Hasil untuk "Heating and ventilation. Air conditioning"

Xin Xin, Zhihao Zhang, Yong Zhou et al.

N. Asim, M. Badiei, M. Mohammad et al.

Increasing demand on heating, ventilation, and air-conditioning (HVAC) systems and their importance, as the respiratory system of buildings, in developing and spreading various microbial contaminations and diseases with their huge global energy consumption share have forced researchers, industries, and policymakers to focus on improving the sustainability of HVAC systems. Understanding and considering various parameters related to the sustainability of new and existing HVAC systems as the respiratory system of buildings are vital to providing healthy, energy-efficient, and economical options for various building types. However, the greatest opportunities for improving the sustainability of HVAC systems exist at the design stage of new facilities and the retrofitting of existing equipment. Considering the high available percentage of existing HVAC systems globally reveals the importance of their retrofitting. The attempt has been made to gather all important parameters that affect decision-making to select the optimum HVAC system development considerations among the various opportunities that are available for sustainability improvement.

Asma Parkar, R. Yadav, Rupali Chopade et al.

This paper reports on designing an automated two-dimensional duct of Heating, Ventilation, Air Conditioning and Refrigeration (HVACR) using computer programming. The designed system uses an algorithmic approach attempting to accelerate the design process, decrease the human error of the designers and make the process of designing HVACR systems more efficient. It applies a system of interacting algorithms to solve duct sizing, routing and layout optimisation tasks. This facilitates both the saving of time and the increment of accuracy and consistency in the final design. By applying advanced computational techniques, it could analyse complex geometries of buildings and easily produce optimised designs for ducts. This research contributes to the field of HVACR engineering by providing a practical solution for automating a critical design phase. The automation system may be configured to work with existing Computer Aided Design (CAD) software, allowing for close collaboration between designers and engineers. Major Findings: A computer program using Hyper Text Markup Language (HTML), Cascading Style Sheets (CSS) and JAVA allows for quick and accurate determination of duct size by applying standard empirical formulas that relate height, width, and diameter. The programmer can effortlessly adjust the design by simply modifying the input data and tailoring the duct specifications to meet specific requirements.

Jie Lu, Xiangning Tian, Chaobo Zhang et al.

Maher Abuhussain, Ali Hussain Alhamami, Khaled Almazam et al.

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction.

赵巍, 韩雅倩, 张华 et al.

In order to investigate the critical snow formation height of the mixed single-aperture nucleator within the artificial snow machine, an industrial microscope was used to observe the microstructure of the snow crystals, measure the critical snow formation height threshold, and analyse the effect of the air-to-water pressure ratio (0.4MPa:0.4MPa, 0.5MPa:0.45MPa, and 0.5MPa:0.4MPa) and the ambient temperatures (-5℃, -10℃, and -15℃) on the critical snow formation height. The results showed that under the working condition of air-water pressure ratio of 0.4MPa:0.4MPa, when the temperatures were -5℃ and -10℃, the threshold of critical snow formation height did not exist, and when the temperature was -15℃, it was able to form snow, and the threshold of critical snow formation height was 50~55cm; when the air-water pressure ratios were 0.5MPa:0.45MPa, 0.5MPa:0.4MPa, the three ambient temperatures can form snow. And the gas-water pressure ratio and ambient temperature will have a certain effect on the critical snow height, under the same ambient temperature, the larger the gas-water pressure ratio, the smaller the critical snow height; The critical snow formation height increases as the ambient temperature increases from -15°C to -5°C while keeping the air-water pressure ratio constant. When the gas-water pressure ratio is 0.5MPa:0.45MPa, the trend of critical snow formation height with temperature is larger, and the temperature has a greater impact on the critical snow formation height. The results of the study can provide a basis for the design of the optimized arrangement between the nucleator and the nozzle of the snow-making machine.

