A Review on Applications of CFD Modeling in COVID-19 Pandemic

Creating respiratory pathogen-free environments in healthcare and nursing-care settings: a comprehensive review

Hospital- and nursing-care-acquired infections are a growing problem worldwide, especially during epidemics, posing a significant threat to older adults in geriatric settings. Intense research during the COVID-19 pandemic highlighted the prominent role of aerosol transmission of pathogens. Aerosol particles can easily adsorb different airborne pathogens, carrying them for a long time. Understanding the dynamics of airborne pathogen transmission is essential for controlling the spread of many well-known pathogens, like the influenza virus, and emerging ones like SARS-CoV-2. Particles smaller than 50 to 100 µm remain airborne and significantly contribute to pathogen transmission. This review explores the journey of pathogen-carrying particles from formation in the airways, through airborne travel, to deposition in the lungs. The physicochemical properties of emitted particles depend on health status and emission modes, such as breathing, speaking, singing, coughing, sneezing, playing wind instruments, and medical interventions. After emission, sedimentation and evaporation primarily determine particle fate. Lung deposition of inhaled aerosol particles can be studied through in vivo, in vitro, or in silico methods. We discuss several numerical lung models, such as the Human Respiratory Tract Model, the LUng Dose Evaluation Program software (LUDEP), the Stochastic Lung Model, and the Computational Fluid Dynamics (CFD) techniques, and real-time or post-evaluation methods for detecting and characterizing these particles. Various air purification methods, particularly filtration, are reviewed for their effectiveness in healthcare settings. In the discussion, we analyze how this knowledge can help create environments with reduced PM2.5 and pathogen levels, enhancing safety in healthcare and nursing-care settings. This is particularly crucial for protecting older adults, who are more vulnerable to infections due to weaker immune systems and the higher prevalence of chronic conditions. By implementing effective airborne pathogen control measures, we can significantly improve health outcomes in geriatric settings.

3D modelling and simulation of thermal effects and dispersion of particles carrying infectious respiratory agents in a railway transport coach

Even though the COVID-19 pandemic now belongs to the long history of infectious diseases that have struck humanity, pathogenic biological agents continue to pose a recurring threat in private places, but also and mainly in places where the public congregates. In our recent research published in this journal in 2022 and 2023, we considered the illustrative example of a commuter train coach in which a symptomatic or asymptomatic passenger, assumed to be infected with a respiratory disease, sits among other travellers. The passenger emits liquid particles containing, for example, COVID-19 virions or any other pathogen. The size spectrum of particles varies depending on whether they are produced during breathing, speaking, coughing or sneezing. More specifically, droplets associated with breathing are in the range of 1-10 µm in aerodynamic diameter, while at the other end of the spectrum, drops associated with coughing can reach 100-1000 µm. In the first part of our research, we used Computational Fluid Dynamics (CFD) to model and simulate in 3D the transport and dispersion of particles from 1 µm to 1 mm in the turbulent flow generated by the ventilation of the railway coach. We used both the Eulerian approach and the Lagrangian approach and showed that the results were strictly similar and illustrated the very distinct aerodynamics, on one hand, of the aerosol of droplets suspended in the air and, on the other hand, of the drops falling or behaving like projectiles depending on their initial speed. In the second part of our research, we developed a model of filtration through a typical surgical mask and possible leaks around the mask if it is poorly adjusted. We resumed the twin experiment of the railway coach and compared the distribution of droplets depending on whether the passengers (including the infected one) wear masks or not and whether the masks are perfectly fitted or worn loosely. Our method made it possible to quantify the particles suspended in the air of the railway coach depending on whether the infected passenger wore their mask more or less well. In this third article, we specifically explore how thermal effects due to the presence of passengers influence the spatio-temporal distribution in the railway coach of aerosols produced by the breathing infected person. We demonstrate that the influence of thermal effects on aerodynamics is very significant and can be very favourable for air decontamination if the ventilation system is judiciously configured. Beyond its application to a commuter train, our work confirms the value of validated CFD tools for describing the airflow and dispersion of particles in complex spaces that do not always allow experimentation. The models that we have developed are applicable to any other semi-confined, ventilated public place, such as a classroom, a hospital room or a performance hall, and they enable the objective assessment of whether the occupation of these spaces could be critical with regard to infectious contamination and of how to limit this ubiquitous risk.

