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Seat modelling and parameters identification to evaluate and predict Ride Comfort of Passenger Cars

Stefano Orlando

Seat modelling and parameters identification to evaluate and predict Ride Comfort of Passenger Cars.

Rel. Massimiliana Carello, Paolo Massai. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2019

Abstract:

The thesis work activity talks about the virtual seat modelling and the evaluation of the most sensitive parameters useful to validate the virtual seat with respect to the experimental one and then predict and evaluate the comfort inside the vehicle. Fist of all, it has been necessary to understand how the vibrations influence the human body in contact with the seat and in which way they are experimentally evaluated, in order to acquire also the main numerical seat and human parameters values which have to be inserted into the input file of the virtual model. Then, the virtual modelling part has been analysed describing all the models proposed from the past up to now and then focusing on the one used, implemented and tuned during this activity and initially furnished by the CRF. Therefore, it has been possible to talk about the fulcrum of the project reporting all the aspects which should be considered during the virtual simulations of the tests performed experimentally and the comfort validation which must be conducted to see if the virtual model results match with the real ones. Finally, an objective comparison of the seats used and the subjective driver perception during the comfort and ergonomics tests are reported in order to consider and have all the information necessary for the evaluation, design and future development of the seat.

Relators: Massimiliana Carello, Paolo Massai
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 114
Additional Information: Tesi secretata. Fulltext non presente
Subjects:
Corso di laurea: Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: MASERATI SPA
URI: http://webthesis.biblio.polito.it/id/eprint/11981
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