Blade tweak boosts vertical-axis wind turbine efficiency by 200% — Study (msn.com)
Story by Jijo Malayil
• 15h
Blade tweak boosts vertical-axis wind turbine efficiency by 200% — Study© Provided by Interesting Engineering
While horizontal-axis wind turbines (HAWTs) dominate the modern wind energy landscape, vertical-axis wind turbines (VAWTs) have a rich historical origin. Their origins date back to the eighth century in the Middle East for grain milling.
VAWTs, spinning perpendicular to the wind, offer advantages such as higher wind energy density. Also, it features quieter operation due to slower rotation and a smaller spatial footprint for equivalent output, both onshore and offshore.
Moreover, their lateral blade movement is more wildlife-friendly, allowing birds to avoid them more easily. Despite these benefits, VAWTs remain rare in today’s wind energy market.
The issue came down to an engineering problem – airflow control, which researchers at the School of Engineering Unsteady Flow Diagnostics Lab (UNFOLD) at EPFL promise to solve. Their approach involves a combination of sensor technology and machine learning to optimize the airflow in VAWT designs.
The team suggests two pitch profiles for VAWT blades that result in a 200 percent boost in turbine efficiency and a 77 percent decrease in vibrations that could damage the structure.
The details of the team’s research were published in the journal Nature Communications.
Addressing a crucial shortcoming
VAWTs have a significant disadvantage despite the multiple benefits mentioned: they work best in environments with moderate, constant airflow. Because of their vertical axis of rotation, the blades’ orientation in relation to the wind is always changing.
Dynamic stall is a phenomenon where a powerful gust increases the angle between the airflow and the blade, creating a vortex. The blades are unable to sustain the momentary structural loads created by these vortices.
The researchers installed sensors on an actuating blade shaft to assess the air forces operating on it and address this lack of resistance to gusts. By varying the angle, speed, and amplitude of the blade’s back-and-forth motion, they produced an array of “pitch profiles.”
After that, they employed a genetic algorithm on a computer, and it completed more than 3500 trial repetitions. The algorithm, akin to an evolutionary process, identified the most robust and efficient pitch profiles and merged these characteristics to produce new and enhanced “offspring.”
“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,” said Sébastien Le Fouest, a researcher in the School of UNFOLD, involved in the project, in a statement.
Transforming VAWT efficiency
The approach employed by the team allowed them to turn the main flaw in VAWTs into a strength and uncover two pitch profile series that considerably increase turbine durability and efficiency. The profiles enhance efficiency by 200 percent and reduce damaging vibrations by 77 percent, optimizing turbine performance.
According to researchers, on a lesser scale, dynamic stall—the same mechanism that destroys wind turbines—can actually advance the blade. Here, by pushing the blade pitch forward to generate power, the team could truly make use of dynamic stalls.
“Most wind turbines angle the force generated by the blades upwards, which does not help the rotation. Changing that angle not only forms a smaller vortex – it simultaneously pushes it away at precisely the right time, which results in a second region of power production downwind,” said Le Fouest.
To develop a proof-of-concept VAWT, the team has been awarded a BRIDGE grant by the Swiss National Science Foundation (SNSF). The intention is to place it outside in order to test how it reacts to actual conditions in real-time.
“We hope this airflow control method can bring efficient and reliable VAWT technology to maturity so that it can finally be made commercially available,” said Le Fouest.
Abstract
Vertical-axis wind turbines are great candidates to enable wind power extraction in urban and off-shore applications. Currently, concerns around turbine efficiency and structural integrity limit their industrial deployment. Flow control can mitigate these concerns. Here, we experimentally demonstrate the potential of individual blade pitching as a control strategy and explain the flow physics that yields the performance enhancement. We perform automated experiments using a scaled-down turbine model coupled to a genetic algorithm optimiser to identify optimal pitching kinematics at on- and off-design operating conditions. We obtain two sets of optimal pitch profiles that achieve a three-fold increase in power coefficient at both operating conditions compared to the non-actuated turbine and a 77% reduction in structure-threatening load fluctuations at off-design conditions. Based on flow field measurements, we uncover how blade pitching manipulates the flow structures to enhance performance. Our results can aid vertical-axis wind turbines increase their much-needed contribution to our energy needs. |
