S
Simonetta Boria
Researcher at University of Camerino
Publications - 58
Citations - 971
Simonetta Boria is an academic researcher from University of Camerino. The author has contributed to research in topics: Impact attenuator & Crashworthiness. The author has an hindex of 14, co-authored 53 publications receiving 749 citations. Previous affiliations of Simonetta Boria include University of Pisa.
Papers
More filters
Journal ArticleDOI
Lightweight design and crash analysis of composite frontal impact energy absorbing structures
TL;DR: In this article, the authors present the steps to follow in order to design specific lightweight impact attenuators, based on a detailed analytical, experimental and numerical analysis of the structural crashworthiness.
Journal ArticleDOI
Experimental and numerical investigations of the impact behaviour of composite frontal crash structures
TL;DR: In this paper, a composite impact attenuator for a Formula SAE racing car is presented, which is manufactured by lamination of prepreg sheets in carbon fibres and epoxy matrix, and has a very similar geometry to a square frusta.
Journal ArticleDOI
Axial energy absorption of cfrp truncated cones
TL;DR: In this paper, the authors studied the energy absorption capability of fiber preconditioned cones made of composite materials, intended for structural applications in the automotive industry, from the point of view of their energy absorbing capability when submitted to axial loading.
Journal ArticleDOI
Impact behavior of a fully thermoplastic composite
TL;DR: In this paper, the authors present the results of an experimental campaign made on a fully thermoplastic composite, where both the reinforcement and the matrix are made in polypropylene, and analyze its behavior under different impact loading conditions using a drop weight testing machine.
Journal ArticleDOI
Kriging-assisted topology optimization of crash structures
Elena Raponi,Mariusz Bujny,Mariusz Bujny,Markus Olhofer,Nikola Aulig,Simonetta Boria,Fabian Duddeck,Fabian Duddeck +7 more
TL;DR: Compared to the state-of-the-art Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the KG-LSM optimization algorithm demonstrates to be efficient in terms of convergence speed and performance of the optimized designs.