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This paper presents the development of a self-tuned HVAC controller that provides customized thermal conditions to satisfy occupant preferences (i.e., online learning) while minimizing energy consumption, and the implementation of this controller in a real occupied office space. The evolution of personalized thermal preference models and the delivery of thermal conditions with model predictive control (MPC) form a closed-loop. To integrate these two parts, we propose a new method that always provides a set of lower and upper indoor temperature bounds. Different from ad hoc rules proposed in previous research, the control bounds are based on a decision-making method that minimizes the expected cost. We implemented the self-tuned controller in an actual open-plan office space conditioned with a radiant floor cooling system with eight independently controlled loops. Localized operative temperature bounds in each radiant floor loop were determined based on occupants’ feedback and personalized thermal preference models, developed using a Bayesian clustering and online classification algorithm. The self-tuned controller can decrease occupant dissatisfaction compared to a baseline MPC controller, tuned based on general comfort bounds. To generalize the findings of this work: (i) we integrated the self-tuned controller with local MPC into a building simulation platform using synthetic occupant profiles, and (ii) demonstrated a method for automatic system adjustment based on comfort-energy trade-off tuning. In this way, decisions resulting in energy waste or occupant dissatisfaction are eliminated, i.e., the energy is deployed where it is actually needed.

This paper presents a method to incorporate personalized visual preferences in real-time optimal daylighting control without using general discomfort-based assumptions. A personalized shading control framework is developed to maximize occupant satisfaction while minimizing lighting energy use in daylit offices with automated shading systems. Personalized visual satisfaction utility functions were used along with model-predicted lighting energy use to form an optimization framework using two approaches. In the multi-objective optimization scheme, the satisfaction utility and predicted lighting energy consumption are used as parallel objectives to provide a set of Pareto solutions at each time step. In the single-objective optimization scheme, the satisfaction utility is converted into a constraint when minimizing lighting energy use. A simulation study with two distinct visual satisfaction models, inferred from experimental data, was conducted to evaluate the implementation feasibility and optimization effectiveness. Daily and annual simulation results are presented to demonstrate the different patterns of optimal points depending on preference profiles, occupant sensitivity to utility function, and exterior conditions. Finally, we present a new way to apply the multi-objective optimization without assigning arbitrary weights to objectives: allowing occupants to be the final decision makers in real-time balancing between their personalized visual satisfaction and energy use considerations, within dynamic hidden optimal bounds. A slider is introduced as a dynamic user interface with mapped and sorted optimal solutions.

In this paper we present a methodology to map individual occupants' thermal preference votes and indoor environmental variables into personalized preference models. Our modeling approach includes a new Bayesian classification and inference algorithm that incorporates hidden parameters and informative priors to account for the uncertainty associated with variables that are noisy or difficult to measure (unobserved) in real buildings (for example, the metabolic rate, air speed and occupants’ clothing level). To demonstrate our approach, we conducted an experimental study in private offices by considering thermal comfort delivery conditions that are representative of typical office buildings. Personalized preference models were developed with the training dataset and the developed algorithms were used in a detailed validation process. The proposed model showed better prediction performance compared to previous methods. Towards realization of preference-based control systems, this study also addresses practical limitations associated with controlling model complexity and data efficiency as well as using effective model evaluation metrics to train reliable personalized preference models in the real world.

In this paper, we explore intelligent operation strategies, based on stochastic model predictive control (SMPC), for optimal utilization of solar energy in buildings with integrated solar systems. Our approach takes into account the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this complex optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process regression, and achieves good solution quality. In the final step, we use an emulator that couples physical system models in TRNSYS with the SMPC controller developed using Python and MATLAB to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate under consideration, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a baseline well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.

