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Rapid urbanization resulted in the drastic expansion of the built infrastructure in urban areas. This eventually led to an increase in energy consumption in the residential and commercial sectors. An appropriate selection of the convective heat transfer coefficient correlation at the design stage is of vital importance as it directly affects the cooling load of the building and consequently buildings’ energy demand. In this context, the comparative analysis of existing convective heat transfer coefficient correlations (CHTCs) used in building simulation programs such as EnergyPlus, ESP-r, IES, IDA, and TAS, are assessed. These correlations are tested against the data of Liu et al.’s [1]. It is observed that some of CHTCs correlations show lower error and others exhibit a significant deviation. In the case of the CHTCs used EnergyPlus, it is observed that the TARP algorithm shows overall better prediction ability for windward, leeward, and roof surface. On the other hand, in the case of the ESP-r, different correlations show a good prediction ability for different surfaces. For windward surface: MoWiTT; for leeward surface: MoWiTT and McAdams; and for roof surface: Liu and Harris show closer prediction with an error of less than 30% among other correlations. The correlation used in IES, IDA, and TAS shows a large deviation for windward, leeward, and roof surfaces under considered input. Based on this analysis, it can be concluded that the choice of such CHTCs uses in the BES tool can lead to the significantly higher energy consumption of the building and hence need the expertise to make the appropriate selection.

Copyright © 2021 The Author(s). The global effects of climate change will increase the frequency and intensity of extreme events such as heatwaves and power outages, which have consequences for buildings and their cooling systems. Buildings and their cooling systems should be designed and operated to be resilient under such events to protect occupants from potentially dangerous indoor thermal conditions. This study performed a critical review on the state-of-the-art of cooling strategies, with special attention to their performance under heatwaves and power outages. We proposed a definition of resilient cooling and described four criteria for resilience—absorptive capacity, adaptive capacity, restorative capacity, and recovery speed —and used them to qualitatively evaluate the resilience of each strategy. The literature review and qualitative analyses show that to attain resilient cooling, the four resilience criteria should be considered in the design phase of a building or during the planning of retrofits. The building and relevant cooling system characteristics should be considered simultaneously to withstand extreme events. A combination of strategies with different resilience capacities, such as a passive envelope strategy coupled with a low-energy space-cooling solution, may be needed to obtain resilient cooling. Finally, a further direction for a quantitative assessment approach has been pointed out. The research is supported by Det Energiteknologisk Udviklings- og Demonstrationsprogram (EUDP) under grant 64018-0578. It was also supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Hybrid ventilation is an effective approach to reduce cooling energy consumption by combining natural and mechanical ventilation. Previous studies of full-scale whole-building measurements of high-rise hybrid ventilation are quite limited due to the complexities of buildings and variable ambient conditions. As a result, validated and accurate whole-building simulations of hybrid ventilation often cannot be found in the literature. This paper reports a series of full-scale measurements of hybrid ventilation in a 17-storey high-rise building and associated whole-building simulations by 15-zone detailed and a 5-zone simplified multizone models. The paper is one of the first studies of using multizone models and real-world full-scale data and sharing key operational and performance experience and case studies of high-rise hybrid ventilation. Both the test data and the validated simulation models can be used for the comparison and validation of simulation models. The 5-zone simplified model developed from this study was able to model such a complex high-rise building by only a few zones, making possible the on-line model predictive control of a high-rise building. This was illustrated in this paper by an example of optimizing the uniformity of the hybrid ventilation on different floors by modifying inlet areas.

Purpose The purpose of this paper is the development of a new density-based (DB) semi-Lagrangian method to speed up the conventional pressure-based (PB) semi-Lagrangian methods. Design/methodology/approach The semi-Lagrangian-based solvers are typically PB, i.e. semi-Lagrangian pressure-based (SLPB) solvers, where a Poisson equation is solved for obtaining the pressure field and ensuring a divergence-free flow field. As an elliptic-type equation, the Poisson equation often relies on an iterative solution, so it can create a challenge of parallel computing and a bottleneck of computing speed. This study proposes a new DB semi-Lagrangian method, i.e. the semi-Lagrangian artificial compressibility (SLAC), which replaces the Poisson equation by a hyperbolic continuity equation with an added artificial compressibility (AC) term, so a time-marching solution is possible. Without the Poisson equation, the proposed SLAC solver is faster, particularly for the cases with more computational cells, and better suited for parallel computing. Findings The study compares the accuracy and the computing speeds of both SLPB and SLAC solvers for the lid-driven cavity flow and the step-flow problems. It shows that the proposed SLAC solver is able to achieve the same results as the SLPB, whereas with a 3.03 times speed up before using the OpenMP parallelization and a 3.35 times speed up for the large grid number case (512 × 512) after the parallelization. The speed up can be improved further for larger cases because of increasing the condition number of the coefficient matrixes of the Poisson equation. Originality/value This paper proposes a method of avoiding solving the Poisson equation, a typical computing bottleneck for semi-Lagrangian-based fluid solvers by converting the conventional PB solver (SLPB) to the DB solver (SLAC) through the addition of the AC term. The method simplifies and facilitates the parallelization process of semi-Lagrangian-based fluid solvers for modern HPC infrastructures, such as OpenMP and GPU computing.

