Experimental GT-POWER Correlation Techniques and Best Practices Low Frequency Acoustic Modeling of the Exhaust System of a Naturally Aspirated Engine

2017-01-1793

06/05/2017

Event
Noise and Vibration Conference and Exhibition
Authors Abstract
Content
As regulations become increasingly stringent and customer expectations of vehicle refinement increase, the accurate control and prediction of exhaust system airborne acoustics are a critical factor in creating a vehicle that wins in the marketplace.
The goal of this project was to improve the predicative accuracy of the GT-power engine and exhaust model and to update internal best practices for modeling. This paper will explore the details of an exhaust focused correlation project that was performed on a naturally aspirated spark ignition eight-cylinder engine.
This paper and SAE paper “Experimental GT-POWER Correlation Techniques and Best Practices Low Frequency Acoustic Modeling of the Intake System of a Turbocharged Engine” share similar abstracts and introductions; however, they were split for readability and to keep the focus on a single a single subsystem.
This paper compares 1D GT-Power exhaust external sound predictions with chassis dyno experimental measurements during a fixed gear, full-load speed sweep. The exhaust system includes an X-pipe and modeling with use of GEM 3D. Predictions were compared with measurements, in terms of overall sound and relevant orders, both with and without (replaced by straight pipes) mufflers.
The primary takeaway from the project is the importance of correctly modeling the geometry in detail utilizing GEM3D and capturing the temperature gradient.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-1793
Pages
6
Citation
Seldon, W., Shoeb, A., Schimmel, D., and Cromas, J., "Experimental GT-POWER Correlation Techniques and Best Practices Low Frequency Acoustic Modeling of the Exhaust System of a Naturally Aspirated Engine," SAE Technical Paper 2017-01-1793, 2017, https://doi.org/10.4271/2017-01-1793.
Additional Details
Publisher
Published
Jun 5, 2017
Product Code
2017-01-1793
Content Type
Technical Paper
Language
English