is limited research available to verify the applicability and usability of the RANS-based two-equation turbulence models of kω and kε groups for a grass bed channel in predicting various flow parameters, the present article will be very helpful to the future researchers in selecting the proper turbulence model for their research work. Future research can be carried out by considering different geometry and roughness conditions (i.e., mobile bed conditions, rigid and flexible vegetations) for better validation of the two-equation turbulence models of kω and kε groups.

The research was supported by the Department of Science and Technology (DST), Science and Engineering Research Board (SERB), Govt. of India (File No.: CRG/2021/003150 and SERB Qualified Unique Identification Document: SQUID-1969-KK-5771).

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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