Hasil untuk "Heating and ventilation. Air conditioning"

B. Chenari, J. Carrilho, M. Silva

H. Hosamo, P. R. Svennevig, K. Svidt et al.

The building industry consumes the most energy globally, making it a priority in energy efficiency initiatives. Heating, ventilation, and air conditioning (HVAC) systems create the heart of buildings. Stable air handling unit (AHU) functioning is vital to ensuring high efficiency and extending the life of HVAC systems. This research proposes a Digital Twin predictive maintenance framework of AHU to overcome the limitations of facility maintenance management (FMM) systems now in use in buildings. Digital Twin technology, which is still at an initial stage in the facility management industry, use Building Information Modeling (BIM), Internet of things (IoT) and semantic technologies to create a better maintenance strategy for building facilities. Three modules are implemented to perform a predictive maintenance framework: operating fault detection in AHU based on the APAR (Air Handling Unit Performance Assessment Rules) method, condition prediction using machine learning techniques, and maintenance planning. Furthermore, the proposed framework was tested in a real-world case study with data between August 2019 and October 2021 for an educational building in Norway to validate that the method was feasible. Inspection information and previous maintenance records are also obtained through the FM sys-tem. The results demonstrate that the continually updated data combined with APAR and machine learning algorithms can detect faults and predict the future state of Air Handling Unit (AHU) components, which may assist in maintenance scheduling. Removing the detected operating faults resulted in

Wuxia Zhang, Yupeng Wu, J. Calautit

M. Kong, B. Dong, Rongpeng Zhang et al.

Abstract Building sensing technologies have evolved rapidly in the last two decades in aid of monitoring building environment and energy system performance. A series of occupancy sensing systems were developed to track the occupant behavior in the indoor space. Occupancy-based building system control is defined as a control method that adjusts the building system operation schedules and setpoints based on the measured occupant behavior and has been identified as a smart building control strategy that can improve building energy efficiency as well as occupant comfort. Some studies demonstrated energy-saving potential and comfort-maintaining capability from occupancy-based control. This study adopted a first-of-its-kind side-by-side experimental approach to quantify the performance of the occupancy-based control in commercial buildings. Three state-of-the-art occupancy sensing technologies were integrated into the real-time Heating, Ventilation, and Air-Conditioning (HVAC) system control in this study. Their detection accuracy and its effectiveness on energy-saving and thermal comfort were analyzed. It was found that the occupancy-based control can maintain good thermal comfort and perceived indoor air quality with a satisfaction ratio greater than 80%. Although the daily energy-saving varied with occupancy sensor accuracy and outdoor environment conditions, the weekly averaged energy saving was between 17 and 24%.

NIU Jianhui, AN Zelong, ZHANG Chenyang et al.

A new type of air source heat pump system combined waterless floor heating and hot water supply was proposed, which is characterized in that the refrigerant is condensed directly in the buried pipe for floor heating, at the same time, the high temperature refrigerant is exchanged in the outer coil water heater to produce domestic hot water. The prototype was processed and tested in three modes: single floor heating, two combined supply and single water heater. The experimental results show that the surface temperature of the floor is maintained between 25 ℃ and 33 ℃, and the air temperature is maintained between 18 ℃ and 22 ℃ whether in the sole floor heating mode, dual supply mode, or single water heater mode. Due to the heat storage effect of the capillary floor, the air temperature in the room remained above 18 ℃ within 113~245 min of stopping heating to the room. When the ambient temperature is -5 ℃, after 572.3 min, the water temperature of the water heater rose to 45 ℃; The minimum system heating capacity and EER can reach to 2.70 kW and 3.55, respectively.

W. J. Fisk

Da Li, C. Menassa, V. Kamat

Anchal Gupta, Y. Badr, Ashkan Negahban et al.

