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Turboshaft Engine Exhaust Noise Identification

Research Field
Resource Type

I am a first year PhD research student in the department of Mechanical & Manufacturing
Engineering, working in the field of Acoustics under the supervision and guidance of Dr.
Gareth Bennett. My research is being conducted in conjunction with TEENI (Turboshaft
Engine Exhaust Noise Identification), a 7th framework EU program aiming to identify noise
sources in turboshaft engines for applications in helicopter engine design. The funding for
my PhD research is provided by this. My research so far has involved working with DLR
(German Aerospace Agency), one of the consortium partners, as they are running tests on a
small-scale experimental rig which simulates the acoustic stages of a turboshaft engine. My
work will involve analyzing the data from these tests and applying novel noise source
identification techniques based on advanced coherence function based numerical
techniques and modal decomposition.
The Need for High Performance Computing
The data from these tests has been acquired over 256 channels. Up to 64 channels of
microphone data needs to be loaded into Matlab for modal decomposition to be applied,
and each channel consists of 1200000 data points of 32-bit floating integers. As such the
memory requirements are considerable. Furthermore, the coherence function based
techniques used require these channels to be modally decomposed for each averaging
block, of which there are 146, and running such a code would take a standard desktop PC
around a day to run (estimate based on initial tests with a simplified version of the actual
code used).