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Occupancy modeling plays a crucial role in building energy simulation in building design, yet studies investigating the influence of model types and their input assumptions remain sparse. This study evaluates the sensitivity of parameters and model robustness to uncertainties in parameter ranges and data availability during the design phase. We assessed three occupancy models—deterministic, probabilistic, and agent-based—using an open dataset with occupancy data across four space types. Our findings reveal that the predictive performance of deterministic and probabilistic models is influenced heavily by a few of the most sensitive parameters. In contrast, more parameters affected the prediction performance of the agent-based model. This suggests a need to prioritize sensitive parameter estimation to enhance prediction accuracy in deterministic and probabilistic models. Specifically, the occupancy density and start/end time, followed by schedule fractions, emerged as highly sensitive due to their significant impact on average occupancy profiles. We also found that for hourly energy predictions, narrowing the range of the top five sensitive parameters decreases the coefficient of variation of the root mean square error (CV(RMSE)) by 3% and reduces its standard deviation by half. These results underscore the importance of careful parameter estimation to improve the reliability of occupancy models in building energy simulations. • The sensitivity and robustness of various occupancy models were evaluated. • Model parameters significantly impact occupancy and energy prediction performance. • Deterministic and probabilistic models are more robust than agent-based model. • Prioritizing estimation efforts for sensitive parameters is important. • Occupancy density, start/end time, and schedule fractions are the most sensitive. [Display omitted] [ABSTRACT FROM AUTHOR]

High-density cities usually surfer deterioration of the urban built environment due to slow or stagnant air movement. This deterioration could be mitigated by improved urban natural ventilation through good urban planning and building design. However, indoor natural ventilation requirements in current building standards only define absolute indoor air speed without considering outdoor conditions. To fill this gap, this paper developed generic urban-scale coupled indoor and outdoor CFD models and proposes a new integrated index (CIOI index) to evaluate indoor natural ventilation potential, which couples both indoor and outdoor wind environments. CIOI v regression models are developed by applying machine learning algorithms based on 3,840 CFD simulation results. The Gradient Boosting (GB) model shows an improved performance in terms of higher correlation ( R 2 =0.8) and lower errors (MAPE=16%) compared with the multivariate linear regression model. Building designers and urban planners can use this CIOI prediction model to quickly check, in the early design stage, the influence of their design variations on indoor ventilation potential. [ABSTRACT FROM AUTHOR]

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator. [ABSTRACT FROM AUTHOR]

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. • Experimental and numerical study on transient droplet and aerosol transmissions. • Downdraft from ceiling fans changes the particle trajectory and dilutes particles arriving at the breathing zone. • Ceiling fans are more effective in reducing direct horizontal transmission than diffuser supply air. [ABSTRACT FROM AUTHOR]

It is important to study the interactions between indoor and outdoor environments for natural ventilation assessment. A 3D isothermal cross ventilation of a high-rise building with surrounding buildings is solved with RNG k-ɛ turbulent model. Openings on four floors are created on north and south walls of the target building. Steady state simulations are performed in a commercial CFD code, scSTREAM, using multi-block method to integrate high-resolution mesh in a neighborhood scale. 72 CFD models are built for parametric analysis to support decision making for early design by evaluating indoor natural ventilation performance in terms of wind directions, building block layouts and opening sizes. [ABSTRACT FROM AUTHOR]

Abstract: Energy modeling approaches have continued to advance to cater for emerging new design concepts toward “greener” solutions that optimize energy consumption in buildings while maintaining thermal comfort as well as healthy environment. Increasing attention is given to passive and mix-mode systems in building. Computational Fluid Dynamics (CFD) model has been widely adopted as effective tool for natural ventilation simulations. However, CFD become unstable for conjugate heat transfer model, which is the transient heat transfer between solid and fluid. Solid and fluid has different respond times to thermal energy. Typically walls respond in hours, and air responds in seconds, causing the system to become stiff. In addressing the issue, a coupled lumped heat transfer model (EnergyPlus) and CFD model (Fluent) was implemented, and 8 days of simulation was conducted. The airflow rates of openings and heat transfer coefficients from the airflow network module in EnergyPlus and those from CFD model were compared. Results show that airflow network model generally predict smaller airflow rates for the openings. Airflow network model generates better results for openings on the south and east facade and internal openings, where there is no immediate adjacent building. Among the heat transfer coefficients calculation methods in EnergyPlus, the TARP algorithm generated closest HTC values to the coupled CFD results. The overall heat transfer coefficients of all the enclosure surfaces are calculated and it is found that the overall thermal resistances generated from the three convective HTC algorithms are almost the same, yet with observable difference from coupled CFD simulations. [Copyright &y& Elsevier]

