An electric vertical take-off and landing flying vehicle (eVTOL) is a potential technology for future urban air mobility. A major challenge for thermal management systems is the high cooling requirement and the variable application scenarios. To overcome this challenge, a multi-scene eVTOL-integrated thermal management system was developed. In this study, an eVTOL thermal management simulation platform based on Amesim simulation software was developed to investigate the effects of flight conditions on thermal management and range. The simulation results show that increasing the cruise altitude can reduce the thermal management energy consumption when the ground temperature is high. The maximum reduction of energy consumption for thermal management energy is 4 kW when the cruising temperature ranges from 10 ℃ to 26 ℃. When the hovering rescue duration is more than 150 s during the emergency rescue operation, the temperature difference inside the battery becomes too pronounced. A reduced payload improves the range, with the unloaded range being 1.33 times greater than the fully loaded range.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Abstract This paper presents a comprehensive review of the significant studies exploited Artificial Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of the relevant studies to the scope of the research, a three-decade time span of the publishing date of the existing studies was taken into account. The review focuses on the studies utilized ANN to analyze the energy-related issues associated with buildings in major areas, including modeling of water heating and cooling systems, heating and cooling loads prediction, modeling heating ventilation air conditioning systems, indoor air temperature prediction, and building energy consumption prediction. Moreover, the findings of the abundant reviewed studies along with the potential future research to be carried out are discussed elaborately. Regarding the comprehensive review conducted, it is found out that the majority of studies focused on building energy consumption and indoor air temperature prediction. Additionally, it is observed that there has been a growing interest in the application of newly-developed ANNs to BEA areas, such as general regression neural network and recurrent neural network, due to their abilities in improving the modeling and prediction of buildings energy analysis. It is believed that this thorough review paper is useful for the researchers and scientific engineers working on the application of AI-based techniques to the building-energy-related areas to find out the relevant references and current state of the field.
Over the last years, there has been a renewed interest in differentiating various contributions to the air concentration in high Froude-number self-aerated flows, see for example Kramer (2024), comprising entrained and entrapped air. The former is characterized by entrained air packets and bubbles, while entrapped air corresponds to air transported along wave peaks and troughs. Entrapped air was first measured by Killen (1968) using a so-called dipping probe, while a physical interpretation of the dipping probe signals was provided only later by Wilhelms and Gulliver (2005). Since then, it has been commonly accepted that two different measurement instruments, for example a dipping probe and a common phase-detection probe, are required to fully quantify entrained and entrapped air. Recently, an article entitled "Resurrecting a Neglected Measurement Technique for Air-Water Flows" was published by Wilhelms and Gulliver (2024), who re-iterated the importance of applying these concepts for cavitation prevention and air-water gas transfer, as well as the need for two separate measurement instruments. The authors are congratulated for their seminal works on entrained and entrapped air (Wilhelms and Gulliver 2005; Wilhelms and Gulliver 2024), and it is stipulated that these concepts have been overlooked in the last two decades. In this discussion, a simple discrimination technique for phase-detection probe signals is proposed, which allows to differentiate entrained and entrapped air from existing datasets, recorded with a state-of-the-art dual-tip phase-detection probe. It is believed that this novel signal processing method will make Killen's (1968) dipping probe redundant, and that it will be useful for the validation of non-intrusive measurements of entrapped air, as well as for the development of physics-based models for air-water mass transfer in self-aerated flows.
Abstract Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiology signals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited in the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. In this review, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.
ABSTRACT This study proposes a novel Digital Twin framework of heating, ventilation, and air conditioning (HVACDT) system to reduce energy consumption while increasing thermal comfort. The framework is developed to help the facility managers better understand the building operation to enhance the HVAC system function. The Digital Twin framework is based on Building Information Modelling (BIM) combined with a newly created plug-in to receive real-time sensor data as well as thermal comfort and optimization process through Matlab programming. In order to determine if the suggested framework is practical, data were collected from a Norwegian office building between August 2019 and October 2021 and used to test the framework. An artificial neural network (ANN) in a Simulink model and a multiobjective genetic algorithm (MOGA) are then used to improve the HVAC system. The HVAC system is comprised of air distributors, cooling units, heating units, pressure regulators, valves, air gates, and fans, among other components. In this context, several characteristics, such as temperatures, pressure, airflow, cooling and heating operation control, and other factors are considered as decision variables. In order to determine objective functions, the predicted percentage of dissatisfied (PPD) and the HVAC energy usage are both calculated. As a result, ANN's decision variables and objective function correlated well. Furthermore, MOGA presents different design factors that can be used to obtain the best possible solution in terms of thermal comfort and energy usage. The results show that the average cooling energy savings for four days in summer is roughly 13.2%, and 10.8% for the three summer months (June, July, and August), keeping the PPD under 10%. Finally, compared to traditional approaches, the HVACDT framework displays a higher level of automation in terms of data management.
