Indoor Airflow Patterns Research Articles

Diffusion characteristics of re-suspended PM2.5 within a radiant floor heating room under various ventilation modes

Few studies have revealed the effects of re-suspension of particulate matter deposited by radiant floor heating on indoor environments. The present study utilized Computational Fluid Dynamics (CFD) to investigate the impact of radiant floor heating and ventilation mode on indoor airflow patterns, as well as the dispersion and migration of re-suspended particulate matter, based on a turbulence model validated by an environmental test cabin. The Archimedes number (Ar) was employed to represent the strength of interaction between the thermal buoyancy force generated by floor radiation and the inertial force of the supply airflow. The results indicates that as Ar increases, the supply airflow exhibits a downward deflection, thereby forming a stable vortex below to prevent the diffusion of heat and re-suspended PM2.5 generated from the heated floor. As the Ar increases, the re-suspended PM2.5 deposited on the surrounding walls increased significantly, and the indoor PM2.5 concentration gradually decreased. When the Ar exceeds 6.03, the increase in Ar will no longer affect the indoor re-suspension PM2.5 diffusion, deposition characteristics, and removal efficiency. Therefore, it is recommended the critical Ar (Arcrit) of 6.03 for a radiant floor heating room under various ventilation modes, beyond which the indoor flow structure and distribution characteristics of re-suspended PM2.5 no longer change with increasing Ar. This study is helpful to further understand the diffusion and distribution characteristics of re-suspended particulates in floor heating and ventilation rooms, and provide technical reference for improving indoor air quality.

Formation and Transport of Secondary Contaminants Associated with Germicidal Ultraviolet Light Systems in an Occupied Classroom.

Germicidal ultraviolet light (GUV) systems are designed to control airborne pathogen transmission in buildings. However, it is important to acknowledge that certain conditions and system configurations may lead GUV systems to produce air contaminants including oxidants and secondary organic aerosols (SOA). In this study, we modeled the formation and dispersion of oxidants and secondary contaminants generated by the operation of GUV systems employing ultraviolet C 254 and 222 nm. Using a three-dimensional computational fluid dynamics model, we examined the breathing zone concentrations of chemical species in an occupied classroom. Our findings indicate that operating GUV 222 leads to an approximate increase of 10 ppb in O3 concentration and 5.2 μg·m-3 in SOA concentration compared to a condition without GUV operation, while GUV 254 increases the SOA concentration by about 1.2 μg·m-3, with a minimal impact on the O3 concentration. Furthermore, increasing the UV fluence rate of GUV 222 from 1 to 5 μW·cm-2 results in up to 80% increase in the oxidants and SOA concentrations. For GUV 254, elevating the UV fluence rate from 30 to 50 μW·cm-2 or doubling the radiating volume results in up to 50% increase in the SOA concentration. Note that indoor airflow patterns, particularly buoyancy-driven airflow (or displacement ventilation), lead to 15-45% lower SOA concentrations in the breathing zone compared to well-mixed airflow. The results also reveal that when the ventilation rate is below 2 h-1, operating GUV 254 has a smaller impact on human exposure to secondary contaminants than GUV 222. However, GUV 254 may generate more contaminants than GUV 222 when operating at high indoor O3 levels (>15 ppb). These results suggest that the design of GUV systems should consider indoor O3 levels and room ventilation conditions.

Opening windows as a supplementary ventilation strategy for large classrooms in a pandemic situation: Field measurements and simulation

The coronavirus disease 2019 (COVID-19) has led to significant global health emergencies in recent years, prompting widespread recommendations across various regions for opening windows in building codes. However, official guidelines have not adequately addressed the details of window opening arrangements related to the supporting evidence from existing literature. This study investigated the risk of disease outbreaks via respiratory transmission in large university studio classrooms. It examined the effectiveness of window-door opening strategies combined with mechanical ventilation in mitigating the infection risk of respiratory diseases in such spaces. A full-scale school ventilation performance test bed was used to measure indoor airflow and CO2 distribution patterns. Comparisons were made with the numerical simulation of exhaled viral emissions. A modified Wells-Riley equation was employed to calculate inhalation exposure and disease infection risks at the room level. Measurements showed that fresh air ventilation from the mechanical system was relatively low compared to existing standards, especially in pandemic situations. The results show that opening windows can lead to a decrease to low probabilities (below 5 %) of infection risk in 85.02 % area of a classroom over an 8-h period, which demonstrates that window-opening behavior is an effective supplementary strategy for large educational spaces with mechanically ventilated systems during emergencies when active measures are insufficient for diluting diseases. Furthermore, the study reveals that maximizing outdoor air rates by opening all windows may occasionally result in adverse indoor airflow redistribution, increasing virus load from 0.0382 to 0.8926 quanta/m3 near the virus source locations.