Abstract Buildings account for roughly 40% of the total energy consumption in the world, out of which heating, ventilation, and air conditioning are the major contributors. Traditional heating controllers are inefficient due to lack of adaptability to dynamic conditions such as changing user preferences and outside temperature patterns. Therefore, it is necessary to design energy-efficient controllers that can improvise occupant thermal comfort (deviation from setpoint temperature) while reducing energy consumption. This research presents a Deep Reinforcement Learning (DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart buildings. We perform extensive simulation experiments using real-world outside temperature data. The results show that the DRL-based smart controller outperforms a traditional thermostat controller by improving thermal comfort between 15% and 30% and reducing energy costs between 5% and 12% in the simulated environment. A second set of experiments is then performed for the case of multiple buildings, each having its own heating equipment. The performance is compared when the buildings are controlled centrally (using a single DRL-based controller) versus decentralized control, where each heater is controlled independently and has its own DRL-based controller. We observe that as the number of buildings and differences in their setpoint temperatures increase, decentralized control performs better than a centralized controller. The results have practical implications for heating control, especially in areas with multiple buildings such as residential complexes with multiple houses.

M. Mohanraj, S. Jayaraj, C. Muraleedharan

In this paper, an attempt has been made to review the applications of artificial neural networks (ANN) for energy and exergy analysis of refrigeration, air conditioning and heat pump (RACHP) systems. The studies reported are categorized into eight groups as follows: (i) vapour compression systems (ii) RACHP systems components, (iii) vapour absorption systems, (iv) prediction of refrigerant properties (v) control of RACHP systems, (vi) phase change characteristics of refrigerants, (vii) heat ventilation air conditioning (HVAC) systems and (viii) other special purpose heating and cooling applications. More than 90 published articles in this area are reviewed. Additionally, the limitations with ANN models are highlighted. This paper concludes that ANN can be successfully applied in the field of RACHP systems with acceptable accuracy.

Wang Jiaxuan, Song Xia, Gao Tianyuan et al.

Aviation applications are facing the challenges of cooling high-power and high-heat-flux electronic equipment. Traditional cooling methods cannot cope with thermal requirements greater than a heat flux of 100 W/cm2. In this study, the ground test bench of a pump-driven two-phase cooling loop (MPCL) system is constructed, and the control strategy of the system is designed. The cooling ability and resistance characteristics of the system are tested, and the mathematical model is developed. The results show that the mechanical pump drives the two-phase cooling system with good thermal performance. The designed copper cold plate is able to effectively handle 6 kW concentrated heat sources with a heat flux of 120 W/cm2. A 10 kW heat source can be effectively cooled by the MPCL system using 70% less working fluid than single-phase cooling under designed working conditions. The surface temperature of the heating element can be stabilized at 63–70 °C, which meets the temperature requirements of the chip. Additionally, the temperature is uniform between the evaporator branches, with a temperature difference below 5 ℃. The pressure drop of the phase-change segment is below 400 kPa, and the resistance characteristics can be described by the Kim and Mudawar models.

M. Prabhakar, M. Saffari, A. Gracia et al.

Abstract In recent years, the building sector has been responsible for continuous increase in energy consumption in the world. Space heating and cooling accounts for 34% of this energy consumption in buildings. In this context, using thermal energy storage (TES) can reduce energy consumption for space air conditioning. The use of phase change materials (PCM) as latent heat thermal energy storage (LHTES) system in the building envelope has been of great interest for passive cooling applications due to the high energy storage capacity of this technology. However, in order to utilize the full potential of a PCM, it needs to be fully charged at each cycle. Ventilation during the night is an effective method which can be used in PCM-enhanced office buildings with the aim of charging the PCM every required cycle. In the present study, PCM melting temperature of office building in various climate conditions was optimized using a simulation-based optimization and coupled with free cooling operation. Ventilation control strategies were used to improve the cooling energy performance of the PCM enhanced building integrated into envelopes. It was found that charging PCM with night ventilation, especially when using some specific control strategies of natural ventilation operated by external windows opening results in considerable cooling energy savings. The study was conducted for 15 different cities around the world. It was found that, in hot arid conditions, PCM passive cooling system was ineffective. Although, the energy savings were improved by coupling PCM and natural ventilation in these climate conditions. But the benefits were more or less similar to using natural ventilation only. On the other hand, in temperate condition, the effectiveness of PCM was increased from 3.32% to 25.62% by coupling a PCM passive system with night ventilation. It was further improved to 40% when using PCM with temperature-controlled ventilation. Moreover, it can be said that smart control of ventilation can lead to considerable energy savings.

