Aero-Structural Analysis of a Wind Turbine Blade Lay-Up as a Preliminary Design Alternative

Abstract

Wind energy has become an essential resource for the development and diversification of the energy sector in México and worldwide. In this context, the mechanical design of turbine blades has emerged as a priority research topic, given its impact on performance and viability. The present research evaluates the aero-structural response of multiple lay-up configurations of a 6 m blade by coupling computational fluid dynamics (CFD) and finite element analysis (FEA). The fluid–structure interaction (FSI) was simulated in ANSYS, a commercial software chosen for its capacity for multivariable analysis. The nominal operating conditions included a wind speed of 10.5 m/s and a rotational speed of 100 rpm, leading to a theoretical power output of 6591 W. For the proposed lay-up configurations, the Tsai-Wu and Puck (Global IRF) criteria were estimated and remained below the critical threshold of 1.0, indicating no risk of structural failure. However, some carbon fiber/epoxy layers, including unidirectional layers in the spar caps and bidirectional layers in the structural shear web, may present failure risks under extreme loading conditions. This applies to configurations with the lowest number of layers in the mid-span spar caps; this fact is reinforced by the main effects analysis. The results emphasize the relevance of conducting comprehensive composite failure evaluations to optimize material selection and structural design, even for small-scale blades.

1. Introduction

In the wind energy industry, the primary components of turbines are the blades. By driving the rotor, the blades convert the kinetic energy of the wind into mechanical energy, which is subsequently transformed into electrical energy by the generator. As illustrated in Figure 1, the blades have an elongated, streamlined, and flexible structure, with an aerofoil-shaped cross-section that produces a pressure difference between the upper and lower surfaces, resulting in a lift force that generates torque [1]. However, under operating conditions, blades are typically exposed to complex loading conditions such as bending (flapwise and edgewise), axial (tensile), compression, and general deformation caused by aerodynamic, centrifugal, and gravitational forces [2], which potentially compromise the structural integrity of the blade.

For the manufacturing of blades, the predominant composite materials are glass fiber reinforced polymers (GFRP) and carbon fiber reinforced polymers (CFRP). These materials offer several advantages, including low weight, high stiffness, minimal deformation, and reduced inter-laminar stress [3]. Therefore, their application, particularly in turbines with a rated power exceeding 1.0 kW, is a critical concern for the wind industry.

As reported, blade manufacturing costs represent between 15 and 20% of the overall cost of a wind power generation system [4]. Despite this share, most of the analytical resources available are insufficient to effectively support the optimization of this activity. For instance, the Blade Element Momentum Theory (BEMT) provides useful information about aerodynamic performance, but it fails to estimate the specific flow field around the blade, which has a considerable impact on load estimations [5]. On the other hand, advanced software packages, such as FOCUS, are extensively used in research institutes to estimate aeroelastic interactions [6]. However, most of these tools do not offer comprehensive capabilities for the evaluation of structural elements associated with different composite lay-up configurations. Moreover, the structural testing methods to validate potential blade geometries, such as full-scale testing of the blade, are expensive and troublesome due to the construction of a test setup [7].

In response to the previous limitations, the coupling of computational fluid dynamics (CFD) and finite element analysis (FEA) offers a promising alternative for both capturing the complex fluid flow patterns derived from the wind turbine operations and transferring them to a comprehensive structural loads analysis. This integrated approach facilitates the estimation of equivalent stresses across multiple scales, from single layers to the entire structure, as well as the evaluation of total deformation and composite material failure criteria, including Tsai-Wu and Puck formulations. For wind turbines, the implementation of this fluid–structure interaction (FSI) analysis is particularly relevant, as it allows the integration of aerodynamic and structural conditions to determine the viability and performance of the blades. Nevertheless, it is worth mentioning that these numerical tools cannot entirely replace experimental testing. Instead, they allow for a preliminary design with reduced time and cost investment.

In the literature, several studies support the implementation of FSI for aero-structural blade design. In 2016, Wang et al. [5] conducted research using an FSI model to evaluate the blade design of a 1.5 MW horizontal-axis WindPACT turbine. The one-way coupling between CFD and FEA was implemented to transfer aerodynamic loads to a structural model, which was validated using computational benchmark tests. Lipian et al. [8] investigated the structural integrity of a small-scale wind turbine with a rated power of about 350 W. The total deformation and principal stresses were quantified for multiple operational conditions. Similarly, Roul & Kumar [1] performed a one-way FSI analysis using ANSYS to systematically evaluate the structural response of the blade by varying the wind speed and angle of attack. This research identified the angle of attack as a critical parameter related to blade deformation and stress distribution. For wind turbines with a rated power equal to or exceeding 1.0 kW, the implementation of FSI tools is justified for better control of the parameters involved in preliminary design, subsequent optimization, experimental testing, and even final manufacturing. In addition, recent studies on the implementation of CFD and FEA have enabled these technologies and their parameters to be implemented properly. In 2024, Khedr and Castellani [9] conducted a study on the parameters used in various projects; the research delves into the critical issues identified, specifically the computational domain cross-section shape and size, discretization scheme accuracy, meshing criteria, freestream turbulence intensity, and turbulence modeling. Also in the same year, Deng et al. [10] proposed a general FSI framework, which combines the advantages of CFD and FEA methods, for analyzing the detailed stress distributions on the composite structures. The results were reasonably in agreement with other literature results.

All the aforementioned studies are related to this work, as they provide the basis on which the methodology was established. However, they may be limited in scope when it comes to more complex manufacturing or analysis where more variables interact. The methodology for many studies was limited to coupling the aerodynamic loads obtained from CFD analysis with the structural assessment in FEA. Although the lay-up of the wind blade was generated, no failure criteria were evaluated to determine areas in need of reinforcement [5]. In others, the research was limited to aerodynamic analysis and its subsequent structural response. Furthermore, it was only evaluated with isotropic material under different operating conditions [8]. The manufacturing stages would represent a problem, as there is no lay-up definition, in addition to not generating a multivariable analysis for structural response.

Unlike previous studies showing CFD and FEA interaction, this study provides a detailed analysis of a turbine blade lay-up, as well as a multivariable analysis of operating conditions for fluid dynamics and the structural behavior of different lay-up configurations with varying numbers of layers. It also includes a main effects analysis and evaluation of failure criteria for composite materials (Tsai-Wu and Puck). Previous studies are limited to fluid–structure interaction or analyze structures under certain loads, or only perform analysis under the aforementioned failure criteria under a single configuration. This study is therefore physically complex, addressing many interacting variables.

