arXiv subjects

Reza Namdar

Publications and source records attributed to Reza Namdar.

3 recordsLinked to original sources

arXiv2025

A low-Mach phase field-lattice Boltzmann-finite difference model for reactive gas flows propagating through complex shaped particle assemblies

The present study provides a systematic derivation of a phase-field version of the momentum, mass and heat transport equations, while accounting for chemical reactions in the fluid phase. To achieve this goal, the volume averaging technique is used to reformulate the conservation equations in the presence of multiple phases and their respective diffuse interfaces. It is shown that the structure of the multiphase/diffuse interface version of the conservation equations is very similar to the original single phase/sharp interface formulation. The multiphase character of the problem is accounted for the coupling terms, which act at the interface between adjacent phases. For the special case of a reactive fluid in contact with an inert solid, two coupling parameters are introduced, which control the exchange of momentum and heat at the interface. For numerical solver, a low-Mach number hybrid lattice Boltzmann-finite difference-phase field (LB-FD-PF) framework is developed and implemented in the open source software OpenPhase Academic. Chemical reactions of reactive flows are included into the model by coupling OpenPhase Academic with the open-source chemical kinetics software CANTERA, which delivers details of the chemical reaction mechanisms and the necessary thermodynamic and transport properties of the reacting chemical species. The model is thoroughly validated against alternative numerical simulations of reactive flows as well as experiments.

physics.flu-dyn↗

arXiv2024

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

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.

physics.flu-dyn↗

arXiv2023

Modeling gas flows in packed beds with the lattice Boltzmann method: validation against experiments

This study aims to validate the lattice Boltzmann method and assess its ability to accurately describe the behavior of gaseous flows in packed beds. To that end, simulations of a model packed bed reactor, corresponding to an experimental bench, are conducted, and the results are directly compared with experimental data obtained by Particle Image Velocimetry measurements. It is found that the lattice Boltzmann solver exhibits very good agreement with experimental measurements. Then, the numerical solver is further used to analyze the effect of the number of packing layers on the flow structure and to determine the minimum bed height above which the changes in flow structure become insignificant. Finally, flow fluctuations in time are discussed. The findings of this study provide valuable insights into the behavior of the gas flow in packed bed reactors, opening the door for further investigations involving additionally chemical reactions, as found in many practical applications.

physics.flu-dyn↗