A Dynamic Analysis of Oscillating Water Column Systems: Design of a 16 kW Wells Turbine for Coastal Energy Generation in Ecuador

Abstract

The work presents the design of an Oscillating Water Column (OWC) system with a nominal capacity of 16 kW, proposed as a contribution to reducing the energy gap in Ecuador, where electricity demand surpasses supply. The province of Santa Elena was selected as a promising site due to its favorable wave conditions and coastal location. The design process involved identifying areas with high wave energy potential, conducting a brief mathematical modeling analysis, and defining the parameters required for the system. Computational Fluid Dynamics (CFD) simulations were carried out in two stages: In the first stage, OpenFOAM was used to evaluate wave behavior, specifically flow velocity and pressure, before the water enters the generation chamber. In the second stage, a different CFD tool was used, incorporating the output data from OpenFOAM to simulate the energy conversion process inside the Wells turbine. This analysis focused on how the turbine captures and transforms the wave energy into usable power. The results show that, under ideal conditions, the system achieves an average power output of 11 kW. These findings suggest that implementing this type of system in coastal regions of Ecuador is both viable and beneficial for local energy development.

1. Introduction

Global energy demand continues to rise, and its environmental repercussions have become increasingly evident due to widespread reliance on fossil fuels. In response, various forms of renewable energy have been proposed as potential solutions to reduce pollution and mitigate environmental impact [1]. In this context, ocean energy sources, specifically wave energy, have emerged as a viable alternative, with systems such as the Oscillating Water Column (OWC) showing promise in converting wave motion into electricity [2].

Wave energy, among renewable energy sources, has recently garnered significant attention due to its notable advantages over other technologies. One of its primary benefits lies in the relative predictability of ocean wave behavior, which allows for more accurate energy production forecasting when compared to solar and wind energy, where higher variability often introduces operational uncertainties. Consequently, wave energy is increasingly regarded as a viable and promising alternative for sustainable electricity generation [3].

With a coastline of approximately 640 km, Ecuador possesses significant potential for wave energy generation. In 2024, the country experienced power outages lasting up to 14 h per day, caused by an energy crisis due to a long-lasting drought that began in September 2023. Ecuador’s energy matrix is heavily dependent on hydropower, which accounts for 69.1% of total generation, followed by thermal energy, 25.6%, energy via interconnection, 3.6%, and other renewable sources such as biomass, 0.9%, wind, 0.6%, solar, 0.1%, and biogas, 0.1% [4]. Figure 1 illustrates the relationship between energy sources in graphical form. This situation highlights the critical vulnerability of the national energy system due to its strong dependence on hydropower.

On the other hand, gravitational wave behavior is analyzed using wave theory by examining the generating and restoring forces, along with the associated relative energy. The generating forces, primarily the gravitational pull exerted by the sun and the moon, cause the interface between two media to be displaced from its equilibrium function [5]. This displacement is illustrated in Figure 2, where the wave profile represents the amount of energy transferred to the fluid by the generating force. In contrast, the restoring forces return the surface to equilibrium, stabilizing the system.

The analysis of wave energy harvesting devices presents complexity due to the wide range of operating modes and development stages. Some devices are patented, and others are undergoing numerical simulations, tested in scale model tanks, or deployed in real marine environments. According to their installation depth, these systems can be classified as deep-water, intermediate, or shallow-water devices. Additionally, based on the mechanisms used to capture energy, wave energy converters are generally grouped into three main types: Oscillating Water Column, overtopping devices, and oscillatory motion devices [6]. The present study is specifically focused on the analysis and performance evaluation of OWC systems.

Although various wave energy conversion technologies exist, including flotation devices, pressure-based systems, OWC devices, and oscillating membrane systems, only offshore installations located at considerable distances from the coastline have been tested in Ecuador [7]. These offshore systems entail high installation and operational costs, which limit their feasibility for large-scale deployment. In contrast, shoreline-based systems, particularly OWC devices, present significant advantages due to their lower implementation costs and greater potential for energy conversion efficiency [4]. An overview of the plant construction, including infrastructure, permitting requirements, and installation variables, is provided in Appendix C.