For the present research, the blade geometry considered for evaluation was partially derived from technical reports published by the CIATEQ A.C. research institute [6]. A CFD simulation was conducted to estimate the aerodynamic loads on the blade surface and transfer them to an equivalent structural model. A three-dimensional model was considered for the structural lay-up configurations, considering the number of layers that cover the entire length of the aerodynamic shells (1 and 2), the spar caps in the root (4 and 6), the spar caps in the mid-span (3 and 4), and the shear web (4 and 5). The structural thickness values were defined in terms of the aerofoil thickness-to-chord ratio (t/c) and the stacking lengths relative to the local-to-total blade lengths (l/lo). The structural response was estimated by applying a one-way FSI analysis. A parametric study was conducted to assess the structural performance of the blade under various loading conditions and lay-up configurations.

2. Materials and Methods

The commercial software ANSYS v2025R1 (ANSYS, Inc., Canonsburg, PA, USA) was chosen as the computational platform because it can analyze different physics in the same environment; in addition, the graphical user interface allows for efficient use of the tool. Compared with experimental validation, different authors who have used it have also obtained good results [1,5,8]. The modules for CAD modeling (SpaceClaim), computational fluid dynamics (Fluent), composite materials (Composite PrePost), and finite element analysis (Mechanical) were used. The aerodynamic loads obtained from the CFD module were transferred to the FEA module and applied as boundary conditions for the structural analysis. Figure 2 illustrates the specific dependencies and coupling between these two modules. On the other hand, Figure 3 introduces the variables of interest, operating conditions, material thicknesses, and response variables considered in the parametric study.

2.1. Aerodynamic CFD Simulations

2.1.1. Computational Domain

The blade geometry design was conducted using the blade element momentum theory (BEMT), with the Wortmann FX-W-258 aerofoil selected as the cross-sectional shape. The polar curve data for this aerofoil were obtained from reports presented by Jacobo et al. (2020) [11], which were developed as part of the activities of the CIATEQ A.C research center.

Table 1. Wind turbine design parameters.

Parameter	Value
Blade length	6 m
Root diameter	0.3 m
Number of blades	3
Design speed	10.5 m/s
Angle of attack	8°
Tip speed ratio	5.93

presents the fundamental design parameters of the wind turbine.

For the computational domain definition, the radial symmetry of the turbine was exploited to reduce the domain to 1/3 of its original size by applying periodic boundary conditions. The application of this approach resulted in a radial section with an aperture angle of 120°. Domain dimensions were defined in terms of the total blade length (L = 6 m) as the reference length. Based on the recommendations of Lipian et al. (2020) [8], a body of influence around the blade and in the downstream region was included to enable the local mesh refinement required to capture complex fluid flow interactions, such as vorticity and turbulence [12]. The dimension of this body of influence includes a radius of 1.88 L, a front extrusion of 1.5 L, and a rear extrusion of 6 L. Figure 4 illustrates the geometry of the conceptual model, and

Table 2. Dimensions of the conceptual CFD model.

Parameter	Dimension
R1	3.75 L (22.5 m)
R2	1.88 L (11.25 m)
L1	3 L (18 m)
L2	6 L (36 m)
L3	1.5 L (9 m)

summarizes the dimensions of the components.

2.1.2. Mesh Independence Study

To guarantee the convergence of the numerical solution with respect to spatial discretization, a mesh independence study was performed [9]. Different researchers performed a mesh sensitivity analysis to validate the optimal mesh size, using between four and six different element sizes [1,5,12], so in this study, seven different meshes were configured. For the conceptual model, an element-predominant poly-hexcore mesh was generated and connected to octree hexahedral elements located near the blade surface. To capture boundary layer interactions, the mesh was refined using isotropic polyhedral prism layers. This mesh configuration achieved a reduction of 20–50% in element count compared to a conventional hexahedral mesh [13].

For the boundary layer, element size was defined using the dimensionless wall distance parameter y+, which determines the height of the first element according to the near-wall treatment requirements. This parameter was estimated by considering the fluid properties and the Reynolds number, a dimensionless quantity that relates the inertial to viscous forces to define the flow regime [14]. The specific magnitudes are included in

Table 3. Fluid properties (air) and dimensionless numbers.

Parameter	Dimension
Density	1.18 kg/m3
Dynamic viscosity	1.84 × 10−5 kg/ms
Kinematic viscosity	1.56 × 10−5 m2/s
Reynolds number	Re = (ρUL/μ) = 2.02 × 105
	Turbulent Regime
Dimensionless number y+	y+ = Uτy/v = 0.1775 mm

.

From the y+ formulation: Uτ is the friction velocity near the wall in m/s, y is the absolute height of the first element in m, and v is the kinematic viscosity of the fluid in m²/s [5].

The selected k − ω SST (Shear Stress Transport) turbulence model requires a dimensionless wall distance y+ ≤ 3 to ensure a proper resolution of the velocity profile and turbulent interactions within the viscous sublayer [8]. Therefore, it was estimated that y ≈ 0.1775 mm, and an inflation refinement was applied to the surface of the blade, incorporating a total of 15 layers, usually between the range of 15 and 20 inflation layers, which were used for researchers [5,12] with a standard growth ratio of 1.2. This value is the default parameter in Ansys, as a smaller growth ratio generates an increment of final count elements. Figure 5a–c show the inflation layers, mosaic meshing structure, and polyhedral elements near the blade, respectively. For the mesh independence study, seven different mesh configurations (A–G) were evaluated, with element sizes described in

Table 4. Results of the mesh independence study.

Parameter	Mesh A	Mesh B	Mesh C	Mesh D	Mesh E	Mesh F	Mesh G
Minimum sizing curvature	5	3.5	2.5	2	1.5	1	1
Element size (surface)	15	15	10	10	8	8	5
Body of influence sizing	350	350	350	350	350	350	350
Average orthogonal quality	0.85	0.87	0.86	0.86	0.87	0.88	0.90
Elements count	1,025,479	1,502,772	1,941,626	2,349,082	2,945,483	3,732,414	5,014,599
Torque (Nm)	165.44	170.27	207.80	204.83	209.85	209.91	211.42
Drag coefficient	0.625	0.620	0.698	0.691	0.702	0.701	0.702
Lift coefficient	2.51	2.36	2.51	2.48	2.49	2.49	2.49

. The torque was defined as the monitoring variable and was computed for operational conditions that include an angle of attack of 8°, a rotational speed of 100 rpm, and a free-stream velocity of 10.5 m/s.