Effectiveness of portable air cleaners in mitigating respiratory virus transmission risk

Portable air cleaners (PACs) have shown promising potential in reducing the risk of SARS-CoV-2 infection by effectively removing pollutant particles and optimizing airflow patterns. This study focused on a simulated scenario where an infected source and a susceptible person engage in conversation within a naturally ventilated room. By combining the Eulerian fluid method with the Lagrangian particle tracking model, a comprehensive insight into indoor airflow patterns and the dispersion of virus-laden droplets was gained. As deposited droplets may be resuspended or in contact thereby increasing the potential risk of infection, the deposition of droplets of different sizes in different susceptible areas was also specifically analyzed. The impacts of three variables, namely the configuration of the PAC’s opening, air flow rate, and positioning, on the transmission of virus-laden droplets were investigated. The results highlighted the significant role of PAC utilization in effectively capturing droplets emitted by the infected source and reducing virus concentration in the vicinity of the susceptible person, thereby mitigating the risk of transmission. Notably, the design and orientation of the suction opening emerged as crucial factors. Among the various cases studied, the optimal control and prevention performance against the virus was achieved with a virus concentration reduction rate of 97.4% when the PAC had an opening configuration with a larger single-sided suction opening facing the infected source, an airflow rate of 200 m3 h−1, and was positioned at the center of the tabletop between the infected source and the susceptible person. This research underscored the importance of employing PACs with appropriate settings to enhance indoor air quality and minimize the potential for SARS-CoV-2 transmission in similar scenarios.

Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach

Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.

Can windcatcher's natural ventilation beat the chill? A view from heat loss and thermal discomfort

Windcatchers provide effective low-energy ventilation and summer passive cooling in temperate climates. However, their use in winter is limited due to significant ventilation heat loss and potential discomfort. Limited research has been conducted on quantifying windcatcher heat loss in cold climates, particularly through field studies. This study aims to evaluate the applicability of windcatchers in low-temperature conditions, with a focus on ventilation heat loss and thermal discomfort. Field experiments were conducted in Nottingham, UK, during an icy period. A 3D-printed prototype windcatcher and a test room were built and tested in such weather conditions. A Computational Fluid Dynamics (CFD) model validated against the field experimental data was employed to investigate the windcatcher's performance in a typical UK primary school classroom. The field experimental results indicate that the indoor airflow patterns are dynamic and continuously change with varying external wind conditions. Using static boundary conditions for ventilation analysis is inadequate, as it may lead to inaccurate predictions due to observed fluctuations and irregular airflow patterns. CFD modelling revealed significant over-ventilation in the classroom at external wind speeds of 3 m/s, despite being previously deemed as “satisfactory”, “adequate”, or “sufficient” ventilation. At wind speeds of 3 m/s or higher, the over-ventilation can cause a minimum 941.4 W heat loss, adding 4.7 kWh heating load and £1.6 electricity cost for a typical-sized single classroom during a 5-h occupied period. The research findings highlighted that control strategies should be introduced to reduce over-ventilation. Integrating heat recovery or thermal storage can enhance winter thermal conditions.

CFD Analysis of Indoor Ventilation for Airborne Virus Infection

CFD Analysis of Indoor Ventilation for Airborne Virus Infection Indoor airflow patterns and air residence times significantly influence the spread of airborne infectious viruses, such as COVID-19. These factors can be quantified using computational fluid dynamics (CFD). In this study, CFD was utilized to assess the indoor airflow patterns and calculate air residence times in a typical restroom with high personnel flow and low ventilation efficiency. The results identified regions with high air residence times, indicating potential risk areas for airborne virus retention. Furthermore, the effects of different ventilation strategies on these high-risk areas were analyzed. Despite meeting air change standards, certain regions were found to potentially pose a higher risk due to prolonged air residence times. Based on these findings, recommendations for improving ventilation systems to reduce the risk of airborne virus infection were proposed. This study highlights the necessity of a more nuanced approach to indoor air assessment than simply calculating air changes per hour. It was concluded that (1) different ventilation strategies can greatly affect the air residence time in the room and (2) the variance of air residence time in the air circulation area are large in some locations, even with simple ventilation adjustments.

