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The purpose of this paper is to establish mass balance model and predict the concentration and diameter distribution of indoor suspended particulate matters (SPM).

Taking the small offices and residences for a research objective, this paper analyzes the major factors to affect the concentration and diameter distribution of indoor SPM, founds the deposition ratio model, the penetration factor model and the mass balance model to predict the concentration and diameter distribution of indoor SPM. According to the real‐time measuring data, the feature of building defence structure and the concentration and diameter distribution of outdoor SPM, the deposition model, the penetration model and indoor air capacity are used as input parameter of the mass balance model.

The size of defence in natural ventilation, the pressure difference of both sides and the friction velocity have less influence on the concentration and diameter distribution of indoor SPM, but the concentration and diameter distribution of outdoor SPM mainly affects that of indoor SPM. Indoor particle concentration change with outdoor particle concentration, and less than later because of indoor particle deposition. The prediction results are basically in agreement with the measuring data.

Real‐time and accuracy of measuring data of outdoor SPM are the main limitations which the prediction model are simulated.

The prediction results can provide scientific theory basis for making environmental standards of particulate matter and the control of indoor air quality.

A new method to predict the concentration and diameter distribution of indoor SPM.

The purpose of this paper is to develop a simple and efficient conservative semi-Lagrangian scheme (SL) for solving advection equation in fast fluid dynamics (FFD), so FFD can provide fast indoor airflow simulations while preserving conservation for energy and species transport.

This study thus proposed a mass-fixing type conservative SL that redistributes global surplus/deficit on the advected field after performing the standard semi-Lagrangian advection. The redistribution weights were designed to preserve the properties of conservatives and monotonicity.

The effectiveness of the conservative SL was validated with several test cases, and the results show that the proposed scheme is indeed conservative with negligible impact on the accuracy of the standard solutions. The numerical tests show that the proposed scheme was indeed conservative with negligible impact on the accuracy of the flow prediction.

The FFD with conservative SL can effectively enforce the energy and species conservation for indoor airflow and predict airflow distributions with reasonable accuracy.

The purpose of this paper is to study the effect of particle shapes (spherical particle and nonspherical fiber) on their orientation distributions in indoor environment.

This paper adopted a particle model to predict the fibrous particle flow and distribution, and analyzed the orientation distributions of nonspherical fiber particles and spherical particles in airflows like indoor places. Fokker-Planck model was employed to solve the orientation behavior of nonspherical fiber particles.

The simulation results discover that the nonspherical airborne fiber particles have very different characteristics and behaviors and their orientation distributions are totally different from the uniform distribution of spherical particles. The investigation of the particle orientation tensor and orientation strength indicates that the airflow field becomes more anisotropic due to the suspended fibers. The airborne fiber particles increase the viscosity of the room airflow due to the fiber induced additional viscosity.

Orientation tensor, strength and additional viscosity in fibrous flow are seldom investigated indoor. This research reveals that the particle shape has to be considered in the analysis of particle transport and distribution in indoor places as most suspended indoor particles are nonspherical.

The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets.

A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart–Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training.

In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.

This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.

The improvement in the quality of life together with thermal comfort, air quality, health, workplace security and energy conservation measures justify the integral education of environmental (outdoor and/or indoor) phenomena. Environmental education, through the appropriate tool, can play an important and vital role in this domain. Computational fluid dynamics (CFD) is the analysis of systems involving fluid flow, heat transfer and associated phenomena such as distribution of pollutants by means of computer‐based simulation. This technique, allowing the simulation and the visualization of environmental problems, represents a powerful tool to motivate, guide and educate on the environment. The main objective of this paper is to introduce this new advanced active tool in environmental education, directed to indoor‐environment quality, that permits the prediction and visualization of air movement, air temperature and air contaminant (such as tobacco smoke) distribution in rooms. With suitable mathematical models and boundary conditions, a computational code has been developed to predict and visualize these phenomena. In order to demonstrate its applicability, the simulation of air contamination distribution in an office room with a smoker was performed.

Biocontaminants represent higher risks to occupants' health in shared spaces. Natural ventilation is an effective strategy against indoor air biocontamination. However, the relationship between natural ventilation and indoor air contamination requires an in-depth investigation of the behavior of airborne infectious diseases, particularly concerning the contaminant's viral and aerodynamic characteristics. This research investigates the effectiveness of natural ventilation in preventing infection risks for coronavirus disease (COVID-19) through indoor air contamination of a free-running, naturally-ventilated room (where no space conditioning is used) that contains a person having COVID-19 through building-related parameters.

This research adopts a case study strategy involving a simulation-based approach. A simulation pipeline is implemented through a number of design scenarios for an open office. The simulation pipeline performs integrated contamination analysis, coupling a parametric 3D design environment, computational fluid dynamics (CFD) and energy simulations. The results of the implemented pipeline for COVID-19 are evaluated for building and environment-related parameters. Study metrics are identified as indoor air contamination levels, discharge period and the time of infection.

