Investigation of the geometric configuration of blades for a micro horizontal axis wind turbine for the semi-arid region of Pernambuco

Abstract

This study investigates how different chord and twist angle distributions: in ideal, linear, and elliptic forms, influence the performance of a micro horizontal wind turbine designed for low-intensity winds in the semi-arid region of Pernambuco. Using a code based on lifting line theory, validated with reference data, airfoils and geometric combinations were evaluated to maximize annual energy production. The results indicate that the GOE 222 airfoil and ideal distribution deliver the best performance, maintaining suitable angles of attack and high lift coefficients. The linear distribution showed intermediate performance, whereas the elliptic distribution was the least efficient because of increased drag. Additionally, twist was found to have greater influence than the chord on aerodynamic efficiency under weak wind conditions.

Keywords:
wind turbine; geometric configuration; annual energy production; blades

List of symbols and variables

. Cp Power coefficient αgeom (°) Geometric angle of attack Cpmax Maximum power coefficient αeff (°) Effective angle of attack L' (N/m) Lift force αaind (°) Induced angle of attack Γ⁡ (m2/s) Circulation reff (m) Effective vortex core radius p (kg/m3) Mass density Vtot (m/s) Total velocity V(m/s) Wind velocity Vund (m/s) Undisturbed velocity vector c (m) Chord β Relaxation factor Cl Lift coefficient P (W) Power Cd Drag coefficient AEP (kWh/year) Annual energy production r (m) Radius, distance λ Tip speed ratio Vind (m/s) Induced velocity A (m/s) Weibull scale factor Kv Correction factor k Weibull shape factor L Length of the vortex filament, lift force vector f probability of wind speed occurrence in a given interval n Vortex core model parameter hw (s/m) Weibull wind speed probability density function α (°) Angle of attack

.
Cp	Power coefficient		αgeom (°)	Geometric angle of attack
Cpmax	Maximum power coefficient		αeff (°)	Effective angle of attack
L' (N/m)	Lift force		αaind (°)	Induced angle of attack
Γ⁡ (m2/s)	Circulation		reff (m)	Effective vortex core radius
p (kg/m3)	Mass density		Vtot (m/s)	Total velocity
V(m/s)	Wind velocity		Vund (m/s)	Undisturbed velocity vector
c (m)	Chord		β	Relaxation factor
Cl	Lift coefficient		P (W)	Power
Cd	Drag coefficient		AEP (kWh/year)	Annual energy production
r (m)	Radius, distance		λ	Tip speed ratio
Vind (m/s)	Induced velocity		A (m/s)	Weibull scale factor
Kv	Correction factor		k	Weibull shape factor
L	Length of the vortex filament, lift force vector		f	probability of wind speed occurrence in a given interval
n	Vortex core model parameter		hw (s/m)	Weibull wind speed probability density function
α (°)	Angle of attack

Introduction

Renewable energy sources are emerging as a sustainable solution to energy challenges in Brazil and worldwide, with significant relevance to the agricultural sector. In addition to reducing dependence on fossil fuels, they promote job creation and provide environmental benefits. The integration of wind, solar, biomass, and geothermal energy makes agricultural practices more efficient and aligned with climate change mitigation (Kar et al., 2023; Rahman et al., 2022; Babu et al., 2021). Among these sources, wind energy stands out because of its rapid expansion and applicability to activities such as irrigation, greenhouse climate control, and food refrigeration, thus contributing to lower operational costs (Majeed et al., 2023; Nazir et al., 2020; Leskovar, 2023). In the semi-arid region of Pernambuco, particularly in Petrolina, a national reference in irrigated fruit production, there is strong potential for the implementation of wind-based solutions. According to Santos et al. (2019), the region’s wind conditions are favorable but underutilized in the agricultural context.

However, harnessing this natural resource is not straightforward, as achieving optimal performance requires blade adjustments to improve efficiency under specific wind conditions within a region. Consequently, a wide range of academic research has focused on enhancing the aerodynamic efficiency of wind turbine blades. In Umar et al. (2022), 1-m blades with NACA 4412 and SG6043 airfoils were optimized for low wind speeds using BEM in MATLAB and QBlade. With a tip speed ratio between 4 and 6, they achieved a Cp of up to 0.45 and power outputs ranging from 144 to 245 W, with cut-in speeds of 1.7–2.2 m/s, thus demonstrating the feasibility for domestic use in rural areas. Abbas et al. (2024) applied Blade Element Theory (BEM), QBlade, and CFD to optimize blades with SG6041 and NACA4711 airfoils at 6 m/s, thereby achieving a Cp of 0.521 and a maximum power output of 563.8 W for the NACA4711 airfoil.

