Hydrodynamic Performance of Seawater Intake Structures Through Numerical Modelling and Particle Image Velocimetry

Abstract

The performance of seawater intake systems affects the reliability and efficiency of desalination plants and water-processing systems. The objective of this work is to gain insights into improving their design by examining the flow patterns around seawater intakes using particle image velocimetry (PIV), image processing techniques, and numerical modeling. Different wave and current conditions are considered, and intake conditions are classified into categories based on hydrodynamic parameters. Numerical simulations indicate complex flow patterns under simultaneous waves and currents. The results revealed that the velocity of the approach current affects the efficiency of seawater intake, and the impact depends on the cap geometry. Square caps, characterized by sharp edges, create flow contractions and instabilities, whereas circular caps result in smoother flow patterns, enhancing efficiency. Wave action exacerbates these effects, particularly as the Keulegan–Carpenter (KC) number increases, and may compromise the stability of intake structures. Circular caps improve overall stability and performance under waves. These results contribute to better designs of seawater intake structures and, thus, improved efficiency and stability.

1. Introduction

Desalinated seawater is a vital resource for many islands and coastal regions, particularly around the Mediterranean Sea, the Middle East, and other water-scarce areas. With the steady decline in desalination costs, these technologies have become more accessible; however, the long-term reliability of desalination plants remains a critical challenge [1,2]. Numerous studies on desalination have explored various aspects, such as technological advancements, energy integration, environmental impacts, and system optimization. These investigations also address the challenges related to brine disposal and its ecological effects, proposing strategies to minimize harm to marine ecosystems. Comprehensive analyses continue to drive innovations, highlighting the vital role of desalination in mitigating global water scarcity amid climate change and increasing demand [3,4]. Huang et al. [5] developed a comprehensive, GIS-based framework for selecting suitable locations for seawater desalination water intakes, with a focus on shallow-water systems. Their method integrates multiple geographic, environmental, industrial, and constraint-related factors and translates them into measurable indicators, such as water depth, sediment concentration, water quality, and proximity to infrastructure. Using multifactor spatial overlay analysis, the approach produces a quantitative suitability map, which is refined through constraint corrections, to identify optimal intake zones. Ntaghry et al. [6] explored strategies to alleviate water scarcity in sub-Saharan Africa, with a focus on Mauritania, by integrating concentrated solar power (CSP) with desalination systems (DS). Using a multi-criteria decision-making framework that combines mathematical analysis with geospatial tools, this study assessed potential sites based on environmental, economic, demographic, and climatic considerations. The results showed that about 10% of Mauritania, mainly in coastal areas, offers favorable conditions for seawater desalination, while densely populated southeastern regions are more suited for brackish water facilities, and sparsely populated northern zones could host decentralized units. This approach not only addresses water supply challenges but also supports energy security, offering valuable insights into future policy development.

One of the crucial factors influencing the efficiency and sustainability of these facilities is the design of the seawater intake system. An optimal intake design minimizes environmental impact, ensures consistent water quality, and reduces operational disruption. Therefore, a careful assessment of intake structures is essential before selecting the most appropriate type for a desalination facility. A comprehensive review of various seawater intake structures can be found in Aghazadeh and Attarnejad [7]. Among the different intake systems, velocity caps are widely employed for offshore seawater intakes, serving both power plants and desalination facilities. Positioned atop vertical intake pipes, velocity caps alter flow patterns to minimize environmental disturbances. Their primary function is to transition vertical intake flows into horizontal flows, thereby reducing surface vortex formation and the entrance of sediments or marine organisms. The design of velocity caps is crucial for mitigating the ecological impact of desalination plants, which have been criticized for their potential harm to marine life. Recent studies, such as those by Aghazadeh and Attarnejad [8], Doni [9], and Missimer et al. [10], have emphasized the importance of proper intake placement—often at depths between 20 m and over 1000 m—to minimize surface pollution and turbulence effects. Additionally, Voutchkov [11] suggested that intake velocities should be limited to 0.1 to 0.15 m/s to prevent marine life entrainment. Figure 1 shows the deep-water intake structure.

Numerical and experimental investigations have been carried out on the performance of velocity caps, focusing primarily on optimizing the intake efficiency and minimizing hydrodynamic disturbances under steady flow conditions. For instance, Lee and Wahab [12] used the Flow-3D computational model to analyze different turbulence models and determined that the k-ε model yielded the best predictions of velocity cap performance. Similarly, Chie and Wahab [13] developed empirical relationships for intake design, whereas Christensen et al. [14] and Gong et al. [15] explored the hydraulic and structural stability of intake structures using OpenFOAM-V4.0 and ANSYS-15.0 simulations. These studies have provided valuable insights into the functionality of velocity caps under steady-state conditions; however, research on their behavior under unsteady flow conditions, particularly in the presence of waves, remains scarce. Wei and Hu [16] conducted a study using CFD to analyze orthogonal wave-current interactions within a rectangular numerical wave basin. They highlighted the complexity and challenges involved in calculating hydrodynamic loads due to wave-current interactions, especially when waves and currents are noncollinear, compounded by disturbances such as reflections from the basin walls. To address this, they developed a numerical flume model utilizing the Reynolds-Averaged Navier-Stokes (RANS) equations combined with the k-ε turbulence model, implemented in the Flow-3D CFD software 11.2. Ma et al. [17] investigated the hydrodynamic performance and energy generation potential of a modified seawater intake caisson integrated with an oscillating water column (OWC) wave-energy converter. Traditional intake caissons often face challenges such as degraded water quality and high-energy pumping demands. The proposed design incorporates an OWC device at the front end of the structure to reduce wave-induced disturbances in the intake zone, promoting sediment settling and improving water quality, while also generating power to partially offset pumping energy needs. Using the OpenFOAM-4.x two-phase flow solver, the flow patterns and velocities of the new and conventional designs were compared. Results indicated that the integrated system reduced the overall flow velocity in the intake area by 10–40%, enhancing water purification, and achieved optimal energy conversion efficiency through adjustments in structural parameters, including the chamber-to-wavelength ratio, orifice dimensions, and draft of the front wall and intermediate baffle.

Hydrodynamic processes in coastal and offshore environments are inherently dynamic and often involve complex interactions between currents and waves. Previous research has mainly focused on the performance of velocity caps under steady flow or reservoir-like conditions, neglecting the influence of unsteady wave-induced forces. Unsteady flow conditions, such as those generated by ocean waves, introduce temporal variations in velocity and pressure fields, which can significantly impact intake efficiency and structural stability. The interplay between waves and currents can alter flow patterns around velocity caps and affect their overall performance. To date, no comprehensive study has evaluated velocity cap functionality under current, wave, and combined wave-current interactions, leaving a crucial gap in the understanding of offshore intake dynamics.

This research gap is the main motivation for the present work. Particle Image Velocimetry (PIV) is employed to capture high-resolution velocity fields in the hydraulic laboratory. PIV is a non-intrusive optical measurement technique that tracks the movement of small tracer particles suspended in a flow. This method has gained widespread use due to its ability to provide detailed velocity distributions without disturbing the natural flow field, unlike intrusive methods such as Acoustic Doppler Velocimetry (ADV). Several researchers have utilized image-based velocimetry to analyze complex flow behaviors in experimental settings [18,19,20]. However, while these techniques have proven effective in controlled laboratory environments, their application in offshore intake studies remains underexplored. A significant number of researchers have employed image velocimetry techniques to analyze current velocities and identify flow patterns. Those interested in further exploration of this methodology can consult various studies, including works by Cameron [21], Kim et al. [22], and Scharnowski and Kähler [23], which provide additional insights and applications of the PIV technique.

This work extends the existing body of research, which has relied primarily on numerical modeling, by offering important insights into the critical elements of seawater intake, particularly through the examination of velocity caps. This research integrates both numerical modeling and experimental work to deepen the understanding of flow patterns around velocity caps under simultaneous waves and currents. By combining these methodologies, this study presents a detailed analysis of the flow dynamics around the caps. The findings of this study are intended to assist hydraulic and marine engineers in refining the design of bottom intake structures, ultimately enhancing the efficiency of water intake systems.

2. Materials and Methods

2.1. Conceptual Framework

This study examines the impact of flow parameters, wave conditions, and the geometry of velocity caps on the flow patterns around an offshore water intake, employing both numerical and experimental approaches. The experimental component of this study focused on analyzing flow dynamics around circular and square caps subjected to current and regular wave conditions. An image processing system was employed to detect and analyze the resulting flow patterns. These experimental measurements, which form a key part of the investigation, are discussed in detail in Section 3.1. In the numerical segment of the study, the Flow-3D numerical model was first validated and then used to simulate the combined effects of wave and current conditions on flow patterns and the volume of diverted discharge through the catchment. These simulations provided valuable insights into the performance of the velocity caps in complex flow interactions. Section 3.2 presents the results of these numerical evaluations, focusing on the behavior of the caps under simultaneous wave and current actions. Finally, a detailed evaluation of the overall flow behavior around the velocity caps under various conditions is provided in Section 4. This section integrates findings from both the experimental and numerical investigations to offer a comprehensive understanding of how different flow scenarios influence the performance and efficiency of the caps. The outline of the experimental and numerical procedures is illustrated in Figure 2.

To analyze the flow pattern around the velocity cap in the presence of waves, two distinct cases were considered (see Figure 3), each representing a critical phase of wave interaction with the structure. The vector beneath the wave pattern shows the direction of the wave particle velocities. In Phase A, it becomes the same as the current direction, and in Phase B, it is opposite to the current direction.

Phase A: The wave crest is directly above the velocity cap. At this stage, the velocity of wave particles is along with the wave propagation and ambient current directions. The flow patterns around the cap in this phase are influenced by increased water pressure, turbulence, and potential vortex formation due to the interaction between the upward motion of the wave and the existing current.

Phase B: The wave trough is directly above the velocity cap. In this phase, the elevation of the free surface is at its lowest, and the wave particles move against the direction of the ambient current, reducing the direct impact of hydrodynamic forces on the velocity cap. However, this stage is characterized by significant variations in flow velocity, potential flow separation, and altered circulation patterns around the structure. The effects of wave-induced return flow and pressure differentials become more pronounced, affecting the overall efficiency and stability of the velocity cap.

By examining these two cases, a comprehensive understanding of the dynamic interaction between waves and the velocity cap can be achieved, offering valuable insights for optimizing the design and performance of intake structures in coastal and offshore environments.

2.2. Analytical Elements

Intake structures are considered one of the most critical hydraulic structures employed to divert a specific portion of flow. Researchers have extensively evaluated various intake structure types, including lateral channels, pipes, and orifice intakes, as referenced by studies such as Firozjaei et al. [24]. The discharge of the intake structures can be extracted using Equation (1), considering various losses (friction, minor, entrance, bend, and transition losses).

$$Q_{in}=C_d \sqrt{2gy}·{\displaystyle \frac{\pi }{4}} D^2$$

(1)

where Q_in is the discharge in the main channel, C_d is the discharge coefficient, y is the head of water above the centerline of the orifice, g is the gravitational acceleration, and D is the diameter of the intake.

Based on our previous studies on seawater intake discharge coefficient [24], C_d depends on Froude number in the main channel, water depth in the main channel, wave height and period, location of intake entrance, and shape of intake. The discharge coefficient represents the amount of water entry and is an important parameter for designing a water intake system with the required capacity. As discussed, some studies have been conducted to propose this coefficient in the presence of currents only. Such values can be accepted for calm tidal areas where waves do not exist. However, in the real world, waves can affect the hydraulic performance and capacity of the water intake system.

The main features of the waves relative to the ambient currents can be captured by the Keulegan–Carpenter number,

$$KC={\displaystyle \frac{U_{max} T}{D}}$$

(2)

where T is the wave period, D is the intake diameter, and U_max is the maximum particle velocity at the MWL. The results in terms of the flow patterns around the intake caps under wave action are presented according to the classification made using these parameters [25,26].

2.3. Experimental Campaign

The experimental component of this study focused on analyzing the flow dynamics around the caps under current and regular wave conditions, employing an image processing system to detect and analyze the resulting flow patterns. The experimental measurements specifically investigated the flow patterns around both circular and square caps under separate wave and current conditions. This research builds upon our previous studies on the seawater intake discharge coefficient, employing similar laboratory channel setups and velocity cap specifications as detailed in our earlier work [24]. As a continuation of these experimental investigations, the methods for measuring flow and wave characteristics—such as current velocity, water depth, wave height, and wave period—were thoroughly described and evaluated. Consequently, the present study utilizes the same validated measurement techniques and instruments to ensure consistency and reliability in data collection. It is important to highlight that these studies considered the specific hydrodynamic conditions of the Persian Gulf. Based on existing experiences and data, the water intake depth was selected to be between 7 and 11 m. Furthermore, the typical annual wave heights in the region were found to range from 1 to 3 m. In addition to wave heights, the studies also considered typical tidal speeds, which were observed to vary between 0.2 and 0.6 ms⁻¹. Based on the prototype conditions, the most typical velocity cap shapes were identified along with their corresponding areas, dimensions, and locations from the seabed. These values were then adjusted to match the constraints of the laboratory setup, taking into account the channel characteristics. For experimental purposes, a final scaling ratio of 1:25 was used.

