Predicting Fluid Driven Whistles in Automotive Intake and Exhaust Systems Escenda Engineering AB

Format:

Conference/Event

Author/Creator:

Karlsson, Karlsson, author.

Contributor:

Abom, Mats

Knutsson, Magnus

Conference Name:

9th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference (2016-06-22 : Graz, Austria)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2016

Summary:

AbstractThis work explores how fluid driven whistles in complex automotive intake and exhaust systems can be predicted using computationally affordable tools. Whistles associated with unsteady shear layers (created over for example side branches or perforates in resonators) are studied using vortex sound theory; vorticity in the shear layer interacts with the acoustic field while being convected across the orifice. If the travel time of a hydrodynamic disturbance over the orifice reasonably matches a multiple of the acoustic period of an acoustic feedback system, energy is transferred from the flow field to the acoustic field resulting in a whistle. The actual amplitude of the whistle is set by non-linear saturation phenomena and cannot be predicted here, but the frequency and relative strength can be found. For this not only the mean flow and acoustic fields needs to be characterized separately, but also the interaction of the two. The flow field is studied using steady state CFD simulations while the acoustic field is calculated with a standard linear acoustics solver. Already from this information one could derive design charts. But to pin point the whistle the interaction of the two fields is needed. This can be achieved numerically or experimentally. In this case available experimental data from other geometries have been rescaled for use in the present case. Finally, linear stability theory is applied to the complete system. Using the proposed method the whistling frequency of a complex intake prototype system was predicted very accurately

Notes:

Vendor supplied data

Publisher Number:

2016-01-1820

Access Restriction:

Restricted for use by site license