Metallurgical Engineering

Metallurgical Engineering

Modeling of fluid flow in porous catalysts base aluminum with simulated using computational fluid dynamics and linear regression methods

Document Type : Research Paper

Authors
MohammadAli Ahmadzadeh 1
Azadeh Jafarizadeh 1
Masoud Panjepour 2
1 Department of Materials Engineering, Isfahan university of technology, Isfahan, Iran.
2 Material processing and fabriction group, Isfahan University of Technology (IUT).Isfahan . Iran
10.22076/me.2024.2027105.1402
Abstract
The unique properties of metal foams have led to the increasing use of these structures in industrial applications such as catalysts. To accurately design engineering systems and high-efficiency catalysts, it is necessary to study the fluid flow behavior. Therefore, this study aimed to investigate the effect of different geometric parameters of the porous medium on fluid flow and to present a linear regression model for predicting Forichmer equation coefficients (ΔP / L = αv + βv2) using computational fluid dynamics (CFD) and linear regression statistical method. For this purpose, in the first stage, foams based on real geometry and with different pores diameter and porosities were made by the Voronoi method. Then, using CFD simulation, the effect of foam structural parameters on fluid flow was investigated. The results indicated that the coefficients of the Forichmer equation and the pressure drop significantly depend on the geometrical parameters of the foam. In the second stage, due to the structural complexity of the foam and the high volume of the computational cost in this method, linear regression models were used to provide a continuous model based on the structural properties of the foam to model the coefficients of the Forichmer equation. The results demonstrated that the model is very accurate in predicting the coefficients of the Forichmer equation in terms of the geometric parameters of the foams. Also, the results obtained from linear regression models demonstrated that the proposed approach effectively predicts fluid flow in large-scale porous catalysts with minimal error.
Keywords
Catalyst
Simulation
Fluid flow
CFD
Linear regression model
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  • Receive Date 22 April 2024
  • Revise Date 25 September 2025
  • Accept Date 01 July 2024