Table 1. Overview of the datasets used in the present study.

Under Current Condition
Parameter		Range of Data
		Minimum		Maximum
Qm (m3/s)		0.006		0.016
h (m)		0.08		0.016
w (m)		0.08		0.016
d (m)		0.28		0.42
V (m/s)		0.032		0.1
Fr		0.017		0.057
Under Wave Condition
Parameter		Wave 01	Wave 02	Wave 03
	Test Condition
d (m)		0.28	0.36	0.42
H (m)		0.04	0.08	0.11
T (s)		0.7	1	1.2

provides a summary of the experimental parameters collected during this investigation.

2.4. Particle Image Velocimetry

This study employs Particle Image Velocimetry (PIV) to explore flow patterns around caps. Figure 4 shows the setup of the PIV system used in the experiment. The system features a continuous laser with an output power of 150 MW and a wavelength of 532 nm, which ensures effective illumination of the flow. Additionally, a camera with a CMOS sensor captures the flow dynamics by recording a video at a resolution of 800 × 600 pixels and a frame rate of 200 fps. This setup guarantees high-quality image acquisition, allowing for a detailed analysis of the flow patterns. Selecting the appropriate tracer particles is crucial for enhancing the accuracy of the image velocimetry method. Ideal tracer particles should exhibit good light reflection properties, have a density similar to that of the fluid, and possess suitable dimensions. Previous studies have indicated that maintaining a particle diameter of approximately 2 to 3 pixels minimizes measurement error [27].

Several studies have explored the cavitating flow characteristics using advanced image-processing approaches. Ge et al. [28] conducted controlled experiments using Particle Image Velocimetry (PIV) in combination with modal decomposition methods to examine flow structures across the entire cavitation zone. Employing Dynamic Mode Decomposition (DMD), they analyzed the influence of temperature on cavitation behavior, focusing on variations in intensity and dynamic response. Their findings revealed distinct flow patterns linked to thermal conditions, providing deeper insights into temperature-driven fluid instabilities and offering guidance for improved engineering design and operational strategies. Further details can be found in the references (e.g., [29,30]).

A custom code written in MATLAB-R2021b is employed to generate synthetic images. Particle Image Velocimetry (PIV) is a highly effective technique for measuring fluid flow by analyzing the movement of small tracer particles introduced into the fluid. The fundamental steps involved include tracer particle injection, where particles must be small enough to accurately follow the fluid flow and large enough to reflect sufficient light. A laser illuminates the region of interest, and a high-speed camera captures sequential images of the particles as they move. These images are then processed using specialized software to convert them into velocity vectors, providing detailed insights into the flow field.

Key to the accuracy of PIV measurements is the selection of particle size and response time, which must be significantly faster than the smallest time scale of the flow, assessed using the Stokes number (Stk):

$$Stk={\displaystyle \frac{t_p}{t_k}}$$

(3)

where t_k is the Kolmogorov time scale, and t_p is the particle time scale, calculated as follows:

$$t_k=\sqrt{{\displaystyle \frac{\nu }{\epsilon }}}$$

(4)

$$t_p={\displaystyle \frac{{\rho }_p}{{\rho }_f}}{\displaystyle \frac{d_p^2}{18 \nu }}$$

(5)

where ν is the fluid kinematic viscosity, ε is the turbulence damping rate, ρ_p is the particle density, ρ_f is the fluid density, and d_p is the particle diameter. For most currents, accurately predicting the damping rate (ε) is a significant challenge. A value of Stk significantly less than 0.1 is ideal for minimizing tracking errors.

Phosphorescent particles have been widely used in previous research for Particle Image Velocimetry (PIV) and related optical measurement techniques due to their favorable optical and flow-tracking properties. For example, Tian et al. [31] and Guerrier et al. [32] applied phosphorescent particle tracking to visualize and quantify flow fields. In the present study, we employed the same class of particles for image acquisition and velocity field determination.

Additionally, Scharnowski and Kähler [23] stated that careful calibration of the optical magnification and time intervals between images is crucial (Figure 5). If the time interval is too long, the particles may follow curved streamlines or experience convective accelerations, leading to significant inaccuracies in the measurement. Based on Equation (6), to estimate the flow velocity (V) in the measurement plane from the displacement of tracer particles ($∆X$) on the image plane, the relationship considers the time interval ($∆t$) between two successive images and the optical magnification M:

$$V= {\displaystyle \frac{∆X}{M∆t}}$$

(6)

The number of PIV measurement repetitions varied slightly depending on the specific test condition; however, on average, each case was repeated three times to ensure statistical reliability. For every run, image acquisition was carried out under carefully controlled conditions, with attention to maintaining uniform seeding density, consistent lighting, and stable flow conditions. Each repetition consisted of capturing a sufficient number of consecutive high-quality image pairs to allow for clear particle tracking and accurate cross-correlation during data processing. This approach minimized random errors and ensured that the derived velocity fields were representative of the actual flows.

To evaluate the accuracy of the image processing results, the average flow velocity calculated using the empirical relationships presented by Firozjaei et al. [24] was compared with the average velocity in the main channel measured using the PIV method. For the PIV-based average velocity calculation, the velocity profile along the channel depth was measured when no structure was present in the flow path. The velocities at 0.2d and 0.8d were extracted, and their mean value was considered the average flow velocity. The results showed that the difference between the average velocity obtained from the PIV measurements and that calculated using the empirical relationships was approximately 3%, indicating the high accuracy of the image-processing approach used in this study.

2.5. Numerical Model

FLOW-3D is a powerful computational fluid dynamics software produced, developed, and supported by Flow Science, Inc. It is designed to investigate the one-, two-, and three-dimensional behaviors of fluid dynamics in a wide range of applications. One of the major capabilities of this software for hydraulic analysis is its ability to model free-surface flows using the VOF method. This method was reported by Deng et al. [33] and Khoshkonesh et al. [34]. FLOW-3D uses a grid of rectangular cells. This grid has the advantages of being easy to generate and well-organized to improve numerical simulations, requiring minimal memory storage. This software solves the equations governing fluid movement using a finite volume approximation, and all variables are calculated at the center of the cell, except for the velocity, which is calculated at the center of the cell faces.

