Definition and Application of a Target Cascading Process on a Fully Trimmed Body, from Vehicle Objectives to Component Objectives Hexagon

Format:

Book

Conference/Event

Author/Creator:

de Walque, Cyril, author.

Contributor:

Jeong, ChanHee

Kong, Taesik

Yoo, Ji Woo

Conference Name:

13th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference (2024-06-19 : Graz, Austria)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Finite element (FE) based simulations for fully trimmed bodies are a key tool in the automotive industry to predict and understand the Noise, Vibration and Harshness (NVH) behavior of a complete car. While structural and acoustic transfer functions are nowadays straightforward to obtain from such models, the comprehensive understanding of the intrinsic behavior of the complete car is more complex to achieve, in particular when it comes to the contribution of each sub-part to the global response. This paper proposes a complete target cascading process, which first assesses which sub-part of the car is the most contributing to the interior noise, then decomposes the total structure-borne acoustic transfer function into several intermediate transfer functions, allowing to better understand the effect of local design changes. This transfer functions decomposition opens the door to cascading full-vehicle objectives, which typically consists of achieving a maximal noise level in the cabin, to component-level objectives. This process is demonstrated on the floor panel of an industrial FE model for which both the structural and acoustic transfer functions have been extensively validated against measurements. Intermediate transfer functions are computed and compared for several alternative designs. The same process is finally applied on reduced models, which consider only the floor panels and acoustic trims. Those reduced models allow much faster design iterations and prove to be reliably predicting trends

Notes:

Vendor supplied data

Publisher Number:

2024-01-2916

Access Restriction:

Restricted for use by site license