Various organizations and societies around the globe have issued guidelines in response to the coronavirus disease (COVID-19) and virus (SARS-CoV-2). In this paper, heating, ventilating, and air-conditioning-related guidelines or documents in several major countries and regions have been reviewed and compared, including those issued by the American Society of Heating Refrigerating and Air-Conditioning Engineers, the Federation of European Heating, Ventilation, and Air Conditioning Associations, the Society of Heating, Air-Conditioning and Sanitary Engineers of Japan, Architectural Society of China, and the Chinese Institute of Refrigeration. Most terms and suggestions in these guidelines are consistent with each other, although there are some conflicting details, reflecting the underlying uncertainty surrounding the transmission mechanism and characteristics of COVID-19 in buildings. All guidelines emphasize the importance of ventilation, but the specific ventilation rate that can eliminate the risk of transmission of airborne particulate matter has not been established. The most important countermeasure, commonly agreed countermeasures, the conflicting content from different guidelines, and further work have been summarized in this paper.
Abstract This paper proposes a novel reinforcement learning (RL) architecture for the efficient scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a commercial building while harnessing its demand response (DR) potentials. With advances in automated building management systems, this can be achieved seamlessly by a smart autonomous RL agent which takes the best action, for example, a change in HVAC temperature set point, necessary to change the electricity usage pattern of a building in response to demand response signals, and with minimal thermal comfort impact to customers. Previous research in this area has tackled only individual aspects of the problem using RL. Specifically, due to the challenges in implementing demand response with whole-building models, simpler analytical models which poorly capture reality have been used instead. And where whole-building models are applied, RL is used for HVAC control mainly to achieve energy efficiency goals while demand response is neglected. Thus, in this research, we implement a holistic framework by designing an efficient RL controller for a whole-building model which learns to optimise and control the HVAC system for improved energy efficiency and thermal comfort levels in addition to achieving demand response goals. Our simulation results show that by applying reinforcement learning for normal HVAC operation, a maximum weekly energy reduction of up to 22% can be achieved compared to a handcrafted baseline controller. Furthermore, by employing a DR-aware RL controller during demand response periods, average power reductions or increases of up to 50% can be achieved on a weekly basis compared to the default RL controller, while keeping occupant thermal comfort levels within acceptable bounds.
Heating, ventilation, and air conditioning (HVAC) are extremely energy consuming, accounting for 40% of total building energy consumption. It is crucial to design some energy-efficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep-reinforcement-learning-based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants’ thermal comfort. We first design a deep feedforward neural network (FNN)-based approach for predicting the occupants’ thermal comfort and then propose a deep deterministic policy gradients (DDPGs)-based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simulation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants’ thermal comfort by 13.6%.
Each day people are exposed to millions of bioaerosols, including whole microorganisms, which can have both beneficial and detrimental effects. The next chapter in understanding the airborne microbiome of the built environment is characterizing the various sources of airborne microorganisms and the relative contribution of each. We have identified the following eight major categories of sources of airborne bacteria, viruses, and fungi in the built environment: humans; pets; plants; plumbing systems; heating, ventilation, and air-conditioning systems; mold; dust resuspension; and the outdoor environment. Certain species are associated with certain sources, but the full potential of source characterization and source apportionment has not yet been realized. Ideally, future studies will quantify detailed emission rates of microorganisms from each source and will identify the relative contribution of each source to the indoor air microbiome. This information could then be used to probe fundamental relationships between specific sources and human health, to design interventions to improve building health and human health, or even to provide evidence for forensic investigations.
Abstract Conventional heating, ventilation, and air-conditioning (HVAC) systems are designed to condition the entire building volume. In contrast, Personal Comfort Systems (PCS) target conditioning only the occupied zones of the space, while maintaining the remaining volume at a relatively under-conditioned state. PCS offer the occupants the choice of modulating their immediate thermal ambience with local controls. The individual-level control helps in improving the subjective thermal and air quality acceptability with the desired thermal sensation. This review paper details on the various types of heating, cooling, ventilation, heating with ventilation, and cooling with ventilation PCS devices. It summarises the thermal ambience created by the respective PCS devices and the resultant subjective responses of the occupants. This review also identifies the energy saving potential of various kinds of PCS devices, the power use of PCS devices, and discusses their economic viability.
