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Exhaust Note Tuning and Correlation Using 1-D Analysis of a Performance Truck
Technical Paper
2021-01-1045
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
Exhaust noise and note are key factors when deciding which high-performance vehicle to buy. Cadence and tone of the exhaust can determine whether a customer is willing to make a purchase. To make sure the exhaust note is desirable, manufacturers must make several prototypes with each subjected to extensive testing, tuning, and customer surveying. This process can be expensive and time consuming. However, even after such processes, the final design may not be well received by consumers. Utilizing computer aided engineering (CAE), specifically computational fluid dynamics (CFD) can provide a financially viable and technically sound solution to this issue.
Recent developments in CAE software have allowed for capturing the effects of aeroacoustics in exhaust systems and reproducing the acoustic signatures as well as a physical exhaust note. Using GT-POWER, Roush Industries created a 1-D model of a pickup truck powertrain with an after-market exhaust and analyzed the noise quality under various scenarios e.g. idle, ramp-up, at speed, and wide-open-throttle. GEM3D was used to discretize a 3D model of the exhaust into a 1-D model to capture spatial acoustic effects. The analyses were correlated to physical testing in an acoustic chamber using Campbell acoustic diagrams relating frequency to engine speed and sound pressure level. Digital microphones were placed in the 1-D analysis to match the physical setup and to record audio files for a customer jury evaluation. Correlation was resolved between the analyses and the chamber tests using audio post processing, jury testing, and Campbell diagram comparisons. This exercise created a well-founded process for exhaust and powertrain acoustic tuning for Roush Industries.
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Kumar, V. and Schigelone, M., "Exhaust Note Tuning and Correlation Using 1-D Analysis of a Performance Truck," SAE Technical Paper 2021-01-1045, 2021, https://doi.org/10.4271/2021-01-1045.Data Sets - Support Documents
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References
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