Three-dimensional (3D) instantaneous velocity fields were measured in a RBC cell filled with (compressed) air in flows from weakly turbulent to fully turbulent. The experimental technique used to acquire the data is stereoscopic PIV. The RBC cell set-up was inserted in the SCALEX facility of TU Ilmenau (Germany), a pressure vessel with several optical accesses that can be pressurized up to 10 bars. To study turbulent flows, the whole experimental facility, including cameras and objective lenses, was pressurized up to 4.5 bars. The main goal of the first part of the project was to reproduce Direct Numerical Simulations (DNS) data of the same flow [1]. Both the experimental data and the DNS ones are used to study extreme events of vorticity. The experimental results were able to reproduce the statistics of DNS data of the same flow well and allowed the study of extreme events of vorticity. From Probability Density Functions (PDFs) statistics both from PIV experiments and from DNS data we observed a transition to intermittency in the turbulent regime. These results were presented at the International Symposium of PIV in August 2021 [3].
In the second part of the project, the experimental investigation was connected with a comprehensive statistical analysis of long-term time series of vorticity and individual velocity derivatives. A statistical convergence for derivative moments up to an order of six is demonstrated. Our results are found to agree well with existing high-resolution direct numerical simulation data in the same range of parameters, including the extreme vorticity events that appear in the far exponential tails of the corresponding probability density functions [2]. The experimental data were used to train a reservoir computing model, one implementation of a RNN, to reproduce highly intermittent experimental time series of the vorticity and thus reconstruct extreme out-of-plane vorticity events. After training the model with high-resolution PIV data, the machine learning model is run with sparsely seeded, continually available, and unseen measurement data in the reconstruction phase. The dependence of the reconstruction quality on the sparsity of the partial observations is also documented [2]. Our latter result paves the way to machine-learning-assisted experimental analyses of small-scale turbulence for which time series of missing velocity derivatives can be provided by generative algorithms. These results were presented at three conferences: (1) the Annual meeting of the American Physical society, Division of Fluid Dynamics, on November 2021, (2) the annual meeting of the priority program SPP 1881 funded by the German Research Foundation on June 2022, and (3) the EuroMech colloquium 619 on July 2022. They are published in [1-3].
[1] Valentina Valori and Jörg Schumacher, Europhysics Letters, 2021. DOI: 10.1209/0295-5075/134/34004
[2] Valentina Valori, Robert Kräuter and Jörg Schumacher, Physical Review Research, 2022, DOI:
https://doi.org/10.1103/PhysRevResearch.4.023180(odnośnik otworzy się w nowym oknie)[3] Valori Valentina, Alexander Thieme, Christian Cierpka, Joerg Schumacher, 14th International Symposium on Particle Image Velocimetry, ISPIV 2021
