Abstract

A novel method for assimilating and extending measured turbulent Rayleigh–Bénard convection data is presented, which relies on the fractional step method also used to solve the incompressible Navier–Stokes equation in direct numerical simulations. Our approach is used to make measured tomographic particle image velocimetry (tomo PIV) fields divergence-free and to extract temperature fields. Comparing the time average of the extracted temperature fields with the temporally averaged temperature field, measured using particle image thermometry in a subdomain of the flow geometry, shows that extracted fields correlate well with measured fields with a correlation coefficient of C_{Ttilde{T}}=0.84. Additionally, extracted temperature fields as well as divergence-free velocity fields serve as initial fields for subsequent direct numerical simulations with and without feedback which generate small-scale turbulence initially absent in the experimental data. Although the tomo PIV data set was spatially under-resolved and did not include any information on the boundary layers, the here-proposed method successfully generates velocity and temperature fields featuring small-scale turbulence and thermal as well as kinetic boundary layers, without disturbing the large-scale circulation contained in the original experimental data significantly. The latter is underpinned by high vertical and horizontal velocity correlation coefficients—computed from velocity fields averaged in time and horizontal x-direction obtained from the measurement and from the simulation without feedback—of C_{vtilde{v}}=0.92 and C_{wtilde{w}}=0.91 representing the large-scale structure. For simulations with feedback, the generated velocity fields resemble the experimental data increasingly well for higher feedback gain values, whereas the temperature fluctuation intensity deviates noticeably from the values obtained from a direct numerical simulation without feedback for gain values alpha ge 1. Thus, a feedback gain of alpha =0.1 was found optimal with correlation coefficients of C_{vtilde{v}}=0.96 and C_{wtilde{w}}=0.95 as well as a realistic temperature fluctuation intensity profile. The xt-averaged temperature fields obtained from the direct numerical simulations with and without feedback correlate somewhat less with the extracted temperature field (C_{Ttilde{T}}approx 0.6), which is presumably caused by spatially under-resolved and temporally oscillating initial tomo PIV fields reflected by the extracted temperature field.Graphical abstract

Highlights

  • Rayleigh–Bénard convection (RBC) is the buoyancy-driven flow of fluid between a heated bottom plate and a cooled top plate and serves as a canonical problem for the more complex thermal convection systems appearing in nature and engineering applications (Lohse and Xia 2010; Chillà and Schumacher 2012)

  • Aiming at the assimilation of measured, spatially under-resolved, threedimensional velocity fields from RBC, we present a novel approach that involves the additional numerical generation of the corresponding temperature fields as representatives of scalar fields

  • In order to compare the coherence of the average flow structure between the tomo particle image velocimetry (PIV) measurement and the Direct numerical simulations (DNS) qualitatively, we compute the correlation coefficient between the flow field seen in Fig. 14a and each of the fields presented in Fig. 14b–d as well as the fields obtained from DNS with = 0.01 and

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Summary

Introduction

Rayleigh–Bénard convection (RBC) is the buoyancy-driven flow of fluid between a heated bottom plate and a cooled top plate and serves as a canonical problem for the more complex thermal convection systems appearing in nature and engineering applications (Lohse and Xia 2010; Chillà and Schumacher 2012). Broyden–Fletcher–Goldfarb–Shanno algorithm described by Nocedal (1980) Evaluating their reconstruction method using synthetic data of forced isotropic turbulence provided by Li et al (2008) by analysing vorticity iso-contours of the reconstructed fields and signal-to-noise ratios, Gesemann et al (2016) show that their method improves with respect to simpler algorithms that penalise the divergence of velocity only or those that do not penalise divergence at all.

Experimental set‐up
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Velocity field assimilation and temperature field generation
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Velocity field assimilation
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Temperature field extraction
Direct numerical simulation
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Conclusion
Findings
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Full Text
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