VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.
 

Characterization of Structure-Borne Tire Noise Using Virtual Sensing

Simple item page
dc.contributor.authorNouri, Arashen
dc.contributor.committeechairTaheri, Saieden
dc.contributor.committeememberWoolsey, Craig A.en
dc.contributor.committeememberAhmadian, Mehdien
dc.contributor.committeememberEskandarian, Azimen
dc.contributor.departmentMechanical Engineeringen
dc.date.accessioned2022-07-22T06:00:27Zen
dc.date.available2022-07-22T06:00:27Zen
dc.date.issued2021-01-27en
dc.description.abstractVarious improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below 800 Hz from road surface through a structure- borne pathand 2) the high frequency (above 800 Hz) air-borne noise that is caused by air- pumpingnoise caused by tread pattern.The structure-borne waves of the circumference of the tire are generated by excitation atthe contact patch due to the road surface texture and characteristics. These vibrations arethen transferred from the sidewalls of the tire to the rim and then are transmitted throughthe spindle-wheel interface, resulting in high frequency vibration of vehicle body panels andwindows.The focus of this study is to develop several statistical-based models for analyzing the roadsurface and using them to predict the tire-road noise structure-borne component. In order todo this, a new methodology for sensing the road characteristics, such as asperities and roadsurface condition, were developed using virtual sensing and intelligent tire technology. In ad-dition, the spindle forces were used as an indicator to the structure-borne noise of the vehicle.Several data mining and multivariate analysis-based methods were developed to extractfeatures and to develop an empirical model to predict the power of structure-borne noiseunder different operational and road conditions. Finally, multiple data driven models-basedmodels were developed to classify the road types, and conditions and use them for the noisefrequency spectrum prediction.en
dc.description.abstractgeneralMultiple data driven models were developed in this study to use the vibration of the tirecontact patch as an input to sense some characteristics of road such as asperity, surface type,and the surface condition, and use them to predict the structure-borne noise power. Also,instead of measuring the noise using microphones, forces at wheel spindle were measuredas a metric for the noise power. In other words, a statistical model was developed that bysensing the road, and using the data along with other inputs, one can predict forces at thewheel spindle.en
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:27867en
dc.identifier.urihttp://hdl.handle.net/10919/111316en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectNVHen
dc.subjectStructure-Borne Noiseen
dc.subjectTire-Pavement Interaction Noiseen
dc.subjectSurface Condition Monitoringen
dc.subjectIntelligent Tireen
dc.subjectStatistical Modellingen
dc.subjectDeep Learningen
dc.subjectMachine Learningen
dc.subjectSignal Denoisingen
dc.subjectPattern Recognitionen
dc.subjectEnd-to-End Learningen
dc.subjectAutoencoderen
dc.subjectDesign of then
dc.titleCharacterization of Structure-Borne Tire Noise Using Virtual Sensingen
dc.typeDissertationen
thesis.degree.disciplineMechanical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Nouri_A_D_2021.pdf
Size:
92.42 MB
Format:
Adobe Portable Document Format
Download

Collections

Doctoral Dissertations