The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach.
Abstract Buildings account for over 32% of total society energy consumption, and to make buildings more energy efficient dynamic building performance simulation has been widely adopted during the buildings’ design to help select most appropriate HVAC (Heating Ventilation and Air Conditioning) systems. Due to the lack of good behavioral models in current simulation packages, many researchers have tried to develop useful behavioral models to improve simulation accuracy, including window behavior models, using field data collected from real buildings. During this work, many mathematical and machine learning methods have been used, and some level of prediction accuracy has been achieved. XGBoost is a recently introduced machine learning algorithm, which has been proven as very powerful in modeling complicated processes in other research fields. In this study, this algorithm has been adopted to model and predict occupant window behavior, aiming to further improve the modeling accuracy from a globally accepted modeling approach, namely, Logistic Regression Analysis. Field data in terms of both occupant window behavior and relevant influential factors were collected from real residential buildings during transitional seasons. Both XGBoost and Logistic Regression Analysis were used to build window behavior models, after a feature selection work, and their prediction performances on an independent dataset were compared. The comparison revealed that XGBoost has solid advantages in modeling occupant window behavior, over Logistic Regression Analysis, and it is expecting that the same finding would be obtained for other behavioral types, such as blind control and air-conditioner operation.
Abstract Decentralized peer-to-peer energy sharing techniques are highly promising to become the next-generation regime for smart building energy management, which can impulse the realization of nearly net-zero energy buildings. In this context, this paper proposes a comprehensive energy sharing framework for smart buildings in considering multiple dynamic components covering heating, ventilation, air conditioning (HVAC), battery energy storage systems (BESS) and electric vehicles (EVs). Specifically, both the power loss and shadow price of shared energy are explicitly modeled in a combined optimization framework, which is aimed to maximize the social welfare through peer-to-peer energy cooperation. Moreover, the role of agents acting as producers or consumers can be endogenously determined in the proposed model. In addition, distinguished with the classical distributed algorithms, we develop a fully decentralized algorithm based on dual-consensus version of alternating direction method of multipliers (DC-ADMM). The proposed algorithm avoids the need of coordinators at both the primal and dual variable updates in the iteration process, which suggests distinctive merits on high-level privacy protection as compared to most of the distributed optimization-based methods. Extensive case studies based on a smart building community demonstrate that the proposed peer-to-peer transactive framework can admirably improve the overall welfare for the involved smart buildings.
Electrocaloric cooling is a solid-state cooling technique based on the manipulation of electric fields. This technology utilizes the temperature variations induced in electrocaloric materials under the influence of an electric field to achieve refrigeration effects. Owing to its advantages, such as zero direct carbon emissions and high efficiency, it has garnered widespread attention, particularly in the context of global warming and carbon reduction objectives. Since the discovery of the giant electrocaloric effect in 2006, electrocaloric cooling technology has undergone rapid development, particularly in improvements in electrocaloric materials and devices. This article provides an analysis and discussion focused on electrocaloric cooling device research, electrocaloric polymer nanocomposite materials, and high-entropy optimization of electrocaloric materials. It commences by introducing the fundamental principles of the electrocaloric effect and current advancements in active regenerative electrocaloriccooling devices. Subsequently, it summarizes the progress in electrocaloric polymer nanocomposite materials, along with strategies for high-entropy optimization and interface polarization enhancement. Finally, it provides insights into future research directions for electrocaloric cooling within the fields of working substances and systems.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Air components, including UAVs, planes, balloons, and satellites have been widely utilized since the fixed capacity of ground infrastructure cannot meet the dynamic load of the users. However, since those air components should be coordinated in order to achieve the desired quality of service, several next-generation paradigms have been defined including air computing. Nevertheless, even though many studies and open research issues exist for air computing, there are limited test environments that cannot satisfy the performance evaluation requirements of the dynamic environment. Therefore, in this study, we introduce our discrete event simulator, AirCompSim, which fulfills an air computing environment considering dynamically changing requirements, loads, and capacities through its modular structure. To show its capabilities, a dynamic capacity enhancement scenario is used for investigating the effect of the number of users, UAVs, and requirements of different application types on the average task success rate, service time, and server utilization. The results demonstrate that AirCompSim can be used for experiments in air computing.