2.5.1. Governing Equations

In FLOW-3D, the governing equations of fluid flow are primarily divided into two categories: continuity equations and momentum equations. These fundamental equations enable the accurate modeling of complex fluid dynamics in various applications, including wave modeling and sediment transport. By solving these equations, FLOW-3D can predict flow behavior and interactions in real-time scenarios. The equations are as follows [35,36,37,38].

$${\displaystyle \frac{\partial \rho }{\partial t}}+{\displaystyle \frac{\partial \rho u}{\partial x}}+{\displaystyle \frac{\partial \rho v}{\partial y}}+{\displaystyle \frac{\partial \rho w}{\partial z}}=0$$

(7)

$${\displaystyle \frac{\partial u_i}{\partial t}}+u{\displaystyle \frac{\partial u_i}{\partial x}}+v{\displaystyle \frac{\partial u_i}{\partial y}}+w{\displaystyle \frac{\partial u_i}{\partial z}}=-{\displaystyle \frac{1}{\rho }}{\displaystyle \frac{\partial p}{\partial x_i}}+{\displaystyle \frac{\partial g}{\partial x_i}}+{\displaystyle \frac{\mu }{\rho }}\left({\displaystyle \frac{{\partial }^2{u}_i}{\partial x^2}}+{\displaystyle \frac{{\partial }^2{u}_i}{\partial y^2}}+{\displaystyle \frac{{\partial }^2{u}_i}{\partial z^2}}\right)$$

(8)

where ρ is the fluid density, μ is dynamic viscosity, g is gravity acceleration, p is pressure, u, v, and w, are local velocity components in x, y, and z directions, respectively. According to studies conducted by Lee and Wahab [12], the k-ε model is well-suited for wave simulations. The k-ε model operates using two transport equations to quantify turbulent kinetic energy (k) and its dissipation rate (ε). These equations, as detailed in Equation (9), provide a foundation for understanding turbulence dynamics. Additionally, the equation for the turbulent dissipation rate is presented in Equation (10), and the kinematic turbulent viscosity is determined using Equation (11). This framework is essential for accurately modeling turbulent flow in various applications.

$${\displaystyle \frac{\partial {\epsilon }_T}{\partial t}}+{\displaystyle \frac{1}{V_F}}\left(u A_x{\displaystyle \frac{\partial {\epsilon }_T}{\partial x}}+v{A}_y{\displaystyle \frac{\partial {\epsilon }_T}{\partial y}}+w{A}_z{\displaystyle \frac{\partial {\epsilon }_T}{\partial z}} \right)={\displaystyle \frac{CDIS1.{\epsilon }_T}{k_T}}\left(P_T+CDIS3.G_T\right)+{Diff}_{\epsilon }-CDIS2{\displaystyle \frac{{{\epsilon }_T}^2}{k_T}}$$

(9)

$${Diff}_{\epsilon }={\displaystyle \frac{1}{V_F}}\left({\displaystyle \frac{\partial }{\partial x}}(v_{ϵ}{A}_x{\displaystyle \frac{\partial {\epsilon }_T}{\partial x}})+R{\displaystyle \frac{\partial }{\partial y}}(v_{ϵ}{A}_{y}R{\displaystyle \frac{\partial {\epsilon }_T}{\partial y}})+{\displaystyle \frac{\partial }{\partial z}}(v_{ϵ}{A}_z{\displaystyle \frac{\partial {\epsilon }_T}{\partial z}})+ϵ{\displaystyle \frac{{v_{ϵ}A}_x{\epsilon }_T}{x}}\right)$$

(10)

$$v_T=CNU{\displaystyle \frac{{k_T}^2}{{\epsilon }_T}}$$

(11)

where P_T and G_T represent the turbulence production resulting from shearing and buoyancy effects, respectively. Diff_ε accounts for the diffusion of the dissipation rate. The coefficients CDIS1, CDIS2, and CDIS3 correspond to the production, decay, and buoyancy effects, respectively. Additionally, R is the viscosity multiplier used to calculate the turbulent diffusion coefficient, and CNU is the coefficient used to evaluate turbulent viscosity. These parameters—CDIS1, CDIS2, CDIS3, R, and CNU—are adjustable, with default values of 1.44, 1.92, 0.2, 1.0, and 0.09 in the k-ε model.

The governing equations of the model are discretized using a finite volume approach, employing structured Eulerian grids with a staggered mesh topology. To represent obstacles, a cut-cell method is applied, which defines area and volume fractions on a cell basis, allowing these fractions to be integrated directly into the conservation equations for mass and momentum. This method is computationally efficient and requires less user intervention than body-fitted meshing; however, its accuracy is constrained by the resolution of the mesh. For momentum advection, the standard approximation is first-order upwind, although second-order schemes, including regular and monotonicity-preserving options, are also available. A fractional two-step method was employed to solve the coupled pressure-velocity equations using a generalized minimal residual algorithm. To track the free surface, the Volume of Fluid (VOF) technique is implemented, where fluid configurations are described by a VOF function F(x,y,z,t). In single-fluid scenarios, FFF is equal to 1 within the fluid and 0 in the void regions. In two-phase modeling, F and 1 − F denote the incompressible (water) and compressible (air) phases, respectively (Vanneste and Troch [39]). A comprehensive overview of the model and its numerical implementation can be found in the works of Lara et al. [40] and Machado et al. [38].

2.5.2. Validation

The mesh is configured so that finer mesh elements are used in critical areas (such as the intake entrances), while a coarser mesh is applied in other regions with an acceptable aspect ratio. One of the most significant factors influencing accuracy and computational cost is the meshing strategy. A coarse mesh can increase the gradients of changes, leading to reduced accuracy of the results. Conversely, a finer mesh implies a greater number of computational cells, which increases solution time. Therefore, a balance must be struck between accuracy and cost to achieve the best results in the shortest time. One solution is to use two or more meshes for the computational domain, allowing for a relatively coarse overall mesh, while finer meshes are employed in regions where flow variables are expected to experience steep gradients, enabling a thorough examination of all changes. Various mesh specifications for different scenarios and a comparison of the obtained results are presented in

Table 2. Sensitivity analysis of the number of numerical model cells for calibration.

