Combined particle image velocimetry and thermometry of turbulent superstructures in thermal convection

Möller, Sebastian; Käufer, Theo; Pandey, Ambrish; Schumacher, Jörg; Cierpka, Christian

doi:10.1017/jfm.2022.538

Cited by 17 publications

(13 citation statements)

References 53 publications

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“…The definition of the convective heat transfer is equal to the definition of the local Nusselt number [17,53]. However, since the local Nusselt number represents the overall heat transfer, the conductive heat transfer must be neglectable, which is only a valid assumption close to the horizontal mid-plane within the Boussinesq-approximation regime.…”

Section: Resultsmentioning

confidence: 99%

Volumetric Lagrangian temperature and velocity measurements with thermochromic liquid crystals

Käufer,

Cierpka

2023

Meas. Sci. Technol.

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We propose a Lagrangian method for simultaneous, volumetric temperature and velocity measurements. As tracer particles for both quantities, we employ encapsulated thermochromic liquid crystals (TLCs). We discuss the challenges arising from color imaging of small particles and present measurements in an equilateral hexagonal-shaped convection cell of height h = 60 mm and distance between the parallel side walls w = 104 mm, which corresponds to an aspect ratio Γ = 1.73 . As fluid, we use a water-glycerol mixture to match the density of the TLC particles. We propose a densely-connected neural network, trained on calibration data, to predict the temperature for individual particles based on their particle image and position in the color camera images, which achieves uncertainties below 0.2 K over a temperature range of 3 K. We use Shake-the-Box to determine the 3D position and velocity of the particles and couple it with our temperature measurement approach. We validate our approach by adjusting a stable temperature stratification and comparing our measured temperatures with the theoretical results. Finally, we apply our approach to thermal convection at Rayleigh number Ra = 3.4 × 10 7 and Prandtl number Pr = 10.6. We can visualize detaching plumes in individual temperature and convective heat transfer snapshots. Furthermore, we demonstrate that our approach allows us to compute statistics of the convective heat transfer and briefly validate our results against the literature.

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Section: Resultsmentioning

confidence: 99%

Volumetric Lagrangian temperature and velocity measurements with thermochromic liquid crystals

Käufer,

Cierpka

2023

Meas. Sci. Technol.

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“…We employ the isothermal condition on the horizontal plates and the adiabatic condition on the sidewalls. This allows direct comparison with other simulations at higher Pr (Pandey et al 2018;Fonda et al 2019) and controlled laboratory experiments, such as those of Moller et al (2022), in exactly the same setting to enable a consistent analysis across the Ra-Pr parameter plane. We use two different solvers for the simulations.…”

Section: Details Of Dnsmentioning

confidence: 99%

“…2019) and controlled laboratory experiments, such as those of Moller et al. (2022), in exactly the same setting to enable a consistent analysis across the – parameter plane.…”

Section: Details Of Dnsmentioning

confidence: 99%

Convective mesoscale turbulence at very low Prandtl numbers

Pandey¹,

Krasnov

Sreenivasan

et al. 2022

J. Fluid Mech.

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Horizontally extended turbulent convection, termed mesoscale convection in natural systems, remains a challenge to investigate in both experiments and simulations. This is particularly so for very low molecular Prandtl numbers, such as occur in stellar convection and in the Earth's outer core. The present study reports three-dimensional direct numerical simulations of turbulent Rayleigh–Bénard convection in square boxes of side length $L$ and height $H$ with the aspect ratio $\varGamma =L/H$ of 25, for Prandtl numbers that span almost 4 orders of magnitude, $10^{-3}\le Pr \le 7$ , and Rayleigh numbers $10^5 \le Ra \le 10^7$ , obtained by massively parallel computations on grids of up to $5.36\times 10^{11}$ points. The low end of this $Pr$ -range cannot be accessed in controlled laboratory measurements. We report the essential properties of the flow and their trends with the Rayleigh and Prandtl numbers, in particular, the global transport of momentum and heat – the latter decomposed into convective and diffusive contributions – across the convection layer, mean vertical profiles of the temperature and temperature fluctuations and the kinetic energy and thermal dissipation rates. We also explore the degree to which the turbulence in the bulk of the convection layer resembles classical homogeneous and isotropic turbulence in terms of spectra, increment moments and dissipative anomaly, and find close similarities. Finally, we show that a characteristic scale of the order of the mesoscale seems to saturate to a wavelength of $\lambda \gtrsim 3H$ for $Pr\lesssim 0.005$ . We briefly discuss possible implications of these results for the development of subgrid-scale parameterization of turbulent convection.

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“…These spectra indicate how the temperature is distributed over the different spatial scales or wavelengths. They are also commonly used to determine the size of the turbulent superstructures [16,66]. To determine the azimuthally averaged spectra, we compute the temperature field's two-dimensional Discrete Fourier Transform (DFT).…”

Section: Temperature Predictionmentioning

confidence: 99%

Inferring the temperature from planar velocity measurements in Rayleigh-Bénard convection by deep learning

Teutsch

Käufer

Mäder

et al. 2023

Preprint

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The simultaneous, spatially- and temporally-resolved direct measurement of velocity and temperature fields in Rayleigh-Bénard experiments is laborious, expensive and sometimes not even feasible. Hence, we assess the capabilities of a deep learning model to support such measurements and reduce the necessary effort. Here, we use a u-net-based model to predict the temperature from the corresponding velocity field obtained from measurements in Rayleigh-Bénard convection at Pr = 7.1 and Ra = 2× 105, 4×105, 7×105. We conduct a hyper-parameter search and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We define two application scenarios, one in which the u-net is trained with data of the same experimental run and one in which the u-net is trained on data of different Ra. Our analysis showed that the u-net can predict temperature fields similar to the measurement data and preserves typical spatial structure sizes. Moreover, the analysis of the heat transfer associated with the temperature fields unveiled that the results are physically reasonable and are in good agreement with the ground truth data when the u-net is trained with data of the same experimental run. We conclude that deep learning has the potential to supplement measurements and can partially replace the requirement of additional temperature measurements.

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Combined particle image velocimetry and thermometry of turbulent superstructures in thermal convection

Cited by 17 publications

References 53 publications

Volumetric Lagrangian temperature and velocity measurements with thermochromic liquid crystals

Volumetric Lagrangian temperature and velocity measurements with thermochromic liquid crystals

Convective mesoscale turbulence at very low Prandtl numbers

Inferring the temperature from planar velocity measurements in Rayleigh-Bénard convection by deep learning

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