A series of experiments were conducted to study the flame spread and radiation characteristics for diesel tray fires against a sidewall. The tray width and the height of the sidewall were varied. The flame spread appearances and rate, subsurface flow and transverse fuel surface temperature were analyzed. Results showed that the sidewall has a great influence on the flame spread behavior due to its restriction on air entrainment. The flame distribution was uneven along the length of the tray, and the front propagating flame inclines to the tray outboard side edge (i.e. the tray's long side that is not attaching to the sidewall) for more oxygen, then more heat transferred from the flame through tray wall to the fuel results in uneven surface temperature profiles. The main flame being one short distance away from the flame spread front is against the sidewall. When the ratio of the sidewall height to the tray width, H/W, is less than 1, both the flame spread rate and subsurface flow increase with the increasing H/W, due to enhanced heat transfer. For H/W > 1, both of these parameters decrease with the increasing H/W, indicating a strong restriction of air entrainment by the sidewall. To estimate the flame radiation to the fuel surface during propagation, a multi-cylinder radiation model was proposed. By dividing the whole flame into several cylindrical sub-flames, the radiation blockage and sidewall effects are considered in the model. The comparison of calculated and measured radiation heat fluxes shows that the proposed model is reliable with a maximum error of 20%. These findings will provide scientific supports to fire monitoring and contribute to estimating radiative heat flux of spreading flame.

Air curtains have been widely used as a mean of reducing infiltration and associated energy losses through building entrances. Since quantifying the infiltration rate through the entrances is directly related to air curtain energy performance, previous studies investigated the infiltration through air curtain doors accounting for door operation, usage frequency, air supply angle and velocity etc. Limited studies have focused on the effect of the wind on air curtain performance, although wind is quite common and could interact with air curtain directly. The purpose of this study is to evaluate experimentally the performance of air curtain under different wind speeds and directions, and their interactions with air curtain jet considering different supply speeds, angles, and pressure differences across the air curtain. The experiments were mostly conducted in an atmospheric boundary layer wind tunnel in a sub-scale building model to represent the actual large-scale air curtain performance in terms of the function of dimensionless air infiltration rate versus the dimensionless pressure difference across the air curtain. The study found that air curtains could resist a certain level of wind speed but could be penetrated by high wind mostly at the lower section of the door, which may be avoided by selecting suitable air curtain speeds and angles, increase of indoor pressures, and entrance door types; the wind effect is the strongest for wind blowing straight-onto the air curtain and decreases with the inclined angles before arriving at the minimal effect at a certain angle, as defined by new wind pressure coefficients.

The semi-Lagrangian method is widely applied to solving the advection term of the Navier–Stokes (N–S) equations whereas the role of time step is often unclear. This article proposed an adaptive tim...

This paper presents an experimental study on the flame merging behaviors of two identical square propane burners with the side length of 15 cm burning under an unconfined ceiling. The total heat release rate of the two burners, burner edge spacing and ceiling height above the burner were varied. The flame merging probability, both the longitudinal and transverse ceiling flame extensions respectively along and perpendicular to the line connecting the centers of the two burners were measured. Based on the dimensional analysis, a piecewise linear function for the flame merging probability was developed and the criteria of the beginning and fully merging of the ceiling flames were thus determined, which provides a guidance of the safety distance between combustibles in buildings. The flame length is defined as the half of the total ceiling flame length in this work. Due to the interaction of two burners, the longitudinal and transverse flame lengths are different. Experimental results showed that as the spacing increases, the longitudinal flame length increases while the transverse flame length decreases. Based on the energy distribution model developed for the vertical and ceiling flames, correlations for predicting the merging ceiling flame lengths in the two directions were respectively established as the functions of the total heat release rate, burner size and spacing, ceiling height and the free flame height of two burners with zero spacing. The proposed models for the ceiling flame lengths can help to better understand the fire ignition and spread probability and flame radiation properties.

The world has faced tremendous challenges during the COVID-19 pandemic since 2020, and effective clean air strategies that mitigate infectious risks indoors have become more essential. In this study, a novel approach based on the Wells-Riley model applied to a multizone building was proposed to simulate exposure to infectious doses in terms of "

The two-node bioheat model is widely used in thermal comfort standards and design tools. In recent years, there have been many new experimental studies and thermoregulatory models developed under stressful heat or cold conditions, but those have not been tested under the two-node model structure. Furthermore, limited validation studies of the two-node model revealed significant discrepancies in the prediction of skin temperature. This study collects relevant thermoregulatory models (six for sweating, three for skin blood flow and shivering, and four for sweat evaporation efficiency) and devises a methodology to compare the accuracy of various model combinations against experimental data. An improved model is developed and validated under heat and cold exposure conditions. The RMSE method is used to compare the accuracy of various model combinations and to optimize the proposed thermoregulatory model constants. The results reveal that only several model combinations can be considered as accurate for the core and skin temperature predictions, amongst which are the proposed models at the first rank.

