Deep particle diffusometry: convolutional neural networks for particle diffusometry in the presence of flow and thermal gradients

Abstract

Diffusion coefficient measurement is a helpful tool in revealing various properties of a fluid such as viscosity and temperature. However, determining the diffusion coefficient often requires specialized equipment. Particle-based techniques allow the use of conventional cameras to determine flow properties without any specialized measurement devices. However, the performance of existing methods such as single-particle and correlation-based measurements degrade drastically in the presence of real-world scenarios such as flow and thermal gradients. This work introduces a new method of estimating diffusion coefficient in the presence of flow and thermal gradients named deep particle diffusometry (DPD). The technique uses temporally averaged particle images as inputs and uses convolutional neural networks to predict the underlying diffusion coefficient. The results show that a high fit coefficient R 2 value of 0.99 was achieved with no or known fluid flow conditions and an R 2 value of 0.95 was achieved if the fluid had an arbitrary flow. Next, the generalization ability of the network was shown by training the DPD models on no gradient datasets and testing on datasets with a diffusion coefficient gradient. The networks maintained comparably high R 2 values of 0.96. Next, the DPD models were tested against three conventional methods on various simulated datasets, showing their superior performance in situations where an arbitrary flow was present along with diffusion. Finally, the networks were tested on experimental data and the predictions were compared with conventional methods which resulted in R2 values of 0.97 under the no-flow condition. The results show that the proposed method provides performance similar to existing methods on datasets with no flow or with a known flow and can surpass their performance on datasets that have an arbitrary flow.