This paper introduces a new multi-agent system approach to optimal control of high performance buildings and presents algorithms for both distributed system identification and distributed model predictive control (DMPC). For the system identification, each thermal zone is divided into sub-systems, and a parameter set for each sub-system is first estimated individually, and then integrated into an inverse model for the whole thermal zone using the dual decomposition algorithm. For the DMPC, a distributed optimization algorithm inspired by the Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) is deployed and multiple MPCs run iteratively while exchanging control input information until they converge. The developed algorithms are tested using field data from an occupied open-plan office space with a radiant floor system with distributed sensing, control, and data communication capabilities for localized comfort delivery. With this tractable approach, agents solve individual optimization problems in parallel, through information exchange and broadcasting, with a smaller scale of the input and constraints, facilitating optimal solutions with improved efficiency that are scalable to different building applications. Using a data-driven model and weather forecast, the DMPC controller is implemented to optimize the operation of an air-cooled chiller while providing different operative temperature bounds for each radiant floor loop. The radiant comfort delivery system with predictive control is capable of providing localized thermal environments while achieving significant energy savings. For the system and climate under consideration, results from the building operation during the cooling season, show 27% reduction in electricity consumption compared to baseline feedback control.

This paper presents a new method for developing personalized visual satisfaction profiles in private daylit offices using Bayesian inference. Unlike previous studies based on action data, a set of experiments with human subjects and changing visual conditions were conducted to collect comparative preference data. The likelihood function was defined by linking comparative visual preference data with the visual satisfaction utility function using a probit model structure. A parametrized Gaussian bell function was adopted for the latent satisfaction utility model, based on our belief that each person has a specific set of neighboring visual conditions that are most preferred. Distinct visual preference profiles were inferred with a Bayesian approach using the experimental data. The inferred visual satisfaction utility functions and the model performance results reflect the ability of the models to discover different personalized visual satisfaction profiles. The method presented in this paper will serve as a paradigm for developing personalized preference models, for potential use in personalized controls, balancing human satisfaction with indoor environmental conditions and energy use considerations.

The objective of this paper is to understand the complex interactions related to visual environment control in private offices of perimeter building zones and to develop a new method for learning occupant visual preferences. In the first step of our methodology, we conduct field observations of occupants’ perception and satisfaction with the visual environment when exposed to variable daylight and electric light conditions, and we collect data from room sensors, shading and light dimming actuators. Consequently, we formulate a Bayesian classification and inference model, using the Dirichlet Process (DP) prior and multinomial logistic regression, to develop probability distributions of occupants’ preference, such as prefer darker, prefer brighter, or satisfied with current conditions. Based on field observations, we encode within the model structure that occupants’ visual preferences are influenced by a combination of measured physical and control state variables describing the luminous environment, as well as latent human characteristics. The latter represent hidden random variables used to determine the optimal number of possible clusters of individuals with similar visual preference characteristics in the studied office building population. In the final step, we learn the visual preferences of new occupants in the dataset, by inferring their cluster values, and we derive the personalized profiles, using a mixture of the general probabilistic sub-models.

In this paper, our goal is to develop user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the model-predictive control (MPC) strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the HVAC energy consumption with MPC can be underestimated by up to 55% without considering occupants’ overrides. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios.

This paper presents a new data-driven method for learning personalized thermal preference profiles, by formulating a combined classification and inference problem, without developing different models for each occupant. Different from existing approaches, we developed a generalized thermal preference model in which our main hypothesis, “Different people prefer different thermal conditions”, is explicitly encoded. The approach is fully Bayesian, and it is based on the premise that the thermal preference is mainly governed by (i) an overall thermal stress, represented using physical process equations with relatively few parameters along with prior knowledge of the parameters, and (ii) the personal thermal preference characteristic, which is modeled as a hidden random variable. The concept of clustering occupants based on this hidden variable, i.e., similar thermal preference characteristic, is introduced. The results, based on a dataset collected from a typical office building population, show clear evidence of the existence of multi-clusters; in particular, the 5-cluster model performed best compared to 2, 3 and higher cluster models using the studied dataset. Subsequently, the thermal preference of a new occupant in the dataset is inferred by using a mixture of the general sub-models for each cluster. The results show that the method developed in this study provides accurate predictions for personalized thermal preference profiles and it is efficient as it only requires a relatively small dataset collected from each occupant. The approach presented in this paper is a significant step towards personalized environments in office buildings using real-time feedback from occupants.

In this paper, we present a hierarchical Bayesian approach to model human interactions with motorized roller shades and dimmable electric lights. At the top level of hierarchy, Bayesian multivariate binary-choice logit models predict the probability of shade raising/lowering actions as well as the actions to increase the level of electric light. At the bottom level, Bayesian regression models with built-in physical constraints estimate the magnitude of actions, and hence the corresponding operating states of shading and electric lighting systems. The models are based on a dataset from a field study conducted in private offices designed to facilitate a large number of participants and to collect data on environmental parameters as well as individual characteristics and human attributes governing human-shading and – electric lighting interactions. In this study, models were developed only for arrival periods due to the low frequency of actions during intermediate time intervals with continuous occupation. Our modeling framework demonstrates the advantages of the Bayesian approach that captures the epistemic uncertainty in the model parameters, which is important when dealing with small-sized datasets, a ubiquitous issue in human data collection in actual buildings; it also enables the incorporation of prior beliefs about the systems; and offers a systematic way to select amongst different models using the Bayes factor and the evidence for each model. Our findings reveal that besides environmental variables, human attributes are significant predictors of human interactions, and improve the predictive performance when incorporated as features in shading action models.

The paper presents a field study on human interactions with motorized roller shades and dimmable electric lights in private offices of a high performance building. The experimental study was designed to (i) extend the current knowledge of human-building interactions to different and more advanced systems, including intermediate shading positions and light dimming levels, and (ii) reveal behavioral characteristics enabled through side-by-side comparisons of environmental controls ranging from fully automated to fully manual and interfaces with low or high level of accessibility (wall switch, remote controller and web interface). The research methodology includes monitoring of physical variables, actuation and operation states of building systems, as well as online surveys of occupant comfort and perception of environmental variables, their personal characteristics and attributes (non-physical variables). The analyzed datasets provide new insights on the dynamics of interdependent human interactions with shading and electric lighting systems. Higher daylight utilization was observed in offices with easy-to-access controls, which implies less frequent use of electric lights and less energy consumption accordingly. Analysis of occupant satisfaction, in terms of comfort with the amount of light and visual conditions, based on datasets from offices with variable accessibility to shading and lighting control, reveals a strong preference for customized indoor climate, along with a relationship between occupant perception of control and acceptability of a wider range of visual conditions.

There is an increasing number of recent studies about personalized thermal preferences and controls in office buildings. Data collection from occupants in real buildings is necessary for training and updating models. However, sufficient quantity and quality of data are required for developing reliable models, along with optimal model complexity, efficient updating modes and robust evaluation metrics. Therefore, long-term collection of occupant feedback is often needed, which might be intrusive and impractical. This paper presents a Bayesian modeling approach which incorporates voluntary feedback data (comfort-related responses), collected via participatory interfaces, along with requested feedback data, into the personal thermal preference learning framework. This is achieved by explicitly considering occupants’ participation –a type of behavior –in the model structure, i.e., integration of thermal preference-related feedback and occupant behavior. The approach was evaluated with two different datasets collected from two experimental setups with human test-subjects. A smart feedback request algorithm was developed, which determines whether to request feedback at any given time based on the quantified value (i.e., information gain) of the request. The value was computed using the expected Kullback-Leibler divergence between the current and updated posterior parameter distributions. In addition, a simulation study was conducted to evaluate the performance of the algorithm. The results show that the new algorithm learns individual thermal preferences with reduced feedback requests, i.e., effective but less-intrusive. Requesting occupant feedback only when truly needed is important for smart and practical human-centered HVAC operation.

Previous studies show differences in thermal comfort among individual occupants and suggest solutions that incorporate building occupants in sensing and control frameworks (a.k.a., human-in-the-loop) and tune heating, ventilation, and air conditioning (HVAC) systems based on their preferences to enable self-tuned thermal environments. The objective of the review presented in this paper is to discuss two key aspects of self-tuned thermal environments: (i) learning individual occupants’ thermal comfort; (ii) HVAC control based on the learned comfort profiles. The review is conducted considering practical issues associated with the implementation of such modeling and control approaches in real buildings. We found that research on learning personalized comfort profiles has rather focused on developing and testing the adopted methods assuming that it is feasible to collect a large amount of training data in real buildings. In addition, previous research has given less attention to the validity of methods for collecting occupants’ feedback responses. Hence, we focus our discussion on data collection, input variable selection, and performance evaluation considering the data efficiency. Regarding HVAC systems control, we found that arbitrary rules have been used to operate the systems with the learned occupant comfort profiles, and we discuss their validity and consistency for different occupants and buildings.

