Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes

Chen, Junwei; Raiola, Marco; Discetti, Stefano

Publication:
Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes

dc.affiliation.dpto	UC3M. Departamento de Ingeniería Aeroespacial	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Ingeniería Aeroespacial	es
dc.contributor.author	Chen, Junwei
dc.contributor.author	Raiola, Marco
dc.contributor.author	Discetti, Stefano
dc.contributor.funder	European Commission	es
dc.date.accessioned	2022-07-27T11:32:50Z
dc.date.available	2022-07-27T11:32:50Z
dc.date.issued	2022-08-01
dc.description.abstract	The most explored path to obtain pressure fields from Particle Image Velocimetry (PIV) data roots its basis on accurate measurement of instantaneous velocity fields and their corresponding time derivatives. This requires time-resolved measurements, which are often difficult to achieve due to hardware limitations and expensive to implement. In alternative, snapshot PIV experiments are more affordable but require enforcing physical constraints (e.g. Taylor’s hypothesis) to extract the time derivative of the velocity field. In this work, we propose the use of data-driven techniques to retrieve time resolution from the combination of snapshot PIV and high-repetition-rate sensors measuring flow quantities in a limited set of spatial points. The instantaneous pressure fields can thus be computed by leveraging the Navier–Stokes equations as if the measurement were time-resolved. Extended Proper Orthogonal Decomposition, which can be regarded as one of the simplest algorithm for estimating velocity fields from a finite number of sensors, is used in this paper to prove the feasibility of this concept. The method is fully data-driven and, after training, it requires only probe data to obtain field information of velocity and pressure in the entire flow domain. This is certainly an advantage since model-based methods can retrieve pressure in an observed snapshot, but show increasing error as the field information is propagated over time. The performances of the proposed method are tested on datasets of increasing complexity, including synthetic test cases of the wake of a fluidic pinball and a channel flow, and experimental measurements in the wake of a wing. The results show that the data-driven pressure estimation is effective in flows with compact POD spectrum. In the cases where Taylor’s hypothesis holds well, the in-sample pressure field estimation can be more accurate for model-based methods; nonetheless, the proposed data-driven approach reaches a better accuracy for out-of-sample estimation after less than 0.20 convective times in all tested cases.	en
dc.description.sponsorship	This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085). Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2022).	en
dc.format.extent	13
dc.identifier.bibliographicCitation	Chen, J., Raiola, M., Discetti, S. (2022). Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes. Experimental Thermal and Fluid Science, 136, 110647	en
dc.identifier.doi	https://doi.org/10.1016/j.expthermflusci.2022.110647
dc.identifier.issn	0894-1777
dc.identifier.publicationfirstpage	1 (110647)
dc.identifier.publicationlastpage	13 (110647)
dc.identifier.publicationtitle	Experimental Thermal and Fluid Science	es
dc.identifier.publicationvolume	136	es
dc.identifier.uri	https://hdl.handle.net/10016/35543
dc.identifier.uxxi	AR/0000030600
dc.language.iso	eng
dc.publisher	Elsevier	es
dc.relation.projectID	info:eu-repo/grantAgreement/EC/H2020/949085
dc.relation.projectID	AT-2022
dc.relation.projectID	AT-22
dc.rights	© 2022 The Author(s)
dc.rights	Atribución 3.0 España
dc.rights.accessRights	open access
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/
dc.subject.eciencia	Aeronáutica	es
dc.subject.other	Pressure estimation	en
dc.subject.other	Extended proper orthogonal decomposition	en
dc.subject.other	Particle image velocimetry	en
dc.subject.other	Flow sensing	en
dc.subject.other	Data-driven methods	en
dc.title	Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication