具有流线型椭圆支板的周期开胞结构对传质受限催化反应器的强化作用

IF 4.3 Q2 ENGINEERING, CHEMICAL
Claudio Ferroni, Mauro Bracconi, Matteo Ambrosetti, Gianpiero Groppi, Matteo Maestri and Enrico Tronconi*, 
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引用次数: 0

摘要

我们设想具有流线型椭圆支柱的周期性开胞结构(POCS)作为潜在的强化结构催化支撑。流线型的椭圆支板与流动方向对齐,取代了传统的圆柱形支板,旨在减小压降的同时增加催化剂沉积的表面积。反应性计算流体力学模拟对传质系数和摩擦因数进行了基本研究。分析了设计参数(孔隙率ε、支板轴线与流向夹角α、支板椭圆伸长率R)的影响。增大椭圆延伸率R和减小角度α对POCS输运性能有显著影响。在低R条件下,得到的舍伍德数和摩擦因数与带圆支板的规则菱形晶格相同。对于高伸长率,几何形状接近蜂窝状,并且蜂窝状的性能在渐近条件下恢复。减小α导致流线型结构,摩擦系数和输运系数减小,与先前对圆形支撑的POCS的观察结果一致。α和R对输运系数和摩擦系数的影响不能与个体贡献分离。为了解决这一复杂性,提出了一种机器学习辅助方法来预测POCS的传质系数和摩擦系数作为设计参数的函数。具有增强性能的POCS的特点是输运系数和压降之间的权衡指数比最先进的蜂窝大2倍。这些优点体现在POCS的各种工作条件和设计参数中,显示了其制造的高度灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Periodic Open Cellular Structures with Streamlined Elliptical Struts for the Intensification of Mass Transfer-Limited Catalytic Reactors

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.

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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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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)
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