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Trinity College Dublin

Turboshaft Engine Exhaust Noise Identification

Research Field: 
Engineering
Resource Type: 
Compute
Lead PI: 
Gareth Bennett
Abstract: 
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).
Start Date: 
01/2010
Duration: 
12.00months

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