Abstract In recent years, photovoltaic thermal (PVT) systems have emerged as an imperative research area due to the escalating demand for energy worldwide. Phase change materials (PCMs) considered as the most suitable materials to harvest thermal energy effectively from renewable energy sources. As such, this paper reviews and explains the various aspects of PCM and Nano-Enhanced PCM (NEPCM) integrated PVT systems. The novel and recent developments in PVT research focusing on cooling and thermal energy storage with PCM and NEPCM and their applications in the heating ventilation and air-conditioning (HVAC), building integrated photovoltaic thermal systems (BIPVT), building integrated concentrated photovoltaic thermal systems (BICPVT) are critically summarized. In addition, this review also accentuates the different methods of preparing NEPCM and their thermo-physical properties at different operating temperatures for targeted applications. The present paper also highlights the use of nanofluid, PCM, and NEPCM in extracting the thermal energy from the commercially available for PVT system. In conclusion, this review recapitulates the effort taken by researchers around the world in enhancing the thermal performance system. It is also expected this review will provide greater insight to the new researchers in recognizing the fundamental science behind the development of thermal performance system and the mechanism to enhance further the overall performance of the PVT system.
Yassine Bouabdallaoui, Z. Lafhaj, Pascal Yim
et al.
The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.
Abstract Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving demand-side energy efficiency. At the same time, the thermodynamics of buildings and uncertainties regarding human activities make effective management challenging. While the concept of model-free reinforcement learning demonstrates various advantages over existing strategies, the literature relies heavily on value-based methods that can hardly handle complex HVAC systems. This paper conducts experiments to evaluate four actor-critic algorithms in a simulated data centre. The performance evaluation is based on their ability to maintain thermal stability while increasing energy efficiency and on their adaptability to weather dynamics. Because of the enormous significance of practical use, special attention is paid to data efficiency. Compared to the model-based controller implemented into EnergyPlus, all applied algorithms can reduce energy consumption by at least 10% by simultaneously keeping the hourly average temperature in the desired range. Robustness tests in terms of different reward functions and weather conditions verify these results. With increasing training, we also see a smaller trade-off between thermal stability and energy reduction. Thus, the Soft Actor Critic algorithm achieves a stable performance with ten times less data than on-policy methods. In this regard, we recommend using this algorithm in future experiments, due to both its interesting theoretical properties and its practical results.
Abstract Automatic fault detection and diagnosis (AFDD) for chillers has significant impacts on energy saving, indoor environment comfort and systematic building management. Recent works show that the artificial intelligence (AI) enhanced techniques outperform most of the traditional fault detection and diagnosis methods. However, one serious issue has been raised in recent studies, which shows that insufficient number of fault training samples in the training phase of AI techniques can significantly influence the final classification accuracy. The insufficient number of fault samples refers to the imbalanced-class classification problem, which is a hot topic in the field of machine learning. In this study, we re-visit the imbalanced-class problem for fault detection and diagnosis of chiller in the heating, ventilation and air-conditioning (HVAC) system. The generative adversarial network is employed and customized to re-balance the training dataset for chiller AFDD. Experimental results demonstrate the effectiveness of the proposed GAN-integrated framework compared with traditional chiller AFDD methods.
This paper investigates and characterizes the row dependent thermal performance of an 8 row finned tube heat exchanger (HEX) with an offset tube arrangement and continuous flat fins. Computational fluid dynamics (CFD) simulations reveal distinct hydraulic thermal behavior across the tube rows, with the first, second, and eighth rows exhibiting significantly higher heat transfer efficiency than the average for the entire HEX under specific flow conditions. At air velocities below 3 ms-1, the first rows exhibit increased efficiency, while the eighth row exhibits comparatively better efficiency at velocities exceeding 6 ms-1. In the fitted range (2.5 to 10 ms-1), orders 1, 2, and 8 outperform orders 3 to 7 by approximately 15 to 25% in Nusselt number, indicating that orders 1, 2, and 8 operate under distinct flow and thermal conditions. The study also determined individual Nusselt number correlations for each order through systematic simulations over the velocity range from 2.5 to 10 ms-1. The analysis further linked these changes to specific flow mechanisms, such as boundary layer development, wake interference, and vortex shedding, which govern the distinct thermal behavior of the first, second, and last orders. These findings can enable dual optimization: lower capital costs by minimizing the number of orders for the target thermal performance, and lower operating costs by reducing pressure drop (reducing fan power).
Pritisha Sarkar, Kushalava reddy Jala, Mousumi Saha
Acknowledging the effects of outdoor air pollution, the literature inadequately addresses indoor air pollution's impacts. Despite daily health risks, existing research primarily focused on monitoring, lacking accuracy in pinpointing indoor pollution sources. In our research work, we thoroughly investigated the influence of indoor activities on pollution levels. A survey of 143 participants revealed limited awareness of indoor air pollution. Leveraging 65 days of diverse data encompassing activities like incense stick usage, indoor smoking, inadequately ventilated cooking, excessive AC usage, and accidental paper burning, we developed a comprehensive monitoring system. We identify pollutant sources and effects with high precision through clustering analysis and interpretability models (LIME and SHAP). Our method integrates Decision Trees, Random Forest, Naive Bayes, and SVM models, excelling at 99.8% accuracy with Decision Trees. Continuous 24-hour data allows personalized assessments for targeted pollution reduction strategies, achieving 91% accuracy in predicting activities and pollution exposure.
