@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} }