Furthermore, among the studies conducted, the chord and twist angle distributions along the blade have been evaluated, with an emphasis on linear, elliptic, and theoretical ideal distributions. Syaukani et al. (2024) optimized horizontal axis wind turbine (HAWT) blades by linearizing chord and twist, thus increasing Cp from 39.71% to 46.43%, and reaching 500.35 W, with an actual average production of 294.19 W, demonstrating good efficiency and low manufacturing cost. Okita & Lourençoni (2025) applied linearization using lifting line theory (LLT) for turbines in rural packing houses, evaluating different airfoils with an emphasis on Joukowsky 6.3%. The ideal configuration, with a 0.40-m chord and 7° twist, reached 32.2 MWh/year, thus balancing performance and construction feasibility. Pradhan & Chitrakar (2022) designed and analyzed blades with distributions, such as elliptic and hyperbolic, using BEM and momentum theory. Simulations indicated higher aerodynamic efficiency and energy extraction, proving that ideal distributions optimize blade geometry and make the turbine more effective for real wind conditions. Akbari et al. (2022) performed a multi-objective optimization of blades for micro wind turbines by combining BEM and genetic algorithms, aiming to maximize annual energy production and reduce costs. The ideal distributions based on the Betz limit served as a reference, showing that nonlinear profiles offer more optimized balance between performance and construction feasibility.

Therefore, this study analyzed the impact of varying chord and twist angle distributions, in linear, elliptic, and ideal forms, on the performance of a micro HAWT operating under typical wind conditions in the semi-arid region of Pernambuco, Brazil. In this context, the main contribution of this research is the investigation of the combination of different geometric distributions, specifically applied to micro wind turbines designed for low-intensity wind conditions that are predominant in the semi-arid region of Pernambuco.

Material and Methods

Prandtl's classical lifting line theory

In the LLT, the blade is represented by a vortex filament known as a bound vortex, positioned at one-quarter of the chord from the leading edge (Abedi, 2013). According to Helmholtz’s theorem, a vortex filament cannot start or end abruptly within the flow domain, resulting in the formation of a horseshoe vortex (Sebastian, 2012). This system comprises a bound vortex and two trailing vortices that extend downstream to infinity, thus forming a closed loop with the starting vortex. This configuration is explained by Kelvin’s circulation theorem, which states that the circulation within a closed domain remains constant over time.

The Kutta–Joukowski theorem relates the lift generated by an airfoil to the circulation (r) of the flow, considering the changes in the velocity and pressure fields induced by this circulation. In [eq. (1)], L' denotes the lift force per unit span, pꝏis the free-stream air density, Vꝏ represents the free-stream velocity, C_l is the sectional lift coefficient, c is the local chord length of the airfoil, and r corresponds to the circulation associated with the flow around the airfoil.

$L^{\prime }=\frac{1}{2}{\rho }_{\infty }{V}_{\infty }^2{C}_{l}cdy={\rho }_{\infty }{V}_{\infty }\Gamma$ (1)

In this model, each rotor blade induces a continuous helical wake composed of trailing vortex filaments shed along the span. These free vortices generate induced velocities in the surrounding flow field, which are computed using the Biot–Savart law. In its vector form, the Biot–Savart formulation relates the velocity induced at an observation point to the circulation and geometry of a vortex filament, as shown in Figure 1. Considering a straight vortex segment bounded by two points, the induced velocity can be expressed as follows:

Figure 1
Geometric representation of the Biot-Savart law for a vortex filament.

$V_{ind}=K_v\frac{\Gamma }{4\pi }\frac{(r_1+r_2)(r_1\times r_2)}{r_1{r}_2+r_1\cdot r_2}$ (2)

where r₁ and r₂ represent the vectors between the observation point and the filament elements. The factor Kv is a viscous core correction introduced to account for the finite radius of the vortex core and to avoid the singular behavior of the Biot–Savart law near the filament centerline. This correction depends on the assumed vortex model and incorporates the effect of viscous diffusion within the core. Following the formulation presented by Abedi (2013), the vortex core model is defined through a parameter n, which controls the velocity distribution inside the core. Typical values of n represent different classical vortex models, such as the Kaufmann (n = 1), Lamb–Oseen (n = 2), and Rankine vortex (n → ꝏ) models.

