Reconstruction of particle image velocimetry data using flow-based features and validation index: a machine learning approach

Abstract

Reconstruction of flow field from real sparse data by a physics-oriented approach is a current challenge for fluid scientists in the AI community. The problem includes feature recognition and implementation of AI algorithms that link data to a physical feature space in order to produce reconstructed data. The present article applies machine learning approach to study contribution of different flow-based features with practical fluid mechanics applications for reconstruction of the missing data of turbomachinery particle image velocimetry measurements. Support vector regression (SVR) and multi-layer perceptron (MLP) are selected as two robust regressors capable of modelling non-linear fluid flow phenomena. The proposed flow-based features are optimally scaled and filtered to extract the best configuration. In addition to conventional data-based validation of the regressors, a metric is proposed that reflects mass conservation law as an important requirement for a physical flow reproduction. For a velocity field including 25% of clustered missing data, the reconstruction accuracy achieved by SVR in terms of R 2-score is as high as 0.993 for the in-plane velocity vectors in comparison with that obtained by MLP which is up to 0.981. In terms of mass conservation metric, the SVR model by R 2-score up to 0.96 is considerably more accurate than the MLP estimator. For extremely sparse data with a gappiness of 75%, vector and contour plots from SVR and MLP were consistent with those of the original field. Comparison of the ML models outcome with the reconstruction made by six additional spatial interpolation methods indicates considerably higher performance of the SVR in terms of either the velocity-based or mass conservation based metrics.