Parametric 3D Convolutional Autoencoder for the Prediction of Flow Fields in a Bed Configuration of Hot Particles

TL;DR

This paper introduces a parametric 3D convolutional autoencoder that predicts flow fields in a bed of hot particles, combining deep learning with physical constraints to achieve accurate results efficiently.

Contribution

It presents a novel data-driven reduced model integrating a parametric 3D convolutional autoencoder with a physical-informed post-processing layer for fluid flow prediction.

Findings

Accurately predicts flow fields with reduced computational cost.

Effectively incorporates physical constraints like no-slip condition.

Demonstrates robustness across variable particle temperatures.

Abstract

The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. In situations where conventional numerical approaches can be computationally expensive, these techniques have shown promise in offering accurate, rapid, and practical solutions for modeling complex fluid flow problems. The success of deep learning is often due to its ability to extract hidden patterns and features from the data, enabling the creation of data-driven reduced models that can capture the underlying physics of the domain. We present a data-driven reduced model for predicting flow fields in a bed configuration of hot particles. The reduced model consists of a parametric 3D convolutional autoencoder. The first part resolves the spatial and temporal dependencies present in the input sequence, while the second part of the architecture is responsible for predicting the solution at the subsequent timestep based on the information gathered from the preceding part. We also propose the utilization of a post-processing non-trainable output layer following the decoding path to incorporate the physical knowledge, e.g., no-slip condition, into the prediction. The evaluation of the reduced model for a bed configuration with variable particle temperature showed accurate results at a fraction of the computational cost required by traditional numerical simulation methods.