Mesh Number		Xmin (mm)	Xmax (mm)	Ymin (mm)	Ymax (mm)	Zmin (mm)	Zmax (mm)
	Direction
Mesh 1		4	40	4	60	6	40
Mesh 2		3	20	4	40	6	20
Mesh 3		2	20	3	40	3	20

. To assess the sensitivity of the numerical model to the mesh, the velocity magnitude around the velocity cap was recorded. A comparison of the results for various mesh sizes at four points is presented in

Table 3. Comparison of velocity magnitude in numerical and experimental models for w = 80 mm, h = 80 mm and N = 4.

	Velocity Magnitude (m/s)	Point A	Point B	Point C	Point D	Error (%)
Model
Experimental Result		0.115	0.11	0.0420	0.112	-
Mesh 01		0.847	0.097	0.03	0.097	12.80
Mesh 02		0.105	0.104	0.036	0.104	6.5
Mesh 03		0.105	0.105	0.038	0.105	5

. It should be noted that the flow simulation was conducted for a circular velocity cap, with a flow rate in the main channel of 10 L/s and a depth of 360 mm. Given that the accuracy of Mesh 2 and Mesh 3 is quite similar, but there is a significant difference in computational costs, Mesh 2 has been selected for modeling the flow.

In the mesh independence study, the Grid Convergence Index (GCI) was calculated to quantitatively assess the numerical accuracy of the simulations. For this purpose, the average mesh size for each mesh was first estimated by averaging the minimum cell dimensions in the X, Y, and Z directions, as provided in the mesh sensitivity Table 3. The average velocity magnitudes at points A, B, C, and D were then computed for each mesh to represent the solution variable consistently. The average mesh sizes and solution values were used to quantify the grid convergence behavior and numerical accuracy.

It is noted that the GCI value between Mesh 1 and Mesh 2 is relatively high, primarily due to the significant difference in mesh resolution and cell sizes, where Mesh 1 is considerably coarser. This discrepancy understandably leads to larger numerical errors and a higher GCI value at this stage (about 117%). However, the GCI between the finer meshes, Mesh 2 and Mesh 3, yielded a much lower value of approximately 3.7%, indicating satisfactory grid convergence and numerical independence. Therefore, despite the large difference observed initially, the results confirm that the solution is mesh-independent at a finer discretization level. This approach aligns with common practice in CFD studies, where the coarsest mesh serves as a baseline, and convergence is validated primarily between the finer mesh refinements.

A detailed schematic of the boundary conditions applied in the numerical model under wave conditions, along with the precise locations of the measurement points around the velocity cap, is shown in Figure 6. The boundary conditions were carefully configured to replicate the experimental setup, including specified velocity or pressure boundaries at the inlet and outlet, and no-slip wall conditions on solid surfaces to accurately capture flow behavior near the structure. Wave boundary conditions were applied at the inlet to enable an accurate numerical simulation of the incoming wave. At the free surface, a symmetry boundary condition was used, following the approach of Al Shaikhli and Khassaf [41], to preserve flow symmetry and minimize errors associated with boundary effects. At the downstream end of the domain, a pressure boundary condition was implemented following the method of Troch and De Rouck [42] to reduce backflow and prevent undesired wave reflections within the computational domain. This combination of boundary conditions was not only chosen through a careful review and alignment with established studies but also designed to model the waves more realistically while minimizing numerical errors arising from the model boundaries.

The simulations were carried out using the standard k-ε turbulence model, consistent with prior related studies, Lee and Wahab [12], which provided a robust framework for modeling turbulent flows in similar hydraulic and marine environments. This choice is justified by its proven reliability in predicting the average turbulent characteristics of the flow, balancing computational efficiency and accuracy. Moreover, the numerical model was implemented in FLOW-3D software, which combines the finite volume method for spatial discretization, pressure-velocity coupling algorithms, and the Volume of Fluid (VOF) technique for free surface tracking. This combination enables an accurate simulation of complex three-dimensional flow phenomena, including wave-structure interactions and multiphase flows, ensuring that the numerical results closely represent the physical experiments.

In addition, the FLOW-3D software automatically calculates the time step size. The logic behind this calculation ensures that the Courant number remains around 1 or less. The Courant number is a dimensionless value that represents the time required for a fluid to travel across one computational cell within a single time step. Maintaining this value near 1 guarantees accurate and stable transient-flow simulations. This condition is essential for preserving the stability and accuracy of the solution in numerical methods. Therefore, FLOW-3D intelligently adjusts the time step to balance the accuracy and computational efficiency.

The discharge deviation and flow pattern around the velocity cap were considered validation criteria. Numerical simulations were carried out for a water depth of 360 mm under the wave condition Wave 02. A comparison of the results is presented in

Table 4. Comparison between numerical and experimental discharge values.

	Qin (m3/s)	Experimental Condition	Numerical Simulation	Error (%)
Run Number
1		0.0072	0.0078	8
2		0.0077	0.0073	6

and Figure 7, which demonstrates the high accuracy of the selected numerical model. As mentioned, the vector beneath the wave pattern indicates the direction of the wave particle velocities. These findings confirm the effectiveness of the modeling approach in accurately capturing the relevant flow dynamics. The wave generally propagated rightward, while the vectors in this figure represent the instantaneous direction of the wave particles at the crest or trough phases. In addition, there is good compatibility between the simulated and observed velocities despite the differences that appeared in the figures. This discrepancy is due to inconsistent horizontal cap dimensions. In other words, the cap dimensions in the numerical and experimental results were continued to x = 0.13 m and x = 0.17 m, respectively. The reason for showing more flow details in the numerical condition is the ability of the numerical model to provide such detailed data, even in the inner parts of the cap. However, the cap boundaries limit the boundary of the measurements in a physical model.

3. Results

3.1. Experimental Observations

Laboratory modeling was conducted under two conditions: flow and wave, to investigate the performance of the intake structure under varying hydraulic scenarios. In the flow analysis, the focus was on examining the flow pattern while considering the geometric shape and location of the intake openings. In the wave analysis, the effects of regular wave parameters, such as wave height and period, were evaluated concerning the flow patterns around the velocity cap. Particle Image Velocimetry (PIV) was employed to provide detailed insights into the flow dynamics, enabling a comprehensive understanding of intake behavior and its efficiency under different conditions. The ultimate goal is to enhance our understanding of the structural behavior of the intake under flow and wave conditions, facilitating a deeper investigation into the interaction between the structure and fluid dynamics.

