https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Identification of swirling air flow velocity by non-neutrally buoyant tracer particle based on machine learning
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
School of Civil and Environmental Engineering, Ningbo University, Zhejiang, 315211, China .
2023 (English)In: Flow Measurement and Instrumentation, ISSN 0955-5986, E-ISSN 1873-6998, Vol. 91, article id 102363Article in journal (Refereed) Published
Abstract [en]

In the non-intrusive measurement of swirling air flow, helium-filled soap bubbles (HFSBs) are ideal neutrally buoyant tracer particles However, there are some researchers that do not use HFSBs in the non-intrusive measurement of swirling air flow, leading to some kind of measurement inaccuracy. Since the flow velocity data has been implicitly included in the physical equations of any kind of tracer particles, it is possible to extract such hidden flow velocity from particle trajectory. In this study we propose a physics-informed procedure of adopting SINDy algorithm to identify the hidden physical equations of non-neutrally buoyant particle dynamics, so that the implicit flow velocity can be discovered. First of all, the numerical experiment is conducted to generate particle trajectory in a 2D swirling air flow in small cyclone separator. Based on the numerical experiment trajectory data, the input variables for SINDy algorithm are properly constructed. The output of SINDy algorithm, which are the identified physical equations, are evaluated and validated on two different-density particle trajectory data. Our results show that the physical equations of tracer particle dynamics can be identified and the discovered flow velocity data has a maximum deviation of 1.4% from the truth (R2≥0.999). The proposed method may remove the requirement of NB tracer particle in non-intrusive measurement of swirling air flow, and may be applied to recognize the physical equations of complex particle laden flow.

Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 91, article id 102363
Keywords [en]
Machine learning, Non-intrusive measurement, Non-neutral buoyant, SINDy, Tracer particle, Air, Buoyancy, Flow velocity, Storms, Trajectories, Buoyant tracer, Machine-learning, Measurements of, Non-intrusive measurements, Non-neutral, Particle trajectories, Physical equations, Swirling air flow
National Category
Fluid Mechanics and Acoustics
Identifiers
URN: urn:nbn:se:mdh:diva-62271DOI: 10.1016/j.flowmeasinst.2023.102363ISI: 000980872000001Scopus ID: 2-s2.0-85152142671OAI: oai:DiVA.org:mdh-62271DiVA, id: diva2:1751686
Available from: 2023-04-19 Created: 2023-04-19 Last updated: 2023-05-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Zhou, Yuanye

Search in DiVA

By author/editor
Zhou, Yuanye
By organisation
Future Energy Center
In the same journal
Flow Measurement and Instrumentation
Fluid Mechanics and Acoustics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 54 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf