Airflow dynamics in an emergency department: A CFD simulation study to analyse COVID-19 dispersion

A model for SARS-CoV-2 virus transmission on the upper deck of a passenger ship bound for a short trip

Public transportation plays a critical role in meeting transportation demands, particularly in densely populated areas. The COVID-19 pandemic has highlighted the importance of public health measures, including the need to prevent the spread of the virus through public transport. The spreading of the virus on a passenger ship is studied using the Computational Fluid Dynamic (CFD) model and Monte Carlo simulation. A particular focus was the context of Bangladesh, a populous maritime nation in South Asia, where a significant proportion of the population utilizes passenger ships to meet transportation demands. In this regard, a turbulence model is used, which simulates the airflow pattern and determines the contamination zone. Parameters under investigation are voyage duration, number of passengers on board, social distance, the effect of surgical masks, and others. This study shows that the transmission rate of SARS-CoV-2 infection on public transport, such as passenger ships, is not necessarily directly proportional to voyage duration or the number of passengers onboard. This model has the potential to be applied in various other modes of transportation, including public buses and airplanes. Implementing this model may help to monitor and address potential health risks effectively in the public transport networks.

A CFD-based framework to assess airborne infection risk in buildings

The COVID-19 pandemic has prompted huge efforts to further the scientific knowledge of indoor ventilation and its relationship to airborne infection risk. Exhaled infectious aerosols are spread and inhaled as a result of room airflow characteristics. Many calculation methods and assertions on risk assume 'well-mixed' flow conditions. However, ventilation in buildings is complex and often not showing well-mixed conditions. Ventilation guidance is typically based on the provision of generic minimum ventilation flow rates for a given space, irrespective of the effectiveness in the delivery of the supply air. Furthermore, the airflow might be heavily affected by the season, the HVAC ventilation, or the opening of windows, which would potentially generate draughts and non-uniform conditions. As a result, fresh air concentration would be variable depending upon a susceptible receptor's position in a room and, therefore, associated airborne infection risk. A computational fluid dynamics (CFD) and dynamic thermal modelling (DTM) framework is proposed to assess the influence of internal airflow characteristics on airborne infection risk. A simple metric is proposed, the hourly airborne infection rate (HAI) which can easily help designers to stress-test the ventilation within a building under several conditions. A case study is presented, and the results clearly demonstrate the importance of understanding detailed indoor airflow characteristics and associated concentration patterns in order to provide detailed design guidance, e.g. occupancy, supply air diffusers and furniture layouts, to reduce airborne infection risk.

Application of virtual product design to the development of HVAC solution for Incheon International Airport Modular COVID-19 testing center

Owing to the spread of COVID-19, the need for an inspection center that can quickly determine whether travelers using the airport are infected has emerged. For rapid determination, not only polymerase chain reaction tests but also antigen-antibody tests and on-site analysis systems are required. However, because it is time- and cost-intensive to construct a building that meets the standards for negative pressure facilities, modular negative pressure facilities are being installed as alternatives. Existing negative pressure facilities have problems such as increased energy consumption due to outdoor air load and condensation due to differences in indoor and outdoor temperatures and humidities caused by excessive external air inflow to achieve the target negative pressure and air change rate (ACH). In addition, owing to the installation of additional devices, additional construction is required to use them for other purposes in the future. To solve these problems, in this study, energy recovery ventilation (ERV) was employed to develop a heating, ventilation and air conditioning (HVAC) solution for the Incheon International Airport COVID-19 Testing Center. To shorten the development period, virtual product design (VPD) using computational fluid dynamics analysis-based design of experiments was performed. Owing to the application of VPD, the Incheon International Airport Modular COVID-19 Testing Center was completed in 2 weeks. The target pressure was measured in all spaces by applying the optimal conditions derived through VPD. In addition, owing to the application of ERV, the ACH of an airborne infectious isolation room exceeded the value suggested by international organizations.

Window-Windcatcher for Enhanced Thermal Comfort, Natural Ventilation and Reduced COVID-19 Transmission

We investigate and test the effectiveness of a novel window windcatcher device (WWC), as a means of improving natural ventilation in buildings. Using ANSYS CFX, the performance of the window-windcatcher is compared to a control case (no window-windcatcher), in three different geographic locations (Cardiff, Doha and Amman) which are representative of three different types of atmospheric conditions. The proposed window-windcatcher has been shown to improve both thermal comfort and indoor air quality by increasing the actual-to-required ventilation ratio by up to 9% compared to the control case as per the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards. In addition, the locations with minimum velocities have been identified. Those locations correspond to the regions with a lower infection risk of spreading airborne viruses such as SARS-CoV-2, which is responsible for the COVID-19 pandemic.