An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets

The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed...

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Main Authors: Lorenzo Scappaticci, Giacomo Risitano, Dario Santonocito, Danilo D’Andrea, Dario Milone
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Vehicles
Subjects:
Online Access:https://www.mdpi.com/2624-8921/3/3/33
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author Lorenzo Scappaticci
Giacomo Risitano
Dario Santonocito
Danilo D’Andrea
Dario Milone
author_facet Lorenzo Scappaticci
Giacomo Risitano
Dario Santonocito
Danilo D’Andrea
Dario Milone
author_sort Lorenzo Scappaticci
collection DOAJ
description The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale.
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spelling doaj.art-63129049f30a455cbccc9305bc3296aa2023-11-22T15:35:34ZengMDPI AGVehicles2624-89212021-08-013354555610.3390/vehicles3030033An Approach to the Definition of the Aerodynamic Comfort of Motorcycle HelmetsLorenzo Scappaticci0Giacomo Risitano1Dario Santonocito2Danilo D’Andrea3Dario Milone4Sustainability Engineering Department, Guglielmo Marconi University, via Plinio 44, 00193 Rome, ItalyDepartment of Engineering, University of Messina, C.da di Dio (Sant’Agata), 98166 Messina, ItalyDepartment of Engineering, University of Messina, C.da di Dio (Sant’Agata), 98166 Messina, ItalyDepartment of Engineering, University of Messina, C.da di Dio (Sant’Agata), 98166 Messina, ItalyDepartment of Engineering, University of Messina, C.da di Dio (Sant’Agata), 98166 Messina, ItalyThe aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale.https://www.mdpi.com/2624-8921/3/3/33aerodynamichelmet vibrationconvolutional neural network
spellingShingle Lorenzo Scappaticci
Giacomo Risitano
Dario Santonocito
Danilo D’Andrea
Dario Milone
An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
Vehicles
aerodynamic
helmet vibration
convolutional neural network
title An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
title_full An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
title_fullStr An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
title_full_unstemmed An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
title_short An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
title_sort approach to the definition of the aerodynamic comfort of motorcycle helmets
topic aerodynamic
helmet vibration
convolutional neural network
url https://www.mdpi.com/2624-8921/3/3/33
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