As shown in Figure 6, the mesh independence study revealed that the difference in estimated torque between Mesh E and Mesh G is approximately 0.75%. Also, a drag and lift coefficient reinforces this fact; the value of this parameter is the same for both meshes (E and G). However, the element count increases by more than 70% between these two configurations, leading to a considerable increase in the computational costs and time. In view of these results, it was concluded that Mesh E is enough to perform the subsequent simulations.

2.1.3. CFD Solver Configuration

For small-scale wind turbines, flows with relatively high turbulence levels are expected both upstream and downstream [15]. Consequently, the selection of the K − ω SST turbulence model is appropriate, as it achieves a relatively good agreement in predicting the boundary layer detachment on aerofoils [13]. This turbulence model implements a blending function that activates the K − ω formulation in the near-wall region, where accurate resolution of the viscous sublayer is critical, while transitioning to κ − ε in the outer flow and wake regions. This hybrid approach ensures predictive capability for flows influenced by adverse pressure gradients [16]. In addition, different researchers have used this turbulence model and achieved accurate results when compared with experimental validation [1,5,8,9,12].

Regarding the rotational motion of the turbine under operational conditions, a stator–rotor approach was adopted, and the computational domain was divided into a static frame and a moving reference frame. In the fluid domain, the fluid acquires a tangential velocity component as it flows through it. While this approach simplifies the time formulation from a transient state to a stationary state, certain limitations arise when attempting to simulate the more complex interactions in the wake. Despite this, relatively low computational requirements, compared to alternative implementations such as sliding mesh, justify its application for preliminary blade design and parametric studies [9].

Figure 7 illustrates the implementation of the moving reference frame approach for a wind turbine and demonstrates how wind speed estimation varies depending on whether it corresponds to a stationary observation (stationary reference frame) or a relative observation (moving reference frame).

The RANS governing equations for mass, momentum, and energy conservation are solved in the moving reference frame attached to the rotating blade. From this perspective, the fluid–rotor interactions become stationary. The fluid velocity relationship between these coordinate frames is described by the following equations [16].

$$\begin{array}{c}{\overrightarrow{v}}_r=\overrightarrow{v}-{\overrightarrow{u}}_r \end{array}$$

(1)

$$\begin{array}{c}{\overrightarrow{u}}_r={\overrightarrow{v}}_t+\overrightarrow{\omega }\times \overrightarrow{r } \end{array}$$

(2)

where ${\overrightarrow{v}}_r$ is the relative velocity (measured in the moving frame), $\overrightarrow{v}$ is the absolute velocity (measured in a stationary frame), ${\overrightarrow{u}}_r$ is the velocity of the moving frame relative to the stationary frame, ${\overrightarrow{v}}_t$ is the translational velocity, $\overrightarrow{\omega }$ is the rotational velocity, and $\overrightarrow{r }$ the radial position. When the equations of motion are solved in the moving reference frame, the acceleration of the fluid is augmented by additional terms that appear in the momentum equations [16]. In this context, the governing equations can be expressed using either relative or absolute velocities as the dependent variables. For the absolute velocity formulation, the governing equations are expressed as follows:

$$\begin{array}{c}\mathrm{Mass} \mathrm{conservation:} \nabla ·\rho \left(\overrightarrow{v_r}\right)=0 \end{array}$$

(3)

$$\begin{array}{c}\mathrm{Momentum:} \rho {\displaystyle \frac{\partial }{\partial t}}\overrightarrow{v}+\nabla ·\rho \left({\overrightarrow{v}}_r\overrightarrow{v}\right)+\rho \left[\overrightarrow{\omega }\times \left(\overrightarrow{v}-{\overrightarrow{v}}_t\right)\right]=-\nabla p+\nabla \cdot \stackrel{̿}{\tau }+\overrightarrow{F} \end{array}$$

(4)

In Equation (3), $\nabla ·\rho \left(\overrightarrow{v_r}\right)$ represent the net mass outflow from the element, also known as the convective flow term [17]. In Equation (4), $\rho {\displaystyle \frac{\partial }{\partial t}}\overrightarrow{v}$ is the local rate of change in velocity, $\nabla ·\rho \left({\overrightarrow{v}}_r\overrightarrow{v}\right)$ is the convective momentum term, $\left[\overrightarrow{\omega }\times \left(\overrightarrow{v}-{\overrightarrow{v}}_t\right)\right]$ accounts for centripetal acceleration and Coriolis effect, ∇p is the pressure gradient, $\nabla \cdot \stackrel{̿}{\tau }$ is the stress tensor, and $\overrightarrow{F}$ represents the external body forces [16].

Loading conditions for the aero-structural evaluation were established based on the International Electrotechnical Commission standards for Wind Energy Geneation IEC 61400-1 and IEC 61400-2 [18,19], which describe design requirements for small-scale wind turbines. The rated and maximum rotational speeds for the blade were 100 and 120 rpm, respectively [6]. As established in the literature, the overloading conditions were assumed to arise when wind speed reaches 21 m/s, corresponding to twice the design wind speed [8].

Table 5. Simulated load cases according to the IEC standard.

Case	Operating Conditions	Load
1	v = 10.5 m/s, 100 rpm	NLC (Normal load case)
2	v = 21 m/s. 100 rpm	ELC (Extreme load case) with rpm control
3	v = 21 m/s. 120 rpm	ELC (Extreme load case) without rpm control

presents the three loading conditions considered for blade evaluation. The first is related to the rated operation, while the remaining are associated with the overloading operation, one incorporating a rotational speed control and the other without such control. These conditions represent the fundamental scenarios for assessing the structural integrity and the response of the composite materials.

For the numerical CFD simulation, the inlet and outlet boundaries of the computational domain were designated as velocity-inlet and pressure-outlet conditions, respectively. The lateral boundaries were defined as a set of conformal radial periodic boundaries. The blade surfaces were modeled as stationary walls with a non-slip condition. Further details are included in

Table 6. Boundary conditions of the CFD model.

Zone	Boundary Condition
Inlet/Inlet top	Velocity inlet V = 10.5 m/s * Turbulence intensity = 5% * Turbulent viscosity ratio = 10
Outlet	Pressure outlet * Manometric pressure = 0 Pa
Periodic boundaries Wall blade	Periodicity to 120° Non-slip wall Stationary wall

* Boundary conditions such as turbulence intensity, turbulent viscosity ratio, and manometric pressure were established according to reports from previous studies [1,8,9].

.

The reference values (area, length, velocity, density, and viscosity) required by the software for the computation of drag and lift coefficients (Equations (5) and (6)), as well as the theoretical power and torque (Equation (7)), are reported in

Table 7. Reference values for the computation of aerodynamic coefficients.

