Periodic Open Cellular Structures with Streamlined Elliptical Struts for the Intensification of Mass Transfer-Limited Catalytic Reactors

• Published in: ACS Engineering Au Vol. 5; no. 2; pp. 168 - 182
• Main Authors: Ferroni, Claudio, Bracconi, Mauro, Ambrosetti, Matteo, Groppi, Gianpiero, Maestri, Matteo, Tronconi, Enrico
• Format: Journal Article
• Language: English
• Published: American Chemical Society 16.04.2025
• Subjects: process intensification; machine learning-based engineering correlations; streamlined struts; computational fluid dynamics; environmental catalysis; periodic open cellular structures
• ISSN: 2694-2488; 2694-2488
• DOI: 10.1021/acsengineeringau.4c00057

We envision periodic open cellular structures (POCS) with streamlined elliptical struts as potential intensified structured catalytic supports. Streamlined elliptical struts aligned to the flow direction substitute conventional cylindrical ones, aiming at reducing the pressure drop while increasing the surface area for catalyst deposition. Reactive computational fluid dynamics simulations are employed for the fundamental investigation of mass transfer coefficients and friction factors. The effects of the design parameters (i.e., porosity ε, angle between the struts’ axis and the streamwise direction α and elliptical strut elongation R) are evaluated. The POCS transport properties are significantly affected by increasing ellipse elongation R and decreasing the angle α. For low R, the same Sherwood number and friction factor are obtained as those for the regular diamond lattice with circular struts. For high elongation, the geometry approaches a honeycomb-like shape, and the properties of the honeycomb are recovered as asymptotic conditions. Decreasing α results in a streamlined structure with a reduced friction factor and a reduced transport coefficient, consistent with previous observations for POCS with circular struts. The effects of α and R on the transport coefficient and friction factor cannot be decoupled from individual contributions. To address this complexity, a machine learning-aided approach was proposed for the prediction of the mass transfer coefficients and friction factors of the POCS as a function of the design parameters. POCS with intensified properties are characterized by a 2-fold larger trade-off index between transport coefficient and pressure drop than the state-of-the-art honeycomb. These advantages are manifested across various operating conditions and design parameters of the POCS, showcasing its high flexibility in manufacturing.