According to the simulation results, higher indoor air temperatures help to reduce the infection risk. Free-running spring and fall seasons can pose higher infection risk as compared to summer. Higher opening-to-wall ratios have higher potential to reduce infection risk. Adjacent window configuration has an advantage over opposite window configuration. As a design strategy, increasing opening-to-wall ratio has a higher impact on reducing the infection risk as compared to changing the opening configuration from opposite to adjacent. However, each building setup is a unique case that requires a systematic investigation to reliably understand the complex airflow and contaminant dispersion behavior. Metrics, strategies and actions to minimize indoor contamination risks should be addressed in future building standards. The simulation pipeline developed in this study has the potential to support decision-making during the adaptation of existing buildings to pandemic conditions and the design of new buildings.

The addressed need of investigation is especially crucial for the COVID-19 that is contagious and hazardous in shared indoors due to its aerodynamic behavior, faster transmission rates and high viral replicability. This research contributes to the current literature by presenting the simulation-based results for COVID-19 as investigated through building-related and environment-related parameters against contaminant concentration levels, the discharge period and the time of infection. Accordingly, this research presents results to provide a basis for a broader understanding of the correlation between the built environment and the aerodynamic behavior of COVID-19.

Indoor environments are characterized by several pollutant sources. Some of these can be sufficiently characterized through the prediction of the airflow and pollutant distribution patterns. The purpose of this study was to simulate, analyze and compare different locations of known pollutant source inside a ventilated room.

Computational fluid dynamics modelling approach was used to analyze the prediction of the airflow and pollutant distribution patterns for different locations of known pollutant source inside a ventilated room by mixing ventilation.

Distinct areas of poor air quality, perfectly identified by concentration fields, were given. The indoor air quality obtained by the different simulated conditions was analyzed and compared.

Pollutant concentration was not measured in the validation experiments (qualitative validation based on the velocity fields).

Once the contaminant concentration fields are calculated based on the source location, the model is very useful to choose the best place to install any pollutant indoor equipment to preserve breathing zones.

Providing an effective indoor air quality assessment to prevent exposure risk. The results would be useful for making decisions to optimize the design procedure, such as establish the best location to install polluting equipment, occupied areas and their interdependence with ventilation systems. In addition, this tool also helps to choose the best location and correct set point adjustment for the pollutant sensors.

This paper aims to investigate the impacts of introducing voids combinations on natural ventilation performance in high-rise residential building living unit.

This study was carried out through field measurement and computational fluid dynamics methods. The parameters of the study are void types and sizes, and a wind angle was used to formulate case studies.

The results indicate that the provision of a single-sided horizontal void larger by 50% increase the indoor air velocity performance up to 322.37% to 0.471 m/s in the living unit and achieves the required velocity for thermal comfort.

Passive design features are the most desirable techniques to enhance natural ventilation performance in the high-rise residential apartments for thermal comfort and indoor air quality purposes.

Natural ventilation is an environmentally friendly effective way of improving thermal comfort and the quality of indoor conditions if applied properly. This study aims to investigate the physical mechanism of the air movement and also the influence of building geometry in a cross-ventilated room through a parametric study of window geometrical characteristics using computational fluid dynamics.

Momentum and continuity equations are solved by the control volume method using a commercially available software. Standard k−ɛ turbulence model is employed to simulate the incompressible airflow and SIMPLE algorithm to solve the conservation equations. Mean air velocity magnitude is measured at three different surfaces of different heights, and the effect of incoming wind velocity inside the building is studied.

The research concluded that window hood and sill projections reduce indoor wind velocity magnitude, play a major role in incoming wind direction and thus have a crucial impact on wind circulation and indoor air quality.

The paper has evaluated redesigning of a both practical and ornamental architectural element named Palekaneh, which is found in many historical buildings in several hot places in the world. Its optimal design could increase indoor natural ventilation quality and decrease a space's cooling load. Therefore, a new passive cooling architectural element could be re-introduced to the regions previously enjoying such ornaments. This is economically efficient because it eventually saves a considerable amount of energy in the long run and is socially important because of the revitalization of architectural identity.

The role of a building envelope's physical features, although being studied for solar absorption and daylight availability, has rarely been investigated for natural ventilation, especially in a small scale, thus making the paper novel in this regard. This provides a guideline for designers to assess the impact of their design on redirecting wind-induced natural ventilation the very early stages of design.

The paper aims to investigate and evaluate the impacts of the voids combination as a passive design feature on wind-driven ventilation performance in high-rise residential building units. It proposes a series of building models and thereon indoor ventilation performance and outlining why and how these building models designed with architectural design features are important. This study aims to provide a comprehensive understanding of how natural ventilation as a passive cooling strategy in living units of high-rise residential buildings can be applied through improving the provision of the architectural design feature of voids configurations.

The study was carried out through field measurements experiment and the computational fluid dynamics methods. A series of numerical simulations were carried out to calculate the indoor ventilation rate inside the case studies of the generated building models based on various variables such as horizontal voids type, size and wind directions.

The results indicate that the provision of a single-sided horizontal voids in building models can improve the indoor ventilation rate in units with cross ventilation mode up to 4 times, depending on wind direction and living unit location. The indoor ventilation performance in units located in models with single-sided horizontal voids is 17.54% higher than the units located in models without voids configuration. Furthermore, higher indoor ventilation performance was achieved in the case scenarios located at higher levels compared to the middle and lower levels in both horizontal voids types.

This study explores the application of voids combinations for natural ventilation performance, investigates the numerical simulation results and validates field measurements experiment data using CFD simulation.