The OWC system has been successfully evaluated in countries such as Spain and Australia, where it has demonstrated favorable performance metrics [8]. In light of these results, the present study aims to analyze Ecuador’s coastal region’s geographic and climatic conditions and the structural and operational characteristics of OWC systems to support their design and optimization for local implementation. Integrating small-scale generation systems, such as OWC plants, can reduce the main power grid load while providing electricity access to remote communities lacking this essential service.

Ecuador’s national power system is experiencing a generation deficit of approximately 1000 MW, highlighting the urgent need to explore and integrate alternative sources. In response to this problem, the present study shows the design of an OWC wave energy conversion system to be installed along the coastal region of Santa Elena Province. Utilizing the open-source OpenFOAM and computational fluid dynamics tools, the system is designed with a projected generation capacity of 16 kW. The scope of this proposal includes the structural design and mechanical energy conversion analysis required to harness the regional wave energy potential effectively.

2. Materials and Methods

2.1. Overview

The OWC system captures wave energy by exploiting the vertical motion of water within a partially submerged chamber. Its operational principle is based on the pioneering work of Professor Yoshio Masuda [9]. As ocean waves enter the chamber, they cause the water column to oscillate, thereby compressing and decompressing the air above it. This bidirectional airflow is directed through a specially designed turbine that rotates in a single direction regardless of the airflow direction. The turbine is coupled to an induction generator connected to the electrical grid, enabling the conversion of mechanical energy into electrical power. The operational concept is illustrated in Figure 3.

OWC systems offer enhanced accessibility when installed along the shoreline, providing key advantages such as significantly reduced capital expenditures and simplified maintenance, collectively lowering operational costs. A vertical interior wall is incorporated into the chamber to redirect airflow. However, this configuration presents certain limitations, including restricted water movement, energy losses due to wave impact against the chamber structure, and destructive interference resulting from bidirectional wave motion [10].

The classical OWC energy conversion process consists of three distinct stages. The first stage involves the capture chamber, where the kinetic energy of ocean waves is transformed into pneumatic energy. The turbine converts the resulting oscillating airflow into mechanical energy in the second stage. Finally, the third stage is the generation phase, during which a generator converts the mechanical motion induced by the turbine into electrical energy.

2.2. Mathematical Approximation

The parameters of energy, energy flux, and power per meter of wave front, kW/m, are used to assess the energy potential of ocean waves. Several studies model wave behavior based on different propagation zones, employing varied values for wave height, period, and direction. The analysis of these parameters begins with Equation (1), which describes the wavelength $\left(\lambda \right)$ using as the distance between two consecutive wave crests. Equation (2) introduces the wave period, representing the interval between two successive crests. Wave celerity, described in Equation (3), refers to the speed at which a wave propagates and is expressed as a function of wavelength and period. Lastly, Equation (4) defines group celerity, representing the average propagation speed of a group of waves and accounts for energy transmission within the wave train [11].

$$\lambda ={\displaystyle \frac{g{T}^2}{2\pi }}$$

(1)

$$T={\displaystyle \frac{1}{f}}$$

(2)

$$C={\displaystyle \frac{\lambda }{T}}$$

(3)

$$C_g={\displaystyle \frac{C}{2}}$$

(4)

In Equations (1)–(4), the symbol $\lambda$ denotes the wavelength, T represents the wave period, $f$ corresponds to the frequency, C indicates the wave celerity, and C_g refers to the group celerity.

The power generated by ocean waves is ideally proportional to the square of the wave height and the wave period. Equation (5) defines the total wave energy as a function of fluid density, gravitational acceleration, and the square of the wave height. Equation (6) expresses the energy flux in terms of density, gravitational acceleration, and wave amplitude. Lastly, Equation (7) describes the wave power as a function of wave amplitude and period [11].

$$E_T={\displaystyle \frac{\rho g{H}^2}{8}}$$

(5)

$$FE={\displaystyle \frac{\rho g^2{H}^{2}T}{32\pi }}$$

(6)

$$P=0.96H^{2}T$$

(7)

In Equations (5)–(7) $\rho$ represents the water density, $g$ gravitational acceleration, $H$ the wave height, and $T$ the wave period.