With the increase in the frequency and duration of heatwaves and extreme temperatures, global warming becomes one of the most critical environmental issues. Heatwaves pose significant threats to human health, including related diseases and deaths, especially for vulnerable groups. Such as the one during the 2018 summer in Montreal, Canada, caused up to 53 deaths, with most lived in buildings without access to air-conditioning. Unlike building energy models that mainly focus on energy performance, building thermal models emphasizes indoor thermal performance without a mechanical system. It is required an understanding of the complex dynamic building thermal physics in which detailed building parameters need to be specified but challenging to be determined in real life. The uncertainty assessment of the parameters estimates can make the results more reliable. Therefore, in this paper, a Bayesian-based calibration procedure was presented and applied to an educational building. First, the building was modeled in EnergyPlus based on an in-site visit and related information collection. Second, a sensitivity analysis was performed to identify significant parameters affecting the errors between simulated and monitored indoor air temperatures. Then, a Meta-model was developed and used during the calibration process instead of the original EnergyPlus model to decrease the requirement of computing load and time. Subsequently, the Bayesian inference theory was employed to calibrate the model on hourly indoor air temperatures in summer. Finally, the model was validated. It is shown that the Bayesian calibration procedure not only can calibrate the model within the performance tolerance required by international building standards/codes but also predict future thermal performance with a confidence interval, which makes it more reliable.

Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as meta-models to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models’ prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed meta-model is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration.

The building sector accounts for nearly 40% of global energy consumption and plays a critical role in societal energy security and sustainability. A building energy model (BEM) simulates complex building physics and provides insights into various energy-saving measures’ performance. The analysis based on BEMs has become an essential approach to slowing down increasing building energy consumption. The reliability and accuracy of BEMs have a high impact on decision-making. However, how to calibrate a building energy model has remained a challenge. In this study, Bayesian inference was applied to the calibration of an office building model under the arid weather conditions of Doha, Qatar. The coefficient of variation with a root-mean-square error of calibration and validation are 1.1% and 1.5%, respectively, which is highly satisfied with the monthly calibration tolerance of 15% required by ASHRAE Guideline 14. Additionally, the calibrated parameter results are with probabilities and degrees of confidence, so they are more reasonable and comprehensive than traditional deterministic calibration methods. This study conducted a sensitivity analysis to select the model’s dominant parameters under hot/arid weather conditions. This study will be among the first studies of stochastic calibration based on Bayesian inference for building energy performances in arid weather.

Ventilation rate plays a significant role in preventing the airborne transmission of diseases in indoor spaces. Classrooms are a considerable challenge during the COVID-19 pandemic because of large occupancy density and mainly poor ventilation conditions. The indoor CO2level may be used as an index for estimating the ventilation rate and airborne infection risk. In this work, we analyzed a one-day measurement of CO2levels in three schools to estimate the ventilation rate and airborne infection risk. Sensitivity analysis and Bayesian calibration methods were applied to identify uncertainties and calibrate key parameters. The outdoor ventilation rate with a 95% confidence was 1.96 ± 0.31ACH for Room 1 with mechanical ventilation and fully open window, 0.40 ± 0.08 ACH for Rooms 2, and 0.79 ± 0.06 ACH for Room 3 with only windows open. A time-averaged CO2level < 450 ppm is equivalent to a ventilation rate > 10 ACH in all three rooms. We also defined the probability of the COVID-19 airborne infection risk associated with ventilation uncertainties. The outdoor ventilation threshold to prevent classroom COVID-19 aerosol spreading is between 3 – 8 ACH, and the CO2threshold is around 500 ppm of a school day (< 8 hr) for the three schools.Practical ImplicationsThe actual outdoor ventilation rate in a room cannot be easily measured, but it can be calculated by measuring the transient indoor CO2level. Uncertainty in input parameters can result in uncertainty in the calculated ventilation rate. Our three classrooms study shows that the estimated ventilation rate considering various input parameters’ uncertainties is between ± 8-20 %. As a result, the uncertainty of the ventilation rate contributes to the estimated COVID-19 airborne aerosol infection risk’s uncertainty up to ± 10 %. Other studies can apply the proposed Bayesian and MCMC method to estimating building ventilation rates and airborne aerosol infection risks based on actual measurement data such as CO2levels with uncertainties and sensitivity of input parameters identified. The outdoor ventilation rate and CO2threshold values as functions of exposure times could be used as the baseline models to develop correlations to be implemented by cheap/portable sensors to be applied in similar situations to monitor ventilation conditions and airborne risk levels.

Airborne transmission of aerosols produced by asymptomatic individuals is a large portion of the SARS-CoV-2 spread indoors. Outdoor air ventilation rate, air filtration, room occupancy, exposure time, and mask-wearing are among the key parameters that affect its airborne transmission in indoor spaces. In this work, we developed a new web-based platform, City Reduced Probability of Infection - CityRPI, to calculate the indoor airborne transmission of COVID-19 in various buildings of a city scale. An archetype library of twenty-nine building types is developed based on several standards and references. Among the mitigation strategies recommended to reduce infection risk, some could result in significant energy impacts on buildings. To study the combined effects of energy consumption and reduced infection probability, we integrated CityRPI with City Building Energy Model. We applied the integrated model to Montreal City and studied the impact of six mitigation measures on the infection risk and peak energy demand in winter. It shows that the same strategy could perform quite differently, depending on building types and properties. In the winter season, increasing the outdoor air ventilation rate may cause massive building energy consumption. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the outdoor air ventilation rate is not as effective as other strategies to reduce the risk with less than 35% reduction. It also significantly increases building peak heating demand with 10-60%.