Airflow Modeling for Citrus under Protective Screens

This study explores the development and validation of an airflow model to support climate prediction for Citrus Under Protective Screens (CUPS) in California. CUPS is a permeable screen structure designed to protect a field of citrus trees from large insects including the vector that causes the devastating citrus greening disease. Because screen structures modify the environmental conditions (e.g., temperature, relative humidity, airflow), farm management and treatment strategies (e.g., pesticide spraying events) must be modified to account for these differences. Toward this end, we develop a model for predicting wind speed and direction in a commercial-scale research CUPS, using a computational fluid dynamics (CFD) model. We describe the model and validate it in two ways. In the first, we model a small-scale replica CUPS under controlled conditions and compare modeled and measured airflow in and around the replica structure. In the second, we model the full-scale CUPS and use historical measurements to "back test" the model's accuracy. In both settings, the modeled airflow values fall within statistical confidence intervals generated from the corresponding measurements of the conditions being modeled. These findings suggest that the model can aid decision support and smart agriculture solutions for farmers as they adapt their farm management practices for CUPS structures.

Modeling Realistic Geometries in Human Intrathoracic Airways

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models.

Numerical simulation application in occupational health studies: a review

Most occupational hazardous agents in workplaces should be evaluated and controlled. Different methods exist for identifying, evaluating and controlling these agents, such as numerical simulation tools. Numerical simulations can help experts to improve occupational health. Due to the importance and abilities of numerical simulations, this study divided occupational hazardous agents into 10 subgroups. These subgroups included air pollution, ventilation, respiratory airways, noise and vibration, lighting, radiation, ergonomics, fire and explosion, risk assessment and personal protective equipment. Recent research studies in each subgroup were then reviewed, and the codes and software used in simulations were determined. The results show that Fluent software and k-ϵ turbulence models are the most used in occupational health studies simulations. Today, different codes and software have been developed for simulation, and we suggest their use in occupational health studies.

Towards optimal design of patient isolation units in emergency rooms to prevent airborne virus transmission: From computational fluid dynamics to data-driven modeling

BACKGROUND

Patient isolation units (PIUs) can be an effective method for effective infection control. Computational fluid dynamics (CFD) is commonly used for PIU design; however, optimizing this design requires extensive computational resources. Our study aims to provide data-driven models to determine the PIU settings, thereby promoting a more rapid design process.

METHOD

Using CFD simulations, we evaluated various PIU parameters and room conditions to assess the impact of PIU installation on ventilation and isolation. We investigated particle dispersion from coughing subjects and airflow patterns. Machine-learning models were trained using CFD simulation data to estimate the performance and identify significant parameters.

RESULTS

Physical isolation alone was insufficient to prevent the dispersion of smaller particles. However, a properly installed fan filter unit (FFU) generally enhanced the effectiveness of physical isolation. Ventilation and isolation performance under various conditions were predicted with a mean absolute percentage error of within 13%. The position of the FFU was found to be the most important factor affecting the PIU performance.

CONCLUSION

Data-driven modeling based on CFD simulations can expedite the PIU design process by offering predictive capabilities and clarifying important performance factors. Reducing the time required to design a PIU is critical when a rapid response is required.

Airborne influenza virus shedding by patients in health care units: Removal mechanisms affecting virus transmission

