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Robust Tire Model and Parameter Identification for NVH Applications

Domenico Minervini

Robust Tire Model and Parameter Identification for NVH Applications.

Rel. Stefano Alberto Malan. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2019


The Noise, Vibration and Harshness (NVH) profile of a vehicle is a key metric by which customers rate vehicle performance. Several sources contribute towards this profile, including wind, drivetrain, rolling, ancillary systems, etc. With the advent of the electrification era rolling noise is set to become a dominant source of noise for electric vehicles, due to the phasing out of the internal combustion engine. Interaction between the road and tire contact patch results in excitation of tire natural frequencies. The resulting vibrations are transferred through the wheel centre to the knuckle and subsequently through the vehicle to the cabin. Automotive OEMs rely on experimental and computational tools which can aid in the vibro-acoustic design of their vehicles in order to meet customer demands for quieter vehicles and adhere to traffic noise emission regulations. Siemens Industry Software currently offers a combined test and model-based solution for tire NVH for the assessment of structure-borne noise. A tire CAE model has been developed for this purpose; however, some limitations are present. These include limited model applicability for higher frequency ranges, and model and parameter robustness. Research efforts are therefore needed to assess alternative approaches for model calibration and implement necessary model improvements. The goal of the thesis project will be to develop an automated optimization process which accounts for the full tire dynamic behavior, including wave propagation, and apply this for a tire over an extended frequency range. Effectiveness of the optimization procedure in capturing target tire behavior and sensitivity of the identified parameters to optimization inputs will be assessed. Modifications to the model itself will be implemented for improved physical representation of the tire and, thus, increased model robustness.

Relators: Stefano Alberto Malan
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 149
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Ente in cotutela: Siemens Industry Software NV (BELGIO)
Aziende collaboratrici: Siemens Industry Software NV
URI: http://webthesis.biblio.polito.it/id/eprint/12490
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