3.1.1. Effect of Velocity Cap Geometry Under Current Condition

This study examines two velocity cap geometries commonly used in desalination projects: circular and square-shaped caps. Figure 8 illustrates the flow patterns and velocity contours for both designs. The streamlines around the circular velocity cap are notably smoother, indicating that water flows more gently and steadily around this shape, a behavior attributed to the absence of sharp edges found in the square cap. In contrast, the sharp edges of the square cap create contraction points and instabilities in the water flow, leading to a more complex and turbulent flow pattern. Velocity contours further reveal that high-velocity zones in the circular cap are primarily concentrated at the front, whereas in the square cap, these zones are located along the lateral edges. Additionally, the low-velocity region behind the square cap is significantly larger due to the flow acceleration near its sharp corners, whereas the curvature of the circular cap minimizes such contractions, resulting in a smaller low-velocity zone.

The reduced intake capacity of the square cap is likely due to its sharp corners, which contribute to complex flow patterns and consequently reduce the diverted flow into the intake structure. In contrast, the curved walls of the circular cap promote smoother flow with minimal contraction, enhancing water intake efficiency. The influence of intake geometry on flow capacity was further explored by Firozjaei et al. [24], who provided deeper insights into the hydrodynamic behavior of different velocity cap designs.

Laboratory observations showed that the performance of the circular intake cap was better than that of the square intake cap, especially under shallow-water conditions. The intake efficiency of the circular cap was superior to that of the square cap at shallow depths. Figure 9 illustrates the performance of intake caps at a shallow depth (0.26 m in this study). As observed, in the case of the square cap, a vortex formed in the downstream region of the cap, whereas no vortex was observed under any condition for the circular cap. It is clear that the formation of the vortex has disrupted the performance of the water intake. It should be noted that all experiments were conducted in the absence of surface vortices. Consequently, a depth of 0.26 m was excluded from further analysis.

3.1.2. Effect of the Location of the Velocity Cap Entrances Under Current

Another critical parameter influencing water intake efficiency is the location of the intake openings. The position of the intake cap opening is determined by two factors: the distance of the intake opening from the bottom of the main channel (w) and the height of the intake cap (h). Figure 10 shows a side view of the flow pattern around the circular intake cap. The observations indicate that as the intake opening is positioned closer to the channel bottom, water enters the intake with greater ease and less flow complexity. Conversely, as the intake opening is positioned higher, the interaction between the intake structure and high-velocity flow region increases, leading to a more complex flow pattern. These significant variations in flow behavior around the intake structure are primarily attributed to hydrodynamic effects. When the intake cap is positioned at a lower height, water flows directly into the intake opening without major obstructions. This results in a relatively smooth and stable flow pattern with minimal turbulence. However, when the intake opening is positioned higher, the likelihood of flow turbulence and complexity increases, causing the streamlines to deviate away from the intake opening. As the height of the intake opening increases, the intake structure acts as an obstacle, creating a low-velocity region downstream of the cap. This low-velocity zone forms due to the resistance imposed by the intake structure against the flow. Within this region, flow velocity decreases, and water pressure increases. The presence of a low-velocity zone behind the intake cap is significant for water collection, as it provides an area where suspended particles, debris, and sediments carried by the flow are more likely to settle due to the reduced velocity. These sediments may include soil particles, sand, and other solid materials transported by water currents. The accumulation of sediment in low-velocity regions can pose several challenges. The buildup of debris may lead to the blockage of the intake opening, restricting water flow, and ultimately reducing intake efficiency. This issue can significantly impact the overall performance and capacity of the intake system. Therefore, the potential for sediment deposition and its effects on intake operations must be carefully considered in the design and management of intake structures to mitigate these challenges.

Given these hydrodynamic considerations, there appears to be no universal recommendation for the optimal placement of an intake opening. The selection of an appropriate intake height should be based on site-specific conditions and operational requirements to balance water intake efficiency while minimizing the risks associated with sediment ingress and flow disturbance.

3.1.3. Flow Pattern Inside the Velocity Cap Under Current

Figure 11 shows a side view of the flow pattern around the circular intake cap. Observations indicate that the velocity cap acts as an obstacle against the flow, causing the water stream to change direction from horizontal to vertical upon impact before it enters the intake. However, in the downstream region of the velocity cap, streamlines follow a more complex path to reach the intake opening. As the flow velocity increases, the interaction between the intake structure and the flow intensifies, leading to greater flow complexity. The velocity contour reveals that the maximum velocity occurs at the front of the intake cap, near the entrance of the intake opening.

Additionally, the use of guide vanes in the intake cap significantly influences the surrounding flow pattern and can improve flow behavior. These vanes are designed to optimize water flow and reduce turbulence. Their primary function is to direct and modify the flow to enhance the overall flow pattern around the cap. To examine the effect of the number of blades (N), two configurations were evaluated: one with four vanes and the other with eight. Figure 12 illustrates the flow patterns around the intake cap for both circular and square geometries. Observations indicate that increasing the number of blades negatively impacts the diversion flow rate but significantly improves the overall flow pattern around the cap. In other words, while an increase in the number of blades (N) may lead to a reduction in the diverted flow rate, it significantly enhances the uniformity of the flow distribution around the intake structure.

As the number of guide blades increases, the velocity distribution around the intake structure becomes more uniform and symmetric. This indicates that the flow disturbances were more evenly spread around the structure, leading to a more homogeneous flow pattern. Consequently, this improvement in flow uniformity reduces environmental concerns related to these intake structures, such as the risk of unintended capture of aquatic organisms within the intake system.

3.1.4. Effect of Wave Parameters on the Flow Pattern Around the Velocity Cap

Wave parameters can significantly impact intake efficiency. The key wave parameters influencing water intake are wave height, period, and direction. To analyze the simultaneous effects of intake entrances, wave characteristics, and water depth, the Keulegan–Carpenter (KC) number was defined. The reported KC values are not the margins selected for flow regime categorization. These values are related to the numerical and experimental runs. Different wave and current conditions were simulated according to the practical ranges of these parameters, and the results are reported in the manuscript, as shown in Figure 13 for the simulated conditions. Within the studied range, different patterns were observed, which are discussed in detail in Section 4. However, KC is a well-known parameter to identify the relative strength of current over wave, and it is a common practice to use a classification for this parameter, as done in many studies, such as [26].