$K_v=\left[\frac{{\left(|L|\left|r_1\right|\right)}^2-{(L\cdot r_1)}^2}{|L{|}^2}\right]{[r_{eff}^{2n}+{\left(\frac{{\left(|L|\left|r_1\right|\right)}^2-{(L\cdot r_1)}^2}{|L{|}^2}\right)}^n]}^{-1/n}$ (3)

Aerodynamic efficiency is determined by the effective angle of attack (α_eff), which is obtained from the difference between the geometric angle of attack (α_geom) and the induced angle (α_ind):

${\alpha }_{eff}={\alpha }_{geom}-{\alpha }_{ind}$ (4)

Based on this perspective, aerodynamic tables that relate the lift coefficient (C_l) and drag coefficient (C_d) to the angle of attack are consulted to update the circulation:

$\Gamma =\frac{1}{2}c{V}_{tot}{C}_l\left({\alpha }_{eff}\right)$ (5)

where $V_{\text{tot}}=V_{\text{und}}+V_{\text{ind}}$, where V_und represents the undisturbed (free-stream) velocity component at the blade element corresponding to the incoming flow without the influence of the rotor wake, and V_ind denotes the velocity induced by the bound circulation of the blade and the trailing vortex wake, thus accounting for the aerodynamic interaction effects within the flow field.

$\Gamma ={\Gamma }_{old}+\beta ({\Gamma }_{eff}-{\Gamma }_{old})$ (6)

The numerical process presented in Figure 2 proceeds as follows. The blade is divided into sections along the span, typically using cosine interpolation, with each section represented by a filament with a constant circulation. Subsequently, the induced velocity is calculated, the effective angle of attack is adjusted, and from the updated values of C_l and C_d, a new circulation is determined from the updated Cl and Cd values. This procedure is repeated until the difference between successive iterations is less than 1%, thereby ensuring convergence. Once convergence is achieved, the final aerodynamic forces on the blade are obtained.

Figure 2
Block diagram of the numerical process.

Annual Energy Production

The annual energy production (AEP) can be calculated by combining the wind turbine power curve with the probability density function (PDF) of the wind speed. From the PDF, the probability that the wind speed lies within a given interval (V_i ≺V₀ ≺ V_i+1) can be determined. By multiplying this probability by the total number of hours in a year, the expected number of operating hours within that wind speed interval is obtained. Subsequently, multiplying this value by the average power output (in kW) corresponding to the same interval yields the contribution to the annual energy production (in kWh) for that interval (Hansen, 2008).

The wind speed probability density function is commonly described using the Weibull distribution, which adequately represents wind regimes in many regions. Local site effects, such as terrain roughness, vegetation, and nearby obstacles, can be incorporated through the Weibull scale factor A and shape (or form) factor k (Hansen, 2008). The Weibull probability density function is expressed as follows:

$h_w\left(V_0\right)=\frac{k}{A}{\left(\frac{V_0}{A}\right)}^{k-1}\exp [-{\left(\frac{V_0}{A}\right)}^k]$ (7)

According to the Weibull distribution, the probability that the wind speed falls between V_i and V_i+1 is calculated by integrating the PDF over this interval, which yields

$f(V_i<V_0<V_{i+1})=\exp [-{\left(\frac{V_i}{A}\right)}^k]-\exp [-{\left(\frac{V_{i+1}}{A}\right)}^k]$ (8)

When the mean wind speed $\stackrel{`}{V}$ is known, the scale factor A can be estimated as a function of the shape factor k using the empirical relation:

$\frac{A}{\overline{V}}=4.534{(0.568+\frac{0.433}{k})}^{-1/k}$ (9)

Finally, the total annual energy production was obtained by summing the energy contributions of all wind speed intervals. The AEP is calculated as follows:

$\mathrm{AEP}=\sum _{i=1}^{N-1}\frac{1}{2}[P\left(V_{i+1}\right)+P\left(V_i\right)]f(V_i<V_0<V_{i+1})8760$ (10)

where P(V_i) and P(V_i+1) are the power outputs corresponding to wind speeds V_i and V_i+1, respectively, and 8760 represents the total number of hours in one year.

Nominal and initial parameters

In this study, a numerical code based on the LLT was developed and implemented in MATLAB to perform aerodynamic analyses of a micro HAWT, whose nominal conditions are presented in Table 1. The investigation focused on evaluating the performance associated with different chord and twist angle distributions along the blade, considering the wind conditions of the Pernambuco semi-arid region. The analyzed geometric distributions included elliptic, linear, and ideal profiles, thereby allowing for a comparison of their effects on the aerodynamic efficiency of the turbine.

Table 1
Nominal and initial conditions.

Conditions	Values
Nominal Power	400 W
Number of blades	3
Nominal aspect ratio	7
Tip radius	0.62 m
Root radius	0.06
Efficiency	0.9

Initially, different airfoils (Clark-Y, GOE 222, GOE 398, NACA 0012, NACA 4412, NACA 23015) will be evaluated using the ideal chord distribution and ideal twist distribution as the initial geometric configuration, as shown in Figure 3, to determine the most suitable profile for the wind conditions of the semi-arid region. Then, considering three main typologies: the ideal distribution, obtained from theoretical criteria of maximum aerodynamic efficiency; the linear distribution, where both the chord and twist decrease proportionally along the radius; and the elliptic distribution, which aims to approximate an elliptical spanwise lift distribution, a classical aerodynamic condition that minimizes induced losses according to lifting-line theory. This configuration promotes more uniform circulation along the blade, reducing variations in induction factors and improving the aerodynamic exploitation of the rotor disk. Eight geometric configurations will be formed through their combinations: ideal chord with linear twist, linear chord with ideal twist, linear chord with linear twist, ideal chord with elliptic twist, elliptic chord with ideal twist, elliptic chord with elliptic twist, linear chord with elliptic twist, and elliptic chord with linear twist, as illustrated in Figure 4.

Figure 3
Ideal chord and twist angle distribution along the radius.

Figure 4
Combinations of linear, elliptic, and ideal distributions along the blade for different geometric configurations.

Results and Discussion

Validation

The turbine analyzed by Hamadani et al. (2025) features three blades with a length of 2.5 m, designed to operate at a wind speed of 10 m/s. Figures 5 and 6 present, respectively, the chord and twist distributions along the blade and the lift and drag coefficients of the NACA 4412 airfoil as a function of the angle of attack, as adopted in the reference study, highlighting its suitability for low-wind-speed applications. Figure 7 shows the numerical validation of the model, with a maximum deviation of 11% in the power coefficient compared with the reference data. Close agreement in both magnitude and trend is observed for tip-speed ratios between approximately λ = 1 and 5. However, for higher values of λ, noticeable deviations arise owing to differences in aerodynamic modeling. At tip-speed ratios exceeding 5, the blades operate at lower effective angles of attack, which enhances the relative influence of induced drag and tip losses, making the prediction of aerodynamic losses increasingly sensitive to the adopted modeling approach. While Hamadani et al. (2025) report a well-defined maximum in the power coefficient (Cp) associated with finite-blade effects and aerodynamic losses, the present results exhibit a nearly constant Cp for λ ≳ 6. This behavior is consistent with the theoretical response of an ideal rotor with an infinite number of blades, where tip losses and three-dimensional effects are inherently minimized.

Figure 5
Chord and twist angle distributions along the radius of Hamadani’s turbine blade.

Figure 6
Lift and drag coefficient along the angle of attack for the NACA 4412 airfoil.

Figure 7
Comparison of the variation of the predicted Cp with the TSR with the results from Hamadani et al. (2025).

Airfoils

The comparative analysis of the airfoils, presented in Figure 8, highlights relevant aerodynamic performance trends under typical wind conditions in the Pernambuco semi-arid region, characterized by moderate speeds predominantly ranging from 3 to 7 m/s. The NACA 4412 airfoil shows a progressive increase in the power coefficient (Cp), reaching values close to 0.50 for wind speeds above 6.5 m/s, while maintaining operational stability. This behavior is associated with its higher camber and ability to sustain elevated lift coefficient (Cl) values without early stall, which is consistent with the results reported by Widiyanto et al. (2021) and Yossri et al. (2021) for microturbine applications.

Figure 8
Power coefficient as a function of wind speed for different airfoils.

The Clark Y profile shows a similar trend, but with slightly lower performance, which is related to its design being less optimized for low to moderate Reynolds regimes. The NACA 23015 and NACA 0012 airfoils, despite showing a gradual increase in Cp with increasing wind speed, exhibit limited performance below 4 m/s, reflecting their lower sensitivity to angle-of-attack variations in low kinetic energy flow conditions.

Conversely, the GOE 222 and GOE 398 profiles demonstrate stable behavior from 2 m/s, indicating good adaptation to weak wind conditions. Notably, GOE 222 maintains Cp ≈ 0.49, thereby showing strong potential for application in micro wind turbines installed in regions with low wind speeds.

Based on the annual energy production (AEP) presented in Table 2, although the NACA 4412 airfoil demonstrated excellent aerodynamic performance at moderate-to-high wind speeds, the GOE 222 airfoil achieved the highest AEP (158.41 kWh/year). This result is directly linked to the fact that GOE 222 maintained a high power coefficient starting from low wind speeds, making it more efficient in harnessing energy under low wind energy density conditions.

Table 2
Annual energy production for the airfoils.

Airfoils	AEP (kWh/year)
Clark Y	148.62
GOE 222	158.41
GOE 398	123.53
NACA 0012	79.20
NACA 4412	152.26
NACA 23015	114.92

Considering that the Pernambuco semi-arid region is characterized by wind speeds that are predominantly below 6 m/s, the ability of the GOE 222 airfoil to operate efficiently under low Reynolds number conditions explains its superior performance, even when compared with profiles such as NACA 4412, which only stand out at relatively high wind speeds. Therefore, the results highlight that the selection of the ideal airfoil for microgeneration must consider the compatibility between the airfoil characteristics and local wind regime; the GOE 222 was the most suitable airfoil for distributed applications in the Brazilian semi-arid region, as it achieved higher efficiency at low wind speeds.

Geometric configuration

The aerodynamic performance analysis of the GOE 222 airfoil, as illustrated in Figure 9, reveals that the efficiency of a wind turbine is strongly influenced by the interaction between blade geometry and the incoming flow. An examination of the performance curves indicates that the combination of an ideal chord with a linear twist outperforms the elliptical twist configuration. This behavior is fundamentally linked to the management of the effective angle of attack along the blade span: while linear twist allows each blade section to operate within a high-lift regime with an excellent lift-to-drag ratio, elliptical twist excessively reduces this angle, forcing the airfoil to operate outside its region of maximum aerodynamic efficiency and resulting in lower torque generation.

Figure 9
Power coefficient as a function of wind speed for the different geometric configurations.

From the perspective of chord distribution, the physical behavior is governed by the relationship between lift generation and vortex formation. Linear or ideal chord configurations preserve a higher local aspect ratio, which is essential for minimizing induced drag. This drag component stems from the downward deflection of the airflow (downwash); by maintaining an optimized chord distribution, the turbine reduces these energy losses. Conversely, an elliptical chord increases the projected area while reducing the effective aspect ratio, thereby intensifying induced drag and compromising overall efficiency.

Furthermore, the results demonstrate that blade twist exerts a more decisive influence on performance than chord variation. This occurs because the twist directly governs the circulation and aerodynamic load distribution, ensuring that the kinetic energy of the wind is converted to mechanical torque with minimal dissipation. As confirmed by the AEP values reported in Table 3, a refined twist adjustment is the primary mechanism for optimizing power extraction, ensuring that the airfoil operates within its ideal aerodynamic regime throughout the blade span.

Table 3
Annual energy production for the different geometric configurations.

Geometric configuration	AEP (kWh/year)
Ideal chord and optimal twist	158.41
Ideal chord and linear twist	123.36
Linear chord and ideal twist	140.78
Linear chord and linear twist	117.37
Ideal chord and elliptic twist	-19.22
Elliptic chord and ideal twist	18.31
Elliptic chord and elliptic twist	-123.99
Linear chord and elliptic twist	-52.49
Elliptic chord and linear twist	47.45

The analysis of the torque distribution in micro wind turbine rotors operating under low wind speed conditions (3 m/s) highlights the critical interdependence between the blade geometric synthesis and underlying aerodynamic flow phenomena, as illustrated in Figure 10. The reference configuration, characterized by ideal chord and twist distributions, establishes a superior aerodynamic equilibrium by optimizing the local velocity triangle at each radial section. Because the tangential velocity increases linearly with the radius, the relative wind direction becomes progressively more oblique toward the blade tip. The application of an ideal twist acts as an angular compensation mechanism, ensuring that the effective angle of attack remains within the range of maximum aerodynamic efficiency. This alignment is essential for maximizing circulation, as postulated by Prandtl’s lifting-line theory, resulting in a lift distribution that maximizes the tangential force component and, consequently, the driving torque.

