Researchers at the University of Stavanger fabricated six versions of the S810 airfoil, a profile from the NREL S-series family designed specifically for horizontal-axis wind turbines. One preserved the original clean contour, while the other five carried controlled material removal centered on the stagnation region, with nominal erosion depths of 0.1, 0.2, 0.5, 0.8, and 1.2 millimeters. These staged geometries were defined in a CAD environment and mapped onto the airfoil profiles by surface projection, guided by the erosion pattern classification of Gaudern and conceptually aligned with the morphological categories of IEA Wind Task 46, which progress from pinholes and shallow pits to connected patches, coating loss, and eventually extensive material removal with laminate exposure.

All six sections were produced by high-resolution fused-filament fabrication with PLA filament at a layer height of 0.1 millimeters, giving each model a chord of 180 millimeters and a span of 410 millimeters. Because every specimen, including the clean baseline, shared the same printing process and layer texture, printer-induced surface features formed a common baseline, and all aerodynamic degradation was evaluated relative to this as-printed reference state. The authors stress that the geometries represent nominal CAD-defined erosion states rather than metrology-validated scans of field-eroded blades.

Testing took place in the low-speed open-circuit wind tunnel at the University of Stavanger, driven by a 22 kW fan and capable of freestream velocities up to 43 meters per second. With honeycomb flow conditioning and multiple mesh screens, turbulence intensity stayed below 0.5 percent. Each blade section was mounted vertically on a rotary table and measured with a six-axis force and torque transducer, with signals acquired at 1 kilohertz and low-pass filtered. The freestream velocity was fixed at 40 meters per second, corresponding to a Reynolds number of approximately 5.3 times ten to the fifth, and the angle of attack was swept from 0 to 10 degrees in steps of 2.5 degrees, covering the pre-stall regime where aerodynamic efficiency peaks for the S810 profile.

The measurements revealed a clear and systematic degradation with increasing erosion severity. The clean airfoil reached a maximum lift coefficient of about 0.92 near 10 degrees angle of attack, while the most severely eroded configuration achieved only roughly 76 percent of that peak. Lift saturation also shifted toward lower angles as erosion deepened, indicating earlier flow separation induced by the roughened leading edge. Drag rose monotonically across all eroded configurations, and the lift-to-drag ratio, the key measure of aerodynamic efficiency, declined steadily from the clean baseline to the extreme case. Comparisons with primary NREL benchmark data for the S810 showed very good agreement in lift-curve slope, confirming that the setup reliably reproduced the airfoil’s fundamental behavior.

To scale these airfoil-level findings to a full rotor, the team propagated the measured lift and drag polars through a Blade Element Momentum Theory model built on the open-source pyBEMT framework, using a representative multi-megawatt turbine consistent with the NREL 5-MW reference turbine, with a 126-meter rotor diameter and three blades discretized into 17 radial elements. For each erosion scenario, the inner 11 stations retained the clean polar while the outer six stations, spanning roughly 0.707 to 0.978 of the rotor radius, received the erosion-specific polar, reflecting the fact that erosion damage concentrates in the outer blade region where local velocities and torque contribution are highest. The maximum power coefficient fell from approximately 0.382 for the reference case to 0.377, 0.372, 0.364, 0.356, and 0.348 for the five eroded levels, representing reductions of 1.3, 2.7, 4.7, 6.8, and 9.0 percent respectively.

The physical mechanism behind these losses is coherent. Leading-edge roughness increases boundary-layer instability and accelerates the transition from smooth laminar flow to turbulence, raising skin friction drag. Geometric disruption of the contour alters the stagnation region and pressure recovery, while earlier separation reduces maximum lift and increases drag. Because these deficits concentrate in the outer blade sections, where local flow speeds are greatest, even shallow leading-edge deterioration produces noticeable rotor-scale penalties. Under below-rated operation, where aerodynamic efficiency governs energy capture, a first-order approximation links relative changes in annual energy production directly to relative changes in the power coefficient, meaning a 9 percent reduction in peak power coefficient implies roughly 9 percent less energy capture unless mitigated through control adaptation or repair.