Zhenan Feng, Ehsan Nekouei

Heating, Ventilation, and Air Conditioning (HVAC) systems are essential for maintaining indoor environmental quality, but their interconnected nature and reliance on sensor networks make them vulnerable to cyber-physical attacks. Such attacks can interrupt system operations and risk leaking sensitive personal information through measurement data. In this paper, we propose a novel attack detection framework for HVAC systems, integrating an Event-Triggering Unit (ETU) for local monitoring and a cloud-based classification system using the Graph Attention Network (GAT) and the Long Short-Term Memory (LSTM) network. The ETU performs a binary classification to identify potential anomalies and selectively triggers encrypted data transmission to the cloud, significantly reducing communication cost. The cloud-side GAT module models the spatial relationships among HVAC components, while the LSTM module captures temporal dependencies across encrypted state sequences to classify the attack type. Our approach is evaluated on datasets that simulate diverse attack scenarios. Compared to GAT-only (94.2% accuracy) and LSTM-only (91.5%) ablations, our full GAT-LSTM model achieves 98.8% overall detection accuracy and reduces data transmission to 15%. These results demonstrate that the proposed framework achieves high detection accuracy while preserving data privacy by using the spatial-temporal characteristics of HVAC systems and minimizing transmission costs through event-triggered communication.

Hong-ah Chai, Seokbin Yoon, Keumjin Lee

Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers. Previous work on modeling the strategies of air traffic controllers and their mental image of traffic situations often centers on specific air traffic control tasks or pairwise interactions between aircraft, neglecting to capture the comprehensive dynamics of an air traffic situation. To address this issue, we propose a machine learning-based framework for explaining air traffic situations. Specifically, we employ a Transformer-based multi-agent trajectory model that encapsulates both the spatio-temporal movement of aircraft and social interaction between them. By deriving attention scores from the model, we can quantify the influence of individual aircraft on overall traffic dynamics. This provides explainable insights into how air traffic controllers perceive and understand the traffic situation. Trained on real-world air traffic surveillance data collected from the terminal airspace around Incheon International Airport in South Korea, our framework effectively explicates air traffic situations. This could potentially support and enhance the decision-making and situational awareness of air traffic controllers.

Deep Narayan Singh, Lagoon Biswal, Girish Naik et al.

In this paper, we study the ventilation airflow in a model classroom, where exhaust fans throw out the used air, to replace it with outdoor air through open door. Hybrid ventilation, or mechanically assisted natural ventilation, of this kind is used as a retrofit design to reduce infection risk from airborne transmission. The air stream entering the door forms a jet-like flow, driven by the suction effect of exhaust fans. We compute the jet velocity using Reynolds averaged Navier Stokes (RANS) method and compare with velocity field measured using particle image velocimetry. Different turbulence models are found to match experimental data near the door, but they over-predict the peak jet velocity further downstream. There is minimal variation between the results obtained using different turbulence models. The computational results are found to be sensitive to inlet boundary conditions, whether the door entry is specified as a pressure inlet or velocity inlet. The geometry of the space outside the door also has a significant effect on the jet velocity. Changing the boundary condition takes the computational results closer to the experimental data; the velocity profiles computed with the extended domain being the closest to the measured peak velocity. Interestingly, the centerline velocity decay computed with the extended domain aligns well with the experimental data. The other cases, irrespective of turbulence model, show much lower decay rate that seem to align with wall jet scaling. This suggests that geometry and boundary conditions at the door is critical to predict the airflow in hybrid ventilation.

Haochen Wu, Lesley A. Weitz, Jeffrey M. Henderson et al.

Advanced Air Mobility (AAM) represents an evolution of the air transportation system by introducing low-altitude, potentially high-traffic environments. AAM operations will be enabled by both new aircraft, as well as new safety- and efficiency-critical supporting infrastructure. Published concepts of operations from both public and private sector entities establish notions such as federated management of the airspace and public-private partnerships for AAM air traffic, but there is a gap in the literature in terms of integrated tools that consider all three critical elements: AAM fleet operators (\emph{lower} layer), airspace service providers (\emph{middle} layer), and overall system governance from the legacy air navigation service provider (\emph{upper} layer). In this work, we explore modeling congestion management within the AAM setting using a bi-level optimization approach, focusing on (1) time-varying, stochastic AAM demand, (2) differing congestion management strategies, and (3) the impact of unscheduled, \enquote{pop-up} demand. We show that our bi-level formulation can be tractably solved using a Neural Network-based surrogate which returns solution qualities within 0.1-5.2\% of the optimal solution. Additionally, we show that our congestion management strategies can reduce congestion by 25.7-39.8\% when compared to the scenario of no strategies being applied. Finally, we also show that while pop-up demand degrades congestion conditions, our congestion management strategies fare better against pop-up demand than the no strategy scenario. The work herein contributes a rigorous modeling and simulation tool to help evaluate future AAM traffic management concepts and strategies.