In this study, we characterize the distribution of airborne viruses (influenza A/B) in hospital rooms of patients with confirmed infections. Concurrently, we monitored fine particulate matter (PM2.5 & PM10) and several physical parameters including the room air exchange rate, temperature, and relative humidity to identify corresponding correlations with virus transport and removal determinants. The results continue to raise concerns about indoor air quality (IAQ) in healthcare facilities and the potential exposure of patients, staff and visitors to aerosolized viruses as well as elevated indoor PM levels caused by outdoor sources and/or re-suspension of settled particles by indoor activities. The influenza A virus was detected in 42% of 33 monitored rooms, with viruses detectible up to 1.5 m away from the infected patient. Active coughing was a statistically significant variable that contributed to a higher positive rate of virus detection in the collected air samples. Viral load across patient rooms ranged between 222 and 5,760 copies/m3, with a mean of 820 copies/m3. Measured PM2.5 and PM10 levels exceeded IAQ daily exposure guidelines in most monitored rooms. Statistical and numerical analyses showed that dispersion was the dominant viral removal pathway followed by settling. Changes in the relative humidity and the room's temperature were had a significant impact on the viral load removal. In closure, we highlight the need for an integrated approach to control determinants of IAQ in patients' rooms.

Dry Powder Formulations for Inhalation Require a Smaller Aerodynamic Diameter for Usage at High Altitude

BACKGROUND

High Altitude Pulmonary Edema (HAPE) seriously threatens the health of people at high altitudes. There are drug treatments for HAPE, and dry powder formulations (DPFs) represent a rapid and accessible delivery vehicle for these drugs. However, there are presently no reports on the inhalability of DPFs in low-pressure environments. Given the reduced atmospheric pressure typical at high altitudes, conventional DPFs might not be suitable for inhalation. Therefore, it is necessary to elucidate the deposition behaviors of dry powder in the respiratory tract at low pressure, as well as to improve their pulmonary deposition efficiency via adjustments to their formulation and design.

METHODS

The effect of air pressure, inspiratory velocity, and particle properties (such as size, density, and aerodynamic diameter) on pulmonary deposition of DPFs was calculated by a computational fluid dynamics (CFD)-coupled discrete phase model. DPFs of various aerodynamic diameters were prepared by spray drying, and the inhalability of these DPFs in a low-pressure environment was evaluated in mice. Finally, a mouse model of HAPE was established, and the treatment of HAPE by nifedipine-loaded DPFs with small aerodynamic diameter was validated.

RESULTS

CFD results showed that low pressure decreased the deposition of DPFs in the lungs. At 0.5 standard atmosphere, DPFs with aerodynamic diameter of ∼2.0 μm could not enter the lower respiratory tract; however, a decrease in the physical diameter, density, and, consequently, the aerodynamic diameter of the DPFs was able to enhance pulmonary deposition of these powders. To validate the CFD results, three kinds of dry powder with aerodynamic diameters of 0.66, 0.98, and 2.00 μm were prepared by spray drying. Powders with smaller aerodynamic diameter could be inhaled into the lungs of mice more effectively, and, consequently could ameliorate the progression of HAPE more effectively than conventional powders. These results were consistent with the CFD results.

CONCLUSIONS

Low atmospheric pressure can prevent the pulmonary deposition of DPFs at high altitudes. Compared with conventional DPFs, powders with smaller aerodynamic diameter can be effectively inhaled at these pressures and thus might be more suitable for the treatment the HAPE.

Resilient Hospital Design: From Crimean War to COVID-19

OBJECTIVES

Serious COVID-19 nosocomial infection has demonstrated a need to design our health services in a different manner. Triggered by the current crisis and the interest in rapid deployable hospital, this article discusses how hospital building layouts can be improved to streamline the patient pathways and thus to reduce the risk of hospital-related infections. Another objective of this work is to explore the possibility to develop flexible and scalable hospital building layouts through modular construction. This enables hospitals to better cope with different future demands and thereby enhance the resilience of the healthcare facilities.

BACKGROUND

During the first wave of COVID-19, approximate one-seventh to one-fifth COVID-19 patients and majority of infected healthcare workers acquired the disease in NHS hospitals. Similar issues emerged during the Crimean War (1853-1856) when more soldiers died from infectious diseases rather than of battlefield casualties in Scutari Hospital. This led to an important collaborative work between Florence Nightingale, who looked into this problem statistically, and Isambard Kingdom Brunel, who designed the rapid deployment Renkioi Hospital which yielded a death rate 90% lower than that in Scutari Hospital. While contemporary medical research and practice have moved beyond Nightingale's concept of contagion, challenges of optimizing hospital building layouts to support healing and effectively combat nosocomial infections still pose elusive problems that require further investigation.

