Mechanical and Civil Engineering Seminar
Near-Wake Dynamics of Cross-Flow Turbine Arrays
Mechanical and Civil Engineering Seminar Series
Title: Near-Wake Dynamics of Cross-Flow Turbine Arrays
Cross-flow turbines (i.e. vertical axis turbines), are devices that can be used to convert the kinetic energy in wind to electricity. A key advantage of cross-flow turbines over axial-flow turbines is that they can efficiently operate in close-proximity in arrays. This talk will explore the high-dimensional space cross-flow turbine arrays occupy. The performance of a two-turbine array in a recirculating water channel was experimentally optimized across a variety of array configurations using a hardware-in-the-loop approach. For each configuration, turbine performance was optimized using tip-speed ratio control, where the rotation rate for each turbine is optimized individually, and using coordinated control, where the turbines are optimized to operate at synchronous rotation rates but with a phase difference. For each configuration and control strategy, the consequences of co- and counter-rotation were also evaluated. Arrays with well-considered geometries and control strategies are found to outperform isolated turbines by up to 30%. Additionally, the performance and wake of a two-turbine array in a fence configuration (side-by-side) are characterized. The turbines are operated under coordinated control. Measurements were made with turbines co-rotating, counter-rotating with the blades advancing upstream at the array midline, and counter-rotating with the blades retreating downstream at the array midline. From the performance and wake data, we found that individual turbine and array efficiency depend significantly on rotation direction and phase difference. Persistent dynamics that exist across all flow fields, as well as differences between cases are identified.
Isabel Scherl is a postdoctoral scholar in the Computational and Data-Driven Fluid Dynamics group with Tim Colonius at Caltech. She recently completed her PhD at the University of Washington advised by Steve Brunton and Brian Polagye in mechanical engineering. Her graduate research augmented experimental fluid mechanics with machine learning. Specifically, she focused on data-driven modeling, control, and optimization of cross-flow (i.e. vertical axis in wind) turbine arrays. Before UW, she graduated with a Bachelor of Science with honors in mechanical engineering from Brown University. Her continued focus is in applying data-driven methods to pressing challenges in fluid dynamics.
NOTE: At this time, in-person Mechanical and Civil Engineering Lectures are open to all Caltech students/staff/faculty/visitors.
Contact: Stacie Takase at (626) 395-3389 Stakase@caltech.edu
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