CFD modeling of room airflow effects on inactivation of aerosol SARS-CoV-2 by an upper-room ultraviolet germicidal irradiation (UVGI) system

Ultraviolet germicidal irradiation (UVGI) systems inactivate microorganisms indoors. Upper-room UVGI systems use wall- or ceiling-mounted fixtures to create an air disinfection zone above the occupied zone. The performance of upper-room UVGI systems varies with indoor airflow patterns induced by mechanical ventilation and thermal plumes from indoor heat sources. Little information is available on the effects of ventilation strategies on upper-room UVGI system performance for the control of viral aerosols in occupied spaces. This study simulated the effects of ventilation system characteristics in an office space on the ability of an upper-room UVGI system to inactivate viral aerosols with UV-C susceptibility representative of coronaviruses. UVGI reduced viral aerosol concentration by two orders of magnitude relative to the concentration without UVGI. Air change rates and air distribution strategy (mixing vs. displacement) had notable effects on the effectiveness of the UVGI system. For mixing ventilation, as the recirculation airflow rate increased from 0 to 5.3 h−1 for a room volume of 108 m3 with a fixed outdoor air change rate of 0.7 h−1, UVGI inactivation increased by 96.7%. Mixing ventilation with 100% outdoor air of 0.7 h−1 yielded airborne virus inactivation that was double that of displacement ventilation, due to enhanced air mixing.

Dispersion of sneeze droplets in a meat facility indoor environment – Without partitions

Spreading patterns of the coronavirus disease (COVID-19) showed that infected and asymptotic carriers both played critical role in escalating transmission of virus leading to global pandemic. Indoor environments of restaurants, classrooms, hospitals, offices, large assemblies, and industrial installations are susceptible to virus outbreak. Industrial facilities such as fabrication rooms of meat processing plants, which are laden with moisture and fat in indoor air are the most sensitive spaces. Fabrication room workers standing next to each other are exposed to the risk of long-range viral droplets transmission within the facility. An asymptomatic carrier may transmit the virus unintentionally to fellow workers through sporadic sneezing leading to community spread. A novel Computational Fluid Dynamics (CFD) model of a fabrication room with typical interior (stationary objects) was prepared and investigated. Study was conducted to identify indoor airflow patterns, droplets spreading patterns, leading droplets removal mechanism, locations causing maximum spread of droplets, and infection index for workers along with stationary objects in reference to seven sneeze locations covering the entire room. The role of condensers, exhaust fans and leakage of indoor air through large and small openings to other rooms was investigated. This comprehensive study presents flow scenarios in the facility and helps identify locations that are potentially at lower or higher risk for exposure to COVID-19. The results presented in this study are suitable for future engineering analyses aimed at redesigning public spaces and common areas to minimize the spread of aerosols and droplets that may contain pathogens.

Large-eddy simulation of buoyant airflow in an airborne pathogen transmission scenario

Indoor airflow patterns and the spreading of respiratory air were studied using the large-eddy simulation (LES) computational fluid dynamics (CFD) approach. A large model room with mixing ventilation was investigated. The model setup was motivated by super-spreading of the SARS-CoV-2 virus with a particular focus on a known choir practice setup where one singer infected all the other choir members. The room was heated with radiators at two opposite walls in the cold winter time. The singers produced further heat generating buoyancy in the room. The Reynolds number of the inflow air jets was set to Re=2750, corresponding to an air-changes-per-hour (ACH) value of approximately 3.5. The CFD solver was first validated after which a thorough grid convergence study was performed for the full numerical model room with heat sources. The simulations were then executed over a time of t=20 min to account for slightly more than one air change timescale for three model cases: (1) full setup with heat sources (radiators+singers) in the winter scenario, (2) setup without radiators in a summer scenario, and (3) theoretical setup without buoyancy (uniform temperature). The main findings of the paper are as follows. First, the buoyant flow structures were noted to be significant. This was observed by comparing cases 1/2 with case 3. Second, the dispersion of the respiratory aerosol concentration, modeled as a passive scalar, was noted to be significantly affected by the buoyant flow structures in cases 1–2. In particular, the aerosol cloud was noted to either span the whole room (cases 1–2) or accumulate in the vicinity of the infected singer (case 3). Turbulence was clearly promoted by the interaction of the upward/downward moving warmer/cooler air currents which significantly affected the dispersion of the respiratory aerosols in the room. The study highlights the benefits of high-resolution, unsteady airflow modeling (e.g. LES) for interior design which may consequently also impact predictions on exposure to potentially infectious respiratory aerosols.

