{"title":"Periodic Open Cellular Structures with Streamlined Elliptical Struts for the Intensification of Mass Transfer-Limited Catalytic Reactors","authors":"Claudio Ferroni, Mauro Bracconi, Matteo Ambrosetti, Gianpiero Groppi, Matteo Maestri and Enrico Tronconi*, ","doi":"10.1021/acsengineeringau.4c0005710.1021/acsengineeringau.4c00057","DOIUrl":null,"url":null,"abstract":"<p >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 <i>R</i>) are evaluated. The POCS transport properties are significantly affected by increasing ellipse elongation <i>R</i> and decreasing the angle α. For low <i>R</i>, 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 <i>R</i> 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.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 2","pages":"168–182 168–182"},"PeriodicalIF":4.3000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.4c00057","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.4c00057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)