Climate change and global warming have been indisputable as supported by mounting evidence of more extended, severe, and frequent occurrences of extreme weather events (EHEs), in particular, summertime heatwaves in recent years. EHEs often interact with buildings in urban area centers, which are densely packed by building blocks with vulnerable populations: the homeless, elderly, children, socially disadvantaged people, the physically challenged, or the sick, creating a unique natural phenomenon, urban heat island (UHI). This chapter covers a comprehensive effort to assess the UHI impacts on buildings and the potentially vulnerable populations through a series of surveys and field measurements in schools and hospitals, and a multi-scale climatic modeling framework from global and regional climates, urban microclimate, to building scale simulations. General methodologies are reported in detail for a better understanding of the levels of impacts by UHIs on buildings, e.g., excessively high indoor temperatures, energy demands and peak loads, and on people, e.g., indoor overheating risks. The effort is essential for developing measures and strategies to mitigate the UHI impacts on buildings and occupants for the current and future climates.

In recent years, stochastic modeling has been increasingly applied to investigate the uncertainties of input parameters in hygrothermal simulation and the moisture damage risks of building envelopes. Generally, stochastic modeling requires hundreds or even thousands of simulations to take into account the uncertainties of input parameters, which is computationally intensive and timeconsuming. This paper aims to apply polynomial and neural network metamodel as a substitute for the traditional hygrothermal model, to predict the hygrothermal performance of building envelopes. In the previous study carried out by the authors, stochastic simulations have been performed based on the traditional hygrothermal model, to investigate the hygrothermal performance of wood-frame walls under different rain leakage levels. The material properties and rain deposition factors were considered as stochastic variables, and stochastic simulations were performed under three rain leakage scenarios: 1%, 0.5% and 0.1% of wind-driven rain. In this paper, the stochastic inputs (the hygric material properties and rain deposition factor) and outputs (the maximum moisture content and mold growth index over a 5-year period of the simulation) of a conventional 2×6 wood-frame wall are used to develop the metamodels through polynomial regression and neural network methods. The metamodels are developed for each rain leakage scenario, and the stochastic data of the three rain leakage scenarios are aggregated together to train another metamodel. It is found that the metamodels generally perform well to predict the maximum moisture content and mold growth index. The metamodels for low rain leakage scenarios are better than those for high rain leakage scenarios and the neural network metamodel is more accurate than polynomial metamodel for high rain leakage scenarios, i.e. 1% of rain leakage.

With the increased severity, intensity, and frequency of “heatwaves” due to climate change, it has become imperative to study the overheating risks in existing buildings. To do so, a building simulation model needs to be calibrated based on measured indoor temperatures under the current weather conditions. This paper presents a robust automated methodology that can calibrate a building simulation model based on the indoor hourly temperature in multiple rooms simultaneously with high accuracy. This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the Multi-Objective Genetic Algorithm to calibrate different rooms simultaneously based on the significant parameters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. Maximum Absolute Difference (MAD), a new metric, that calculates the maximum absolute difference between simulated and measured hourly indoor temperatures, Root Mean Square Error (RMSE), Normalized Mean Bias Error (NMBE) were used as the evaluation criteria. Another new metric is introduced, 1 °C Percentage Error criterion that calculates the percentage of the number of hours with an error over 1 °C during the calibration period, to select the best solutions from the Pareto Front solutions. 0.5 °C Percentage Error criterion is also used for the level of accuracy the model can achieve. It was found that the calibrated model achieved these metrics with RMSE of 0.3 °C, and MAD of 0.8 °C, and 85% of data points with an error less than 0.5 °C for a school building case.

As a consequence of global warming and rapid urbanization around the globe, the magnitudes and frequencies of extreme heat events (EHEs) are increasing and this trend is expected to continue into the future. In this study, the added benefit of modelling climate at convection-permitting spatial resolutions (grid spacing

Natural ventilation is widely applied in buildings considering its potential of improving indoor air quality and saving building energy costs. However, to evaluate its viability and determine the ventilation rates quickly and relatively accurately during early design stage is challenging. This paper explores a fast and accurate evaluation approach in the form of empirical equations to estimate the ventilation rate and potential of wind-driven natural ventilation. By using computational fluid dynamics (CFD) with results validated for both cross and single natural ventilation strategies, this study conducted a series of simulations to determine critical ventilation coefficients for the empirical equations as functions of wind direction, speed and building height. The proposed evaluation approach could help architects and engineers to evaluate the viability of natural ventilation during early building design. This approach was also demonstrated to evaluate the potential of natural ventilation in 65 cities of North America so a series of natural ventilation potential maps were generated for a better understanding of natural ventilation potential in different climates and for the climate-conscious design of buildings in North America.