Parameter	Value
Area	2.12 m2
Density	1.18 kg/m3
Characteristic length	0.3 m
Velocity	10.5 m/s
Viscosity	1.84 × 10−5 kg/ms
Reference zone	Fluid domain

.

$$\begin{array}{c}CD={\displaystyle \frac{FD}{\frac{1}{2}\rho V^{2}A}}\end{array}$$

(5)

$$\begin{array}{c}CL={\displaystyle \frac{FL}{\frac{1}{2}\rho V^{2}A}}\end{array}$$

(6)

$$\begin{array}{c}P=\omega ·T\end{array}$$

(7)

where $FL$ is the lift force, $FD$ is the drag force, $V$ is the fluid velocity, $A$ is the swept area, $\omega$ is the rotational speed, $T$ is the thrust, and $P$ is the power.

Assuming fluid incompressibility, a double-precision pressure-based solver was selected to address the pressure-velocity coupling. Furthermore, a second-order upwind scheme was selected for the spatial discretization to improve the accuracy of numerical results. On the other hand, the initial flow field was estimated using a standard initialization computed from the inlet boundary.

Table 8. Settings of the simulation solution methods.

Parameter	Definition
Simulation state	Steady
Solver	Pressure-based
Velocity formulation	Absolute
Turbulence model	k − ω (SST)
Solution method	Coupled
Spatial discretization	Second order
Initialization	Standard from inlet
Number of iterations	2000

summarizes the solution scheme parameters.

To achieve a weak convergence criterion, with a threshold for residual errors below 1 × 10⁻⁴, no more than 2000 iterations were required using a conservative pseudo-transient time scale. In addition, alternative sensitive variables, including the drag and lift forces, were monitored. The absence of relevant fluctuations in final iterations provided further verification of numerical solution convergence. Finally, continuity was validated by confirming that the mass imbalance between inflow and outflow of the domain did not exceed 0.1%, consistent with best practices documented in the literature [16].

2.1.4. Post-Proccesing

For quantitative post-processing of the results, the most relevant parameters exported included the lift and drag aerodynamic coefficients, pressure coefficients, torque, and power. For qualitative evaluations, contour plots were generated for the static pressure distribution around the blade (extrados and intrados) and the absolute velocity along transversal planes of the domain (upstream and downstream). In addition, streamline visualizations were produced to improve the understanding of physical interactions and their relationship with operational configurations.

2.2. Structural FEA Simulation

Advanced composite materials offer an effective solution for manufacturing wind turbine blades. These materials provide unique characteristics, including high resistance to deflection and elevated stress tolerance, which are essential for withstanding operational conditions ranging from normal to extreme wind scenarios, including severe gust events [20]. Moreover, weight reduction improves the overall performance while potentially reducing the project costs and, as a consequence, the levelized cost of energy.

The blade surface was used as the reference surface for stacking the composite material layers, replicating actual manufacturing processes. Figure 8 shows the blade geometry including the root and tip sections, the aerodynamic shells (extrados and intrados), and the shear web.

2.2.1. Mesh Generation

For finite element simulations, the blade surface was discretized employing a hybrid mesh that incorporates both quadrilateral and triangular elements. In this case, shell elements were used because they are suited for modeling thin-walled to moderately thick-walled structures, are compliant in bending, and give good deformation results while being computationally inexpensive [21]. A local refinement near the shear web was included to enable the prediction of stress concentrations (see Figure 9).

Table 9. Mesh characteristics for finite element analysis.

Element Size (mm)	Curvature Size Minimum (mm)	Minimum Element Quality	Average Element Quality	Maximum Element Quality	Number of Elements
10	0.1	0.096	0.837	1	134,980

presents the global mesh controls, element size, and minimum curvature, as well as the mesh quality (minimum, average, and maximum) and total element count.

2.2.2. Composite Materials Modeling

In order to generate the different lay-up configurations over the blade surface, the composite materials module embedded in ANSYS software was employed. Material properties were extracted from the ANSYS Workbench database for composite materials (see

Table 10. Composite materials properties.

Material Type	Property	Glass Fiber/Epoxy	Carbon Fiber/Epoxy
Orthotropic	Density (kg/m3)	2000	1540
	Young’s Modulus x direction (Pa)	4.5 × 1010	2.09 × 1011
	Young’s Modulus y direction (Pa)	1 × 1010	9.45 × 109
	Young’s Modulus z direction (Pa)	1 × 1010	9.45 × 109
	Poisson’s Ratio XY	0.3	0.27
	Poisson’s Ratio YZ	0.4	0.4
	Poisson’s Ratio XZ	0.3	0.27
	Shear Modulus XY (Pa)	5 × 109	5.5 × 109
	Shear Modulus YZ (Pa)	3.84 × 109	3.9 × 109
	Shear Modulus XZ (Pa)	5 × 109	5.5 × 109
Material Type	Property	SANFoam	Gelcoat
isotropic	Density (kg/m3)	81	1230
	Young’s Modulus (Pa)	6 × 107	3.44 × 109
	Poisson Ratio	0.3	0.3
	Shear Modulus (Pa)	2.3 × 107	1.32 × 109
	Bulk Modulus (Pa)	5 × 107	2.86 × 109

). It is important to note that gelcoat material properties were not available in this database and thus were retrieved from the published work of Wang et al. (2016) [5].

Orthotropic materials were integrated in the different structural elements of the blade, including the shear web, aerodynamic shells, and spar caps. These materials are characterized by directionally dependent mechanical properties along three orthogonal axes. In contrast, isotropic materials, such as the SANFoam core and gelcoat, used in the shear web and aerodynamic shells for structural reinforcement and surface finishing, respectively, present uniform mechanical properties invariant across directions.

The mechanical properties of these materials, along with factors such as stacking sequence, stacking location, fiber orientation angles, and the number of layers, are determinant in the structural performance of the turbine blade [10].

The reference orientation of fibers of 0°, from which additional fiber reinforcements are measured, was aligned with the longitudinal axis of the blade, consistent with conventions adopted in the literature [20,22,23]. The structural lay-up configuration was proposed based on the thickness and length relationships reported in previous research [5,6,11,24]. The length of layers was defined by the ratio between local and total length of the blade, expressed as l/lo. Based on this parameter, the root section occupies 0–12% of the blade length, the mid-section 12–90%, and the tip section 90–100%. Similarly, the width and thickness of the layers were described by the ratios c/co (local chord to aerofoil chord) and t/c (thickness to aerofoil chord), respectively. A description of the structural elements is included below.