Figure 13 shows the flow pattern surrounding the circular velocity cap at different stages of the wave cycle. The vectors beneath the wave pattern represent the direction of wave particle velocities. At Phase A, the wave particle velocity aligns with the current direction, while in Phase B, it opposes the current direction. When KC is 0.135, the influence of wave parameters on the flow pattern is minimal, resembling conditions in a reservoir where a symmetrical difference in water elevation directs flow into the intake. However, at a KC of 0.76, under the wave crest (Phase A), a significant portion of the flow enters the intake from the front, while some pass over the cap, entering from the downstream side. Conversely, under the wave trough (Phase B), the inflow direction reverses, with more water entering from the rear of the cap. As the KC number increases, waves become more prominent than current, drastically altering the flow pattern. For example, at KC = 1.44, substantial changes occur, including the formation of a recirculating flow region downstream of the cap. The upward force exerted by the wave attempts to lift the cap, a phenomenon observed at both Phase A and B. Overall, as the KC number increases—indicating greater drag force—the flow path becomes increasingly irregular, and the upward force on the velocity cap intensifies, ultimately leading to a reduction in intake discharge capacity [26].

Experimental observations revealed that high KC values combined with shallow water depths for the intake structure significantly compromised the stability of the velocity cap, potentially leading to damage or destruction of the intake system. Additionally, the shape of the velocity cap edges was found to play a crucial role in mitigating the destructive effects of waves. The circular cap demonstrated greater stability than the square cap. This enhanced stability is attributed to the increased curvature of the circular cap, which allows the waves to flow around it more smoothly and with less turbulence. As a result, wave energy is reduced, minimizing vibrational and interaction effects on the intake structure, thereby enhancing its overall resilience in challenging conditions [24].

Notably, the circular cap demonstrated better performance than the square cap in terms of intake discharge efficiency, flow patterns, and structural stability. Due to its smooth, curved edges, the circular cap minimizes turbulence and enhances the overall flow dynamics, leading to more efficient water intake. As a result of these advantages, the circular cap shape was selected for numerical modeling to investigate the cap’s performance under simultaneous regular wave and current conditions.

3.2. Numerical Simulation

The main objective of this section is to numerically cap under simultaneous regular wave and current conditions. The primary objective of this section is to numerically simulate the combined effects of regular waves and flow on the intake velocity cap structure. Experimental data from a laboratory wave channel were used to evaluate the intake performance under these conditions. Observations reveal that the intake flow rate in the presence of both waves and current is lower than that observed under wave-only conditions. The reduction in intake efficiency is influenced by the intensity of the main channel flow and the wave parameters.

To evaluate intake efficiency under simultaneous regular wave and current conditions, 36 numerical simulations were conducted for the circular cap shape with d = 360 mm, N = 4, h = 80 mm, and w = 80 mm.

Table 5. Discharge coefficient under simultaneous regular wave and current conditions for different conditions.

V (m/s)	T (s)	H (mm)	Cd
0.05	0.69	40	0.78
	0.85	60	0.765
	1	80	0.76
	1.18	100	0.645
	1.35	120	0.63
0.07	0.69	40	0.76
	0.85	60	0.75
	1	80	0.65
	1.18	100	0.625
	1.35	120	0.588
0.09	0.69	40	0.785
	0.85	60	0.74
	1	80	0.63
	1.18	100	0.6
	1.35	120	0.57
0.11	0.69	40	0.71
	0.85	60	0.7
	1	80	0.61
	1.18	100	0.567
	1.35	120	0.52
0.13	0.69	40	0.67
	0.85	60	0.66
	1	80	0.58
	1.18	100	0.55
	1.35	120	0.49

shows the discharge coefficients for the different cases.

Observations showed that the intake flow rate in the presence of both waves and currents was lower than that under the wave-only condition. This reduction is due to the increased interaction between the waves, currents, and intake structure, which enhances the complexity of the flow around the intake cap. The decrease in intake efficiency depends on the intensity of the main channel flow and the strength of the waves. Figure 14 illustrates the longitudinal velocity contours along with streamlines at the mid-height (h) and center of the velocity cap, with data collected during the wave trough phase. This figure specifically presents the velocity magnitude and streamlines at the center of the cap for N = 8, w = 80 mm, h = 80 mm, and Ym = 360 mm in Wave 02. The vectors under the wave pattern indicate wave-particle velocities. In Phase A, they align with the current direction, while in Phase B, they oppose it. The results reveal that the presence of flow significantly influences the behavior of the streamlines behind the velocity cap, causing them to deviate away from the intake opening, particularly in Phase B. This deviation effectively reduces the amount of flow diverted toward the intake, emphasizing the intricate interplay between wave and flow dynamics in determining the efficiency of the intake system. These findings highlight the importance of considering both hydrodynamic forces and intake positioning when optimizing water intake structures under wave-influenced conditions.

4. Discussion

Investigations to assess the combined effects of regular waves and flow on the cap of the intake structure revealed that the discharge coefficient decreased as the wave height and flow intensity increased. This finding indicates a significant interaction between the hydrodynamic forces exerted by waves and flow conditions, which can adversely affect the performance of the intake system. Figure 15 shows the contour lines of the water conductivity coefficient for various scenarios, illustrating how different combinations of wave and flow conditions influence the overall hydraulic performance.

After analyzing the experimental data and numerical results, it was observed that the intake structure experiences different conditions depending on its distance from the shore and the hydrodynamic conditions of the sea. Therefore, various zoning classifications were established based on the wave and flow conditions (Figure 16), which are discussed in this section. The zoning conducted in this study is based on the prototype conditions of waves and flow specific to the Persian Gulf, which provides a relevant framework for assessing real-world scenarios in this region. This approach allows for a more accurate representation of the hydrodynamic environment encountered by the intake structure. It is important to note that the zoning depicted in the figure below is approximate and reflects the variability of wave and flow patterns in the Gulf. Such approximations are essential for better understanding the potential impacts on the structure’s performance and for guiding future design considerations.

Zone 0 refers to a location where the intake structure is positioned in a deep offshore area, where the intensity of the current and the strength of the waves are minimal, or where water is drawn from a reservoir without the presence of waves and currents. In this zone, the flow pattern around the intake structure is expected to follow the behavior illustrated in Figure 17. Studies have shown that under these conditions, water intake occurs symmetrically. Additionally, the findings indicate that this scenario results in the highest intake flow rate and efficient water intake. In summary, when the intake is situated in a deep offshore region with no significant wave or current influence, the intake process remains symmetric, leading to a maximum diversion flow rate and optimal intake efficiency.

