Prediction of particle-laden pipe flows using deep neural network models

Abstract

An accurate and fast prediction of particle-laden flow fields is of particular relevance for a wide variety of industrial applications. The motivation for this research is to evaluate the applicability of deep learning methods for providing statistical properties of the carrier and dispersed phases in a particle-laden vertical pipe flow. Deep neural network (DNN) models are trained for different dependent variables using 756 high-fidelity datasets acquired from point-particle large-eddy simulations for different values of Stokes number, St, bulk particle volume fraction, Φ¯v, and wall roughness, Δγ, for the range St=10−500, Φ¯v=5×10−5−10−3, and Δγ=1°−6°. The considered parameter space corresponds to the inertia-dominated regime and covers a large extent of the typical conditions in powder-based laser metal deposition. We find that the DNN models capture the nonlinear dynamics of the system and recreate the statistical properties of the particle-laden pipe flow. However, DNN predictions of the particle statistics are of higher accuracy compared to the fluid statistics, which is attributed to the highly non-monotonic dependence of the fluid statistics on the control parameters. Owing to significantly decreased time-to-solution, the trained DNN models are promising as surrogate models to expedite model development and design process of various industrial applications.