Accurate and reliable prediction of smoke development process is critical for building fire protection design. Although modern computers allow us to use sophisticated models, e.g. CFD, to simulate certain fire scenarios, the simulation time is often drastically longer than zone models. This paper applies a real-time forecasting method, Ensemble Kalman filter (EnKF) algorithm, to a zone model, CFAST, for the simulation of a full-scale fire experiment in a two-story building structure. The results of the CFAST simulation with and without the EnKF algorithm are compared to the experimental data. It shows that the EnKF algorithm can significantly improve the accuracy of the CFAST simulation of the smoke layer temperature and height. To solve the problem of filter divergence and further reduce the computational cost, a general statistical interference method, the bootstrap method, is added to the EnKF algorithm. It shows that the bootstrap method improves the accuracy with fewer ensemble members and less computing time, thus even achieving a real-time forecasting on a personal computer. A sensitivity analysis is also conducted in nine case studies for demonstrating practical engineering applications of the real-time forecasting process.

Air curtains have been widely used to reduce infiltration through door openings and save heating/cooling energy in different types of buildings. Previous studies have found that there exist three aerodynamics conditions: optimum condition (OC), inflow break-through (IB), and outflow break-through (OB) conditions, which are important for categorizing air curtain performance subject to such key parameters including supply speed and angle, and presence of a person during an actual operation. However, few studies have focused on the effects of these parameters on air curtain performance in terms of resisting infiltration and reducing exfiltration. This research presents a parametric study of air curtain performance based on reduced-scale experiments and full-scale numerical simulations. It was found that increasing air curtain supply angle improves air curtain performance when it is operated under the OC and IB conditions but creates excessive exfiltration under the OB condition. Increasing supply speed of air curtain generally improves the air curtain performance whereas this improvement deteriorates with the increase of supply angle under the OB condition. The presence of person, either directly under or below the air curtain, almost has no effect on the infiltration/exfiltration during the OC condition. Moreover, the person in the doorway can block airflow from both directions, contributing to less infiltration under the IB condition and less exfiltration under the OB condition than without the person. This study provides valuable insights into air curtain aerodynamics performance under different operational conditions and key contributing parameters.

Real-time prediction of smoke layer temperature and height of tunnel fires are crucial in guiding emergency rescue. However, current fire simulation tools are often not able to provide reliable modeling results due to poorly known input parameters and model errors. Besides, fire modeling are subject to computer resources, for instance, fire modeling by computational fluid dynamics (CFD) tools is often time-consuming. Moreover, sensors located in tunnels can only detect certain physical quantities within a certain level of uncertainties. In order to gain more reliable predictions of temperature and smoke layer height of tunnel fires in real time, a proposed method, inverse modeling based on Ensemble Kalman Filter (EnKF), is presented in this study to improve the predictability and address problems of demanding computer resources of tunnel fire simulation by doing data assimilation. The basic formulas of EnKF method are introduced and the application of EnKF to tunnel fires is implemented by connecting the fire simulation tool, CFAST, with a data assimilation software, OpenDA. In current study, observation data are generated under the framework of Observation System Simulation Experiment (OSSE), i.e., synthetic observations are generated by CFAST simulation assuming true value of control parameters are known. Studies are conducted to show the feasibility of real-time predicting smoke movement during tunnel fires. Results show that prediction performance are improved after applying the EnKF method compared to the standalone tunnel fires modeling.

Thermal stratification in large warehouses is a common phenomenon in winter, especially for cold climates. Buoyancy-driven warm air tends to move upwards towards ceilings creating vertical temperature gradients with upper higher temperatures, and consequently leading to excessive heat losses through roofs. These warehouses therefore require thermal destratification through certain air mixing strategies, the selection of which often relies on engineering rules of thumb and experiences but can be better determined through combined experimental and numerical approaches. This paper investigated different thermal destratification methods in two industrial warehouses based on field measurements and CFD simulations with the focus on the quantification of thermal stratification, and the suitable methods modeling air mixing devices, e.g. ceiling rotating fans and bucket fans. The numerical models were first validated by field measurements and then used to simulate different destratification strategies and to compare associated energy impacts. The CFD methods for modeling air mixing devices and their performances can be applied to other similar cases.

Analytical solutions in terms of dimensionless numbers for the smoke spread through high-rise shafts during fires are essential to provide a fundamental understanding of smoke transport physics, which is a complex coupled heat and mass transfer problem. Existing solutions are often dimensional based on simplification of the problem such as assuming adiabatic conditions. In order to obtain the dimensionless analytical solutions, energy balance equation, mechanical energy equation and mass balance equation were established for smoke spread in high-rise buildings under both mechanical and natural venting conditions. Experiments were designed and conducted on two scaled shafts with different sizes and materials, and the measured results were compared to the dimensionless analytical solutions. It was found that the dimensionless analytical solutions could predict temperature profiles, mass flow rate and neutral plane level accurately. The effect of the adiabatic assumption on the accuracy was also discussed. For example, due to the adiabatic assumption, the error of the calculated mass flow rate required during mechanical venting to maintain a high-rise shaft smoke free was found to increase with a dimensionless number, ω , defined by the geometrical and thermal properties of the shaft.

