Interaction between a jet and a turbulent boundary layer

Ott, Célestin; Gallas, Quentin; Delva, Jérôme; Lippert, Marc; Keirsbulck, Laurent

doi:10.2514/6.2019-3501

Cited by 1 publication

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On those proposed planes, the velocity fields are colored by U ϕ (m/s) using the dark end rainbow color scale, and the associated RMS fields are colored by blue-red color scale ( g , h , i for the conditional approach and j , k , l for the neural network approach). The spatial development of the jet reconstituted using the 3D conditional approach is considered as the reference flow thanks to its validation in Ott et al (2019b) and using comparisons with literature about slotted and pulsed jet development Eroglu and Breidenthal ( 2001), Vernet et al (2009), Mahesh (2013), Sau and Mahesh (2010). The Fig.…”

Section: In-depth Study Of the Resultsmentioning

confidence: 99%

“…The conditional approach used in this paper is based on a method already used and validated on 2D cases Ott et al (2019a) Chovet et al (2016, and extrapolated to the 3D cases Ott et al (2019b) Ott (2020. This approach is implemented through a Python program, and processed using eight core processors (10240 Kb each).…”

Section: Conditional Approachmentioning

confidence: 99%

“…(2013) • 2D instantaneous velocity fields Zaman et al (1989), Chovet et al (2016, Ott et al (2019a) • 3D instantaneous velocity fields Ott et al (2019b), or to estimate a 3D velocity field based on multiple parallel 2D velocity fields, synchronized with each other. The conditional approach shows good results when coupled with a phase-average process, acting as filtering Schaeffler et al (2002) Hardy et al (2010) Ostermann et al (2015.…”

Section: Introductionmentioning

confidence: 99%

“…In order to obtain a 3D space-time-resolved velocity field based on these local measurements, we propose in the present study a Neural Network-based flow field estimation method able to give a 3D spacetime-resolved velocity field using local time-resolved velocity signals captured with a hot-wire traveling along a 3D measurement grid. To validate this method, the same experimental data set is processed using a previouslyvalidated conditional-based approach Ott et al (2019b), which allows to obtain a 3D space-time-resolved baseline flow field. The paper is outlined as follows.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Pulsed jet phase-averaged flow field estimation based on neural network approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

Single hot-wire velocity measurements have been conducted along a three-dimensional measurement grid to capture the flow-field induced by a 45°inclined slotted pulsed jet. Based on the periodic behavior of the flow, two different estimation methods have been implemented. The first one, considered as the reference base-line, is the conditional approach which consists in the redistribution of the experimental data into spaceand time-resolved three-dimensional velocity fields. The second one uses a neural network to estimate 3D velocity fields given spatial coordinates and time. This paper compares the two methods for a complete flow-field estimation based on hot-wire measurements. Results suggest that the neural network is tailored to capture the phase-averaged dynamic response of the jet induced by the actuator, and identify the coherent structures in the flow field. Interesting performances are also observed when degrading the learning database, meaning that neural networks can be used to drastically improve the temporal or spatial resolution of a flow field estimation compared to the experimental data resolution.

show abstract

Section: In-depth Study Of the Resultsmentioning

confidence: 99%

Section: Conditional Approachmentioning

confidence: 99%