Figure 10
Torque distribution along the radius for different geometric configurations.

However, even in optimized geometries, the efficiency is reduced near the blade extremities owing to three-dimensional effects, notably tip losses. Physically, the pressure gradient between the pressure and suction sides induces a transverse flow that culminates in the formation of tip vortices. These vortices generate a downward induced velocity (downwash), which deflects the relative wind vector and consequently tilts the lift force in the flow direction, thereby generating induced drag. This phenomenon dissipates kinetic energy that would otherwise be converted into mechanical work, thus explaining the torque reduction observed in the vicinity of the blade tip.

The replacement of the ideal twist with simplified distributions, such as linear or elliptical ones, disrupts the synchronization between the airfoil geometry and incoming flow. The lack of appropriate angular scaling forces large portions of the blade to operate at suboptimal angles of attack, as shown in Figure 11, thereby destabilizing the circulation distribution and reducing the local lift coefficient. Similarly, modifications to the chord distribution alter the blade aspect ratio and aerodynamic loading. When the ideal chord distribution is replaced by linear or elliptic profiles while preserving the ideal twist, the local aspect ratio is modified, and the projected blade area is increased in certain regions. Although a larger chord may locally enhance lift, it also intensifies the induced drag, particularly in small-scale rotors. In microturbines operating at low Reynolds numbers, where viscous effects are dominant, an excessive or poorly dimensioned chord fails to improve aerodynamic performance and exacerbates skin-friction drag and wake-related turbulence losses, thus ultimately reducing torque generation and the overall efficiency of energy extraction.

Figure 11
Effective angle of attack distribution along the radius for different geometric configurations.

The most severe degradation scenario occurs in the configuration that combines elliptical chord and twist distributions, where a collapse of aerodynamic efficiency is observed. Under these conditions, the excessive reduction in the effective angle of attack leads to marginal lift coefficients, as demonstrated in Figure 12. From a vectorial standpoint, the magnitude of the aerodynamic drag, which acts in the opposite direction of motion, exceeds the projection of the lift onto the plane of rotation. This imbalance results in a reversal of the resultant force vector and emergence of negative torque, a phenomenon where the blade behaves as a dissipative (braking) element, thereby preventing the extraction of useful energy from the flow and compromising the operational viability of the wind turbine under low-wind regimes.

Figure 12
Lift coefficient distribution along the radius for different geometric configurations.

Conclusions

This study analyzes the impact of optimal, linear, and elliptic chord and twist angle distributions on the performance of a micro wind turbine designed for the semi-arid region of Pernambuco. A code based on LLT was developed in MATLAB and validated using reference data to ensure reliability. The results indicate that the optimal distribution delivers the best performance by maintaining the effective angle of attack at high and uniform levels, promoting high lift coefficients and greater torque, which results in a higher AEP. The linear distribution showed lower performance owing to the reduction of the effective angle of attack and the decrease in the blade aspect ratio, which increased drag. The elliptic distribution presented the least optimal results, as it combined high chord and twist values, generating a larger projected area and intensifying drag losses. Moreover, the twist variation has a greater influence on the aerodynamic performance than the chord, particularly under low Reynolds conditions typical of weak wind regimes. Analyses of the geometric configurations presented in this study may serve as a basis for future investigations, including experimental studies aimed at collecting additional data on the performance of the micro wind turbine in the semi-arid region of Pernambuco.

Acknowledgments

The first author thanks the National Council for Scientific and Technological Development (CNPQ) and the Pernambuco State Science and Technology Support Foundation (FACEPE) for the postdoctoral scholarship.

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• Data Availability Statement:
  The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Edited by

• Area Editor:
  Juliana Lobo Paes

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Publication Dates

• Publication in this collection
  31 July 2026
• Date of issue
  2026

History

• Received
  21 Oct 2025
• Accepted
  24 Apr 2026