• Aerodynamic shells: These structural elements are intended to resist torsional and shear forces while preserving the aerodynamic shape of the blade [20]. They are typically manufactured using a triaxial glass fiber-reinforced epoxy matrix composite with fiber orientations of −45°, 0°, and 45 °. In the present research, a five-layer configuration with variable lengths was adopted based on recommendations reported by Medrano (2022) [6].
• Spar caps: Typically manufactured from unidirectional fibers, spar caps are designed to withstand flapwise and edgewise bending loads induced by aerodynamic forces and weight [20]. The width of the spar caps was defined using the ratio c/co = 0.75–0.90 [24], assuming a maximum blade chord of 0.72 m. Therefore, the absolute width was estimated to be approximately 0.1 m. The structural composition of this element incorporated unidirectional layers of glass and carbon fiber-reinforced epoxy matrix composites. On the other hand, the thickness was selected as t/c = 1.5% based on relationships reported by Medrano (2022) [6].
• Shear web: The shear web constitutes the primary structural element of the blade and provides shear stress resistance due to fiberglass reinforcements [6]. The shear web length was defined as a function of the ratio l/lo =0.05–0.95. The structural configuration incorporated bidirectional glass fibers oriented at ±45° and embedded in an epoxy matrix. The thickness of the SANFoam core was set to be 1% of the maximum chord length [5].

For the blade manufacturing, sandwich-type panels were considered, wherein an intermediate lightweight core is positioned between two composite reinforcement layers. The core is usually constructed from a foam or a honeycomb structure, and its function is to improve structural stiffness [20]. As a final treatment, a unidirectional glass fiber layer was applied over the entire blade structure.

To anticipate the requirements of actual manufacturing processes, certain lengths and thicknesses were slightly modified to prevent material overlap. Figure 10 presents the final distribution of structural elements along with a thickness map across the blade. The thickness configurations, stacking sequences, stacking directions, lengths, and materials for each structural element are summarized in

Table 11. Lay-up of the composite materials for a wind turbine blade.

Composite Material	Structural Element	Fiber Direction	Length (l/lo)	Thickness by Layer
Triaxial glass fiber/epoxy	Aerodynamic shells	(−45°/0°/+45°)	−5 layers (l/lo = 37%, 45%, 52.5%, 100%)	0.72 mm
Triaxial glass fiber/epoxy	Root	(−45°/0°/+45°)	−l/lo = 0–5%	0.72 mm
BD glass fiber/epoxy	Shear web	(−45°/+45°)	Shear web length	0.65 mm
UD glass fiber/epoxy	Spar caps Aerodynamic shells	(0°)	Width = 0.75–0.95 c/co Length (l/lo = 0.95)	t/c = 1.5% 0.75 by layer
UD carbon fiber/epoxy	Spar caps	(0°)	Width = 0.75–0.95 c/co Length (l/lo = 0.95)	t/c = 1.5% 0.75 by layer
Gelcoat	Aerodynamic shells	(0°)	l/lo = 100%	0.1
SANFoam core	Shear web Aerodynamic shells	(0°)	l/lo = 90%	1% c 0–15% c 50–85%c

.

The total weight of the initial lay-up configuration was estimated at 61.91 kg, with triaxial glass fiber/epoxy constituting the largest proportion at 43.92 kg.

Table 12. Final weight of the composite materials applied to the blade.

Composite Material	Weight (kg)
Triaxial glass/epoxy	43.92
Bidirectional glass/epoxy	2.65
Unidirectional glass/epoxy	10.24
Unidirectional carbon/epoxy	3.29
SANFoam Core	1.19
Gelcoat	0.6
Total weight	61.91

presents the weight distribution of the remaining materials.

2.2.3. Boundary Conditions

For the structural simulation, a fixed support condition was set for the blade surface directly connected to the turbine hub. As a consequence, the six degrees of freedom were constrained. The primary loads acting on the blade surface were directly imported from the aerodynamic simulation, while gravitational and centrifugal forces were configured using the ANSYS Mechanical module. The gravitational forces induce tension, compression, bending, and torsion in the blade, while the centrifugal forces generate tension, bending, and torsion. In contrast, aerodynamic loads primarily contribute to bending and torsion [25].

2.2.4. Fluid–Structure Interaction

In the FSI model, finite element analysis (FEA) is used to evaluate the structural response of the blade based on aerodynamic loads derived from CFD simulations. For this research a one-way coupling method was employed; in this scheme the aerodynamic loads are exported from the CFD simulation and mapped to the structural model. Nevertheless, deformations are not fed back to the aerodynamic model. Given its relatively low computational cost, this strategy is recommended for preliminary evaluations [5]. Different researchers [5,8,15] have carried out one-way FSI analysis to obtain an accurate and concise initial estimate of the stresses and deformations in a warped structure under certain boundary conditions.

For this research, one-way FSI analysis was adopted to facilitate a parametric study. As a preliminary approximation, this strategy is considered enough to predict the most relevant deformations and stresses in the structural model of the blade. However, future work should consider the implementation of the two-way coupling, including the numerical validation using experimental data.

3. Results and Discussion

3.1. Aerodynamic Analysis

The main objective of the aerodynamic analysis was to estimate the aerodynamic loads on the blade surface. In addition, relevant parameters were quantified, including lift and drag coefficients, pressure coefficient, and power output. Figure 11 shows the velocity flow fields at different blade sections for three load cases. At the root section, the relatively high twist angles, which are derived from the BEMT implementation reported by Jacob et al. [11], induce a premature boundary layer detachment near the leading edge. The aerofoil-shaped cross-sections in this region are intentionally designed with a maximum thickness to withstand bending and axial stresses. Consequently, the structural stiffness and integrity are prioritized over the aerodynamic performance.

On the other hand, the streamlined mid-span transversal geometry delays the boundary layer detachment to the aerofoil trailing edge. However, a velocity stagnation effect was observed in both extreme load cases and, as a result, the static pressure increased on the intrados (see Figure 12). While the impact of lift generation is relatively low, due to the pressure–lift correlation, the local aerodynamic efficiency is reduced as a consequence of the increased drag and a deficient pressure recovery process. For the first case, the absolute velocities estimated were 27 m/s, 62 m/s, and 75 m/s for the root, mid-span, and tip sections, respectively. In the second case, these velocity magnitudes increased to 48.5 m/s, 70 m/s, and 100 m/s. Similarly, in the third case, velocities were 50 m/s, 76.8 m/s, and 123 m/s. The increased velocities in these last two cases are directly attributed to the increase in the free-stream velocity of the fluid and the rotational speed of the blade. Moreover, higher velocities are consistently reported for the tip section, as tangential velocity increases linearly with radius.