The study then demonstrated how these aerodynamic penalties could be propagated through a realistic site wind distribution using an illustrative SCADA-based case study on the openly available Kelmarsh wind farm dataset from the UK. The researchers derived a clean reference power curve from a year of 10-minute operational records of a Senvion MM92 turbine, applying careful filtering to screen curtailment and non-representative operating points, and then imposed the modelled power-coefficient reductions as a first-order derating. The scenario-based estimates produced energy deficits of 63.5, 132.0, 229.7, 332.3, and 439.9 megawatt-hours over the analysed period for the mild through extreme erosion cases. The authors emphasize that no direct blade-inspection evidence of erosion was available for the analysed turbine, so these figures are hypothetical performance-propagation illustrations rather than measured field losses.

Beyond the immediate energy numbers, the findings carry significant implications for maintenance planning. Leading-edge erosion is a major driver of operational expenditure through increased inspection frequency, unplanned downtime, and recurrent blade repair or recoating campaigns, yet maintenance strategies still predominantly rely on fixed intervals rather than quantified relationships between erosion severity and energy-yield impact. The study introduces an aerodynamic health index, defined as one minus the relative power-coefficient reduction, as a compact ranking metric for fleet prioritisation, and maps erosion levels to indicative maintenance responses: mild and moderate erosion primarily motivate enhanced monitoring, while advanced, severe, and extreme erosion produce losses large enough to materially affect turbine-level energy capture, making repair prioritisation or corrective intervention increasingly justified.

The authors are careful to frame their contribution as a traceable integration of established methods rather than a new erosion model or validated maintenance decision model. Important limitations remain: the experiments covered a single airfoil profile at a single Reynolds number representative of laboratory-scale testing, no blockage or wall-interference corrections were applied to the reported coefficients, and the transfer of S810 erosion polars to the reference rotor geometry is a model-specific simplification. The power-coefficient penalties should therefore be viewed as relative sensitivity bounds for an outer-span erosion scenario rather than field-validated losses. Future work should validate the erosion-performance relationships with long-term SCADA data from operating turbines, multi-Reynolds-number experimental campaigns, and inspection-informed spanwise damage maps, potentially integrating drone- or lidar-based erosion inspection records with operational performance trends to build site-specific degradation trajectories for erosion-aware maintenance planning.

Subject of Research: Experimental evaluation of progressive leading-edge erosion as an aerodynamic failure mechanism affecting wind turbine performance and maintenance planning

Article Title: Experimental evaluation of progressive leading-edge erosion as an aerodynamic failure mechanism with implications for wind turbine maintenance planning

Article References: Ali, W., Teigen Giljarhus, K. E., Delimitis, A., & El-Thalji, I. (2026). Experimental evaluation of progressive leading-edge erosion as an aerodynamic failure mechanism with implications for wind turbine maintenance planning. Results in Engineering, 32, Article 113411. https://doi.org/10.1016/j.rineng.2026.113411

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113411

Keywords: wind energy, leading-edge erosion, wind turbine blades, aerodynamics, wind tunnel testing, blade element momentum theory, SCADA, maintenance planning, power coefficient, annual energy production, Experimental, evaluation

The problem, according to Hamza Latif Mehr, Imran Akhtar, and Haris Moazam Sheikh, is decades of inherited intuition. Drag-based Savonius rotors and lift-based Darrieus turbines were shaped by engineering judgment built on horizontal-axis research, but vertical-axis turbines live in a fundamentally different aerodynamic world. Their blades sweep through the wind, stall, and recover in a continuously rotating flow, so the shapes that work best do not necessarily resemble anything a designer would sketch by hand. The team’s answer was to remove human intuition from the loop almost entirely, letting an algorithm morph blade shapes into configurations no conventional parameterization would ever produce.

The centerpiece of the framework is a technique called Design-by-Morphing, or DbM. Instead of manipulating control points on a blade surface the way traditional shape parameterization methods do, DbM treats entire geometries as points in a continuous space. Any two shapes bounded by a single continuous surface are topologically compatible, meaning they can be mapped onto one another through a set of collocation points. New blades are then generated by morphing among a set of baseline shapes with smooth weight factors, allowing both interpolation between existing designs and, crucially, extrapolation beyond them through negative weights that produce geometries unlike any of the originals.