There is concern about the hazard of acute residential CO exposures from portable gasoline-powered generators, which can result in death or serious adverse health effects in exposed individuals. To address this hazard, the U.S. Consumer Product Safety Commission has developed low CO emission prototype generators by adapting off-the-shelf emission control technologies onto commercially available generators. A series of tests was conducted to characterize the indoor CO concentrations resulting from portable generators operating in the attached garage of a research house under seven different test house/garage configurations. The tested generators include both unmodified and modified low CO emission prototypes. It was found that CO concentrations varied widely, with peak house CO concentrations ranging from under 10 ppm to over 10,000 ppm. The highest concentrations in the house resulted from operation of the unmodified generator in the garage with the garage bay door closed and the house access door open. The lowest concentrations resulted from operation of a modified low CO emission prototype in the garage with the garage bay door open and the house access door closed. These tests documented reductions of up to 98% in CO concentrations due to emissions from two low CO emission portable generators compared to a stock generator.Improper portable generator use has caused 800 U.S. deaths in the past 14 years. Generators operated in attached garages can cause CO to quickly reach deadly levels. Two low-emission prototypes generators were tested and had CO emissions reduced by up to 98%. Low-emission generators can reduce the risk of consumer poisonings and deaths.

Building entrance doors are a major source of air infiltration and energy loss in commercial buildings. Previous studies have calculated entrance doors air infiltration and energy saving potential of vestibules with the simplified method which is based on pressure factors. However, challenges are still faced in estimating the pressure difference and the resultant infiltration rates across doors as well as validating the used airflow coefficients under different flow conditions. In this paper, an experimental study is used to validate the airflow coefficient for a fully open single door under both infiltration and exfiltration conditions. The study presents four methods for modeling air infiltration across automatic single and vestibule doors for two reference building models: two methods use the pressure factors and the two others are based on airflow simulations. Energy simulations are then conducted using the air infiltration rates obtained from each method. The results revealed that the design methods overestimate the pressure difference across doors, the air infiltration rates as well as the vestibule savings potentials in comparison to the simulation methods. In conclusion, airflow simulations were found to provide more realistic estimates of pressure differences and infiltration rates across entrance doors when compared to the widely-used design methods.

The efficiency of air curtain in reducing infiltration and associated energy usage is currently evaluated statically by using an efficiency factor, η air, based on single steady/static condition, which is often not the case for actual buildings under variable weather conditions and door usages. Based on a new method to consider these dynamic effects on air curtains, this study uses a dynamic efficiency factor η B in terms of whole building site end-use energy to assess the efficiency of air curtains when compared to single doors (i.e. without air curtains) and vestibule doors. Annual energy simulations were conducted for two reference building models considering their specific door usage schedules in 16 climate zone locations in the North America. The variations of the proposed efficiency factor for different climate zones illustrated the dynamic impacts of weather, building, unit fan energy and door usage frequency on air curtain efficiency. A sensitivity study was also conducted for the operation temperature conditions of air curtain and showed that η B also considers these operational conditions. It was thus concluded that using whole building site end-use energy to calculate the efficiency factor, ηB, can provide more realistic estimates of the performance of air curtains operations in buildings than the existing static efficiency factor.

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Urban atmospheric flow is characterized by meteorological processes and street-scale perturbations of microclimate wind velocity and air temperature distributions. The Weather Research and Forecasting model (WRF) is applied to investigate the effects of meteorological processes on temperature distribution over the Greater Montreal Area during the 2017 heatwave. The single-layer Urban Canopy Model is coupled with the WRF to estimate the heat fluxes from the canopy to the atmosphere. The mesoscale simulation results provide the boundary conditions for a new city-scale microclimate LES model, CityFFD. This study first validated the WRF model through multiple weather station measurements and the CityFFD model through the literature data, then investigated the impacts of three canyon aspect ratios and three anthropogenic heat regimes, i.e., surface temperature differences, on the boundary conditions setups. The strong anthropogenic heat assumption was applied to modeling the 2017 heatwave for three consecutive days using the integrated model. The transient results show that a difference of ∼4 m/s wind speed could result in a spatial temperature variation of up to 4 °C for the area under consideration. This study shows the importance of microclimate simulations for regional climate models when studying urban heatwaves.

Background and objective Pigmented villonodular synovitis (PVNS) is a lesion of uncertain etiology that involves the synovial membranes of joints or tendon sheaths, representing a diffuse and non-encapsulated form of the more common giant cell tumors of the synovium (GCTTS). PVNS was reclassified to denote a diffuse form of synovial giant cell tumor (TSGCT), while ‘giant cell tumor of the tendon sheath (GCTTS)’ was used for localized lesions. These pathologies rarely affect the axial skeleton. We provide an unprecedented and extensive systematic review of both lesions highlighting presentation, diagnostic considerations, treatment, prognosis, and outcomes, and we report a short case-series. Method We describe two-cases and conduct a systematic review in accordance with PRISMA guidelines. Result PVNS was identified in most of the cases reviewed (91.6 %), manifesting predominantly in the cervical spine (40 %). Patients commonly presented with neck pain (59 %), back pain (53 %), and lower back pain (81.2 %) for cervical, thoracic, and lumbar lesions, respectively. GTR occurred at rates of 94 %, 80 %, and 87.5 %. Recurrence was most common in the lumbar region (30.7 %). GCTTS cases (8%) manifested in the cervical and thoracic spine at the same frequency. We reported first case of GCTTS in the lumbosacral region. Both poses high rate of facet and epidural involvements. Conclusion Spinal PVNS and GCTTS are rare. These lesions manifest most commonly as PVNS within the cervical spine. Both types have a high rate of facet and epidural involvement, while PVNS has the highest rate of recurrence within the lumbar spine. The clinical and radiological features of these lesions make them difficult to differentiate from others with similar histogenesis, necessitating tissue diagnosis. Proper management via GTR resolves the lesion, with low rates of recurrence.