Development of non-isothermal wall jet integrated zonal model with enhanced accuracy using a multi-flow coefficient calibration method

A zonal model is an intermediate model between a nodal model and a computational fluid dynamics (CFD) model. This type of models has been widely used to predict indoor temperature distribution and airflow patterns by compromising prediction accuracy and computational efficiency. However, the zonal model was mainly used to predict the indoor environment of rooms with natural convection or isothermal air supply in previous literature, neglecting its application in non-isothermal air supply. Since driving flow under non-isothermal air supply is more common in air-conditioned spaces, this paper proposes a jet-integrated zonal model (JIZM) to predict the indoor environment of rooms with non-isothermal wall jet supply, extending the applicability of the zonal modelling technique. Meanwhile, a multi-flow coefficient calibration method was developed to improve the performance of the JIZM using a multi-objective genetic algorithm (MOGA). The applicability of the JIZM was evaluated under typical air supply scenarios. The results demonstrated that the JIZM with the proposed calibration method is able to using limited measurement data with calibrated model parameters to predict the air distributions with reasonable accuracy in a fast way, thus offering a better technique in predicting the vertical temperature distribution in air-conditioned rooms compared with a conventional zonal model.

Ventilation methods to prevent the spread of airborne pathogens from teachers to students

This study investigates different ventilation systems to prevent the spread of airborne pathogens from teachers in an elementary school classroom setting. The analyzed systems include a general mechanical ventilation system and a hybrid ventilation system. The hybrid ventilation system used a combination of natural ventilation, general mechanical ventilation, and local mechanical ventilation systems. For natural ventilation, wind velocities of 1.1 m/s and 0.11 m/s were considered. To analyze the patterns of the spread of airborne pathogens, the indoor airflow patterns and concentrations of airborne pathogens (passive scalar) were examined using Star-CCM + . Comparing the methods confirmed that natural ventilation was more effective than general mechanical ventilation in removing the airborne pathogens discharged from the teacher. The proposed hybrid ventilation method with combined natural and mechanical ventilation also showed promise in removing airborne pathogens. However, for natural ventilation with low wind velocity, the buoyancy effect around the occupants creates airflow vortices in the front of the classroom which spread airborne pathogens from the teacher toward the students seated in the front of the classroom. Furthermore, operating a local ventilation system close to the teacher reduced the spread of airborne pathogens that occurred under natural ventilation conditions with low wind velocity.

Mitigation of breathing contaminants: Exhaust location optimization for indoor space with impinging jet ventilation supply

Densely occupied spaces (e.g., classrooms) are generally over-crowded and pose a high risk of cross-infection during the pandemic of COVID-19. Among various ventilation systems, impinging jet ventilation (IJV) system might be promising for such spaces. However, the exhaust location of the IJV system used for densely occupied classrooms is unclear. This study aims to investigate the effects of exhaust location on the removal of exhaled contaminants in a classroom (15 × 7 × 5 m3) occupied by 50 students. Exhaled contaminants are modeled by a tracer gas released at the top of each manikin. The reference case has three exhausts evenly distributed in the ceiling. The results indicate that: a) a recirculation airflow entraining exhaled contaminants exists above the occupied zone; b) this recirculation air flow entrains contaminants and accumulates them at the upper part of the room near the diffuser; c) locating merely one exhaust on the same side of the supply diffuser leads to the best indoor air quality, i.e., it reduces the mean age of air from 278 s to 243 s, the mass fraction of CO2 from 753 ppm to 726 ppm, and the concentration of tracer gas from 305 ppm to 266 ppm; d) this layout still performs the best when the supply velocity drops to 0.5 m/s. It is worth noting that the proposed layout has fewer exhausts than the reference case but performs better. These results conclude that the exhaust for large spaces is not evenly distributed but depends on the indoor airflow pattern: the key is locating the exhaust near the region with high contaminant concentration. Factors determining the recirculation airflow are suggested to be further studied.