Ozone is known to cause adverse health effects such as decreased lung function and respiratory symptoms. Indoor ozone originates mainly from the outdoor environment and enters a building through three different ventilation mechanism: infiltration, natural ventilation, and mechanical ventilation. This study investigated the relationship between ventilation and indoor/outdoor ozone concentration by measuring the concentration and the ventilation rate in two chambers and in an actual office space with different ventilation systems. The ventilation rate was determined by using the decay method with sulfur hexafluoride (SF6) as a tracer gas. The surface removal rates were estimated from the information provided in the previous literature. The results show that within the range of our investigation, the indoor/outdoor ozone concentration ratio can be predicted by a simple steady-state model within 80% accuracy. By using the model and according to the ventilation rate and surface removal rate data collected from literature, the most common indoor-to-outdoor ozone ratios were found to be 0.09, 0.19, and 0.47 for infiltration, mechanical ventilation, and natural ventilation, respectively.

This study investigates the interactions between the underlying turbulent features that together make up the complex flow behaviour observed in corrugated channel flows. Higher order analyses using proper orthogonal decomposition (POD) and wavenumber spectra were conducted on the turbulent velocity data for heated and unheated air flow through the channel. Results showed that at a given flow rate, the turbulent flow energy was produced by the corrugation and transported into the bulk flow at both heated and unheated conditions. At low flow rates, the heated wall increased the total turbulent energy and affected its distribution across the modes. Strong energies were seen close to the corrugations, which contributed to sustaining structures at higher modes, while the flow energy was more evenly distributed for the unheated condition. At the highest flow rate, the energy strength and distribution was very similar at low modes ( n ≤ 20) and heating effects were most prominent at high modes with higher energies associated with small-scale flow patterns. It is observed that the corrugation waveform has a larger impact on the turbulence generation compared to heating. The addition of heat primarily increased and maintained the strength of turbulent structures.

Natural ventilation is a promising approach to reducing building energy use if designed properly. Most of the previous design models for calculating airflow due to single-sided natural ventilation have been based on the assumption of simple openings. Since, most windows are not simple openings, but rather can create flow obstructions when opened; the impact of window structure on ventilation needs to be accounted for in order to accurately predict the ventilation rate in buildings. This paper presents an experimental and numerical evaluation of the impact of three types of windows—hopper, awning, and casement—on airflow in the case of single-sided natural ventilation. Semiempirical models for predicting the ventilation rate were developed for these window types and validated by both large-eddy simulations and full-scale measurements. In general, the predictions agreed with the measured results within an error of 25%, and the new models can be used for the design of natural ventilation systems.

The objective of this study was to determine the effects of geometry on the wind loads acting on photovoltaic panel arrays with modules mounted parallel to roof surfaces of low-rise buildings. Specific attention was made to determine the effects of varying the spacing between individual modules, G, and the mounting height above the roof surface, H. The photovoltaic system was modeled as an array of 28 modules on a 1/20 scale building model with a roof slope of 30°. In addition, limited studies were carried out with the array mounted on a flat-roof to assess the impact of roof slope. In general, it was found that larger gaps between modules, G, and smaller gaps between the panels and the roof surface, H, were found to yield lower net wind loads. Minimum loads tend to occur for G/H>~1, for the particular panel size considered in the study. Pressure equalization between the upper and lower surfaces of the modules results in the magnitudes of the net panel pressures typically being lower than those for the bare roof surface. A pressure equalization factor, Ceq, was used as a measure of how much the peak net wind loads on the panels are reduced relative to the peak external loads.