The ability to predict the concentration of hydrogen in a partially confined space is significant to the safe use of hydrogen-related products such as fuel cell vehicles. Hydrogen release and subsequent dispersion are frequently investigated using commercially-available computational fluid dynamic (CFD) models once those are calibrated with available experimental data. Due to the explosion safety concerns of using hydrogen, accidental scenarios are often replicated with helium as a hydrogen simulant in experiments. Currently, there is no validated, theoretical analogy to correlate the helium data in order to predict the spatial and temporal distribution of hydrogen in the enclosure. The aim of this paper is to assess different theoretical relationships for the similarity between hydrogen and helium leakage in an enclosure. Experiments were first carried out to obtain measured data with helium leakage in a set of scenarios and to validate the present CFD model. Three methods, namely equal volumetric flow rate, equal buoyancy and a newly proposed correlation derived from equal concentration, were employed to determine the equivalent hydrogen release rate, using which the validated CFD model was then used, with the physical property values for hydrogen, to stimulate hydrogen dispersion as compared to that of helium. The accuracy of these different methods at different stage of release and location is discussed. The present result thus provides a guide when using helium experiment to validate hydrogen simulation in different scenarios, which is of importance to the investigation of hydrogen safety.

Forecasting building fire growth and smoke dispersion is a challenging task but can provide early warnings to first responders and building occupants and thus significantly benefit active building fire protection. Although existent computer simulation models may provide acceptable estimations of smoke temperature and quantity, most simulations are still not able to achieve real-time forecast of building fire due to high computational requirements, and/or simulation accuracy subject to users’ inputs. This paper investigates one of the possibilities of using ensemble Kalman filter (EnKF), a statistical method utilizing the real-time sensor data from thermocouple trees in each room, to estimate the spread of an accidental building fire and further forecast smoke dispersion in real time. A general approach to forecasting building fire and smoke is outlined and demonstrated by a 1:5 scaled compartment fire experiment using a 1.0 kW to 2.8 kW propane burner as fire source. The results indicate that the EnKF method is able to forecast smoke transport in a multi-room building fire using 40 ensemble members and provide noticeable accuracy and lead time. Unlike other methods that directly use measurement data as model inputs, the developed model is able to statistically update model parameters to maintain the forecasting accuracy in real time. The results obtained from the model can be potentially applied to assist mechanical smoke removal, emergency evacuation and firefighting.

Air infiltration through building entrances is one of the main sources of energy loss in modern buildings. Previous studies have shown that air curtains, when used at building entrances, can reduce infiltration-related energy loss significantly. A recent computational fluid dynamics (CFD) study proposed a new empirical model to capture air curtain door infiltration/exfiltration characteristics under varying operation conditions and pressure differences. Extending the recent CFD study, this paper presents an experimental study to verify and further investigate the flow characteristics of building entrances equipped with air curtains. A small scale chamber of 2.44 m × 2.44 m × 1.3 m (L × W × H) was constructed and used for the measurements of infiltration/exfiltration and pressure differentials, which were then used for developing the empirical model across the operating air curtain. A 2-D particle image velocimetry (PIV) system with helium filled soap bubbles as seeds was used to visualize the airflow fields captured at the doorway. Both the PIV and the measurement-based correlations were also compared to CFD simulations. The flow/pressure measurements confirmed that, for the tested pressure difference range, air curtains can significantly reduce infiltration. The PIV results confirmed the existence of multiple flow characteristics subject to pressure differences across the air curtain. The experimental results also validated the CFD modeling methods for air curtain, and verified that the empirical model of air curtain from the literature is valid in estimating infiltration through building entrances equipped with air curtains.

The location of neutral plane level (NPL) of a high-rise shaft is an important factor for the evaluation of risks of smoke spreads in high-rise buildings, where the shaft internal pressure is equal to that of the building floor at the same height. The current method to determine the location of NPL assumes uniform temperature distribution inside a shaft, which causes concerns over accuracy of the predicted NPL for high-rise shafts with non-uniformly distributed temperatures during fires. To address the effect of temperature distribution on NPL location, this paper introduces a method to calculate temperature distribution and its associated NPL location based on a coupled model of smoke temperature profile, flow rate and pressure distribution inside a shaft. The measured data from a 1/3 scale experiment is used to validate the method and used to develop two empirical equations for NPL locations in terms of dimensionless numbers: one empirical equation based on shaft top temperature and the second based on shaft bottom temperature. A sensitivity study of the empirical equations is then conducted to evaluate the applicability of the developed equations when compared to the existing NPL method. It was found that for the existing NPL equation based on uniform temperature assumption may under-/overestimate the NPL locations, and the NRMSE would be over 50%, while the NRMSE of the empirical equation is only around 6% based on the results of coupled equations. For non-adiabatic shafts, the effects of non-uniform temperature distribution on NPL should be considered and the suggested empirical equations can predict NPL locations with a reasonable accuracy.

