Vehicle Level Acoustic Sound Pack Sensitivity and Test Correlation by Utilizing Statistical Energy Analysis (SEA) Technique for Premium SUV 2015-26-0135
Due to increased awareness by customer perceived sound characteristics, advance simulation technique emerged in NVH domain for mid-high frequency like BEM, Hybrid and Statistical Energy Analysis (SEA).
One of the most widely and accepted practice in high frequency NVH is SEA technique to assess and optimize acoustic sound pack for Air Borne Noise (ABN) in the range of 400 Hz to 6300 Hz typically for Powertrain and Tyre Patch Noise Reduction.
As Prof. Lyon states that “The most obvious disadvantage of statistical approaches is that they give statistical answers, which are always subject to some uncertainty” . It is always challenge for SEA engineer to get correlation for full vehicle level model for Tyre Patch Noise Reduction (TPNR) and Powertrain Acoustic Transfer Function (PT ATF) to acceptable level. Appropriate correlated SEA model is developed and few challenges associated with SEA modeling are also discussed in this paper.
Present study demonstrates utilization of SEA technique on premium segment SUV correlation at vehicle level and further study and correlate critical sound pack systems performance. NVH Test group invest large amount of time to investigate each sound pack by DOE study. SEA tool was simultaneously utilized to identify sensitivity of critical sub systems and also achieved good agreement between SEA and actual vehicle level test results. Optimization can be performed on correlated model to achieve optimum NVH performance with respect to cost requirement.
Citation: Mistry, K., Badhe, N., and Fisher, S., "Vehicle Level Acoustic Sound Pack Sensitivity and Test Correlation by Utilizing Statistical Energy Analysis (SEA) Technique for Premium SUV," SAE Technical Paper 2015-26-0135, 2015, https://doi.org/10.4271/2015-26-0135. Download Citation
Kalpesh Mistry, Nitin Badhe, Steve Fisher
Tata Technologies Ltd., Jaguar Land Rover Ltd.
Symposium on International Automotive Technology 2015