Experimental assessement of in-duct modal content of fan broadband noise via iterative Bayesian inverse approach
Résumé
Next generation of ultra-high-bypass-ratio (UHBR) engines are associated with an increase in fan diameter, a reduction of the exhaust jet speed and shorter intake and exhaust ducts. A direct impact is that the fan module is expected to be the major noise source of commercial aircrafts. It is estimated that the broadband part of fan noise contribution fluctuates about 80-90% in approach conditions and about 40% at take-off. Within this context, an experimental campaign to characterize fan broadband noise has been conducted in the framework of the European project TurboNoiseBB. The experiment has been carried out at the UFFA fan test rig operated by AneCom AeroTest. Different configurations of fan/outlet guide vanes (OGV)-spacing at different working lines have been tested. The radiated noise has been measured by in-duct microphone arrays located at the intake, interstage and downstream of the fan module. A main goal was to assess the modal content of fan broadband noise and further estimate the associated sound power levels. This is done in the present work via an iterative Bayesian Inverse Approach (iBIA). One outcome of this approach is an algorithm allowing to control the sparsity degree of the modal content. Thus, different sparsity levels may be tailored to specific noise components (e.g. tonal and broadband contributions) based on a priori knowledge. Other advantages are that no assumption regarding inter-modal correlation is necessary and no parameter has to be tuned manually by the end-user.