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Data-driven Estimation of Scalar Quantities from Planar Velocity Measurements by Deep Learning Applied to Temperature in Thermal Convection by Philipp Teutsch; Theo Käufer; Patrick Mäder; Christian Cierpka is a Engineering article available to read on EtoBox.

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## Abstract The measurement of the transport of scalar quantities within flows is oftentimes laborious, difficult or even unfeasible. On the other hand, velocity measurement techniques are very advanced and give high-resolution, high-fidelity experimental data. Hence, we explore the capabilities of a deep learning model to predict the scalar quantity, in our case temperature, from measured velocity data. Our method is purely data-driven and based on the u-net architecture and, therefore, well-suited for planar experimental data. We demonstrate the applicability of the u-net on experimental temperature and velocity data, measured in large aspect ratio Rayleigh–Bénard convection at $$\textrm{Pr} =7.1$$ Pr = 7.1 and $$\textrm{Ra} =2\times 10^5,4\times 10^5,7\times 10^5$$ Ra = 2 × 10 5 , 4 × 10 5 , 7 × 10 5 . We conduct a hyper-parameter optimization and ablation study to ensure appropriate training convergence and test different architectural variations for the u-net. We test two application scenarios that are of interest to experimentalists. 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 $$\textrm{R

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Author

Philipp Teutsch; Theo Käufer; Patrick Mäder; Christian Cierpka

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Engineering (Physical Sciences)