The Ursell number is employed to evaluate the similarity between the reference study waves in the zone. This dimensionless parameter in fluid dynamics quantifies the degree of nonlinearity of gravity surface waves in a fluid layer. The Ursell number is expressed by Equation (8).

$$U={\displaystyle \frac{H}{h}}{\left({\displaystyle \frac{\lambda }{h}}\right)}^2={\displaystyle \frac{H{\lambda }^2}{h^3}}$$

(8)

where H corresponds to the wave height, $\lambda$ to the wavelength, and h to the required depth for the system [12].

2.3. Wells Turbine

The Wells turbine is part of the second and third stages of the energy conversion process. Its bidirectional configuration allows continuous rotation in a single direction, regardless of the airflow’s direction during the intake and exhaust phases within the air chamber. The turbine incorporates symmetrically curved blades positioned at a 90° angle relative to the airflow, facilitating effective operation under oscillatory conditions. It is characterized by a straightforward design, favorable efficiency, and the ability to achieve high rotational speeds, often exceeding 1500 rpm. However, certain limitations are associated with its operation. Optimal performance typically requires steady airflow as an idealized condition. Additionally, the turbine may exhibit difficulties during startup and, at higher speeds, is prone to generating high noise levels and considerable axial forces [13].

$$\varnothing ={\displaystyle \frac{V_x}{r{\Omega }_m}}$$

(9)

$$C_a={\displaystyle \frac{∆p}{K_t{\rho }_{atm}\frac{1}{A_t}\left[{V_x}^2+{\left(r{\Omega }_m\right)}^2\right]}}$$

(10)

$$C_t={\displaystyle \frac{T_t}{K_t{\rho }_{atm}r\left[{V_x}^2+{\left(r{\Omega }_m\right)}^2\right]}}$$

(11)

$$\eta ={\displaystyle \frac{C_t}{C_a\varnothing }}$$

(12)

Equation (9) defines the flow coefficient, which quantifies the linear airflow $V_x$ velocity in relation to the angular velocity of rotation ${\Omega }_m$. Equation (10) estimates the inlet pressure variation $∆p$, incorporating the thermophysical properties of air and geometric parameters like turbine constant $K_t$ encapsulates the relationship between the number of blades, blade chord and length, and blade height. Additionally, ${\rho }_{atm}$ denotes the air density, $A_t$ represents the cross-sectional area of the duct enclosing the turbine, and $r$ is the turbine radius. Equation (11) calculates the coupling coefficient, which expresses the torque $T_t$ as a function of the turbine constant, air density, turbine radius, and linear and angular velocities [14]. Finally, Equation (12) corresponds to the turbine’s aerodynamic efficiency coefficient, which is determined by the relationship between the pressure, coupling, and flow coefficients. This parameter measures the efficiency of converting pneumatic input into mechanical output power.

2.4. Design Methodology

2.4.1. Procedure of the Design of an OWC Plant

Figure 4 illustrates the design of an OWC plant methodology. It begins with a literature review, evaluates potential sites, and selects the most suitable location. Subsequently, design parameters and criteria are established, leading to conceptual design development. The performance of the OWC system is assessed in two stages. The first stage, conducted using OpenFOAM, simulates ocean behavior at the selected site to determine water pressure and velocity characteristics. The second stage analyzes the pneumatic airflow that drives the Wells turbine.

2.4.2. Evaluation of Potential Sites

The selection criteria were categorized into five groups: suitable wave heights, optimal wave periods, appropriate water depth, presence of low coastal cliffs, and access to existing infrastructure.

Table 1. Categories used to discretize the site selection.

Category	Description
Suitable wave heights	Higher average wave heights correspond to greater available energy.
Optimal wave periods	The frequency at which waves reach the shore must remain relatively constant. Longer wave periods contribute to increased energy potential and operational stability.
Ideal depth	In shallow waters, waves break prematurely, resulting in energy loss before reaching the energy capture structures. In contrast, deeper waters allow waves to maintain their height and energy potential over greater distances.
Presence of low cliffs	The presence of coastal cliffs or bluffs at the ends of the beach facing the sea is advantageous, as they provide structural support for plant installation and enhance the interaction with wave energy.
Access and installation complexity	Potential obstacles related to limited accessibility and high human activity in the area, which may hinder plant installation and maintenance, are considered.

briefly describes the five categories that discretize the site selection process.