The estimated theoretical power output of the turbine at rated operating conditions was 6591 W, with a corresponding power coefficient of Cp ≈ 0.4830, as reported by CIATEQ [11].

Table 13. Aerodynamic force results.

Load Case	CD	Drag (N)	CL	Lift (N)	Torque (Nm)	Power (W)	Pressure Coefficient
1	0.70	96.86	2.49	343.88	209.85	2197.10	1.60
2	0.69	384.02	2.70	1489.33	808.22	8462.02	0.97
3	0.80	439.68	3.12	1721.50	1015.98	10,637.35	1.20

presents the aerodynamic forces’ results computed from the CFD simulations. Although higher power outputs are predicted for the two extreme load cases, these conditions are not representative as energetic performance metrics because they are defined only for structural validation under extreme wind events and lie outside normal operation.

3.2. Stress, Deformation, and Failure Criteria Analysis

A parametric study with 16 different lay-up configurations was conducted to evaluate the structural integrity of the blade. The maximum deformation, predominantly observed in the tip section, was designated as the response variable. For composite materials, Tsai-Wu and Puck (Global IRF) criteria were also estimated to assess the equivalent stresses and failure under extreme load conditions.

Table 14. Design of experiments of the lay-up configuration of the wind blade.

Design Point	Triaxial GF/Epoxy	UD GF and CF/Epoxy (Root)	UD GF and CF/Epoxy (Mid-Span)	BD GF/Epoxy (Shear Web)	Weight (kg)
Number of Layers
DP-1	2	4	3	4	61.91
DP-2	1	4	3	4	55.28
DP-3	1	4	3	5	55.94
DP-4	2	4	3	5	62.57
DP-5	1	6	3	4	55.73
DP-6	2	6	3	4	62.37
DP-7	1	6	3	5	56.40
DP-8	2	6	3	5	63.03
DP-9	1	4	4	4	57.50
DP-10	2	4	4	4	64.13
DP-11	1	4	4	5	58.16
DP-12	2	4	4	5	64.80
DP-13	1	6	4	4	57.95
DP-14	2	6	4	4	64.59
DP-15	1	6	4	5	58.62
DP-16	2	6	4	5	65.25

presents the design points, where input variables included the number of triaxial glass fiber/epoxy layers applied to the full-length aerodynamic shell surfaces (i.e., the first two layers), unidirectional glass and carbon fiber/epoxy layers in the spar caps at the root and mid-span sections, and bidirectional glass fiber/epoxy layers in the main shear web.

Two critical operational scenarios were considered for structural analysis. In scenario A, the blade was positioned parallel to the ground, where gravitational effects produced maximum deformations. In scenario B, the flapwise bending mode was replicated, wherein the aerodynamic forces deform the blade out of the rotational plane, potentially causing a blade-tower impact. The loads applied to the blade for both scenarios are presented in Figure 13.

For the initial configuration (i.e., DP-1), there was a reduction in the final weight compared to the reference weight reported by CIATEQ [6]. This reduction was associated with the blade geometry, lay-up thickness, material properties, and stacking length, particularly in the spar caps. The glass fiber/epoxy with triaxial weave contributed 70% of the total structural weight for this research (see Figure 14). According to research developed by Medrano (2022) [6], a 6 m blade with a similar stackup configuration, made of glass and carbon fibers, has a weight of approximately between 74 kg and 93.7 kg. The triaxial glass weight is above 50 kg, which represents 53% of the total weight (see Figure 14). Aerodynamic design affects the distribution and final weight of each composite material, since the stacking area is different, but the lay-up proposed in this work lies within the weight range reported [6].

The maximum deformation at the blade tip was also observed for the initial configuration, with values of 0.188 m and 0.155 m for scenarios A and B, respectively (see Figure 15). This structural response is a direct consequence of the reduced number of layers at the root section. The tip deformation was registered in all design points due to the fixed boundary condition at the root-hub interface. However, the maximum deformations were reported for scenario A. It is possible to conclude that gravitational and weight-induced effects must be regarded as critical parameters in structural design.

In contrast, the minimum deformations observed were 0.164 m in scenario A (DP-15) and 0.137 m in scenario B (DP-13). This reduction, compared to DP-1, is attributed primarily to the incorporation of six unidirectional glass-carbon fiber/epoxy layers at the root section in both design points. The total weights estimated were 61.93 kg for DP-1, 57.95 kg for DP-13, and 58.62 kg for DP-15. Appendix A provides further information about stresses and deformations for each design point and scenario.

For both scenarios, the Von Mises analysis reveals stress concentration zones at the root, spar caps, and the shear web-aerodynamic shells interface (Figure 16). A maximum equivalent stress of 35 MPa was identified in regions where a lay-up failure was subsequently observed. Regarding the Tsai-Wu and Puck criteria, for the three load cases (rated, extreme with rpm control, and extreme without rpm control), the DP-1 exhibited the greatest deformations, reaching 0.739 m in scenario A. The suction side of the blade was identified as the most critical zone, being susceptible to combined bending and axial stresses. The Tsai-Wu index was estimated at 0.59 for the entire structure, while the IRF index evaluated using both criteria (Tsai-Wu and Puck) was 0.66. Since values exceeding unity indicate material failure, and the global IRF for the nominal load case remained below this threshold, the blade demonstrated structural integrity to withstand aerodynamic loads.

As shown in Figure 17, the predominant failure mode is “pmA” in both the aerodynamic shells and the shear web, indicating a tensile matrix failure. For the first two layers of triaxial glass fiber/epoxy, the maximum IRF value of 0.26 is observed in the 0° fiber orientation of the first layer.

A more detailed analysis was conducted by including monitoring points on the aerodynamic shells and shear web. Figure 18a presents the critical zone for the aerodynamic shells, where the z-coordinate indicates the thickness of each material and the horizontal axis shows the inverse reserve factor (IRF) values. The triaxial glass fiber/epoxy layers and the core exhibit an IRF below 0.1, while the unidirectional glass fiber/epoxy layer presents a maximum value of 0.36. Despite these values, no risk of failure is associated with the layers. In addition, Figure 18b reveals a potential core failure “cf” on the SANFoam, with maximum IRF values exceeding 0.225 and reaching 0.3 in certain zones. The value for fiber reinforcements remained below 0.1. For the spar caps and the aerodynamic shells, IRF values proved to be greater than 0.3 in the carbon fiber/epoxy layers at the root zone. The maximum global IRF was approximately 0.6, then it can be concluded that the normal load does not directly affect or cause catastrophic failures in the structure, as the IRF value of 1 is not exceeded in any case.

