Master Assignment
Research aimed for 2 MSc students (1 modeling and 1 experimental)
With proper acoustic propagation models and analyses, we are now capable of extracting seemingly invisible acoustic signals and turn a microphone array into an acoustic camera capable of creating images of acoustic sources. As science continues pushing the boundaries of knowledge, sensors are reaching the limit of what can be measured using traditional direct (beamforming-based) approaches. Therefore, to extend the capability of conventional sensors, we must be creative and invent new strategies to recover information from indirect (inverse) measurements.
Acoustic imaging plays a critical role in advancing the fundamental understanding of the physics of aerodynamic noise sources. The technique provides spatial separation and quantification of individual acoustic sources, creating the background to the understanding of novel noise production mechanisms towards the design of innovative noise reduction solutions for technological applications of societal interest.
Through the combination of (novel) aeroacoustic processing and sensing strategies, it is possible to design acoustic imaging systems that exceed fundamental theoretical limitations of fundamental measurement methods. However, these non-traditional imaging systems generally come with a trade-off once researchers are left with increasingly sparse and/or noisy measurements that require incorporating additional structure to extract anything meaningful from a complex indirect measurement dataset.
Your MSc research will exploit the physics behind the acoustic data structure to uncover noise sources previously invisible.
The thesis will include:
- Literature review
- Modelling and Model Reduction (by Machine Learning)
- Parameter and State Identification
- Writing thesis
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