The present study explores efficient integration approaches of photovoltaic-thermal systems coupled with corrugated transpired solar collectors (building-integrated photovoltaic-thermal, BIPV/T), Heating, Ventilation and Air Conditioning (HVAC) systems and thermal storage devices, to enable optimal collection and utilization of solar energy in high performance buildings. The objective is to (a) develop models that capture the relevant system dynamics and are computationally efficient for subsequent use within model-predictive control (MPC) algorithms; (b) evaluate the energy saving potential of the integrated system and the predictive controller in comparison with baseline operation strategies. An open plan office space at Purdue’s Living Laboratory is used as test-bed, in which the BIPV/T system preheats ventilation air, while also, it is coupled with the building through an air-to-water heat pump and a thermal energy storage (TES) tank that serves as the heat source for the radiant floor heating (RFH). A detailed energy prediction model developed in TRNSYS is considered as a true representation of the building and it is used to identify the parameters of low-order linear time-invariant state-space models. Both gray-box and subspace state-space system identification (4SID) methods are investigated. A simulation study is performed using TMY3 data for West Lafayette, IN during the heating period. The results show that implementation of a deterministic MPC algorithm for the optimal set-point trajectory of the TES tank can reduce the electrical energy consumption of the heat pump by 34.5%. For the BIPV/T configurations tested, the energy saving of the integrated solar system can be up to 45% compared to the baseline operation of the radiant floor heating. The study also investigates the impact of forecast uncertainty for the horizontal solar irradiance on the performance of the predictive controller, with the results showing considerable impact on thermal comfort conditions when the prediction error is higher than 38%.

The paper presents a control-oriented modeling approach for multi-zone buildings with mixed-mode cooling, based on the linear state-space representation with varying coefficient matrices. Key features are the time-variant thermal resistances, associated with the heat extraction due to airflow, calculated using an airflow network model. This approach was validated with experimental data collected in a two-zone test-building under four operation modes. A forward linear time-variant state-space (LTV-SS) model, developed based on first principles, was then used as a true representation of the building, to identify the parameters of a low-order LTV-SS gray-box model. The low-order model can predict the building thermal dynamics with sufficient accuracy with a root mean square error (RMSE) of 0.58 °C for the air and 1.08 °C for the area-weighted mean surface temperature in the south direct gain zone. Furthermore, the study develops a progressive refinement (ProRe) optimization method, following the multi-level optimization topology and branch and bound decision trimming strategy, to find sequences of binary (open/close) decisions for the motorized windows. Due to the significant improvement in computing time, the models and algorithms presented in this paper enable long-term simulation for MPC performance evaluation and implementation of predictive strategies in real controllers.

This paper is the second of two companion papers focused on energy modeling and performance analysis of building-integrated photovoltaic thermal (PV/T) systems with corrugated unglazed transpired solar collectors (UTCs). In Part 1, energy models are presented for two configurations: UTC only and UTC with PV panels. The models predict the energy output of the system for different weather and system design conditions and are validated using measured data from an outdoor test facility. In this paper (Part 2), the system performance is evaluated based on data drawn from the literature and simulations with Computational Fluid Dynamics (CFD) and energy models. The analysis includes parameters that are unique for this system, such as the corrugation geometry and the collector orientation. Validated, high resolution CFD simulations are used to study the impact of plate orientation and incident turbulence intensity, based on the comparison of exterior and interior Nusselt (Nu) number and the cavity exit air temperature, as well as the PV surface temperature when UTCs are integrated with PV panels. It is found that for configurations with UTC only, both exterior and interior convective heat transfer is enhanced in the ‘vertical’ installation, while similar results were obtained for increased incident turbulence intensity levels. However, only minor influences from these two parameters are observed for UTCs with PV panels. The energy model is used to investigate the optimal geometry for both configurations. It is found that parameters such as slope length and corrugation wavelength have the most significant impact on UTC performance while the wavelength and PV panel height have the largest effect for UTCs with PV panels.