For this study, the researchers chose three conventional vertical-axis turbine profiles as their morphing baselines: the lift-based Darrieus NACA 0021 airfoil, a hybrid cusped Gottingen 395 profile, and the drag-based Savonius bucket shape. By varying two spherical weight factors across a discretized range, the framework generated 169 distinct blade morphologies, many of them hybrids that combine drag and lift characteristics in proportions no human designer would have proposed. When blade tilt, inter-blade spacing, vertical placement, tip-speed ratio, and rotation direction were added, the complete design space swelled to roughly 5.8 million unique configurations.

Extrapolation comes with a catch. Negative morphing weights occasionally produce self-intersecting blade profiles or blades that overlap one another in physically impossible ways. Rather than discarding these candidates and shrinking the design space, the team built automated geometric correction algorithms that detect intersections, reorder coordinate points, and smooth sharp curvature until a valid continuous contour emerges. The corrections proved remarkably gentle: among the corrected geometries that produced positive power, the enclosed area changed by less than one percent on average, and none of the three highest-performing designs required any correction at all.

Evaluating each candidate required an unsteady computational fluid dynamics simulation, which is expensive enough that brute-force search across millions of designs would be hopeless. The optimizer therefore needed to be stingy with its simulations, and the researchers turned to Bayesian optimization through a framework called MixMOBO. The algorithm builds a Gaussian-process surrogate model of the design space, balancing exploration of uncertain regions against exploitation of promising ones, and adaptively selects from a portfolio of acquisition strategies including Expected Improvement, Probability of Improvement, GP Upper Confidence Bound, and Thompson Sampling. Before unleashing it on the turbines, the team validated MixMOBO on five standard benchmark functions, confirming it reliably approached the global optimum within the allotted budget.

The optimization campaign began with 50 randomly sampled designs evaluated by CFD, followed by 180 sequentially proposed candidates, for a total of 230 simulations. The best configuration emerged remarkably early, after just 13 optimizer-guided evaluations, landing at the 63rd simulation of the entire campaign. Its blade geometry, defined by morphing angles of 120 and 179 degrees with zero horizontal offset and full vertical placement, reached a power coefficient of 0.3285 at a tip-speed ratio of 1.17. That represents a 21.26 percent improvement over the maximum power coefficient of the reconstructed Savonius benchmark and a 77.8 percent improvement over the Darrieus benchmark, within the same two-dimensional numerical framework.

The winning design is a strange creature: an extrapolated DbM geometry that resembles a high-solidity drag-based shape with contours borrowed from lift-based profiles. Its azimuthal torque rises smoothly from a minimum near the start of rotation to a sharp peak of 10.01 at about 115 degrees of azimuth before declining with stall, and the curve mirrors itself across the second half of the revolution. Fast Fourier analysis of the torque signal revealed a dominant spectral peak at 4.44 hertz, almost exactly matching the expected two-blade passing frequency of 4.46 hertz, indicating that the principal torque fluctuation comes from clean, periodic blade loading rather than chaotic aerodynamic instability. Vorticity contours showed a coherent vortex forming along the blade, detaching, and convecting downstream in a stable periodic street.

Sensitivity analyses added further texture to the result. Conventional wisdom suggests that introducing an overlap gap between Savonius buckets can improve efficiency by directing flow through the inter-bucket passage, but the optimizer’s best geometry performed best with zero spacing, achieving 29.4 percent higher maximum power than a spacing ratio of 0.09 and 38.8 percent higher than 0.18. Pressure contours revealed why: the gap creates a localized pressure depression that equalizes the pressure difference across the torque-producing blade surfaces, bleeding away useful aerodynamic torque. A Reynolds number sweep showed the design is tuned for moderate Reynolds numbers around 4 times 10 to the fifth, performing 15.83 percent better there than at 1.3 times 10 to the sixth, where lift-based designs generally dominate.

