  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.
