Hydrogen is a promising energy carrier, exploitable to extract energy from fossil fuels, biomasses as well as intermittent renewable energy sources and its generation from fossil fuels with CO₂ separation at the source is one of the most promising pathway for fossil fuels utilization. This work is focused on a particular configuration, called Reformer and Membrane Module (RMM), which alternates stages of Steam Reforming (SR) reaction with H2 separation stages to overcome the thermodynamic limits of the conventional SR. The configuration has numerous advantages with respect to the more studied and tested membrane reactors and has been tested on a pilot scale during a pilot-scale research project. Although the numerous modelling works appeared in the literature, the design features of the material exchanger (in the so-called RMM architecture) of different geometrical configurations have not been developed and the mass transfer correlations, capable of providing design tools useful for such membrane modules, are not available. The purpose of this work is therefore to apply a physical-mathematical model of the mass transfer, in three different geometries, considering both concentration polarization and membrane permeation, in order to: i) simulate the cited experimental results, ii) estimate the scaling-up correlations for the “material exchange modules”; iii) identify the mass transfer limiting regime in relation to the gas mass flow rate.

Computational rhinology is a specialized branch of biomechanics leveraging engineering techniques for mathematical modelling and simulation to complement the medical field of rhinology. Computational rhinology has already contributed significantly to advancing our understanding of the nasal function, including airflow patterns, mucosal cooling, particle deposition, and drug delivery, and is foreseen as a crucial element in e.g. the development of virtual surgery as a clinical, patient-specific decision support tool. The current paper delves into the field of computational rhinology from a nasal airflow perspective, highlighting the use of computational fluid dynamics to enhance diagnostics and treatment of breathing disorders. The paper consists of three distinct parts – an introduction to and review of the field of computational rhinology, a review of published literature on in vitro and in silico studies of nasal airflow, and the presentation and analysis of previously unpublished high fidelity CFD simulation data of in silico rhinomanometry. While the two first parts of the paper summarize the current status and challenges in the application of computational tools in rhinology, the last part addresses the gross disagreement commonly observed when comparing in silico and in vivo rhinomanometry results. It is concluded that this discrepancy cannot readily be explained by CFD model deficiencies caused by poor choice of turbulence model, insufficient spatial or temporal resolution, or neglecting transient effects. Hence, alternative explanations such as nasal cavity compliance or drag effects due to nasal hair should be investigated.