The authors are careful about what the numbers mean. Because the entire campaign ran in two-dimensional unsteady Reynolds-averaged Navier-Stokes simulations, the reported 21.26 percent gain is a relative prediction within that common numerical framework, not an experimentally demonstrated performance improvement. Three-dimensional effects such as tip losses and spanwise flow remain uncaptured, and the best-found geometry has not yet been validated experimentally or in 3D CFD. The optimization also targeted a single objective, cycle-averaged power coefficient, leaving starting torque, structural loads, and noise for future multi-objective work. Still, the framework itself is the real prize: a scalable, sample-efficient pipeline that couples morphing-based geometry generation with Bayesian optimization, ready to be pointed at any expensive aerodynamic design problem where human intuition has run out of ideas.

Subject of Research: A data-driven framework combining Design-by-Morphing and Bayesian optimization to discover high-performance vertical-axis wind turbine blade shapes beyond conventional designs.

Article Title: A data-driven design framework for wind turbines via Design-by-Morphing and Bayesian optimization

Article References: Mehr, H. L., Akhtar, I., & Sheikh, H. M. (2026). A data-driven design framework for wind turbines via Design-by-Morphing and Bayesian optimization. Results in Engineering, 32, Article 113463. https://doi.org/10.1016/j.rineng.2026.113463

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113463

Keywords: vertical-axis wind turbines, Design-by-Morphing, Bayesian optimization, computational fluid dynamics, blade shape optimization, Savonius turbine, Darrieus turbine, power coefficient, aerodynamic design, renewable energy, data-driven, design

The research, published in the journal Results in Engineering, was carried out by Maziyar Moghtadaeifar, Ramin Farzadi, and Majid Bazargan, who built their design around a radical idea that has been gathering momentum in wind engineering: the J-shaped airfoil. A conventional blade cross-section, such as the standard NACA0021 profile used here as a baseline, is a closed teardrop shape. A J-shaped airfoil takes that profile and simply removes part of the pressure side, the face of the blade that faces away from the turbine’s rotation axis, leaving a hollow, scoop-like section bounded by a wall just one millimeter thick. The opening begins at the point of maximum thickness and runs all the way to the trailing edge, transforming the blade into something between a wing and a curved trough.

That hollow matters. When wind sweeps across a J-shaped blade, air circulates inside the concave cavity, and the resulting changes in pressure distribution alter how lift and drag act on the rotor. Earlier experimental and numerical work had shown that such profiles can improve self-starting, because the open cavity behaves somewhat like a drag device at low speeds, but often at a cost to peak efficiency. The innovation in the new study is to combine this profile with a helical configuration: three blades, each twisted sixty degrees around the rotation axis, mounted on a rotor with a radius of 0.99 meters and a height of 1.15 meters. Helical blades smooth the torque output because some section of some blade is always optimally positioned relative to the wind, reducing the violent fluctuations that plague straight-bladed designs and making the rotor less dependent on the angle at which it happens to have stopped.

To test the concept, the team ran three-dimensional unsteady Reynolds-averaged Navier-Stokes simulations using the shear-stress transport k-omega turbulence model, a combination that previous validation studies have identified as among the most accurate for capturing the complex, separated flows around vertical-axis rotors, including the transient phenomenon of dynamic stall. The computational domain stretched thirty-two rotor radii downstream and twelve radii in the other directions, with a blockage ratio of just 1.61 percent, and a sliding-mesh cylinder carried the spinning blades. The researchers verified their setup against published experimental data for two different variable-pitch turbines, then ran careful mesh and time-step independence studies, refining the grid until the cycle-averaged torque coefficient changed by less than one percent and settling on a time step of half a degree of rotation per step.

The first headline result concerns how the J-shaped profile itself performs. Across low and medium tip speed ratios, the ratio of blade speed to wind speed that governs rotor behavior, the J-shaped helical turbine outperformed a geometrically identical machine with conventional NACA0021 blades. At a tip speed ratio of 1.11, for example, the J-shaped rotor produced a torque coefficient 26.59 percent higher than the full-profile machine, with the advantage concentrated in the upwind passage between azimuth angles of thirty and one hundred twenty degrees, where dynamic stall dominates. The cavity appears to reshape the internal flow structure in a way that enlarges the pressure difference between the two sides of the blade, and the first torque peak shifts to a later azimuth angle and grows taller. Averaged across all the operating conditions examined, the profile swap alone was worth roughly a ten percent improvement in torque.

