@article{HasanuzzamanEivaziMerboldetal.2022, author = {Hasanuzzaman, Gazi and Eivazi, Hamidreza and Merbold, Sebastian and Egbers, Christoph and Vinuesa, Ricardo}, title = {Enhancement of PIV measurements via physics-informed neural networks}, doi = {10.1088/1361-6501/aca9eb}, year = {2022}, abstract = {Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component (2D3C) stereo particle-image velocimetry datasets, to reconstruct the mean velocity field and correct measurements errors in the data. Within this framework, the PINNsbased model solves the Reynolds-averaged-Navier-Stokes (RANS) equations for zeropressure-gradient turbulent boundary layer (ZPGTBL) without a prior assumption and only taking the data at the PIV domain boundaries. The TBL data has different flow conditions upstream of the measurement location due to the effect of an applied flow control via uniform blowing. The developed PINN model is very robust, adaptable and independent of the upstream flow conditions due to different rates of wall-normal blowing while predicting the mean velocity quantities simultaneously. Hence, this approach enables improving the mean-flow quantities by reducing errors in the PIV data. For comparison, a similar analysis has been applied to numerical data obtained from a spatially-developing ZPGTBL and an adverse-pressure-gradient (APG) TBL over a NACA4412 airfoil geometry. The PINNs-predicted results have less than 1\% error in the streamwise velocity and are in excellent agreement with the reference data. This shows that PINNs has potential applicability to shear-driven turbulent flows with different flow histories, which includes experiments and numerical simulations for predicting high-fidelity data.}, subject = {Machine learning; Particle image velocimetry; Turbulent boundary layer; Large Eddy Simulation; Measurement; Maschinelles Lernen; Turbulente Grenzschicht; Physikalisch informiertes neuronales Netz; Messung; Turbulente Grenzschicht; Neuronales Netz; Maschinelles Lernen; Particle-Image-Velocimetry}, language = {en} } @article{BuchwaldHasanuzzmanMerboldetal.2023, author = {Buchwald, Tom and Hasanuzzman, Gazi and Merbold, Sebastian and Schanz, Daniel and Egbers, Christoph and Schr{\"o}der, Andreas}, title = {Large-scale flow field and aerosol particle transport investigations in a classroom using 2D-Shake-The-Box Lagrangian Particle Tracking}, series = {Heliyon}, volume = {9}, journal = {Heliyon}, number = {12}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2405-8440}, doi = {10.1016/j.heliyon.2023.e22826}, year = {2023}, abstract = {Infections with COVID-19 in enclosed public spaces, where virus-laden aerosol particles can accumulate over time, have significantly contributed to the rapid spread of the virus. It is therefore of great importance to understand the transport and dispersion process of aerosol particles in such spaces, especially against the background of future pandemics. In this work, we present a Lagrangian-Particle-Tracking experiment to assess the mixed convective flow in a classroom with different ventilation strategies. For this purpose, thermal plumes were created by heated dummies, and a collimated LED light-sheet with ~0.4 m thickness was used for illumination of helium filled soap bubbles (HFSB) acting as passive tracer particles. In this way, the Lagrangian trajectories of the particles were recorded at two approximately 4.2 m × 2.8 m large fields using the novel 2D-Shake-The-Box-Method. As a result, time-resolved trajectories of over 300,000 simultaneously tracked HFSB have been reconstructed, so that both small-scale and large-scale properties of the flow are visualized quantitatively across the entire cross-section of the room. The trajectories show that the thermal plumes create lengthwise circulating vortices, which cannot be destroyed across the entire cross-section of the room by opening or tilting a window. Furthermore, the mixing in the room through the operation of an air purifier is higher compared to opening a window, which suggests that this strategy in combination with its air filtering capability is the most effective strategy to prevent infections.}, subject = {Shake-the-box; Lagrangian-particle-tracking; Aerosol particle spreading; Room ventilation; Covid-19; Lagrangesche Partikelverfolgung; Ausbreitung von Aerosolpartikeln; Raumbel{\"u}ftung; Aerosol; Lagrange-Darstellung ; Bel{\"u}ftung; Klassenzimmer}, language = {en} }