AI Morphs Wind Turbine Blades Into Shapes No Engineer Would Guess

Vertical-axis wind turbines have long been the quiet underachievers of the wind energy world. They spin no matter which way the wind blows, they install easily on rooftops and in tight urban spaces, and they hum at noise levels that would never bother a neighbor. Yet while their horizontal-axis cousins convert 50 to 55 percent of the wind’s energy into electricity, vertical-axis machines typically manage only 30 to 40 percent. That stubborn efficiency gap has kept them on the sidelines of the renewable energy boom, and a new study in Results in Engineering argues the reason is not physics but imagination: engineers have simply been designing these turbines with the wrong mental models.

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