Transient transmission of droplets and aerosols in a ventilation system with ceiling fans

Different droplet and aerosol particles are generated in various respiratory activities, and their transmissions behave differently under various indoor airflow patterns. Since the judicious use of fans can reduce aerosol transmission by dispersing nearfield aerosol concentration in steady-state tracer gas experiments, this study further investigates the effect of ceiling fans on the transient droplet and aerosol transmission in the coughing process. Both large-eddy simulation (LES) and experiments, including particle image velocimetry (PIV) technique, concentration measurements, and video recordings with the high-speed camera were conducted. The results showed that the operation of ceiling fans changed the particle trajectory downwards up to 0.4 m above the floor surface. Since the airflow from ceiling fans could settle down the large particles within a short period and dilute the concentration of small-diameter particles, the aggregated concentrations in the breathing zone were reduced by 87%. Compared to the supply air from diffusers, the ceiling fan airflow had a more significant effect on the droplet and aerosol transmissions and achieved better protection for the manikin located underneath from the cough exposure. The ceiling fans strongly affected the indoor airflow pattern and also showed a potential to reduce the exposure risk to horizontally directed coughs in the transient process.

Influence of the ventilation strategy on the respiratory droplets dispersion inside a coach bus: CFD approach

The airborne transmission of the COVID-19 virus was considered the main cause of infection. The increasing concern about the virus spread in confined spaces, characterized by high crowding indexes and an often-inadequate air exchange system, pushes the scientific community to the design of many studies aimed at improving indoor air quality. The risk of transmission depends on several factors such as droplet properties, virus characteristics, and indoor airflow patterns. The main transmission route of the SARS-CoV-2 virus to humans is the respiratory route through small (<100 μm) and large droplets. In an indoor environment, the air exchange plays a fundamental role on the dispersion of the droplets. In this study, an integrated approach was developed to evaluate the influence of the ventilation strategy on the dispersion of respiratory droplets emitted inside a coach bus. There are no specific guidelines and standards on the air exchange rate (AER) values to be respected in indoor environments such as coach buses. The aim of this work is to analyse the influence of ventilation strategy on the respiratory droplet concentration and distribution emitted in a coach bus. Ansys FLUENT was used to numerically solve the well-known transient Navier-Stokes equations (URANS equations), the energy equation and using the Lagrangian Discrete Phase Model (DPM) approach to construct the droplet trajectories. The geometry is representative of an intercity bus, a vehicle constructed exclusively for the carriage of seated passengers. The 3D CAD model represented a coach bus with an HVAC system, within which an infected subject was present. The positions of exhaust vents and air-conditioning vents were chosen to ensure complete air circulation throughout the bus. The infected subject emitted droplets with a well-defined size distribution and mass through the mouth. The air exchange is provided in two different ways: general ventilation (from air intakes positioned along the bus windows and top side of central corridor) and personal ventilation (with air intakes for each passenger). For the general ventilation a single AER value was set (0.3 m3 s−1). The first results obtained showed a slight particle dispersion in the computational domain due to the airflow rate entered through the HVAC system, but a still elevated level of particle concentration tended to accumulate on the area near to infected subject. Additional analysis was executed to evaluate the beneficial effects linked to further addition of airflow through personal air-conditioning vents placed above every passenger’s head. The results show the importance of the use of the ventilation system inside a coach bus, highlighting how the contribution linked to of the personal air exchange rate can lead to a significant reduction of droplet concentration exposure and consequently a reduction of the risk of infection from airborne diseases.

A review of human thermal plume and its influence on the inhalation exposure to particulate matter

This paper reviews studies on the human thermal plume and its influence on the inhalation exposure to particulate matter in the breathing zone under different conditions. The human thermal plume transports particle pollutants from the floor to the breathing zone, increasing the inhaled particulate matter concentration. The concentration can be four times higher than that in the ambient environment. Studies have reported that the human thermal plume may prevent particulate matter from entering the breathing zone under specific conditions. Indoor airflow patterns significantly affect the dispersion of pollutants, especially in rooms equipped with displacement ventilation at low airflow velocities. It has been shown that the particle concentration is two times lower in the breathing zone of a rotating manikin than a static manikin. Understanding the characteristics and influencing factors of the human thermal plume is crucial to formulate measures to mitigate the inhalation exposure to particulate matter, achieve independent and personalized control of the human microenvironment, and create a healthy, intelligent and energy-saving indoor environment.

Event based approach for modeling indoor airflow patterns

People spend a significant proportion of their time indoors, where the quality of indoor air affects the productivity, efficiency, and well-being of occupants. One of the oldest challenges in building construction is designing a ventilation system that ensures optimum indoor air quality. Acceptable indoor air should provide thermal comfort and minimize human exposure to contamination. Characterizing these two elements requires information on both heat/mass transfer in the microenvironment and the time-specific activity of individuals who move among these microenvironments. While researchers have utilized simulation tools to investigate this complex human-environment interaction, current numerical techniques severely limit the simulations to overly simplified, unrealistic scenarios. To address these issues, this paper proposes a new and innovative approach called event-based modeling (EBM) to simulate airflow patterns for realistic human-environment interactions. EBM can provide an accurate approximation to simulate the patterns of air movement in indoor environments. EBM can also provide a path to simulate complex, random human-environment interactions that are pragmatically impossible to solve by current approaches. This paper formulates and evaluates this novel approach, and then validates it via simple cases of a door opening and human walking.