2.4.3. Site Selection

It is important to highlight that not all coastal locations are suitable for installing an OWC plant. The criteria include excluding all coastal zones designated as protected areas, as these are not eligible for project development. In the case of the Santa Elena province, this includes the “El Pelado” Marine Reserve and the “Puntilla de Santa Elena” Coastal Marine Fauna Production Reserve. In addition to environmental restrictions, coastal geomorphology must be considered. Sites characterized by high cliffs or barrier beaches are considered unsuitable for equipment installation due to physical limitations. Furthermore, existing coastal structures such as groins, breakwaters, and revetments are only viable if they are consistently exposed to wave action. Structures lacking this exposure are omitted from consideration.

After a thorough analysis, the locations were selected based on their wave characteristics and compliance with the selection criteria previously described. The sectors with high potential are presented in Figure 5. Data collection focused primarily on significant wave height, a parameter commonly used in designing energy conversion devices. It represents the average height of the highest one-third of recorded waves [15]. A general evaluation of the locations where the study was conducted is presented.

General evaluations of the locations are as follows:

• Monteverde

The significant wave height at the study site is 0.8 m, with a wave period of 14 s and a predominant direction from the southwest. During the low tide phase, wave celerity reaches a maximum of 0.22 m/s, with an average velocity of 0.04 m/s. In the high tide phase, the maximum velocity is 0.20 m/s, and the average velocity increases to 0.18 m/s.

• Anconcito-Punta Carnero

Significant wave heights in the study area range from 1.0 to 1.5 m, with wave periods of less than 8 s and a predominant direction from the west-southwest [16]. Along the Ecuadorian coastline, wave activity predominantly originates from the southwest. This region is notably characterized by its commercial and touristic significance. Wave celerity varies between 0.30 and 0.35 m/s.

• La Entrada-Olón

This area is characterized by significant wave heights ranging from 2 to 3 m, with wave periods of 12 s and a predominant direction from the southwest. During the high tide, the maximum wave velocity reaches 0.22 m/s in a northwesterly direction, while during the low tide, the maximum velocity is 0.11 m/s, predominantly toward the southwest. Figure 6a shows the low beach relief characteristic of the La Entrada–Olón sector. It is important to highlight that this area is free from any type of commercial activity. In contrast, Figure 6b displays a coastal zone with pronounced erosion and high cliff relief, illustrating the ocean’s energy potential and the available space and conditions suitable for the construction of a power plant. Both images were taken from the same location, with a 180° rotation of the camera.

2.4.4. Air Chamber Design

The characteristics of the incident waves directly influence the design of the pneumatic compression chamber. Figure 7 illustrates the key dimension considered in the geometric sizing of the Oscillating Water Column system. In this context, “a” denotes the submerged depth of the system’s front wall, “b” represents the width of the air chamber, “c” indicates the height of the air chamber, “d” corresponds to the sea depth measured from the reference point at the seafloor, and “e” refers to the diameter of the flow conduit directing air toward the turbine.

3. Results

3.1. Methods and Programs

Fluid dynamics in engineering is governed by differential equations derived from the principles of conservation of mass, momentum, and energy. Computational Fluid Dynamics (CFD) was employed to approximate numerical solutions to these equations using solvers and post-processing tools to interpret the results. The simulations were conducted using both OpenFOAM and Solidworks 2024 CFD software to address the critical stages of the design process, with a particular focus on the plant’s operational parameters. OpenFOAM was utilized to simulate wave interactions with the power plant structure, including the behavior of the OWC within the chamber, which compresses and expels air through a duct. At this stage, the resulting air velocity, pressure, and mass flow rate are input data for the subsequent turbine performance analysis.

3.2. OpenFOAM Post-Processing

Following the simulation process using OpenFOAM and the ParaView visualization tool, velocity contour plots were obtained based on the velocity field’s magnitude and directional components (x, y, and z). Concurrently, additional hydrodynamic parameters, such as water volume fraction, dynamic pressure, and static pressure, were evaluated. The simulation replicated the fluid–structure interaction along a defined channel, modeling the wave flow and its impact with the OWC structure over a total simulation duration of 120 s.