When water intake occurs in the presence of a current, it is categorized into three zones: Zone 1, Zone 2, and Zone 3. The flow pattern differs significantly from the previous conditions, as shown in Figure 18. Observations indicate that the most significant differences in the flow pattern occur in the downstream region of the intake structure. When the flow velocity in the main channel is low (e.g., approximately 0.06 m/s, as considered in this study), the suction effect of the intake effectively draws more water in (Figure 18a). As the velocity increases (e.g., around 0.1 m/s), this influence diminishes, resulting in reduced flow directed toward the intake (Figure 18b). Finally, with a further increase in approaching flow velocity (e.g., 0.15 m/s), the downstream inflow is minimized, and the flow continues along its path in the main channel with minimal deviation toward the intake (Figure 18c).

The water intake process in the presence of waves is characterized by greater complexity, divided into two zones (Zone 4 and Zone 5). This complexity arises from various wave parameters, including wave height and period, which significantly influence the flow pattern around the intake structure.

As illustrated in Figure 19, the reciprocating nature of the waves can create intricate flow patterns that lead to changes in flow direction around the intake. Due to the reciprocating nature of the waves, the flow pattern around the structure can include more complex flows. This complexity can lead to further changes in the flow pattern and direction around the intake. Generally, the performance of the intake structure varies depending on the wave characteristics, exhibiting better efficiency on one side than on the other. Observations indicate that if the KC decreases (in this study, for example, KC = 0.7), the intake is more effective at absorbing upstream flow at the wave crest (Phase A), directing more flow toward the intake from that side. Conversely, during the wave trough (Phase B), the intake performed better by absorbing the flow from the downstream side (Figure 19a,b). However, as current velocity increases leading to larger values of KC number (in this study, for example, KC = 1.4), the intake performance becomes predominantly one-sided under the wave crest, with flow absorption occurring only from upstream under the wave crest (Phase A) and exclusively from downstream under the wave trough (Phase B) (Figure 19c,d). These variations in intake performance between the wave crest and trough have a significant impact on overall efficiency, with higher elevations of the free surface during the wave cycle (such as under the wave crest) leading to a decrease in intake efficiency due to the intensified influence of the waves on the structure.

Zone 6 and Zone 7 represent regions where water intake occurs under the influence of both waves and currents, with flow patterns highly dependent on the approaching current velocity and wave intensity (see Figure 20). In Zone 6, where KC is high and the current speed governs the intake flow pattern, the flow exhibits unique behaviors during the wave trough phase. Here, the reversed flows from the trough interact with the incoming current, leading to a collision of flows approaching the intake structure from downstream. This interaction results in a distinct flow pattern that channels more water toward the intake from the upstream side, similar to the conditions observed during the crest phase in wave-only scenarios. However, the inflow from the downstream side is contingent on the dominance of the current. When wave energy is high and current velocity is low, more flow is directed toward the intake, whereas high current velocities significantly diminish this inflow. In Zone 7, which is characterized by high wave parameters and current velocities, similar interactions occurred. However, the collision between wave- and current-induced flows creates a low-velocity zone in front of the intake cap. If the current velocity remains low, wave-induced flows dominate, promoting flow reversal. Overall, the flow behavior in these zones underscores the critical interplay between waves and currents, emphasizing that as the current velocity increases, the flow near the downstream intake diminishes. Understanding this interplay is vital for assessing intake performance under combined conditions.

5. Conclusions

This study employed a combination of experimental analysis using Particle Image Velocimetry (PIV) and numerical simulations to investigate the flow patterns around velocity caps under various flow and wave conditions. The primary objective of this study was to quantitatively and qualitatively assess the hydrodynamic behavior of circular and square intake caps and evaluate their impact on water intake efficiency. The results demonstrate that the approaching flow negatively affects the intake efficiency, and its influence depends on the intake cap geometry. The intake capacity of the circular cap was significantly greater than that of the square cap, particularly at shallow flow depths. This suggests that at lower depths, the circular cap is more efficient for water collection. The sharp corners of the square cap may contribute to this inefficiency, as they can create constriction points and enhance flow speed near them. This leads to a more turbulent and unstable flow, resulting in water loss. In contrast, the smooth curvature of the circular cap allows for a calmer flow, reducing constriction and ultimately increasing water intake capacity. In addition, the intake depth plays a critical role in balancing efficiency and sediment intrusion. A lower intake position allows for easier water entry but increases the risk of sediment accumulation, potentially leading to blockages and reduced intake capacity. This trade-off underscores the necessity of site-specific sediment evaluations for optimizing intake performance. Furthermore, the presence of flow-guiding blades significantly improves flow uniformity and symmetry around the intake, although it slightly reduces the diverted discharge. This contributes to minimizing environmental impact and preventing aquatic organisms from entering the intake.

Under wave conditions, the influence of intake cap shape becomes more pronounced as the Keulegan–Carpenter (KC) number increases. Experimental findings indicate that at high KC values and shallow depths, the structural stability of the intake cap is severely compromised, posing a risk of damage or failure. The rounded edges of the circular caps effectively ….-induced forces, enhancing overall stability. Additionally, the flow pattern around the intake is strongly correlated with the KC number, where lower values reduce flow complexity and improve intake performance. Moreover, simultaneous wave and current conditions introduce intricate flow interactions that significantly alter the intake efficiency depending on the dominant hydrodynamic force. When currents dominate, most of the diverted flow enters from the upstream side, whereas under wave-dominant conditions, strong return currents at wave troughs lead to increased intake from the downstream side. These findings highlight the importance of accurately characterizing both flow and wave parameters when designing and operating intake structures in dynamic environments. Finally, this study provides a comprehensive understanding of the hydrodynamic behavior of intake caps under different flow and wave conditions. These results offer valuable insights into optimizing the intake structure design to enhance efficiency and stability while mitigating sedimentation and environmental challenges.

Future research should extend to the investigation of directional and irregular waves, as a deeper understanding of these conditions will help refine design guidelines and ensure that intake systems perform optimally across diverse environmental scenarios. In addition, the complex interplay between flow and wave interactions requires further examination because of its substantial impact on the hydraulic performance of intake structures. While the present study primarily addresses the crest and trough phases of waves, the intermediate phases may exhibit distinct flow characteristics that warrant a detailed analysis in future work.