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In recent years, a great emphasis has been put on engineering the acousticsignature of vehicles that represents the overall comfort level for passengers.Due to highly uncertain behavior of production cars, probabilistic metamodelsor surrogates can be useful to estimate the NVH dispersion and assess differentNVH risks. These metamodels follow physical behaviors and shall aid as a designspace exploration tool during the early stage design process to support the NVHoptimization. The measurement databases constitute different noise paths suchas aerodynamic noise (wind-tunnel test), tire-pavement interaction noise(rolling noise), and noise due to electric motors (whining noise). Thisresearch work proposes a global NVH metamodeling technique for broadband noisessuch as aerodynamic and rolling noises exploiting the Bayesian framework thattakes into account the prior (domain-expert) knowledge about complex physicalmechanisms. Generalized additive models (GAMs) with polynomials and Gaussianbasis functions are used to model the dependency of sound pressure level (SPL)on predictor variables. Moreover, parametric bootstrap algorithm based ondata-generating mechanism using the point estimates is used to estimate thedispersion in unknown parameters. Probabilistic modelling is carried out usingan open-source library PyMC3 that utilizes No-U-Turn sampler (NUTS) and thedeveloped models are validated using Cross-Validation technique.

Bayesian NVH metamodels to assess interior cabin noise using measurement databases