Two VAWT blade profiles enhance efficiency by 200% and reduce damaging vibrations by 77%, optimizing turbine performance.
Although horizontal-axis wind turbines (HAWTs) have become the primary choice in today’s wind energy landscape, vertical-axis wind turbines (VAWTs) boast a rich historical legacy, dating back to the eighth century in the Middle East for grain milling purposes.
VAWTs, rotating perpendicular to the wind, offer several advantages over their horizontal counterparts. They harness higher wind energy density, operate with reduced noise due to slower rotation, and occupy a smaller spatial footprint while delivering comparable output, both onshore and offshore.
A revolutionary advancement in VAWT technology has led to the development of a motionless wind turbine that boasts a remarkable 50% increase in efficiency compared to conventional turbines.
Furthermore, the lateral movement of VAWT blades proves to be more wildlife-friendly, allowing birds to navigate around them more effectively. Despite these advantages, VAWTs remain relatively uncommon in today’s wind energy market.
The main challenge hindering their widespread adoption is an engineering one – airflow control. Researchers at the School of Engineering Unsteady Flow Diagnostics Lab (UNFOLD) at EPFL have embarked on a mission to address this issue. Their innovative approach combines sensor technology and machine learning to optimize airflow within VAWT designs.
The team proposes two pitch profiles for VAWT blades, resulting in a remarkable 200% increase in turbine efficiency and a significant 77% decrease in vibrations that could potentially damage the structure.
Details of the team’s groundbreaking research have been published in the prestigious journal Nature Communications.
Tackling a critical limitation
Despite the numerous benefits, VAWTs suffer from a notable disadvantage: they perform optimally in environments with consistent, moderate airflow. Due to their vertical axis of rotation, the orientation of the blades in relation to the wind is constantly changing.
Dynamic stall, a phenomenon where powerful gusts alter the angle between the airflow and the blade, results in the formation of vortices. The blades struggle to withstand the sudden structural loads generated by these vortices.
To address this challenge, researchers installed sensors on an actuating blade shaft to assess the air forces acting upon it and mitigate the lack of resistance to gusts. By adjusting the angle, speed, and amplitude of the blade’s movement, they created an array of “pitch profiles.”
Subsequently, they employed a genetic algorithm on a computer, conducting over 3500 trial repetitions. This algorithm, mimicking an evolutionary process, identified the most robust and efficient pitch profiles and amalgamated these characteristics to generate new and enhanced “offspring.”
Sébastien Le Fouest, a researcher at the School of UNFOLD involved in the project, commented, “Our study represents, to the best of our knowledge, the first experimental application of a genetic learning algorithm to determine the best pitch for a VAWT blade.”
Enhancing VAWT efficiency
The innovative approach adopted by the team effectively transformed VAWTs’ primary flaw into a strength, revealing two pitch profile series that significantly enhance turbine durability and efficiency. These profiles enhance efficiency by 200% and reduce damaging vibrations by 77%, thereby optimizing turbine performance.
According to researchers, dynamic stall, the same mechanism that poses a threat to wind turbines, can actually propel the blade forward on a smaller scale. By adjusting the blade pitch forward to generate power, the team leveraged dynamic stalls effectively.
“Most wind turbines direct the force generated by the blades upwards, which hinders rotation. By altering that angle, we not only reduce the size of the vortex – we also push it away at precisely the right moment, resulting in a secondary region of power production downwind,” explained Le Fouest.
In a bid to create a proof-of-concept VAWT, the team secured a BRIDGE grant from the Swiss National Science Foundation (SNSF). Their aim is to deploy it outdoors to evaluate its performance under real-world conditions.
“We envision that this airflow control technique could propel efficient and reliable VAWT technology towards commercial viability,” added Le Fouest.
Abstract
Vertical-axis wind turbines present promising opportunities for wind power extraction in urban and offshore settings. However, concerns regarding turbine efficiency and structural integrity have impeded their widespread adoption. Flow control offers a potential solution to these challenges. In this study, we experimentally demonstrate the efficacy of individual blade pitching as a control strategy and elucidate the flow dynamics responsible for performance enhancement. Through automated experiments utilizing a scaled-down turbine model coupled with a genetic algorithm optimizer, we identify optimal pitching kinematics under both on- and off-design operating conditions. We derive two sets of optimal pitch profiles that achieve a three-fold increase in power coefficient at both operating conditions compared to non-actuated turbines and a 77% reduction in load fluctuations threatening the turbine’s structural integrity at off-design conditions. Based on flow field measurements, we elucidate how blade pitching manipulates flow structures to enhance performance. Our findings can contribute to the advancement of vertical-axis wind turbine technology, augmenting its role in meeting our energy demands.
