Field
Value
Language
dc.contributor.author
Pan, Zhao
datacite.creator.affiliationIdentifier
https://ror.org/01v4tq883;https://ror.org/01aff2v68
en_US
datacite.creator.affiliation
Florida A&M University - Florida State University College of Engineering; University of Waterloo
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Zhang, Yang
datacite.creator.affiliationIdentifier
https://ror.org/01v4tq883
en_US
datacite.creator.affiliation
Florida A&M University - Florida State University College of Engineering
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Gustavsson, Jonas P.R.
datacite.creator.affiliationIdentifier
https://ror.org/01v4tq883
en_US
datacite.creator.affiliation
Florida A&M University - Florida State University College of Engineering
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Hickey, Jean-Pierre
datacite.creator.affiliationIdentifier
https://ror.org/01aff2v68
en_US
datacite.creator.affiliation
University of Waterloo
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Cattafesta III, Louis N.
datacite.creator.affiliationIdentifier
https://ror.org/01v4tq883
en_US
datacite.creator.affiliation
Florida A&M University - Florida State University College of Engineering
en_US
datacite.creator.nameIdentifier
en_US
dc.date.accessioned
2020-03-26T23:41:07Z
dc.date.available
2020-03-26T23:41:07Z
dc.date.issued
2020-03-26
dc.identifier.uri
https://www.frdr-dfdr.ca/repo/dataset/482a8b73-5fb8-85b1-43bc-a0a9336fdac1
dc.identifier.uri
https://doi.org/10.20383/101.0222
dc.description
This repository contains the raw data (9 snapshots) of a turbulent channel flow at a bulk Mach number of 0.3 and a Re_tau of about 750. Postprocessing scripts are available in GitLab at https://gitlab.com/mpilab_waterloo/dns-post-processing. This data was used for the analysis of the following paper:
Pan, Z., Zhang, Y., Gustavsson, J.P.R., Hickey, J.-P. and Cattafesta III, L. N. Unscented Kalman filter (UKF) based nonlinear parameter estimation for a turbulent boundary layer: a data assimilation framework, Meas. Sci. and Technol. 2020.
en_US
dc.publisher
Federated Research Data Repository / dépôt fédéré de données de recherche
dc.rights
Creative Commons Public Domain Dedication (CC0 1.0)
en_US
dc.rights.uri
https://creativecommons.org/publicdomain/zero/1.0/
en_US
dc.subject
DNS
en_US
dc.subject
turbulent channel flow
en_US
dc.subject
direct numerical simulation
en_US
dc.title
Unscented Kalman filter (UKF) based nonlinear parameter estimation for a turbulent boundary layer: DNS data
en_US
globus.shared_endpoint.name
f163c1b3-9c88-42f6-a7bb-5839ed6c4063
globus.shared_endpoint.path
/1/published/publication_221/
frdr.preservation.status
AIP generation and transfer successful
frdr.preservation.datetime
2020-03-27 09:52:01
datacite.publicationyear
2020
datacite.resourcetype
Dataset
en_US
datacite.relatedidentifier.IsSupplementedBy
https://gitlab.com/mpilab_waterloo/dns-post-processing
datacite.descriptionType.Other
Please email for additional information on the dataset.
en_US
datacite.fundingReference.funderName
Compute Canada
en_US
datacite.fundingReference.awardNumber
en_US
datacite.fundingReference.awardTitle
en_US
frdr.crdc.code
RDF1010205
frdr.crdc.group_en
Mathematics and statistics
en_US
frdr.crdc.class_en
Applied mathematics
en_US
frdr.crdc.field_en
Computational fluid mechanics
en_US
frdr.crdc.group_fr
Mathématiques et statistique
fr_CA
frdr.crdc.class_fr
Mathématiques appliquées
fr_CA
frdr.crdc.field_fr
Mécanique computationnelle des fluides
fr_CA