Panza, Maria Antonietta (2017) VIBRO-ACOUSTIC ANALYSES IN VEHICLES: EXPERIMENTAL ENGINE NOISE EVALUATION AND PASSIVE CONTROL, SOUND QUALITY ASSESSMENT BY MEANS OF NEURAL NETWORKS ALGORITHM. [Tesi di dottorato]

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Tipologia del documento:	Tesi di dottorato
Lingua:	English
Titolo:	VIBRO-ACOUSTIC ANALYSES IN VEHICLES: EXPERIMENTAL ENGINE NOISE EVALUATION AND PASSIVE CONTROL, SOUND QUALITY ASSESSMENT BY MEANS OF NEURAL NETWORKS ALGORITHM
Autori:	Autore Email Panza, Maria Antonietta mariaantonietta.panza@unina.it
Data:	Ottobre 2017
Numero di pagine:	180
Istituzione:	Università degli Studi di Napoli Federico II
Dipartimento:	dep11
Dottorato:	phd046
Ciclo di dottorato:	30
Coordinatore del Corso di dottorato:	nome email Grassi, Michele michele.grassi@unina.it
Tutor:	nome email Bozza, Fabio [non definito] Siano, Daniela [non definito]
Data:	Ottobre 2017
Numero di pagine:	180
Parole chiave:	Automotive, engine noise, acoustic materials, passive control, vehicle interior comfort, psychoacoustics, neural networks, sound quality classification, gaseous fuels, air and noise pollution
Settori scientifico-disciplinari del MIUR:	Area 09 - Ingegneria industriale e dell'informazione > ING-IND/08 - Macchine a fluido
Depositato il:	06 Gen 2018 14:32
Ultima modifica:	19 Mar 2019 10:52
URI:	http://www.fedoa.unina.it/id/eprint/12243

Abstract

The NVH (Noise, Vibration & Harshness) comfort of a vehicle represents a key factor in vehicle development process nowadays, since it strongly affects customer judgement and expectations. This in terms of noise levels reduction and pleasantness of sound perception for user comfort, as well as noise pollution to the environment. In the automotive panorama, development cycles are getting tighter, giving NVH engineers less time to deal with typical today’s challenges concerning fuel economy, cost-effective and light-weight design. Increasingly advanced vibro-acoustic experimental and virtual tools can help the vehicle development process from its early stages, favouring synergy with all automotive research activities. The studies presented in this thesis follow the main pillars of a joint effort. In a first analysis, the use of alternative sound absorbing materials for engine noise passive control was explored to then identify the optimal trade off in terms of cost-effective solution and acoustic performance. The subsequent studies, in addition to assess the potential of natural gas vehicles for traffic noise mitigation, rely on the interior acoustic comfort characterization and Neural Networks algorithms-based analysis, carried out through a wide experimental campaign for several diesel engine vehicles, tested in parallel to evaluate emission levels across different driving cycles and conditions.

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