Building-integrated photovoltaic–thermal (BIPV/T) systems with unglazed transpired solar collectors (UTCs) can provide a key solution for on-site electricity and thermal energy generation. Although the energy saving potential of this technology is significant, no systematic thermal analysis model has been developed for optimal system design and integration with building operation. This paper is the first of two companion papers focused on modeling and performance analysis of BIPV/T systems with UTC. In Part 1, energy models are presented for two configurations: UTC only and UTC with PV panels, to predict the cavity exit air temperature and plate surface temperature with weather (incident solar radiation, ambient air temperature, dew point temperature and wind speed) and design (airflow rate or suction velocity and geometry) parameters used as inputs. Nusselt number and effectiveness correlations, representing both the exterior and interior convective heat transfer processes, have been obtained from experimentally validated, three-dimensional, Reynolds-Averaged Navier–Stokes (RANS), Computational Fluid Dynamics (CFD) simulations, using high resolution grids and the ReNormalization Group Methods k–e (RNG k–e) turbulence closure model. The energy models were validated with measurements in an outdoor test-facility. Good agreement was observed between the model prediction and the experimental data, with the root mean square error (RMSE) being within 1 °C for the UTC-only model and within 2 °C for the model of UTC with PV modules. In the companion paper, Part 2, the effects of important parameters on system performance are demonstrated based on information from the literature and simulations using CFD and energy models. The optimal geometry is investigated for both configurations and the performance curves, under different levels of solar radiation, wind speed and suction velocity, are presented to provide guidelines for system design.

The paper presents model predictive control (MPC) strategies for buildings with mixed-mode cooling (window opening position, fan assist, and night cooling schedule) and demonstrates their potential performance bounds in terms of energy savings within thermal comfort constraints, in comparison with standard heuristic rules used in current practice. The study also identifies optimal control sequences coordinated with shading, for the control of solar gains. A transient, multi-zone building energy prediction model, with a coupled thermal and airflow network, is developed in MATLAB, and it is used within an offline MPC framework with Particle Swarm Optimization (embedded in GenOpt) as an optimizer. Simulations are performed for a period of six consecutive summer days with mixed-mode cooling strategies decided by the predictive controller, based on weather forecast and cooling load anticipation over a 24 h planning horizon. The results show that MPC can significantly reduce the cooling requirements compared to baseline night setback control while maintaining the operative temperature during the occupied period within acceptable limits. On the contrary, rule-based control strategies for the window opening position, based on simple heuristics for the outdoor conditions, create an increased risk of overcooling with lower thermal comfort acceptability.

The flow structure and convective heat transfer mechanisms in Unglazed Transpired solar Collectors (UTCs) are crucial to their performance. High-resolution, 3-dimensional, steady, Reynolds-Averaged Navier–Stokes (RANS), Computational Fluid Dynamics (CFD) simulations have been used to analyze the convective heat transfer processes for both flat and corrugated UTCs. The performance of five potentially suitable turbulence closure models: Standard k – e , Renormalization Normal Group k – e (RNG k – e ), Realizable k – e , Shear Stress Transport k – ω (SST k – ω ) and Reynolds Stress Model (RSM), has been evaluated. Two scenarios have been considered: a flat UTC under free stream approaching flow and a corrugated UTC subjected to a plane wall jet flow. The results were compared against experimental data from the literature and those obtained using a full-scale experimental set-up in a solar simulator, in terms of the velocity and turbulent kinetic energy of the airflow as well as the plate surface and air temperature. The validation study showed that the RNG k – e model has the best overall performance for both UTC geometries at reasonable accuracy and computing cost. A parametric analysis has been conducted using the RNG k – e model at 3 m/s approaching flow velocity, for different suction flow rates (0.01 and 0.06 m/s) and free stream turbulence intensities (0.1% and 20%). The results showed that in UTCs, it is the suction velocity, rather than the suction ratio of V s / U ∞ , that has the most profound impact on the boundary layer development and thermal efficiency. For flat UTCs, the presence of perforations is more significant than the level of turbulence intensity in the approach flow. However, for corrugated UTCs, the incident turbulence intensity plays a more important role in the system performance than the perforation dimensions due to the turbulent interaction between the corrugated geometry and the incoming flow. Although a high suction rate can help maintain the asymptotic boundary layer profiles and, hence simplify the energy analysis of UTCs, it is not applicable in practice due to the requirement of large fan power. These outcomes will not only further advance the development of UTCs and optimize their performance but also provide insights for the development of simplified thermal analysis models for use in building energy simulation.