METHODS

Through case study investigations, this article evaluates the risk of nosocomial infections of airborne transmissions under different building layouts, and this provides essential data for infection control in the new-build or refurbished healthcare projects.

RESULTS

Improved hospital layout can be achieved through reconfiguration of rooms and concourse. Design interventions through evidence-based infection risk analysis can reduce congestion and provide extra separation and compartmentalization which will contribute the reduced nosocomial infection rate.

CONCLUSIONS

A resilient hospital shall be able to cope with unexpected circumstances and be flexible to change when new challenges arise, without compromising the safety and well-being of frontline medical staff and other patients. Such an organizational resilience depends on not only flexible clinical protocols but also flexible hospital building layouts. The latter allows hospitals to get better prepared for rapidly changing patient expectations, medical advances, and extreme weather events. The reconfigurability of an existing healthcare facility can be further enhanced through modular construction, standardization of building components, and additional space considered.

Large eddy simulation of sneeze plumes and particles in a poorly ventilated outdoor air condition: A case study of the University of Houston main campus

Since the outbreak of the COVID-19 pandemic, many previous studies using computational fluid dynamics (CFD) have focused on the dynamics of air masses, which are believed to be the carriers of respiratory diseases, in enclosed indoor environments. Although outdoor air may seem to provide smaller exposure risks, it may not necessarily offer adequate ventilation that varies with different micro-climate settings. To comprehensively assess the fluid dynamics in outdoor environments and the efficiency of outdoor ventilation, we simulated the outdoor transmission of a sneeze plume in "hot spots" or areas in which the air is not quickly ventilated. We began by simulating the airflow over buildings at the University of Houston using an OpenFOAM computational fluid dynamics solver that utilized the 2019 seasonal atmospheric velocity profile from an on-site station. Next, we calculated the length of time an existing fluid is replaced by new fresh air in the domain by defining a new variable and selecting the hot spots. Finally, we conducted a large-eddy simulation of a sneeze in outdoor conditions and then simulated a sneeze plume and particles in a hot spot. The results show that fresh incoming air takes as long as 1000 s to ventilate the hot spot area in some specific regions on campus. We also found that even the slightest upward wind causes a sneeze plume to dissipate almost instantaneously at lower elevations. However, downward wind provides a stable condition for the plume, and forward wind can carry a plume even beyond six feet, the recommended social distance for preventing infection. Additionally, the simulation of sneeze droplets shows that the majority of the particles adhered to the ground or body immediately, and airborne particles can be transported more than six feet, even in a minimal amount of ambient air.

A Review of Artificial Neural Network Models Applied to Predict Indoor Air Quality in Schools

BACKGROUND

Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures.

METHODS

This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion.

RESULTS

After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models.

CONCLUSIONS

This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.

Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence

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.

Infection control for COVID-19 in hospital examination room

Healthcare providers are vulnerable to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) because of their close proximity to patients with coronavirus disease 2019. SARS-CoV-2 is mainly transmitted via direct and indirect contact with respiratory droplets, and its airborne transmission has also been identified. However, evidence for environmental factors is scarce, and evidence-based measures to minimize the risk of infection in clinical settings are insufficient. Using computational fluid dynamics, we simulated exhalation of large and small aerosol particles by patients in an otolaryngology examination room, where medical procedures require the removal of a face mask. The effects of coughing were analyzed, as well as those of humidity as a controllable environmental factor and of a suction device as an effective control method. Our results show that a suction device can minimize aerosol exposure of healthcare workers by efficiently removing both large (11.6-98.2%) and small (39.3-99.9%) aerosol particles. However, for coughing patients, the removal efficiency varies inversely with the particle size, and the humidity notably affects the aerosol behavior, indicating the need for countermeasures against smaller aerosols. Overall, these results highlight the potential and limitation of using a suction device to protect against SARS-CoV-2 and future respiratory infections.