But the J-shape carried a hidden liability that emerged at high speed. At a tip speed ratio of 1.77, the open-tipped J-shaped turbine’s performance collapsed, falling more than fifty percent below the conventional rotor. The culprit was the blade tip. Because the J-profile is hollow, vortices do not merely shed from the tip the way they do on a solid wing; they can leak out through the open cavity itself, spilling high-energy flow and destroying the pressure difference the blade depends on. Tip vortices, the swirling structures that form wherever lift-generating surfaces end abruptly, are a familiar efficiency tax on all rotors, but the open J-section makes the leakage far more damaging precisely when the turbine is spinning fast enough to generate its best power.

The team’s fix was disarmingly simple: cap the tips. They added thin NACA0021-shaped plates, one millimeter thick, to both ends of each blade, sealing the hollow section like lids on a tube. The effect was enormous. At the problematic tip speed ratio of 1.77, the caps boosted the torque coefficient by roughly 122 percent compared with the uncapped J-shaped turbine, lifting it about eight percent above even the conventional full-profile machine. Averaged over all five tip speed ratios studied, from 0.44 to 1.77, the caps raised the J-shaped turbine’s torque coefficient by about 46 percent, and the capped J-shaped design beat the conventional helical turbine by about 48 percent overall. In power-coefficient terms, obtained by multiplying torque by tip speed ratio, the capped machine dominated across the operating range.

By slicing each blade into twenty sections along its height and tracking the torque of each, the researchers traced exactly where the gains came from. At a mid-range tip speed ratio of 1.33, the upwind half of the rotor improved by just over twenty percent with caps installed, while the downwind half gained a more modest eight percent, with the largest benefits near the tips themselves. Flow visualizations revealed the mechanism: without caps, the vortex forming inside the blade’s concave cavity escapes outward over the open edge; with caps in place, the vortex is redirected toward the trailing edge, and pressure builds inside the cavity, strengthening the force that drives rotation. The caps essentially convert a leaking bucket into a sealed one, letting the J-profile’s pressure advantage accumulate instead of draining away through the blade ends.

Perhaps most important for the urban applications that motivate this whole line of research, the capped design starts better. The team held the rotor stationary in a seven-meter-per-second wind and computed the starting torque at azimuth angles from zero to 105 degrees in fifteen-degree increments. The capped J-shaped turbine beat the conventional helical rotor at every single angle, with improvements ranging from about four percent to a striking 46.6 percent at ninety degrees, and an average gain of roughly 21.5 percent across all positions. For a technology whose central weakness has always been the dead zone where lift-based rotors cannot overcome their own drag, a design that simultaneously raises efficiency, smooths torque, and strengthens self-starting is a meaningful step forward. The authors caution that they fixed the cut ratio and cap thickness rather than optimizing them, so further gains may remain on the table. But the message of the study is clear: sometimes the path to better wind energy is not a bigger machine or a fancier material, but a hollow blade with its ends sealed shut, turning the city’s messy gusts into cleaner, steadier power.

Subject of Research: Aerodynamic performance and self-starting behavior of a helical J-shaped vertical-axis wind turbine with capped blade tips

Article Title: Aerodynamic performance and self-starting characteristics of a novel helical J-shaped vertical-axis wind turbine with blade-tip caps

Article References: Moghtadaeifar, M., Farzadi, R., & Bazargan, M. (2026). Aerodynamic performance and self-starting characteristics of a novel helical J-shaped vertical-axis wind turbine with blade-tip caps. Results in Engineering, 32, Article 113308. https://doi.org/10.1016/j.rineng.2026.113308

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113308

Keywords: vertical-axis wind turbine, J-shaped airfoil, helical blades, blade-tip caps, tip vortices, self-starting torque, CFD simulation, URANS, SST k-omega, dynamic stall, urban wind energy, power coefficient