In the study of new solutions for motorcycle passive safety, FE models of full-scale crash tests play a strategic role. The most important issue in the development process of FE models is their reliability to reproduce real crash tests. To help the engineering in the validation phase, a sensitivity analysis of a FE model for motorcycle-car crash tests is carried-out. The aim of this study is to investigate the model response subjected to variations of specific input parameters. The DOE is performed generating a list of simulations (each one composed by a unique combination of 8 parameters) through Latin Hypercube Sampling. The outputs monitored are the Head Injury Criterion (HIC) and Neck Injury Criteria (Nij). The analysis of the results is performed using scatter plots and linear regression curves to identify the parameters that have major impact on the outputs and to assess the type of dependency (linear or non-linear).

Sensitivity Analysis of a FE Model for Motorcycle-Car Full-Scale Crash Test / Daniele Barbani; Niccolò Baldanzini; Marco Pierini. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - ELETTRONICO. - (2014), pp. 1-8. (Intervento presentato al convegno SAE/JSAE 2014 Small Engine Technology Conference & Exhibition tenutosi a Pisa nel 18-20 novembre 2014) [10.4271/2014-32-0023].

Sensitivity Analysis of a FE Model for Motorcycle-Car Full-Scale Crash Test

BARBANI, DANIELE;BALDANZINI, NICCOLO';PIERINI, MARCO
2014

Abstract

In the study of new solutions for motorcycle passive safety, FE models of full-scale crash tests play a strategic role. The most important issue in the development process of FE models is their reliability to reproduce real crash tests. To help the engineering in the validation phase, a sensitivity analysis of a FE model for motorcycle-car crash tests is carried-out. The aim of this study is to investigate the model response subjected to variations of specific input parameters. The DOE is performed generating a list of simulations (each one composed by a unique combination of 8 parameters) through Latin Hypercube Sampling. The outputs monitored are the Head Injury Criterion (HIC) and Neck Injury Criteria (Nij). The analysis of the results is performed using scatter plots and linear regression curves to identify the parameters that have major impact on the outputs and to assess the type of dependency (linear or non-linear).
2014
Proceedings of the SAE/JSAE 2014 Small Engine Technology Conference & Exhibition
SAE/JSAE 2014 Small Engine Technology Conference & Exhibition
Pisa
18-20 novembre 2014
Daniele Barbani; Niccolò Baldanzini; Marco Pierini
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/935139
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