An experimental study was conducted to examine the air flow behavior in the channel of a transpired air collector under different heating conditions. Velocity fields were measured using Particle Image Velocimetry (PIV). Mean velocities and turbulent properties were computed and evaluated. Results show that at high flow rates, the flow was dominated by forced convection while at the lowest flow rate the flow was primarily buoyancy driven, where buoyancy-induced stabilities and heating effects were strongest. It was observed that the buoyancy-induced instabilities enhanced the magnitude and modified the structure of mean and turbulent properties as compared to the unheated flow. The flow rate influenced the relative magnitudes of the normalized mean and turbulent velocities that were enhanced with a decrease in the flow rate at a given heating condition. Collector efficiencies up to 70% were observed, which could be attributed to the corrugation surface geometry that enhanced turbulence and provided a larger heat transfer surface area.

An experimental investigation of the flow dynamics in a channel with a corrugated surface is presented. Particle image velocimetry was used to obtain two-dimensional velocity fields at three different locations along the channel length, over a range of Reynolds numbers. The results show a significant impact of the corrugation waveform on the mean and turbulent flow structure inside the channel. Strong bursting flow originating from the trough, sweeping flow from the bulk region and the vortex shedding off the crest were observed. Their interactions created a complex three-dimensional flow structure extended over almost the entire channel. The mean velocity profiles indicate a strong diffusion of shear. The profiles of various turbulent properties show the enhancement of turbulence in the vicinity of the waveform. It was found that the turbulence in the channel was almost entirely produced in this region above the corrugation trough. Significant momentum transfer from the corrugation wall by the turbulent velocity field was also observed. The mean and turbulent flow behaviour was found to be periodic with respect to the waveform over most of the channel length. The results show the presence of strong turbulence even at the Reynolds number that falls within the conventional laminar range.

The paper explores mixed-mode cooling strategies in buildings with hybrid ventilation and high levels of exposed thermal mass and presents data collected over an extensive period of time using a full-scale experimental set-up in an occupied institutional building with motorized facade openings integrated with an atrium. Night cooling strategies for thermal mass pre-cooling, with variable low temperature setpoint for control of airflow through operation of motorized grilles or a possible variable speed fan, are evaluated. Results for the thermal conditions in the highly-glazed atrium zone and the wind and buoyancy-driven airflow rates show that free cooling covers a significant portion of the building's cooling requirements while maintaining a comfortable indoor environment. The heat removal from concrete floor slabs is 2–5 times higher with the inlet air stream at an average temperature of 12 °C, compared to that with an air stream at 15 °C or 18 °C respectively. These findings provide insights for space conditioning using hybrid ventilation that can support the development of model-predictive control strategies.

The present work evaluates forced convective heat transfer from the inclined windward roof of an isolated low-rise building, with application to building-integrated Photovoltaic/Thermal (PV/T) systems. High resolution, 3-D, steady, Reynolds-Averaged Navier–Stokes (RANS) Computational Fluid Dynamics (CFD) simulations of the wind flow field near the roof of a building with plan dimensions of 4.2 m by 6 m, a 3 m eaves height and a 30° roof slope, are conducted, with the results validated by experimental data from a 1:50 scale model tested in a boundary layer wind tunnel. The heat transfer model is validated using the boundary layer correlation for an isothermal horizontal flat plate in uniform flow. Full-scale simulations, with the same building geometry and numerical model, for Reynolds numbers (Re) from 2.2 × 105 to 7.7 × 105 based on wind speed at eaves height and sloped roof length, are also carried out, for four roughness length (z0) values representing open and suburban terrain. From these, dimensionless correlations for the exterior convective heat transfer coefficient (CHTC), expressed as Nusselt number (Nu), for the windward roof are developed which include the Re and the incident turbulence intensity at eaves height. The windward roof average CHTC is dependent upon the presence and extent of the leading edge separated flow region. This region reduces in size as both Re and eaves height turbulence intensity are increased, such that at high turbulence levels it disappears and the value of the exponent on the Re term in the Nu correlation approaches that for a flat plate turbulent boundary layer.