Increasing the number of layers affects the structural performance of the wind blade, such as the mid, root, and shear web, as well as the aerodynamic shells. This can be seen through main effects analysis, which shows how mean deformation changes if the number of layers increases or decreases. For scenario A, Figure 19a shows that deformations are greater for triaxial glass fiber/epoxy when two layers are used. For reinforcements in the spar caps at the mid-span zone, deformation decreases when four fiber layers are employed. For the remaining variables, mean deformation changes are not relevant. These results indicate that the number of triaxial glass fiber/epoxy layers directly affects deformation.

For scenario B, as is shown in Figure 19b, the main effects changed. The number of unidirectional glass/epoxy and carbon/epoxy layers on the spar caps and the mid-span becomes the most relevant variable. Unlike scenario A, the triaxial weave did not exhibit the same tendency, but the remaining two variables followed similar patterns without being significant. These results align with monitoring points, which confirm that the initial two triaxial layers exert a predominant influence on aerodynamic shell response.

In the second load case, maximum deformation was also detected in DP-1 for both scenarios. The measured deformations were 0.592 m for scenario A and 0.559 m for scenario B. This increase in deformation is primarily attributed to aerodynamic loads, with stresses reaching 91.41 MPa and 82.62 MPa, respectively. In contrast, the most favorable configuration was observed at DP-15, with deformations of 0.524 m and 0.498 m reported for scenarios A and B, respectively. The Tsai-Wu failure criterion produced a value of 1.53, and the IRF for both criteria reached 1.63. Therefore, matrix tensile failures (pmA) and core failure (cf) modes are expected in specific layers and critical zones. Figure 20 illustrates the maximum stress distribution. The critical zones susceptible to failure are located on the suction side of the blade near the root section of the aerodynamic shells, as well as in the unidirectional glass fiber/epoxy of the main shear and spar caps.

Additional inverse reserve factor (IRF) plots were generated at critical structural locations. Figure 21a presents results for the aerodynamic shells, where IRF values in the five triaxial glass fiber/epoxy layers range from approximately 0.1 to 0.17. For the unidirectional glass fiber/epoxy layer associated with the sandwich structure, the IRF value reached 0.9. Figure 21b focuses on the spar caps at root sections. The IRF value approached 0.8 from the first glass fiber/epoxy layer and became predominant at the fourth carbon fiber/epoxy layer near the root section. These results demonstrate that layer count in this zone is critical for structural integrity.

The fiber orientation of 0° of the first triaxial layer exhibited a maximum IRF of 0.68 in certain zones. Figure 22 illustrates these results, as well as the failure criterion variation across the first two layers for each triaxial weave. The IRF value remains below 0.2 within the core, indicating minimal failure risk. However, near the blade leading edge and the spar caps, the IRF value increases to 0.63.

Figure 23 shows the IRF failure criterion distribution across the carbon fiber/epoxy layers for the spar caps in the root and mid-span zones. The maximum IRF arises at the fourth carbon fiber/epoxy layer, where maximum stresses concentrate. At the blade mid-span, the maximum criterion changed to the third layer with an IRF of 0.63, reflecting the reduced stress magnitudes compared to those observed in the root region.

The structural analysis of the shear web (Figure 24) reveals compressive failure in both the SANFoam core and the composite matrix, with IRF values exceeding 1. Figure 24a illustrates core failure, where the maximum IRF reaches 0.82 in the SANFoam core, while the bidirectional reinforcements maintain IRF values below 0.4. In contrast, the aerodynamic shell connection zones (Figure 24b) demonstrate critical failure conditions, with bidirectional reinforcement IRF values exceeding 1.5.

According to Figure 25, for the shear web, the structural integrity is compromised from the first to the last layer, with IRF values of 1.60 and 1.69, respectively. These values are presented for the aerodynamic shell connections and correspond to the maximum structural stress observed. For this zone, the number of layers employed represents a critical design parameter. As demonstrated in DP-15, incorporating five layers in both this zone and spar caps effectively reduces deformations, a correlation validated through main effects analysis.

Figure 26 presents the main effects for the second load case, corresponding to a wind speed of 21 m/s and a rotational speed of 100 rpm. Both scenarios exhibited similar patterns, with the most pronounced effects observed in the glass and carbon fiber layers at the blade mid-span. When four layers were included, the mean deformation ranged between 0.53 and 0.54 m for scenario A and between 0.50 and 0.51 m for scenario B, which is evidence of an inverse relationship between layer count and deformation. Compared to the first load case, variations in the number of layers within the aerodynamic shells produced a minimal effect on deformation, even though the IRF values exceeded those obtained under nominal operating conditions.

DP-1 in scenario A exhibited maximum deformation and stress for the third load case, with 0.739 m and 108.39 MPa, respectively. For scenario B, DP-2 achieved a maximum deformation of 0.708 m, a maximum stress of 106.44 MPa, and a total weight of 55.28 kg. Once again, DP-15 was identified as the most favorable configuration, with maximum deformations below 0.656 m for scenario A and 0.629 m for scenario B. These results demonstrate that increasing the number of layers in both root and mid-span spar caps reduces structural deformation.

Compared to the previous load cases, the Tsai-Wu criterion yielded a value of 1.81, while the global IRF reached a maximum of 2. These results indicate a “pmC” failure attributable to shear stress in the matrix layers. In addition, IRF values exceeded unity on both the pressure and suction sides, as illustrated in Figure 27. These results are attributed to the absence of rotational speed control.

Replicating the aforementioned approach, critical monitoring points were generated on the aerodynamic shells. The maximum inverse reserve factor (IRF) values were observed in the first two layers on the leading edge (Figure 28a). The triaxial weaves in the first layer exhibited IRF values up to 0.72, decreasing to 0.2 in the second layer and fluctuating between 0.1 and 0.2 in subsequent layers. Another critical point was the unidirectional glass fiber/epoxy layer integrated in the sandwich structure (Figure 28), where the maximum IRF reached 1.26, which indicates a potential failure. In contrast, the underlying triaxial fiber layers exhibited maximum IRF values of 0.2.

The distribution of IRF values and critical zones is introduced in Figure 29. The highest values were located at the intersection between the spar caps and the shear web on both suction and pressure sides near the root, as well as the leading edge. Given these critical stress concentrations, structural reinforcement in these areas is advisable.