The pre-configuration parameters used in OpenFOAM are presented in

Table 2. Configuration of OpenFOAM: system parameters, constants, and simulation results.

System	Constant	Results
blockMeshDict	g	alpha.water
controlDict	transportProperties	p_rgh
decomposeParDict	turbulenceProperties	U
fvSchemes	waveProperties
fvSolution
meshQualityDict
refineMeshDict
setFieldsDict
snappyHexMeshDict

. The system commands listed were selected to achieve accurate simulation results. These configurations include definitions for constants such as gravitational acceleration, conserved mass transport schemes, turbulence modeling parameters, and the thermophysical properties of water as the working fluid. The simulation outputs primarily evaluate the system’s mass flow rate, pressure distribution, and velocity profiles. The OWC structure was defined as a wall-type obstacle for the boundary conditions, allowing it to be interpreted as a solid geometry within the computational domain. The pneumatic outlet interface between the OWC structure and the environment was also designated a wall-type boundary.

The multiphase solver in OpenFOAM was employed to simulate multiphase flow bevahior. The specific path used was multiphase/interFoam/laminar/waves/stokesV, where interFoam refers to the solver used for simulating multiphase flows based on the Volume of Fluid method. The laminar flow type was selected, given that the scenario involves regular wave patterns. The waves directory indicates the simulation of wave phenomena, while stokesV corresponds to a specific case representing Stokes wave conditions.

The blockMeshDict configuration, located in the system directory, was used to generate the computational mesh using hexahedral blocks. Vertices were defined to delimit the desired computational domain. Within the boundary conditions, the Oscillating Water Column structure was modeled as a wall-type obstacle, ensuring its recognition as part of the computational geometry. Similarly, the outlet boundary was also defined as a wall-type obstacle for the same purpose. As previously stated, the domain depth was set to a nearly negligible value of 0.04 m, supporting the assumption of a two-dimensional simulation.

To enhance accuracy in critical regions, a refined mesh was generated in areas of interest, such as the free surface, the wave–air interface, and the interior of the OWC chamber, to better quantify wave impacts and internal effects. This detailed meshing was performed using the snappyHexMeshDict configuration, which further subdivides the base mesh to improve resolution. Additional refinement was conducted using the meshQualityDict and refineMeshDict utilities. The resulting mesh characteristics are presented in

Table 3. Configuration of OpenFOAM: mesh characteristics of the domain.

Parameter	Value
Points	1,169,089
Faces	2,956,059
Internal faces	2,424,684
Cells	894,658

.

The controlDict file defines the main simulation control parameters, specifying a total simulation of 120 s, a time step of 0.01 s, and a data acquisition interval of 1 s. The decomposeParDict file is used to decompose the computational domain into multiple subdomains, enabling parallel processing across several CPU cores. This configuration is particularly useful for optimizing computational efficiency and reducing simulation time. Additionally, the fvSchemes and fvSolution files contain the numerical discretization schemes and solver settings, respectively. These defines the mathematical procedures and solution algorithms applied during the simulation, depending on the selected physical model. Finally, the setFieldsDict file initializes the fluid region within the computational domain. In this case, it sets the water level inside the wave channel, covering an x-directional range from 0 to 104 m and a water depth of 9 m.

The boundary conditions are presented in

Table 4. Configuration of OpenFOAM: boundary conditions of the domain.

Contour	Dynamic Pressure	Velocity	Volume Fraction
Inlet	fixedFluxPressure	waveVelocity	waveAlpha
Outlet	fixedFluxPressure	waveVelocity	zeroGradient
Ground	fixedFluxPressure	fixedValue	zeroGradient
Top	totalPressure	pressureInletOutletVelocity	inletOutlet
Sides	Empty	Empty	Empty
Obstacle	fixedFluxPressure	fixedValue	zeroGradient

. Wave generation and absorption are handled with the interFoam solver, applying different specifications at the inlet and outlet of the computational domain. At the inlet (left boundary), the volume fraction and velocity are prescribed according to OWC design. At the outlet (right boundary), a shallow water absorption model is imposed. The seabed, the structural surfaces of the OWC, and the outlet structure are treated as no-slip walls, labeled grounds, and obstacles, respectively. The top boundary is assigned atmospheric conditions, and the lateral boundaries are defined as empty to emulate two dimensional flow.