Abstract Understanding occupants’ thermal sensation and comfort is essential to defining the operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings. Due to the continuous impact of human and environmental factors, occupants’ thermal sensation and comfort level can change over time. Thus, to dynamically control the environment, thermal comfort should be monitored in real time. This paper presents a novel non-intrusive infrared thermography framework to estimate an occupant's thermal comfort level by measuring skin temperature collected from different facial regions using low-cost thermal cameras. Unlike existing methods that rely on placing sensors directly on humans for skin temperature measurement, the proposed framework is able to detect the presence of occupants, extract facial regions, measure skin temperature features, and interpret thermal comfort conditions with minimal interruption of the building occupants. The method is validated by collecting thermal comfort data from a total of twelve subjects under cooling, heating and steady-state experiments. The results demonstrate that ears, nose and cheeks are most indicative of thermal comfort and the proposed framework can be used to assess occupants’ thermal comfort with an average accuracy of 85%.
This research investigates the compatibility of conventional air conditioning with the principles of green building, highlighting the need for systems that enhance indoor comfort while aligning with environmental sustainability. Though proficient in regulating indoor temperatures, conventional cooling systems encounter several issues when incorporated into green buildings. These include energy waste, high running costs, and misalignment with eco-friendly practices, which may also lead to detrimental environmental effects and potentially reduce occupant comfort, particularly in retrofit situations. Given the emphasis on sustainability and energy conservation in green buildings, there is a pressing demand for heating, ventilation, and air conditioning (HVAC) solutions that support these goals. This study emphasises the critical need to reconsider traditional HVAC strategies in the face of green building advances. It advocates for the adoption of innovative HVAC technologies designed for eco-efficiency and enhanced comfort. These technologies should integrate seamlessly with sustainable construction, use greener refrigerants, and uphold environmental integrity, driving progress towards a sustainable and occupant-friendly built environment.
PM2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM2.5, the impact of PM2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM2.5, and control of indoor PM2.5, and finally presents analysis and suggestions for future research.
A fault diagnosis method of heating, ventilation, and air conditioning (HVAC) systems based on the ReliefF-recursive feature elimination based on cross validation-support vector machine (ReliefF-RFECV-SVM) combined model is proposed to enhance the diagnosis accuracy and efficiency. The method initially uses ReliefF to screen the original features, selecting those that account for 95% of the total weight. The recursive feature elimination based on cross validation (RFECV), based on a random forest classifier, is then applied to select the optimal feature subset according to diagnostic accuracy. Finally, a support vector machine (SVM) model is constructed for fault classification. The method is tested on seven typical faults of the ASHRAE 1043-RP water chiller dataset and three typical faults of an air-cooled self-built air conditioner simulation dataset. The results show that the ReliefF-RFECV-SVM method significantly reduces diagnosis time compared to SVM, shortening it by about 50% based on the ASHRAE 1043-RP dataset, while achieving an overall accuracy of 99.98%. Moreover, the proposed method achieves a comprehensive diagnosis accuracy of 99.97% on the self-built simulation dataset, with diagnosis time the reduced by about 65% compared to single SVM.
Materials of engineering and construction. Mechanics of materials, Production of electric energy or power. Powerplants. Central stations
Tanmay Agrawal, Narsing K. Jha, Vamsi K. Chalamalla
Air curtains are commonly employed in building applications to facilitate aerodynamic sealing against the exchange flow that occurs through an open doorway due to the density differences owing to buoyancy. Such situations often prevail due to temperature gradients across a doorway of an air-conditioned building, e.g., during the summer season in an Indian subcontinental situation. In the present study, we numerically investigate the performance of `positively buoyant' air curtains. In such installations, the density of the jet fluid is larger than the density of the fluid contained within the building space. Using the two-dimensional Reynolds-averaged Navier-Stokes (2D RANS) formulation, we compute the temperature distribution in the flow domain and estimate the associated sealing effectiveness for various values of positive jet buoyancy and operating velocities of the air curtain. These estimates of sealing effectiveness are compared with that of a neutrally buoyant air curtain to assess the influence of positive buoyancy. We report an increase in sealing effectiveness of up to 10%, whereas its peak value improves by about 5%.
Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.