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SUMMARY:"JJCAB2#4 - Stochastic assessment of electric powertrain whining n
 oise under early-stage design uncertainties"
DTSTART;VALUE=DATE-TIME:20230710T082500Z
DTEND;VALUE=DATE-TIME:20230710T083000Z
DTSTAMP;VALUE=DATE-TIME:20260308T101519Z
UID:indico-contribution-764@events.isae-supaero.fr
DESCRIPTION:Speakers: Vinay PRAKASH ()\nDespite the advantage of being qui
 eter than traditional internal combustion engine vehicles\, electric vehic
 les are often distinguished by high-frequency tonal components\, which can
  be perceived as unpleasant to the occupants. To ensure optimal acoustic c
 omfort in electric vehicles\, it is important to analyze the NVH behavior 
 of e-powertrains during the early stages of the design process which poses
  inherent uncertainties\, such as varying operating conditions\, partial k
 nowledge of design parameters\, dispersion in measurement data\, etc. To e
 ffectively address these uncertainties\, it is necessary to use fast and c
 omprehensive stochastic models during the design phase.\n	\nIn this work\,
  a probabilistic framework is presented to estimate the electric powertrai
 n’s interior whining noises considering the structure-borne contribution
 . Hence\, two different stochastic metamodels are developed for efficient 
 quantification and propagation of uncertainties from electric motor stage 
 to powertrain mounting system. Multivariate Bayesian regression models hel
 p to incorporate prior knowledge on the uncertain parameters and generate 
 the respective posterior distributions using Markov chains Monte Carlo (MC
 MC) techniques. The data is generated through weakly-coupled multi-physica
 l domains estimated using semi-analytical approaches and combined with mea
 sured vehicle transfer functions. Importantly\, the validation of each dom
 ain is conducted separately to ensure accurate representation. The results
  obtained from the developed probabilistic framework will aid in the early
  design stages by guiding engineers in making informed decisions to optimi
 ze NVH performance.\n\nhttps://events.isae.fr/event/22/contributions/764/
LOCATION:MFJA
URL:https://events.isae.fr/event/22/contributions/764/
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