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Nick Virgilio

Publications and source records attributed to Nick Virgilio.

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Surface-access limitation in catalytic porous monoliths: Performance diagnosis using pore-resolved CFD

Porous monoliths are promising catalyst supports due to their high surface area, interconnected channels, thermal stability and mechanical robustness. However, their tunable topology complicates design: trade-offs between conversion and pressure drop are not reliably captured by macroscopic descriptors, such as porosity, specific surface area, or tortuosity. Pore-resolved computational fluid dynamics~(PRCFD) addresses this gap by resolving pore-scale flow and transport, enabling diagnostics and discrimination between macroscopically similar structures. We investigate surface-access-boundedness: a case where conversion is limited by flow maldistribution and incomplete utilisation of the catalytic surface, even at low Damköhler numbers (Da<1). Using palladium-nanoparticle-coated silicone monoliths for p-nitrophenol reduction, we perform reactive PRCFD in microcomputed-tomography-based geometries, calibrate a pseudo-heterogeneous eggshell reaction model, and validate transferability across samples and flow rates. We then diagnose surface-access-boundedness via the limited influence of diffusivity and reaction kinetics on conversion. Furthermore, we compare synthesised random monoliths with triply periodic minimal surface structures under matched porosity and surface area. Significantly, the required pumping power can decrease by up to an order of magnitude for the same molar production rate, depending on topology. These results show that, in heterogeneous systems affected by surface-access limitations, reactor performance is governed by structure-dependent surface accessibility rather than intrinsic kinetics or molecular diffusion alone, and that validated reactive PRCFD provides a practical framework to diagnose and compare porous reactor geometries under realistic operating conditions.

physics.flu-dyn

Robust pore-resolved CFD through porous monoliths reconstructed by micro-computed tomography: From digitization to flow prediction

Porous media are ubiquitous in energy storage and conversion, catalysis, biomechanics, hydrogeology, as well as many other fields. These materials possess high surface-to-volume ratios and their complex channels can restrict and guide the flow. However, optimizing design parameters for specific applications remains challenging due to the intricate structure of porous media. Pore-resolved CFD reveals the effects of their structure on flow characteristics, but is limited by the performance of mesh generation algorithms for such complex geometries. To alleviate this issue, we use a sharp immersed boundary method which enables usage of Cartesian, non-conformal grids, within a massively parallel finite element framework. This method preserves the order convergence of the scheme and allows for adaptive mesh refinement (AMR). We introduce a radial basis function-based representation of solids that allows to solve the flow through complex geometries with precision. We verify the method using the method of manufactured solutions. We validate it using pressure drop measurements through porous silicone monoliths digitized by X-ray computed microtomography, for pore Reynolds numbers up to 30. Simulations are conducted using grids of 200M cells distributed over 8k cores, which would require 16 times more cells without AMR. Results reveal that pore network structure is the principal factor describing pressure evolution and that preferential channels are dominant at this scale. In this work, we demonstrate a robust and efficient workflow for pore-resolved simulations of porous monoliths. This work bridges the gap between sub-millimetric flow and macroscopic properties, which will open the door to design and optimize processes through the usage of physics-based digital twins of complex porous media.

physics.flu-dyn