The physical properties defined for the simulation are as follows: Gravity was applied in the z di-rection with a magnitude of 9.81 m/s². The kinematic viscosity was set to 1.0 × 10⁻⁶ m²/s for water and 1.48 × 10⁻⁵ m²/s for air. The density values used were 1025 kg/m³ for sea-water and 1.2 kg/m³ for air. Additionally, a surface tension coefficient of 0.07 N/m was defined between the water and air phases. Wave generation was configured with a wave height of 2 m, based on the average wave conditions at the selected site, and a wave period of 12 s, with a phase angle of 0°. A ramp time of 4 s was established to allow the wave amplitude to increase gradually, reaching the full height of 1 m. This approach minimizes transients and abrupt disturbances at the start of wave generation. Active wave absorption was also implemented at the inlet to reduce unwanted reflections. At the channel outlet, a shallow water absorption model was employed to dissipate wave energy and prevent wave reflections from re-entering the domain, thereby ensuring more realistic flow behavior and avoiding interference with the internal wave dynamics.

The computational domain can be seen in Figure 8 and was constructed using hexahedral mesh blocks, with a bottom element size of 0.04 m to ensure adequate resolution near the seabed. The complete domain size is 104.5 m in length and 20 m in height. The water phase within the domain ranges from a minimum wave through height of 5.5 m in width and 10 m in height, with a wall thickness of 0.5 m. The outlet duct, which channels compressed air toward the turbine, has a width of 0.8 m. It is important to note that one of the key assumptions is the use of two-dimensional (2D) geometry in the OpenFOAM simulations. The three-dimensional (3D) modeling will be developed in the second stage, based on the assumption that the results are proportional across each unit of transverse area.

Figure 9 presents the velocity magnitude contours at 60 s and 120 s at two specific time instants. As this is a dynamic analysis, these time points were selected to assess the development of the flow field and observe system behavior approaching the end of the simulation period. At 60 s, a concentrated region of elevated water velocity is evident within the chamber, corresponding to the formation of the oscillating water column. Additionally, the incident waves can identify high-velocity profiles before reaching the structure’s front wall. By 120 s, the influence of the front wall on flow acceleration becomes more pronounced. The seabed also contributes to flow acceleration through boundary effects. The parameter under analysis shows a marked increase in velocity near the structure, primarily attributed to the constructive interference of incoming waves. This interaction facilitates a partial energy transfer to the structure, with the most significant effect being the localized rise in velocity both around and within the internal chamber of the system.

Figure 10 illustrates the distribution of velocity components within the channel, where the flow is predominantly confined to the XZ plane. This configuration allows for a two-dimensional analysis of the flow behavior. The velocity component along the y-axis can be considered negligible in the overall velocity estimation, as it exerts minimal influence on the flow dynamics. In contrast, the X and Z axes reveal the regions with the highest velocity magnitudes. On the other hand, turbulence phenomena are observed to reach velocities exceeding 4 m/s near the rear boundary of the computational domain and adjacent to the structural elements. The visual output corresponds to a single wave cycle analysis, wherein one wave crest is visible outside the structure. In contrast, the subsequent crest is captured within the pneumatic chamber.

Figure 11 illustrates the water volume fraction obtained from the two-phase simulation. Regions in red, corresponding to a numerical value of 1, represent the water phase, while regions in blue, with a value of 0, denote the air phase. The interface between these two fluids can be approximated as a pneumatic piston, effectively simulating a horizontal boundary within the chamber. The simulation and analysis were conducted over a single wave period. Results indicate that the mass distribution of water inside and outside the structure remains acceptable, preventing overtopping and flooding of the pneumatic chamber. The simulations are parameterized by wave period or frequency, which facilitates accurate modeling of wave height and the dynamic motion of the pneumatic piston within the chamber. The observed height differential between internal and external water levels also supports forming a linear profile, enabling a stable and proportionally consistent pneumatic airflow.