In contrast to the first two load cases, Figure 30 revealed IRF values above the 0.8 threshold on the blade pressure side, predominantly in the spar caps. At the root, a maximum IRF of 0.92 was estimated in the fourth layer and a minimum of 0.4 in the first layer (see Figure 31). Similar to load case 2, the most critical condition for potential lay-up failure takes place in the carbon fiber layers at the mid-span spar cap. Increasing the number of layers could reduce the IRF, though at the expense of increased weight.

Figure 32a illustrates the core failure in the shear web with an inverse reserve factor of 0.98. In the fourth bidirectional glass fiber/epoxy layer, the maximum IRF values in the core remained below 0.5. However, as is shown in Figure 32b, the IRF values increased in the connection between the shear and the aerodynamic shells, with all layers exceeding 1.9 and reaching 2. Figure 33 confirms that maximum failure criterion values are concentrated at this connection zone.

Main effects analysis (Figure 34) suggests that triaxial fiber layer count (one or two) has no meaningful effect in either scenario A or scenario B. In a similar way, reinforcements applied to the shear web and spar caps in the root region showed no relevant differences. However, a notable effect emerges for the spar cap layers in the mid-span: three layers increase mean deformation in both scenarios, while four layers decrease it to a mean value of 0.67 m.

To conclude, it should be noted that design point 16 presents the highest mass at 65.25 kg. This configuration consists of 2 triaxial weave layers, 6 unidirectional glass and carbon fiber/epoxy layers in the root, reduced to 4 layers in the mid-span zone, and 5 bidirectional glass fiber-epoxy layers in the shear web. Nevertheless, this design does not exhibit the lowest deformation among the candidates. Therefore, the configuration is deemed oversized, which could represent a disadvantage in terms of the manufacturing costs.

To reinforce the obtained results, they were validated by analyzing the stresses and deformations generated on blades of a similar size. In [7] was developed a structural analysis of a 5 m turbine blade subjected to different loads according to IEC, using the ACP and structural modules of ANSYS, in which total deformation occurs at the tip of the blade. The maximum equivalent stresses in the structure are 78 MPa at the leading edge. Compared to the present work, these stresses occur in the second load case; on the other hand, for this case, the maximum deformation is almost 1 m. For this research, the maximum deformation is for the third load case with 0.739 m, close to the 1 m mentioned above. According to their results, stress concentration occurs on the suction side in the area near the root, which is consistent with the behavior obtained in the present analysis. In [6] was performed an analysis of the structural lay-up of a 6 m blade using FOCUS V6.3 Educational Software (Wind turbine Materials and Constructions, Wieringerwerf, Netherlands), analyzing different load cases. For the nominal operation, a deformation close to 0.1 m was observed related to the nominal load case [7]. Compared to the results obtained in this analysis, the maximum deformation is 0.188 m under nominal operation. The difference is due to the accuracy and scope of the methods and numerical models used in each analysis and the coupling between physics and the aerodynamic design.

4. Conclusions

In the present research, an aero-structural assessment of a blade was conducted by coupling CFD and FEA. The boundary conditions derived from the aerodynamic simulations were considered to evaluate the structural response of 16 different lay-up configurations, which varied in thickness, number of layers, and materials. The blade was analyzed under three different load cases. The most representative case is associated with rated conditions with a wind speed of 10.5 m/s and a rotational speed of 100 rpm. Assuming a wind speed of 21 m/s, two extreme load cases were also considered: one for a rotational speed of 100 rpm with braking control and another for 120 rpm without braking control.

• For rated conditions, the theoretical power output was 6591 W. While the power increases in both extreme load cases, results were excluded from the power factor estimation, as they represent critical conditions outside the turbine operating range.
• Regarding the CFD analysis, the adoption of poly-hexcore mesh topology combined with a moving reference frame formulation yielded substantial reductions in computational cost and time. For the finite element analysis, the ability to assess failure criteria for each layer facilitated a comprehensive layer-by-layer evaluation of the composite material response and the total lay-up configuration.
• Using blade tip deformation as the performance metric, configuration DP-15 exhibited the most favorable weight-to-performance ratio, with a total mass of 58.62 kg and maximum deformations ranging from 0.498 m to 0.656 m across all load cases and operational scenarios.
• For the first load case (rated conditions), a global inverse reserve (IRF) of 0.66 was estimated. Maximum values for individual layers reached 0.26 in the first triaxial glass fiber/epoxy layer oriented at 0° and 0.36 in the unidirectional glass fiber/epoxy layer incorporated in the sandwich structure. These results indicate that the proposed reinforcement satisfies the load requirements and provides a suitable basis for blade manufacturing and subsequent experimental validation through structural testing.
• For the second load case, the global IRF of 1.63 indicates structural failure in certain layers, particularly in the carbon fiber layers of the spar caps at the mid-span area (IRF = 0.8) and the shear web core (IRF = 0.82).
• For the third load case, critical IRF values approaching 2.0 were observed in both the shear web core and the bidirectional reinforcements, indicating that the structural integrity is compromised under these specific conditions.
• The main effects analysis for the first load case indicated that the number of triaxial weave layers implemented in the aerodynamic shells was the most relevant variable. Hence, using a single layer is enough to reduce deformation and weight. For load cases 2 and 3, the main effects were observed in the reinforcements at the mid-span spar caps, where four material layers were employed, thus producing the most significant effect.
• The initial lay-up configuration established through the FSI-CFD-FEM analysis scheme exhibits a 33.29% weight reduction compared to the configuration reported by Medrano (2022) [6]. Therefore, the proposed method represents a viable alternative for preliminary design and structural evaluation of blades, offering multi-parametric results, qualitative visualizations, and quantitative comparisons that exceed the capabilities of conventional approaches.

Future research should explore advanced methods for optimizing the blade geometry, wherein objective functions prioritize aerodynamic performance maximization while maintaining structural integrity through minimizing deformation in critical zones. From a structural perspective, fatigue and modal analyses should be incorporated to provide comprehensive assessments and mitigate failure risks from phenomena such as aerodynamic flutter. Although two-way fluid–structure interaction (FSI) analysis is recommended, computational cost and time constraints suggest that such approaches should be reserved just for final-stage prototypes. This research allows for a simplified analysis method, replicability, numerical validity, and mathematical support for aerodynamic and structural analysis under a lay-up scheme. Specific criteria and variables enable the identification of failure zones and types under certain operating conditions. This improves analysis time under low computational cost schemes, which is beneficial for the implementation and manufacturing of wind turbine blades today.