Atria are becoming an increasingly common feature of new buildings. They are often included for their aesthetic appeal; however, their effect on building indoor environment can be significant. Building simulation tools have the potential to assist designers in enhancing energy efficiency by providing information on the temperature and velocity fields inside the atrium for specified geometries and ambient conditions. The unique nature of the physical phenomena that govern the complex flows in atria, however, are not usually considered in traditional building energy simulation programs. These physical phenomena include turbulent natural convection, radiative heat transfer and conjugate heat transfer. Computational fluid dynamics (CFD) has the potential for modeling fluid flow and heat transfer resulting from the phenomena; however, careful validation is required in order to establish the accuracy of predictions. This paper provides a systematic validation of a commercial CFD code against experimental measurements of the underlying physical phenomena. The validation culminates in the simulation of an existing atrium. This work indicates that CFD can be used to successfully simulate the heat transfer and fluid flow in atria geometries and provides recommendations regarding turbulence and radiative heat transfer modeling.

This paper presents an experimental study of basic cross-ventilation flow characteristics that are essential inputs for accurate natural ventilation modelling and design. The study focuses on a generic single-zone building model tested in a wind tunnel under isothermal flow conditions (wind-driven ventilation). An advanced experimental method based on particle image velocimetry (PIV) was developed to investigate the air velocity field in buildings with cross-ventilation. It was found that airflow patterns in rooms with cross-ventilation are complex and cannot be predicted by simplified macroscopic models such as the orifice equation. Inlet-to-outlet ratio and relative location of openings on a building facade are important parameters to be considered, in addition to the wall porosity. This study provides new insights that enable improved design and control of operable facade elements to enhance space cooling using natural ventilation.

This paper presents a simulation case study of facade and envelope preliminary design options for the new Engineering building of Concordia University in Montreal. A major principle of the analysis was to create a high quality building envelope in order to optimally control solar gains, reduce heating and cooling energy demand and reduce electricity consumption for lighting, while at the same time maintain a comfortable and pleasant indoor environment. The stated approach of the design team was to aim for an energy-efficient building, employing innovative technologies and integrating concepts such as daylighting and natural ventilation. Detailed energy simulations were therefore performed from the early design stage, in order to present recommendations on the choice of facade, glazings, shading devices, lighting control options, and natural ventilation. Integrated thermal studies, a daylighting analysis and the impact of the above on HVAC system sizing were considered. Simulation results showed that, using an optimum combination of glazings, shading devices and controllable electric lighting systems, the energy savings in perimeter spaces can be substantial. Perimeter heating could be eliminated if a high performance envelope is used. The building is currently being commissioned.

The paper presents and discusses internal pressure and discharge coefficients for a building with wind-driven cross-ventilation caused by sliding window openings on two adjacent walls. The study found that both coefficients vary considerably with the opening area (porosity of wall(s)) and the inlet to outlet ratio. Comparisons with previous work were also carried out. Experimental results verify the unsteady pressure and velocity field, particularly in the case of cross-ventilation with large opening areas. For such cases, a simulation sensitivity analysis of wind-induced building ventilation confirms that airflow rates vary considerably when different discharge coefficient values are used.

This study presents a full-scale experimental investigation of the air leakage characteristics of two trickle ventilator types, namely slot and pressure-controlled. Furthermore, the performance of trickle ventilators under the influence of the wind, which is the main driving force of the air flow through low-rise buildings, and the validity of the orifice equation to represent their behaviour have been assessed. Finally, the potential of trickle ventilator integration in ventilation design of office buildings is investigated through flow network simulations and design solutions for the opening area of trickle ventilators to satisfy the fresh air requirements, as recommended by ASHRAE, are presented. It was found that the air flow through trickle ventilators may be modelled/predicted accurately and utilized in the design of natural/hybrid ventilation systems. However, the pressure-controlled ventilator appears to be preferable since it provides for better IAQ, and its performance is better than that of the slot ventilator with respect to comfort and energy use to warm up the ventilated air during the heating season