Figure 12 and Figure 13 in the final analysis present static and dynamic pressure distributions, respectively. Figure 12 shows that the maximum pressure values are concentrated in the lower region, indicating that the flow exerts a significant hydrostatic load on the analyzed volume. Static pressure increases moderately with the passage of waves and provides a critical support element for the structure by maintaining a relatively constant pressure gradient over time at the lower inlet. This stability facilitates the formation of a piston-like water level within the chamber. Conversely, reducing pressure on the front wall leads to more turbulent flow conditions. The regions of high dynamic pressure are located near the structure at the moment of wave impact, indicating that an increase in wave velocity directly corresponds to an increase in dynamic pressure within the zone of influence.

Conversely, static pressure remains relatively stable over time, as the water and air levels within the chamber tend to stabilize due to the periodic nature of the bidirectional flow. It is important to emphasize that the oscillation frequency of the water column within the chamber must resonate with the frequency of the incident waves. This resonance amplifies the oscillatory motion, enhancing the pneumatic compression and decompression, thereby maximizing overall energy conversion efficiency.

3.3. Computational Fluid Dynamics Post-Processing

In the second stage of the analysis, emphasis is placed on the use of Computational Fluid Dynamics (CFD) software. The data obtained from the first stage, using OpenFOAM, specifically velocity, pressure, and mass flow rate, serve as time-dependent input conditions for this phase. These parameters vary according to wave period and are imported into the CFD tool to assess airflow dynamics. With the theoretical framework established, the design of the Wells turbine and the corresponding flow redirectors was developed to evaluate the interaction of air with the turbine blade profiles.

The structural design of the wave power plant includes a 10-m-high front wall, of which 3.5 m are submerged and impacted by incoming waves. The wall thickness is 0.5 m. The internal air chamber measures 5 m in width, 3 m in length, and an average height of 6 m. These dimensions are based on optimization ratios and the reference design of the Mutriku wave power plant in Spain. At the top of the chamber, the air duct has a diameter of 0.8 m. It is concentrically connected to the turbine compartment, which has a rectangular prism shape with a square base, measuring 1.2 m in height and 1.1 m in width. The turbine duct was designed within a computational domain of an internal cylindrical geometry enclosed by an external cubic volume. The cylinder’s diameter corresponds to the outlet diameter derived from the OpenFOAM simulation. A flow director was incorporated to guide the airflow trajectory, ensuring it effectively impacts the turbine blades. This component is essential to prevent adverse effects such as flow recirculation, backpressure, and excessive compressive stress on the turbine shaft. Lastly, structural supports were included to secure the energy conversion assembly, ensuring operational stability.

The configuration settings within the CFD tool were defined to simulate internal flow, incorporating gravity effects, rotational dynamics, thermodynamic properties, and the boundaries of the computational domain. Boundary conditions were established based on the inlet parameters derived from the OpenFOAM simulations, while the outlet was set to atmospheric pressure.

Figure 14 illustrates the behavior and pressure phenomena on a shear plane across the Wells turbine blades. The atmospheric pressure, defined as the upper boundary condition, creates a region of vector field stability. The lower section of the turbine shows high pressure because the turbine is in the impulse phase. As a result, the opposite side of the turbine experiences a low-pressure (vacuum) region, and this pressure differential generates the rotational inertia required to drive the turbine. This same effect can be observed when the pressure zones are reversed, due to the return motion of the ocean wave, which induces a vacuum state within the air chamber. However, due to the symmetrical airfoil profile of the Wells turbine blades, the rotation is maintained in a single direction, regardless of the airflow direction. Due to the idealized simulation conditions, a relatively low pressure distribution is seen throughout the domain except in the vicinity of the turbine blades, where localized variations are more pronounced.

Figure 15 shows the velocity vector field and its interaction with the duct and the Wells turbine. A change in velocity can be observed after the airflow comes into contact with the turbine blades, indicating a reduction in air energy. The shape and arrangement of the NACA airfoil enable the observation of an inertial rotational motion. Additionally, Figure 15 presents a velocity shear profile along the blade section. It is evident that the maximum air velocity occurs at the leading and trailing edges of the profile, which enhances the rotational tendency of the shaft.