Abstract: Building industry is closely related to current energy and environmental issues. Several green building codes and rating systems addressing the problems have been developed. Leadership in Energy and Environmental Design (LEED) rating system is recognized as one of the effective and widely adopted commercial building standards. LEED buildings were investigated in several green city and green building studies but only used as instances in static matrices. These studies were not able to answer the question why a particular city favors LEED. However, in this paper, three commonly used machine learning algorithms – Linear Regression, Locally Weighted Regression and Support Vector Regression (SVR) – are compared and SVR is used to investigate, discover and evaluate the variables that could influence LEED building markets in U.S. East Coast cities. Machine learning models are first created and optimized with the features of city geography, demography, economy, higher education and policy. Then SVR model identifies the key factors by dynamic self-training and model-tuning using the dataset. Via optimization, the correlation coefficient between the model''s prediction and actual value is 0.79. The result suggests that population and policy can be important factors for developing LEED buildings. It is also interesting that higher education institutions, especially accredited architecture schools could also be driving forces for LEED commercial building markets in East Coast cities. [Copyright &y& Elsevier]

Abstract: Computational Fluid Dynamics (CFD) has been introduced to the architectural engineering and HVAC (Heating Ventilation and Air Conditioning) industry for decades. However, the mesh generation process is complicated and time consuming, especially for free form geometric artifacts, e.g., human bodies in the room or buildings in complex terrains. This paper presents an effort to apply quality mesh generation for CFD simulations in the architectural context. A prototype meshing tool is developed to construct adaptive quadrilateral meshes from two-dimensional image data, e.g., architecture drawings. First the quadtree based isocontouring method is utilized to generate initial uniform quadrilateral meshes in the immediate region of the objects. Meshes are further decomposed into finer quads adaptively near the surface of the object without introducing any hanging nodes. Boundary layers are then generated using the pillowing technique and the thickness of the boundary layer is controlled to achieve the desired y + values for different near wall turbulence models. Finally, meshes are extended to the ambient domain with desired sizes, where flow fields are assumed to be relatively steady. The developed tool has been employed to generate meshes for CFD simulations of scenarios commonly existing in the indoor and outdoor environment. [Copyright &y& Elsevier]

We present BACH, a computational tool for the simulation of air flow in and around buildings in the early stages of the design process. This tool is part of an integrated simulation environment towards comprehensive performance-based building design evaluation. We describe the rational behind the development of this tool, its architecture, and its predictive performance. [Copyright &y& Elsevier]

Indoor airborne transmission largely depends on air distribution and ventilation. This study experimentally and numerically investigates steady-state aerosol transmission characteristics in a full-size room using a dedicated outdoor air system coupled with ceiling fans. Tracer gas was used to simulate the exhaled droplet nuclei from the infector, and a breathing thermal manikin was employed as a seated exposed person. Effects of air supply rates, ceiling fan speeds, source positions, and breathing modes of the exposed person on the aerosol transmission were studied. The increase of air change per hour from 4.5 to 5.6 and 7.5 reduced the averaged concentrations at sampling points by 18% and 38%, respectively. The ceiling fans generated local air movement and mixed air within the space, and a higher operating speed contributed to a more uniform concentration distribution. With better dispersion of the aerosols, the ceiling fan operation reduced the concentrations at the exposed person's breathing zone by more than 20%. The ceiling fans show the potential to reduce the cross-infection risk in an air-conditioned space. • Experimental and numerical study of ceiling fans' effect on aerosol transmission. • Analysing the relationship between ceiling fans and ventilation. • Ceiling fans reduced the exposure risk of the breathing zone by more than 20%. • Ventilation affects the overall concentration while limited to local distribution. • Ceiling fans have the potential to improve indoor air distribution. [ABSTRACT FROM AUTHOR]

Effective urban ventilation through decent urban planning and building design can alleviate the deterioration of the urban built environment. However, natural ventilation requirements and guidelines in current building codes and standards are either qualitative or quantitative but subject to an absolute indoor airspeed threshold without considering the outdoor wind environment. To fill this gap, this paper develops an urban-scale coupled indoor and outdoor computational fluid dynamics (CFD) model and defines a novel ventilation index to assess natural ventilation potential. The index considers wind environments of both indoor and outdoor spaces. First, the coupled CFD model is developed to study wind-driven cross ventilation in high-density cities. A 3D isothermal CFD simulation is solved using the RNG k-ε turbulent model. The simulation results are compared with wind tunnel experiment data from literature. Second, six key design variables are used to generate 3840 parametric design variations for natural ventilation assessment. Third, a novel integrated index C I O I v (coupled indoor and outdoor interaction) is proposed to evaluate the wind speed ratio between the indoor area and outdoor reference area. For demonstration, C I O I v , F 1 is used to represent C I O I v on the ground level. Data-driven C I O I v , F 1 models are developed to predict indoor building ventilation potential for quick early design support. Compared with multivariate linear regression model, the Gradient Boosting non-linear model displays much higher prediction accuracy (mean absolute percentage error = 0.16 with R 2 = 0.8). In early design stage, designers and engineers can skip the computational expensive CFD simulations and use this data-drive model to quickly check the building natural ventilation potentials of different design options in an urban environment. • An urban-scale coupled indoor and outdoor CFD model is developed to study cross ventilation. • Urban planning and building design features are parametrized and quantified using six key variables. • A novel ventilation evaluation index is proposed to integrate indoor and outdoor wind environments. • An advanced machine learning algorithm is used to develop data-driven CIOI prediction model for fast early design support. [ABSTRACT FROM AUTHOR]