William Seeley, Francesca Coke, Radostin Simitev
et al.
We investigate the linear onset of thermal convection in rotating spherical shells with a focus on the influence of mechanical boundary conditions and thermal driving modes. Using a spectral method, we determine critical Rayleigh numbers, azimuthal wavenumbers, and oscillation frequencies over a wide range of Prandtl numbers and shell aspect ratios at moderate Ekman numbers. We show that the preferred boundary condition for convective onset depends systematically on both aspect ratio and Prandtl number: for sufficiently thick shells or for large $\text{Pr}$, the Ekman boundary layer at the outer boundary becomes destabilising, so that no-slip boundaries yield a lower $\text{Ra}_c$ than stress-free boundaries. Comparing differential and internal heating, we find that internal heating generally raises $\text{Ra}_c$, shifts the onset to larger wavenumbers and frequencies, and relocates the critical column away from the tangent cylinder. Mixed boundary conditions with no-slip on the inner boundary behave similarly to purely stress-free boundaries, confirming the dominant influence of the outer surface. These results demonstrate that boundary conditions and heating mechanisms play a central role in controlling the onset of convection and should be carefully considered in models of planetary and stellar interiors.
An aircraft consists of various cavities including air intake ducts, cockpit, radome, inlet and exhaust of heat exchangers, passage for engine bay/other bay cooling etc. These cavities are prime radar cross-section (RCS) contributors of aircraft. The major such cavity is air intake duct, and it contributes significantly to frontal sector RCS of an aircraft. The RCS reductions of air intake duct is very important to achieve a low RCS (or stealthy) aircraft configuration. In general, radar absorbing materials (RAM) are getting utilized for RCS reduction of air intake duct. It can also be noticed that a large number of fasteners are used for integration of air intake duct with the aircraft structures. The installation of fasteners on RAS may lead to degradation of RCS performance of air intake. However, no such studies are reported in the literature on the impact of rivets on the RCS performance of RAS air intake duct. In this paper, radar absorbing material of thickness 6.25 mm is designed which givens more than -10 dB reflection loss from 4 to 18GHz of frequencies. Next, the effect of rivet installation on these RAS is carried out using three different rivet configurations. The RCS performance of RAS is evaluated for duct of different lengths from 1 to 18GHz of frequencies. In order to see the RCS performance, five different air intake cases are considered The RCS performance with increase in percentage surface area of rivet heads to RAS is reported in detail. At the last, an open-source aircraft CAD model is considered and the RCS performance of RAS air intake with and without rivets is evaluated.
T&M Associates is a national engineering firm headquartered in Middletown, New Jersey that specializes in many different fields of engineering. Throughout the past semester, I have had the opportunity to collaborate with Mechanical, Electrical, and Plumbing (MEP) engineers at T&M, working on computer-aided design (CAD) drawings of heating, ventilation, and air conditioning (HVAC) systems. These drawings play a crucial role in the construction process, guiding contractors through the installation of the HVAC systems of a building.
T&M Associates is a national engineering firm headquartered in Middletown, New Jersey that specializes in many different fields of engineering. Throughout the past semester, I have had the opportunity to collaborate with Mechanical, Electrical, and Plumbing (MEP) engineers at T&M, working on computer-aided design (CAD) drawings of heating, ventilation, and air conditioning (HVAC) systems. These drawings play a crucial role in the construction process, guiding contractors through the installation of the HVAC systems of a building.
Abstract Buildings are responsible for a significant amount of energy consumption resulting in a considerable negative environmental impact. Therefore, it is essential to decrease their energy consumption by improving the design of new buildings or renovating existing buildings. Heat losses or gains through building envelopes affect the energy use and the indoor condition. Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems are responsible for 33% and 25% of the total energy consumption in office buildings, respectively. However, renovating building envelopes and energy consuming systems to lessen energy losses is usually expensive and has a long payback period. Despite the significant contribution of research on optimizing energy consumption, there is limited research focusing on the renovation of existing buildings to minimize their Life Cycle Cost (LCC) and environmental impact using Life Cycle Assessment (LCA). This paper aims to find the optimal scenario for the renovation of institutional buildings considering energy consumption and LCA while providing an efficient method to deal with the limited renovation budget. Different scenarios can be compared in a building renovation strategy to improve energy efficiency. Each scenario considers several methods including the improvement of the building envelopes, HVAC and lighting systems. However, some of these scenarios could be inconsistent and should be eliminated. Another consideration in this research is the appropriate coupling of renovation scenarios. For example, the HVAC system must be redesigned when renovating the building envelope to account for the reduced energy demand and to avoid undesirable side effects. A genetic algorithm (GA), coupled with an energy simulation tool, is used for simultaneously minimizing the energy consumption, LCC, and environmental impact of a building. A case study is developed to demonstrate the feasibility of the proposed method.