Data reconstruction of homogeneous turbulence using Lagrangian Particle Tracking with Shake-The-Box and machine learning

Abstract

This paper proposes a data reconstruction of homogeneous turbulent flow combined machine learning (ML) approach using experimental Lagrangian Particle Tracking (LPT) data with Shake-The-Box (STB). The LPT with STB was adopted to measure a von Kármán flow with a homogeneous turbulent region in the center [1]. The STB results have been stored and a temporal filter using 3rd order B-splines has been applied with optimal weighting coefficients to be used as input for FlowFit data assimilation method [2]. FlowFit data was used as ground truth to train ML algorithm. The low-resolved data of the velocity and acceleration field was reconstructed using an Adaptive Neuro-Fuzzy Inference System (ANFIS) with the downsampled LPT data as an input to predict homogeneous turbulent flow [3]. The training process can be mathematically regarded as an optimization problem to determine the weighting factor.