Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Prediction

Simple item page
dc.contributor.authorZhang, Xin-Leien
dc.contributor.authorXiao, Hengen
dc.contributor.authorWu, Tingen
dc.contributor.authorHe, Guoweien
dc.date.accessioned2022-02-15T14:12:49Zen
dc.date.available2022-02-15T14:12:49Zen
dc.date.issued2021-12-13en
dc.date.updated2022-02-15T14:12:45Zen
dc.description.abstractThe Reynolds-averaged Navier–Stokes (RANS)-based method is a practical tool to provide rapid assessment of jet noise-reduction concepts. However, the RANS-based method requires modeling assumptions to represent noise generation and propagation, which often reduces the predictive accuracy due to the model-form uncertainties. In this work, the ensemble Kalman filter-based acoustic inversion method is introduced to reduce uncertainties in the turbulent kinetic energy and dissipation rate based on the far-field noise and the axial centerline velocity data. The results show that jet noise data are more effective from which to infer turbulent kinetic energy and dissipation rate compared to velocity data. Moreover, the inferred noise source is able to improve the estimation of the turbulent flowfield and the far-field noise at unobserved locations. Further, the noise model parameters are also considered uncertain quantities, demonstrating the ability of the proposed framework to reduce uncertainties in both the RANS and noise models. Finally, one realistic case with experimental data is investigated to show the practicality of the proposed framework. The method opens up the possibility for the inverse modeling of jet noise sources by incorporating far-field noise data that are relatively straightforward to be measured compared to the velocity field.en
dc.description.versionAccepted versionen
dc.format.extent16 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.2514/1.J060876en
dc.identifier.eissn1533-385Xen
dc.identifier.issn0001-1452en
dc.identifier.orcidXiao, Heng [0000-0002-3323-4028]en
dc.identifier.urihttp://hdl.handle.net/10919/108364en
dc.language.isoenen
dc.publisherAmerican Institute of Aeronautics and Astronauticsen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000742546900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectEngineering, Aerospaceen
dc.subjectEngineeringen
dc.subjectDATA ASSIMILATIONen
dc.subjectMIXING NOISEen
dc.subjectMEAN-FLOWen
dc.subjectOPTIMIZATIONen
dc.subjectANALOGYen
dc.subjectSPACEen
dc.subjectMODELen
dc.subjectSPEEDen
dc.subjectAerospace & Aeronauticsen
dc.subject0901 Aerospace Engineeringen
dc.subject0905 Civil Engineeringen
dc.subject0913 Mechanical Engineeringen
dc.titleAcoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier-Stokes-Based Jet Noise Predictionen
dc.title.serialAIAA Journalen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherEarly Accessen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Aerospace and Ocean Engineeringen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/University Research Institutes/Fralin Life Sciences/Durelle Scotten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Zhang et al. - 2021 - Acoustic Inversion for Uncertainty Reduction in Re.pdf
Size:
9.19 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download

Collections

All Faculty Deposits
Scholarly Works, Aerospace and Ocean Engineering