4. Discussion

The global analysis of the plant’s performance was conducted analytically through CFD simulation, considering three case scenarios: real, optimal, and ideal. The real values for the study were obtained via simulation, as they incorporate the wave parameters of the selected area and the design dimensions. These yielded a mechanical power output at the turbine shaft of 1.5 kW and a pneumatic power of 11.4 kW, based on an air mass flow rate of 0.06 kg/s at a speed of 10 m/s and a shaft rotation of 0.12 rad/s. An analysis and iteration process were conducted to determine the input values to find the optimal air inlet velocity, using the simulated velocity from OpenFOAM as a reference. The values obtained are derived using Equations (5)–(7), demonstrating that the energy available from wave action is directly dependent on wave amplitude and period. However, to accurately assess the influence of the liquid medium on the overall energy conversion process, it was essential to characterize the water flow as a form of mass transport. This approach enabled evaluating the interaction between the water and the air column and the subsequent mechanical work produced to drive the turbine. It is important to note that the pressure, mass flow, and velocity analyses were conducted under ideal conditions, assuming the presence of a single wave front. Greater velocity, pressure, and power output values are observed under constructive wave interference scenarios. Conversely, alternative conditions may lead to destructive interference, significantly reducing the efficiency and performance of the OWC system. Additionally, it is important to emphasize that the results obtained do not account for the resistance of the shaft and generator. In practical applications, there is a necessary load resistance that must be overcome to ensure effective operation. While this factor is indeed relevant to the overall system performance, it falls outside the scope of the present study and was therefore not included in the simulation or analysis. Finally, Ecuador’s extensive coastal profile allows the application of the projected average power output of 16 kW at a specific site, replicating this energy generation approach at multiple locations along the coastline. On the other hand, it is important to discuss the absence of shaft influence in the current analysis. The shaft was not considered because, in the actual Wells turbine prototype, flow redirectors are positioned at both ends, prior to the airfoils, to optimize the active surface area and enhance rotational speed. These redirectors play a crucial role in minimizing flow disturbances caused by structural interference. Without them, the turbine efficiency could be significantly reduced, as reverse or recirculating flows may develop around the shaft region. For this reason, the present analysis was limited to the core working area, focusing on the aerodynamic behavior of the turbine blades without the additional complexities introduced by shaft geometry.

5. Conclusions

The design of the Oscillating Water Column (OWC) plant was developed in parallel with the site selection process, targeting locations with high potential for small-scale power generation. The simulation strategy was implemented in two stages. The first stage approximated wave behavior under idealized conditions using data acquired through open-source software. This provided the necessary velocity and pressure parameters to be used as inputs for the second simulation stage, which involved the application of Computational Fluid Dynamics (CFD) tools. Initial projections estimated a power output of 16 kW; however, results from the second simulation stage indicated an actual power output of 11 kW, yielding a plant factor of 0.69. This discrepancy is interpreted in the context of the Wells turbine efficiency, calculated in the appendices to be approximately 37%. These findings support the conclusion that the idealized projection of a 16 kW plant provides a realistic and valuable baseline for the design.

In terms of the simulation results, the pressure and velocity values, obtained from the two-dimensional OpenFOAM analysis, serve as the initial input conditions for the subsequent CFD-based pneumatic simulation. These values are idealized as constant across a representative surface section to facilitate the second simulation stage. The results of the pneumatic analysis of the Wells turbine indicate that the average air velocity reaches approximately 20 m/s, with a pressure differential of up to 2000 Pa between minimum and maximum values. These parameters confirm that the Wells turbine possesses sufficient energy potential to generate rotational motion and, consequently, produce electrical power. It is important to note that the shaft geometry was excluded from the analysis, as flow redirectors are positioned at both ends of the turbine. These components are designed to optimize airflow and improve the overall performance and efficiency of the system, thereby justifying the omission of the shaft in the current simulation framework.

The use of high-strength materials is essential to ensure the durability of this type of energy converter, which is exposed to a corrosive environment and significant dynamic forces. The Wells turbine, in particular, must be constructed from lightweight materials to minimize inertia during start-up.