ESR 5 : Ranim Najib

PhD title :

Manufacturing margins and robustness of NVH prediction for lightweight transmissions

Ranim Najib

Recruiting universityUniversity of Naples (IT)
Academic supervisorsProf. Sergio De Rosa,
Dr. Giuseppe Petrone
Industrial partnerVibratec (FR)
Industrial supervisorMs. Anne Coulon,
Dr. Alexandre Carbonelli
SecondmentsPowerflex (IT), Enis (TN)
Start date 01/11/2020
Duration36 months

Short Bio

Studied Mechanical Engineering at the Lebanese University Faculty of Engineering Tripoli- Lebanon. Holds a Research Master Degree in Computational Mechanics (Master Mirroir with École Centrale de Nantes ECN). Been an intern in Mechanical Maintenance and Diesel departments at Mercedes Benz and Cimenterie Nationale. She is interested in the computational tools for manufacturing applications and materials. As master project, she worked on modeling the friction behavior of woven fabric interlocks during the forming process. She joined Lebanon Green Building Council as student member and Hult Prize international committee for social entrepreneurship, also participated in workshops and international competitions – ECOSOC youth forum 2019.

Project description

A gearbox is a complex system and, as consequence, its NVH performance is ruled by many parameters. Some of them cannot be precisely defined froma deterministic point of view and/or depend on manufacturing issues. In addition, some of them can introduce a non-linear behaviour. The research topic of this ESR is basically linked to theseaspects. First of all, the ESR has to identify the source of uncertainties for a NVH predictive algorithm. Different numerical approaches have to be investigated in order to obtain a predictive tool which is able to fulfil the industrial needs and, specifically, reduce the computational cost. This aspect is mandatory due to the complexity of the specific structural-acoustic problem, the number of possible uncertainties and the need to bound the vibroacoustic response. Some aspectsto be considered further in this work are: uncertainties and optimization algorithms; Defining the relevance of the uncertainties versus weight reduction; mathematical predictive tools; Ranking of standard and hybrid gearboxes versus uncertainties.


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