Variation of Indoor Airflow Patterns under Dynamic Outdoor Wind Conditions in Large Space Naturally Ventilated Buildings

Understanding Indoor Airflow Patterns (IAPs) helps control air contaminants in large naturally ventilated buildings (NVBs). However, the effect of random and unpredictable changes in outdoor wind conditions (OWC) is a major contributor to the variation in IAPs in the NVBs, making the IAP uncontrollable. This study presents the results of field measurements and numerical simulation in a NBV to study the IAP variation characteristic under the dynamical OWC. The OWC data monitored in real time was processed with Kalman Filtering (KF) and the gradient method to decompose the data prior to being entered into the CFD solver. The trend was similar between the simulated and measured data of a full size NVB. In addition, the distribution of internal turbulence intensity varied widely depending on the spatial locations and time intervals. The variation in speeds in the vicinity of windbreaks greatly affected the variation in IAP on a certain frequency scale. The results not only prove that CFD simulation to be an efficient tool for the prediction of time-averaged IAP, but also initialize efficient measures for the control of IAQ in dynamic OWC of large space NVBs.

Research progress on indoor environment of mushroom factory

Mushroom factory is an emerging production mode that is relied on facility and equipment by creating a suitable, steady, and uniform growing environment to accommodate the demand of edible fungus at each stage of growth. Therefore, an optimal range of key environmental factors (temperature, humidity, light, and CO2 concentrations) in mushroom facilities is crucial to achieving satisfactory production rates. This research aimed to provide an overview of recent progress on indoor environmental studies of mushroom cultivation facilities from three perspectives, 1) the development of environmental monitoring and controlling systems; 2) the application of computer modelling in addressing indoor environmental issues; 3) the refinement of mushroom facility design, including structure and ventilation scheme. With the aid of cutting-edge technologies, accomplishments have been made in developing smart farming systems that facilitate real-time recording of environmental parameters and automatic regulation of equipment, which helps the establishment of growth models for specific mushroom species. Computational Fluid Dynamics (CFD) modelling has been adopted by researchers to investigate indoor airflow patterns and assess the distribution of critical environmental parameters. Studies have been conducted to modify the design of mushroom facilities to improve the performance in structural stability, ventilation efficiency, and internal environmental condition. However, some existing problems still need further investigations, such as the lack of design guidelines, energy-saving strategies, and Artificial Intelligent (AI) quality control. Therefore, this overview was expected to provide constructive insights for future studies in addition to references of previous studies. Keywords: modern farming, mushroom factory, indoor environment control, computational fluid dynamics, remote sensing DOI: 10.25165/j.ijabe.20221501.6872 Citation: Chen L, Qian L, Zhang X, Li J Z, Zhang Z J, Chen X M. Research progress on indoor environment of mushroom factory. Int J Agric & Biol Eng, 2022; 15(1): 25–32.

Performance evaluation of different pressure-velocity decoupling schemes in built environment simulation

Simulations of indoor airflow can effectively assist in the design of a built environment. However, the intensive computational cost of current computational fluid dynamics (CFD) techniques restricts their efficient deployment, especially in transient airflow simulations. The fast fluid dynamics (FFD) method and fractional-step (FS) method have demonstrated their potential to reduce computational cost by reforming conventional pressure-velocity decoupling algorithms. Thus, this study evaluated the computational accuracy and computational efficiency of four promising schemes, which were chosen and developed from the FFD and FS methods, by applying them to lid-driven cavity flow and forced convection flow. All cases were simulated under constant boundary conditions and periodic boundary conditions with different time step sizes. The results showed that the FS method can achieve similar computational accuracy as the pressure-implicit with splitting operators (PISO) algorithm with approximately half the computational cost. The accuracy of one scheme of the FS method decrease rapidly with increasing time step size. The FFD method can reduce computational cost significantly because it is stable with a large time step size and is more suitable for large-scale numerical simulations. Simulation results of ventilation cases employing these methods also proved that time-periodic boundary conditions induced periodic changes in indoor airflow patterns that could facilitate the dispersion of air pollutants, especially in airflow stagnation areas.