Manuel Aguiar Ferreira, Carlos Navas Rodríguez, Gunnar Jacobi et al.

The present study experimentally investigates the onset of ventilation of surface-piercing hydrofoils. Under steady-state conditions, the depth-based Froude number $Fr$ and the angle of attack $α$ define regions where distinct flow regimes are either locally or globally stable. To map the boundary between these stability regions, the parameter space $(α,Fr)$ was systematically surveyed by increasing $α$ until the onset of ventilation, while maintaining a constant $Fr$. Two simplified model hydrofoils were examined: a semi-ogive profile with a blunt trailing edge and a modified NACA 0010-34. Tests were conducted in a towing tank under quasi-steady-state conditions for aspect ratios of $1.0$ and $1.5$, and $Fr$ ranging from $0.5$ to $2.5$. Ventilation occurred spontaneously for all test conditions as $α$ increased. Three distinct trigger mechanisms were identified: nose, tail, and base ventilation. Nose ventilation is prevalent at $Fr<1.0$ and $Fr<1.25$ for aspect ratios of $1.0$ and $1.5$, respectively, and is associated with an increase in the inception angle of attack. Tail ventilation becomes prevalent at higher $Fr$, where the inception angle of attack takes a negative trend. Base ventilation was observed only for the semi-ogive profile but did not lead to the development of a stable ventilated cavity. Notably, the measurements indicate that the boundary between bistable and globally stable regions is not uniform and extends to significantly higher $α$ than previously estimated. A revised stability map is proposed to reconcile previously published and current data, demonstrating how two alternative paths to a steady-state condition can lead to different flow regimes.

M. Krajčík, M. Arıcı, Zhenjun Ma

Dasheng Lee, Shang-Tse Lee

Kaiyun Jiang, Tianyu Shi, Haowei Yu et al.

Heating, ventilation and air conditioning (HVAC) systems could significantly impact indoor environmental quality, particularly in terms of thermal comfort and indoor air quality. Achieving a high-quality indoor environment poses challenges to the energy consumption of HVAC systems. Thus, balancing thermal comfort, indoor air quality (IAQ) and energy consumption becomes a challenging task. Currently, indoor environment prediction methods are considered effective solutions to address this issue. However, the published literature usually concentrates on single aspects like thermal comfort, air quality or energy consumption, with multi-aspect prediction methods being rare. The present work reviews research spanning the last decade that employs machine learning methods for predicting indoor environments and HVAC energy consumption through separate and multi-output predictive models. Separate predictive models focus on HVAC systems’ impact on the indoor environment, while multi-output models consider the interplay of various outputs. This article gives a thorough insight into machine learning prediction models’ workflow, detailing data collection, feature selection and model optimization for each research goal. A systematic assessment of methods for data collection of diverse prediction targets, machine learning algorithms and validation approaches for different prediction models is presented. This review highlights the complexities of data management, model development and validation, enriching the knowledge base in indoor environmental quality optimization.

Saber Abrazeh, S. Mohseni, Meisam Jahanshahi Zeitouni et al.

