Computational analysis of a new biomimetic active ventilation paradigm for indoor spaces

Abstract

Purpose Ventilation of indoor spaces is required for the delivery of fresh air rich in oxygen and the removal of carbon dioxide, pollutants and other hazardous substances. The COVID-19 pandemic brought the topic of ventilating crowded indoors to the front line of health concerns. This study developed a new biologically inspired concept of biomimetic active ventilation (BAV) for interior environments that mimics the mechanism of human lung ventilation, where internal air is continuously refreshed with the external environment. The purpose of this study is to provide a detailed proof-of-concept of the new BAV paradigm using computational models. Design/methodology/approach This study developed computational fluid dynamic models of unoccupied rooms with two window openings on one wall and two BAV modules that periodically translate perpendicular to or rotate about the window openings. This study also developed a time-evolving spatial ventilation efficiency metric for exploring the accumulated refreshment of the interior space. The authors conducted two-dimensional (2D) simulations of various BAV configurations to determine the trends in how the working parameters affect the ventilation and to generate initial estimates for the more comprehensive three-dimensional (3D) model. Findings Simulations of 2D and 3D models of BAV for modules of different shapes and working parameters demonstrated air movements in most of the room with good air exchange between the indoor and outdoor air. This new BAV concept seems to be very efficient and should be further developed. Originality/value The concept of ventilating interior spaces with periodically moving rigid modules with respect to the window openings is a new BAV paradigm that mimics human respiration. The computational results demonstrated that this new paradigm for interior ventilation is efficient while air velocities are within comfortable limits.

Numerical analysis of the SIS infectious disease model with spatial heterogeneity

“…This part primarily examines the impact of host migration and spatial variation on disease dynamics. Mathematical software is used for numerical simulation based on the references (Zhenxiang and Linfei, 2022; Erdogan et al , 2019; Mesgarani et al , 2022; Li et al , 2022; Marom et al , 2023b).…”

“…While there are numerous models in the literature (Jahromi and Rolland, 2022; Abukhaled et al , 2022; Marom et al , 2023a), few consider spatial heterogeneity, and the numerical prediction is far from practical applications. Heterogeneity refers to a common situation found in biological populations, where individual differences arise, such as gender, region and population.…”