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Details of Grant 

EPSRC Reference: EP/P015980/1
Title: Understanding and developing new noise reduction mechanisms for aerofoils in unsteady flow through the use of analytical mathematics
Principal Investigator: Ayton, Dr L
Other Investigators:
Researcher Co-Investigators:
Project Partners:
University of Southampton Virginia Polytec Inst & State University
Department: Applied Maths and Theoretical Physics
Organisation: University of Cambridge
Scheme: EPSRC Fellowship
Starts: 10 July 2017 Ends: 09 July 2022 Value (£): 605,042
EPSRC Research Topic Classifications:
Continuum Mechanics Mathematical Analysis
EPSRC Industrial Sector Classifications:
Aerospace, Defence and Marine Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 Jan 2017 EPSRC Mathematical Sciences Fellowship Interviews January 2017 Announced
29 Nov 2016 EPSRC Mathematical Sciences Prioritisation Panel November 2016 Announced
Summary on Grant Application Form
This project centres on finding mathematical solutions to complicated fluid-structure interaction problems, bringing together a variety of advanced mathematical techniques to solve key problems in the aerospace industry, such as reducing aircraft noise. Excessive environmental noise from airports and wind farms is a key issue affecting both public health and the expansion of the aviation and sustainable energy industries.

A current expanding area of research in aeroacoustics is in leading-edge (at the front) and trailing-edge (at the rear) adaptations to standard blades on aeroplanes or wind turbines. It is believed that these adaptations can lead to significant decreases in generated noise and are largely inspired by nature, in particular the silent flight of owls. It is well known that owls are unique among birds in that they fly almost silently. This is believed to be possible due to a number of features their wings possess which are not present in other species. By creating new designs aimed at mimicking these owl-like features, this project shall attempt to significantly reduce noise generated by aircraft and wind turbine blades.

A primary source of noise generated by aeroengine blades is leading-edge noise, arising from the interaction of an unsteady fluid with the front of the solid body. Conversely, trailing-edge noise is a dominant contributor to airframe and wind turbine noise, arising due to the interaction of turbulence above the blades with the rear of the blade (the trailing edge).

Understanding and quantifying the mechanisms generating these different types of noise is vital to knowing what adaptations can be made to current designs in order to reduce the overall sound emitted by these systems. Potential noise reduction designs include; blades with serrated, or porous and flexible trailing edges, or fringed leading edges. Research into the effectiveness of these designs is typically experimental or numerical, which cannot delve deeper in to the results to tell you why such a design is effective, merely if it is or not. This project ultimately aims to provide the answer to why.

Mathematical solutions derived using analytic methods can be incredibly powerful as they preserve the vital physics of these fluid-structure interaction problems, but significantly reduce computational costs and can address cases in which numerical models struggle, such as generation of high-frequency noise. The speed of mathematical predictions can identify important areas that require further investigation, giving a head start to developing new noise-reducing or increased-performance technologies.

Through mathematical analysis, this project will give insight into the mechanisms by which these adapted leading- and trailing-edge designs reduce noise, and obtain relationships between key design features, such as the porosity of a trailing-edge adaptation or original blade geometry, and the total noise reduction, allowing for quick optimisation of the design without the need for large numbers of experiments or costly computation.
Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.cam.ac.uk