In this paper, a novel self-adaptive control method based on a digital twin is developed and investigated for a multi-input multi-output (MIMO) nonlinear system, which is a heating, ventilation, and air-conditioning system. For this purpose, hardware-in-loop (HIL) and software-in-loop (SIL) are integrated to develop the digital twin control concept in a straightforward manner. A nonlinear integral backstepping (NIB) model-free control technique is integrated with the HIL (implemented as a physical controller) and SIL (implemented as a virtual controller) controllers to control the HVAC system without the need for dynamic feature identification. The main goal is to design the virtual controller to minimize the distinction between system outputs in the SIL and HIL setups. For this purpose, Deep Reinforcement Learning (DRL) is applied to update the NIB controller coefficients of the virtual controller based on the measured data of the physical controller. Since the temperature and humidity of HVAC systems should be regulated, the NIB controllers in the HIL and SIL are designed by the DRL algorithm in a multi-objective scheme (MO). In particular, the simulations of the HIL and SIL environments are coupled by a new advanced tool: function mockup interface (FMI) standard. The Functional Mock-up Unit (FMU) is adopted into the FMI interface for data exchange. The extensive research of HIL and SIL controllers shows that the system outputs of the virtual controller are controlled exactly according to the physical controller.

Liang Anqi, Zeng Shuang, Ren Jiahang et al.

To improve the comprehensive utilization of regional energy and promote low-carbon development, this study constructs an integrated energy system for typical areas, such as parks, including a new energy power generation system driven by photovoltaic and wind power, heating and cooling energy supply systems for ground-source/air-source heat pumps, water chillers, and energy storage equipment. TRNSYS? software is used to simulate and study the dynamic characteristics of the system under six climate conditions in Beijing, and the game theory is used for intelligent operation, which is then compared with the logic control method. The results show that the logic control method can meet the load demand but cannot realize the efficient operation of the heat pump unit and the charge and discharge balance of the energy storage device. The integrated energy system after optimization via game theory can not only realize flexible energy scheduling and distribution through electric-thermal coordination, but also save the entire energy consumption of the heat pump unit and achieve the goal of regional energy economic benefits. The research presented in this paper provides an important theoretical basis for the intelligent operation of heat pump systems in integrated electric-thermal cooperative grids.

Kang Chen, Siliang Chen, Xuejiao Zhu et al.

Bashar Mahmood Ali, Mehmet Akkaş

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.

M. Xia, Fangjian Chen, Qifang Chen et al.

Residential heating, ventilation and air condition‐ ing (HVAC) provides important demand response resources for the new power system with high proportion of renewable ener‐ gy. Residential HAVC scheduling strategies that adapt to realtime electricity price signals formulated by demand response program and ambient temperature can significantly reduce elec‐ tricity costs while ensuring occupants’ comfort. However, since the pricing process and weather conditions are affected by many factors, conventional model-based method is difficult to meet the scheduling requirements in complex environments. To solve this problem, we propose an adaptive optimal scheduling strategy for residential HVAC based on deep reinforcement learning (DRL) method. The scheduling problem can be regard‐ ed as a Markov decision process (MDP). The proposed method can adaptively learn the state transition probability to make economical decision under the tolerance violations. Specifically, the residential thermal parameters obtained by the leastsquares parameter estimation (LSPE) can provide a basis for the state transition probability of MDP. Daily simulations are verified under the electricity prices and temperature data sets, and numerous experimental results demonstrate the effective‐ ness of the proposed method.

D. Sekartaji, Y. Ryu, Didit Novianto

Over the past three years, regulations have been implemented to combine natural ventilation (NV) and air conditioning to mitigate the risk of disease transmission, particularly in response to the COVID-19 outbreak. As we know, simultaneous use of NV and air conditioning can make it challenging to achieve indoor thermal comfort. This paper aims to analyze the effect of NV on the air conditioning`s cooling and heating load in a classroom through simulation. A simulation model was developed using EnergyPlus software with an OpenStudio interface software. Simulation results demonstrate that continuous use of NV alongside an air conditioner increases the cooling load from 1.06 to 1.75 times during summer and a 1.54 to 9.49 times heating load increase during winter. On the other hand, intermittent NV every hour results in a cooling load increase from 1.05 to 1.46 times in summer and a heating load increase from 1.13 to 4.63 times in winter. Moreover, employing NV based on the outside air temperature can reduce the cooling load at the air conditioner with set-point 26℃—28℃ from 0.94 to 0.88 times. The outcomes of this study are expected to serve as a reference for determining strategies that effectively combine NV and air conditioning to meet various needs without causing a significant increase in energy consumption. Additionally, the results are expected to be useful for reducing AC energy consumption in extremely hot and cold weather with some strategies of NV application.