Preprints
https://doi.org/10.5194/wes-2024-4
https://doi.org/10.5194/wes-2024-4
22 Feb 2024
 | 22 Feb 2024
Status: this preprint is currently under review for the journal WES.

On optimizing the sensor spacing for pressure measurements on wind turbine airfoils

Erik Fritz, Christopher Kelley, and Kenneth Brown

Abstract. This research article presents a robust approach to optimizing the layout of pressure sensors around an airfoil. A genetic algorithm and a sequential quadratic programming algorithm are employed to derive a sensor layout best suited to represent the expected pressure distribution and, thus, the lift force.

The fact that both optimization routines converge to almost identical sensor layouts suggests that an optimum exists and is reached. By comparing against a cosine-spaced sensor layout, it is demonstrated that the underlying pressure distribution can be captured more accurately with the presented layout optimization approach. Conversely, a 39–55 % reduction in the number of sensors compared to cosine spacing is achievable without loss in lift prediction accuracy. Given these benefits, an optimized sensor layout improves the data quality, reduces unnecessary equipment and saves cost in experimental setups.

While the optimization routine is demonstrated based on the generic example of the IEA 15 MW reference wind turbine, it is suitable for a wide range of applications requiring pressure measurements around airfoils.

Erik Fritz, Christopher Kelley, and Kenneth Brown

Status: open (until 11 May 2024)

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Erik Fritz, Christopher Kelley, and Kenneth Brown

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Supporting data belonging to the publication On optimizing the sensor spacing for pressure measurements on wind turbine airfoils Erik Fritz, Christopher Kelley, and Kenneth Brown https://doi.org/10.4121/99662eaf-ac79-4952-ad80-6d7de3708427

Erik Fritz, Christopher Kelley, and Kenneth Brown

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Short summary
This study investigates the benefits of optimizing the spacing of pressure sensors for measurement campaigns on wind turbine blades/airfoils. It is demonstrated that local aerodynamic properties can be estimated considerably more accurately when the sensor layout is optimized compared to commonly used simpler sensor layouts. This has the potential to reduce the number of sensors without losing measurement accuracy and, thus, reduce the instrumentation complexity and experiment cost.
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