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Upper-room ultraviolet germicidal irradiation (UVGI) system is recently in the limelight as a potentially effective method to mitigate the risk of airborne virus infection in indoor environments. However, few studies quantitatively evaluated the relationship between ventilation effectiveness and virus disinfection performance of a UVGI system. The objective of this study is to investigate the effects of ventilation strategy on detailed airflow distributions and UVGI disinfection performance in an occupied classroom. Three-dimensional computational fluid dynamics (CFD) simulations were performed for representative cooling, heating, and ventilation scenarios. The results show that when the ventilation rate is 1.1 h -1 (the minimum ventilation rate based on ASHRAE 62.1), enhancing indoor air circulation with the mixing fan notably improves the UVGI disinfection performance, especially for cooling with displacement ventilation and all-air-heating conditions. However, increasing indoor air mixing yields negligible effect on the disinfection performance for forced-convection cooling condition. The results also reveal that regardless of indoor thermal condition, disinfection effectiveness of a UVGI system increases as ventilation effectiveness is close to unity. Moreover, when the room average air speed is >0.1 m/s, upper-room UVGI system could yield about 90% disinfection effect for the aerosol size of 1 μm-10 μm. The findings of this study imply that upper-room UVGI systems in indoor environments (i.e., classrooms, hospitals) should be designed considering ventilation strategy and occupancy conditions, especially for occupied buildings with insufficient air mixing throughout the space., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by CFD data can be used for fast and accurate prediction of indoor airflow, but current methods have limitations, such as only predicting limited outputs rather than the entire flow field. Furthermore, conventional AI models are not always designed to predict different outputs based on a continuous input range, and instead make predictions for one or a few discrete inputs. This work addresses these gaps using a conditional generative adversarial network (CGAN) model approach, which is inspired by current state-of-the-art AI for synthetic image generation. We create a new Boundary Condition CGAN (BC-CGAN) model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter, such as a boundary condition. Additionally, we design a novel feature-driven algorithm to strategically generate training data, with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the AI model. The BC-CGAN model is evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box. We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria. The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5% relative error and up to about 75,000 times faster when compared to reference CFD simulations. The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the AI models while maintaining prediction accuracy, particularly when the flow changes non-linearly with respect to an input., (© Lawrence Berkeley National Laboratory, under exclusive licence to Tsinghua University Press 2023.)

The ongoing Covid-19 pandemic, and its associated public health and socioeconomic burden, has reaffirmed the necessity for a comprehensive understanding of flow-mediated infection transmission in occupied indoor spaces. This is an inherently multiscale problem, and suitable investigation approaches that can enable evidence-based decision-making for infection control strategies, interventions, and policies; will need to account for flow physics, and occupant behavior. Here, we present a mesoscale infection transmission model for human occupied indoor spaces, by integrating an agent-based human interaction model with a flow physics model for respiratory droplet dynamics and transport. We outline the mathematical and algorithmic details of the modeling framework, and demonstrate its validity using two simple simulation scenarios that verify each of the major sub-models. We then present a detailed case-study of infection transmission in a model indoor space with 60 human occupants; using a systematic set of simulations representing various flow scenarios. Data from the simulations illustrate the utility and efficacy of the devised mesoscale model in resolving flow-mediated infection transmission; and elucidate key trends in infection transmission dynamics amongst the human occupants., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier B.V. All rights reserved.)

The spread of respiratory diseases via aerosol particles in indoor settings is of significant concern. The SARS-CoV-2 virus has been found to spread widely in confined enclosures like hotels, hospitals, cruise ships, prisons, and churches. Particles exhaled from a person indoors can remain suspended long enough for increasing the opportunity for particles to spread spatially. Careful consideration of the ventilation system is essential to minimise the spread of particles containing infectious pathogens. Previous studies have shown that indoor airflow induced by opened windows would minimise the spread of particles. However, how outdoor airflow through an open window influences the indoor airflow has not been considered. The aim of this study is to provide a clear understanding of the indoor particle spread across multiple rooms, in a situation similar to what is found in quarantine hotels and cruise ships, using a combination of HVAC (Heating, Ventilation and Air-Conditioning) ventilation and an opening window. Using a previously validated mathematical model, we used 3D CFD (computational fluid dynamics) simulations to investigate to what extent different indoor airflow scenarios contribute to the transport of a single injection of particles ( 1 . 3 μ m ) in a basic 3D multi-room indoor environment. Although this study is limited to short times, we demonstrate that in certain conditions approximately 80% of the particles move from one room to the corridor and over 60% move to the nearby room within 5 to 15 s. Our results provide additional information to help identifying relevant recommendations to limit particles from spreading in enclosures., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.)

The world is having an unprecedented time due to the pandemic. Currently, more than 93 million people have been infected, and over 2 million people have passed away since 2020. SARS-CoV-2 has forced people to change their lifestyles and patterns. Under the pandemic, buildings are no longer safe shelters. The infected transmit infectious viruses to other occupants by direct contact or indirect contact (i.e., indoor airflow). In addition, the airflow from electric heat pump systems can propel indirect contact in indoor spaces. However, the impact of airflow is still not sufficiently identified to develop virus control strategies in buildings. Therefore, this study selected a restaurant in Seoul, Korea, to experiment with airborne virus transmission of direct airflow in winter using virus-similar particles. The results of this study verified the potential exposure of droplets or aerosols to occupants that can be delivered by air current from heating systems in winter. The effect of kitchen hoods was also confirmed as additional ventilation equipment without additional budget investment in restaurants. The recommendations of this study are expected to improve the guidelines for restaurants to ensure occupant's safety during the COVID-19 period., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors. Published by Elsevier Ltd.)

Human activity is known to leave significant effects on indoor airflow patterns. These patterns are carefully designed for many facilities such as cleanrooms, pharmaceutical settings, and healthcare environments, where human-induced wakes contribute to the transport of contaminants. Therefore, the knowledge about these wakes as it relates to indoor air quality is critical. As a result, a series of experiments were conducted in a controlled chamber to study the three-dimensional effects of true human walking on airflow. Experiments were designed to capture the effect of human walking under three different flow conditions, and for two different walking schemes. The results show that the effect of walking on the airflow is not negligible and can sustain up to 10 seconds after the moving body has passed. Walking on a straight line creates significant change in the velocity normal to the walking path and vertical to the plane of walking movement. These changes were detectable till 1.0 m away from the walking track. Also, the similarity between airflow patterns of walking once and twice illustrated a promising opportunity of predicting the flow patterns of random walk from a set of base cases., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

The three-dimensional multiple-relaxation-time LB (MRT-LB) and Lagrangian particle tracking methods were applied to simulate turbulent airflow and particle dispersion in a ventilated room with a partition. The turbulent airflow was simulated by large eddy simulation (LES) using the MRT-LB method with the Smagorinsky model. This method was verified by comparing it with experimental and other numerical results. Good agreement was observed between airflow simulation and experimental data. It is also demonstrated that the LES carried out by the MRT-LB method can produce airflow results very similar to the RNG LES and provide better prediction than the standard and RNG k - ε models. In order to further improve the efficiency of the MRT-LB method, the multi-block grid refinement (MBGR) technique was used. The accuracy and efficiency of the MBGR and the consistency of physical quantities across the interface were investigated. In simulation of particle dispersion in the model room, particles with diameters of 1 and 10 µm were considered. It is shown that this model can successfully capture dispersion characteristics of particles., (Copyright © 2013 Elsevier Ltd. All rights reserved.)