A numerical approach for preventing the dispersion of infectious disease in a meeting room

Airborne transmission of respiratory aerosols carrying infectious viruses has generated many concerns about cross-contamination risks, particularly in indoor environments. ANSYS Fluent software has been used to investigate the dispersion of the viral particles generated during a coughing event and their transport dynamics inside a safe social-distance meeting room. Computational fluid dynamics based on coupled Eulerian-Lagrangian techniques are used to explore the characteristics of the airflow field in the domain. The main objective of this study is to investigate the effects of the window opening frequency, exhaust layouts, and the location of the air conditioner systems on the dispersion of the particles. The results show that reducing the output capacity by raising the concentration of suspended particles and increasing their traveled distance caused a growth in the individuals' exposure to contaminants. Moreover, decreasing the distance between the ventilation systems installed location and the ceiling can drop the fraction of the suspended particles by over 35%, and the number of individuals who are subjected to becoming infected by viral particles drops from 6 to 2. As well, the results demonstrated when the direction of input airflow and generated particles were the same, the fraction of suspended particles of 4.125%, whereas if the inputs were shifted to the opposite direction of particle injection, the fraction of particles in fluid increased by 5.000%.

Multi-objective performance assessment of HVAC systems and physical barriers on COVID-19 infection transmission in a high-speed train

A computational fluid dynamics (CFD) simulation was performed to model and study the transmission risk associated with cough-related SARS-CoV-2 droplets in a real-world high-speed train (HST). In this study, the evaporating of the droplets was considered. Simulation data were post-processed to assess the fraction of the particles deposited on each passenger's face and body, suspended in air, and escaped from exhausts. Firstly, the effects of temperature, relative humidity, ventilation rate, injection source, exhausts' location and capacity, and adding the physical barriers on evaporation and transport of respiratory droplets are investigated in long distance HST. The results demonstrate that overall, 6–43% of the particles were suspended in the cabin after 2.7 min, depending on conditions, and 3–58% of the particles were removed from the cabin in the same duration. Use of physical barriers and high ventilation rate is therefore recommended for both personal and social protection. We found more exhaust capacity and medium relative humidity to be effective in reducing the particles' transmission potential across all studied scenarios. The results indicate that reducing ventilation rate and exhaust capacity, increased aerosols shelf time and dispersion throughout the cabin.

Investigation of the effects of mechanical and underfloor heating systems on the COVID-19 viruses distribution

Investigation of the spread of pollutants and especially pathogenic particles in the interior of today's buildings has become an integral part of the design of such buildings. When the Coronavirus is prevalent in the world, it is necessary to pay attention to the spread of the virus in the interior of residential apartments. In the present study, the Coronavirus particles emitted from the sneezing of a sick person in the bedroom of a residential apartment were tracked. Meanwhile, the degree of exposure of a mannequin that has been placed in the living room playing the role of a healthy person is examined. In this research, a segregated solution of steady-state flow and an unsteady particle solution have been separately used: a suitable, accurate, and optimal solution in particle studies. A comparison of the results shows that underfloor heating creates a healthier space around the healthy person's respiratory system, but instead, we will see more polluted areas around the sick person. According to the PRE results, the PRE value for a mechanical heating system is higher than a floor heating system. Therefore, it is recommended to use mechanical heating system in the apartments where the person with COVID-19 is hospitalized.

An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements

The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO2 concentrations as a proxy for exhaled air can help to shed light on potential aerosol pathways. While the former typically lack accurate boundary conditions as well as spatially and temporally resolved validation data, currently existing measurement systems often probe rooms in non-ideal, single locations. Addressing both of these issues, a large and flexible wireless array of 50 embedded sensor units is presented that provides indoor climate metrics with configurable spatial and temporal resolutions at a sensor response time of 20 s. Augmented by an anchorless self-localization capability, three-dimensional air quality maps are reconstructed up to a mean 3D Euclidean error of 0.21 m. Driven by resolution, ease of use, and fault tolerance requirements, the system has proven itself in day-to-day use at ETH Zurich, where topologically differing auditoria (at-grade, sloped) were investigated under real occupancy conditions. The corresponding results indicate significant spatial and temporal variations in the indoor climate rendering large sensor arrays essential for accurate room assessments. Even in well-ventilated auditoria, cleanout time constants exceeded 30 min.