An air curtain is often installed at a building entrance as an air barrier to reduce infiltration and resultant energy and air quality concerns. Due to its direct contact with the ambient environment, wind could affect the air curtain’s performance significantly, whereas its interaction with the wind has not been well understood. This study carried out a series of experiments on a sub-scaled building model with an air curtain in an atmospheric boundary layer wind tunnel. A CFD model was also built to further investigate the air curtain jet interactions with the wind. The CFD model was verified and validated through Particle Image Velocimetry measurements for different air curtain and wind conditions and in terms of the function of the dimensionless air infiltration rate versus the dimensionless pressure difference across the air curtain. This study defined the wind pressure coefficient for air curtains based on their performance under wind compared to that without wind. The wind pressure coefficient was found to be constant for different air curtain supply speed, supply angle, and wind speed, but varies with wind direction, so its profile was provided as a function of wind direction. This study provides insights into air curtain performance under various wind conditions.

Many urban and building microclimate problems are featured with a big computational domain and a large number of computing grids, so a fast and accurate solution by computation fluid dynamics (CFD) remains a challenge. The simulations are often only steady-state and, if there exists a transient model, the solution is often limited for a short period. For these computationally expensive simulations, large timesteps and coarse meshes (LTCM) seem preferable among users with limited computing resources, whereas many existing CFD models may not perform well for LTCM problems. This study developed a new fast fluid dynamics (FFD) model based on the semi-Lagrangian approach with high-order temporal and spatial schemes designed for LTCM problems. For large timesteps, a 2nd-order temporal scheme estimates the characteristic curve of each fluid's particle by accounting for the acceleration of the flow field. For coarse meshes, a 4th-order spatial scheme applies a backward-forward sweep technique for better accuracy in space. The performance of the proposed method is verified and validated by five benchmark cases, including a pure advection problem, forced convection in a single room, transient wind flow around a cylinder, airflow around a group of low-rise buildings, and airflow around a high-rise building. Then, we demonstrated the model for the study of the urban wind flows in a city downtown area with 2500 buildings and 35 million grids. As one of the first studies of applying FFD to urban aerodynamics, this study shows its strong potential for solving big transient problems with large timesteps and coarse meshes.

An air curtain installed at a building entrance acts as a barrier against infiltration/exfiltration of airflow between the outdoor environment and the indoor space. Several factors may affect air curtain performance, including air discharge velocity, angle, door height and width, and indoor and outdoor conditions. The current methodological standards/codes evaluate air curtain performance based on 1). aerodynamics performance characterization through the measurements of the jet velocity distribution and degradation, and 2). the laboratory measurements of their effectiveness in terms of blocking infiltration/exfiltration rates with/without air curtains under different operating conditions, i.e. pressure difference across doors. The 1st method is easier to conduct but cannot directly evaluate the effectiveness of an air curtain in reducing infiltration/exfiltration. The 2nd method can evaluate each specific unit, though the tests are often prohibitively costly and time-consuming. This study developed a new cost-effective method to assess the aerodynamic performance of an air curtain with respect to its capability of reducing infiltration/exfiltration. This method was demonstrated by a case study and validated against previous experimental data. The new method was found to be more efficient and cost-effective for various air curtain products and installation scenarios, as compared to existing methods.

The interaction of multiple pool fires might lead to higher burning rate and flame height than single pool fire, raising the possibility of fire ignition and flame spread and increasing the risks to people, buildings and environment. To quantify the burning rate and flame height of multiple pool fires from the view of physical mechanism, this paper presents an experimental study on two identical square pool fires. Heptane was used as fuel. The pool size and spacing were varied. Results showed that both the burning rate and flame height change non-monotonically with spacing. From the view of air entrainment, a correlation for the flame height of two pool fires is developed involving pool size, spacing and the flame height of zero spacing. The comparison with experimental results shows that the developed correlation is suitable for two heptane or propane fires. A theoretical study based on energy balance at one of the pool surfaces is performed to evaluate the burning rate of two fires, which is finally expressed as a function of pool size, spacing, burning rate and the flame height of single fire. The proposed model is validated using the experimental and literature data, which presents a reasonable reliability.

Thermal stratification is established when warmer air rises and cooler air descends under thermal buoyancy. It occurs in indoor environment situations including large warehouse-type buildings, buoyancy-driven ventilated spaces with displacement, underfloor ventilation, and/or natural ventilation, and enclosure fires with hot smoke layer on top of cold air layer. This paper reports a recent study that thermal stratification of indoor environment follows the statistical Beta distribution so the vertical temperature distribution is the Cumulative Distribution Function of the Beta distribution defined by two shape parameters, Alpha (α) and Beta (β), despite ventilation types, heat source and other details. It is then possible to estimate a complete vertical temperature profile under thermal stratification by four temperature points (ie, 4-point Beta distribution), or as few as two points (ie, 2-point Beta distribution) with a slight loss of accuracy. The study was confirmed by the field measurement data of five warehouse-type buildings, and eleven thermal stratification studies from the literature. A few applications were demonstrated including quantitative characterization of thermal stratification; estimation of mean and spatial temperature uniformities and other key parameters. The dimensionless nature of the methodology may also be potentially applied to other indoor stratification phenomena.