Chen Ren, Shi-Jie Cao

Abstract It has been of great importance to develop better control techniques for heating, ventilation and air conditioning (HVAC) systems to provide occupancy driven energy and comfort management due to the significance of building energy conservation. Following with our previous work, where low-dimensional linear ventilation model (LLVM) and artificial neural network (ANN) were incorporated to realize online control of indoor air quality (IAQ), we continued to expand the work on indoor thermal comfort (ITC) with low-dimensional linear temperature model (LLTM) and contribution ratio of indoor climate (CRI) to provide dependable support for HVAC online control. Two steps were required to be implemented, respectively considering pollutant and temperature responses. As a premise for control, the database was constructed by CFD, which was verified by the corresponding experiments. Linear ventilation model (LVM) and linear temperature model (LTM) could be well employed to expand the CFD database. Then, combined with satisfying ANN and CRI along with low-dimensional linear model (LLM) for database reconstruction, LLVM-based ANN and LLTM-based CRI could rapidly predict the distribution of indoor environmental parameters (e.g., CO2 concentration and temperature) within acceptable errors. Next, the comprehensive evaluation indices were defined to provide weighting factors for indoor environment (IAQ and ITC) and air conditioning energy. By using the current control strategy, the HVAC energy consumption resulting from ventilation and air conditioning loads could be significantly decreased up to 50% and 32% respectively. This study will further promote an intelligent control strategy to improve the deteriorated condition between occupant comfort and HVAC energy conservation.

Zhengxuan Liu, Mingjing Xie, Yuekuan Zhou et al.

Geothermal energy with abundance and large quantity can partially cover building heating/cooling loads and promote the carbon-neutrality transitions. Shallow geothermal ventilation (SGV) system, with a little initial investment cost, is one of promising technologies to partly replace the conventional air-conditioning system for air pre-cooling/pre-heating. This paper reviews applications of SGV system for improving thermal performance over latest two decades, which mainly includes the reclassification of SGV system, coupling with other advanced energy-saving technologies, application potentials for building cooling/heating under various weather conditions. Heat transfer mechanism and mathematical modelling techniques have been reviewed, together with in-depth analysis on current research trends, existing limitations, and recommendations of SGV system. Phase change materials, with considerable latent energy density, can stabilize the thermal performance with high reliability. The review identifies that optimization designs and advanced approaches need to be investigated to address the existing urgent issues of SGV system (e.g., large land occupation, difficulty in centralized collection of condensate water timely for horizontal buried pipe, bacteria growth, polluted supply air, and high construction cost for vertical buried pipe). A plenty of studies show that the SGV system could greatly expand the application scope and improve system energy efficiency by combining with other energy-saving technologies. This paper will provide some guidelines for the scientific researchers and engineers to keep track on recent advancements and research trends of SGV system for the building thermal performance enhancement and pave path for future research works.

Jianyun Wu, Wenhe Zhou, Xiaowei Wang et al.

Yongjin Chai, Shunian Zhao, Shaoxiong Zhang et al.

Liangdong Ma, Tixiu Ren, Tianyi Zhao et al.

Zhixian Ma, Nan Zhao, Anping Zhou et al.

Siwei Li, Jaewan Joe, Jianjun Hu et al.

Qin Sheng, Liu Dongpeng, Li Wei et al.

At present, the international community pays more attention on global warming issue caused by the increased emissions of greenhouse gases. In this study, we establish a set of simulated atmospheric experimental devices to investigate the reactions of greenhouse gases with OH radicals or other atmospheric oxidants and evaluate their atmospheric chemistry implications, e.g., global warming potential (GWP). To estimate the accuracy of the experimental devices, the reaction rate constant of 1,1-difluoroethane (R152a) with OH radicals is measured using the relative rate method, and the infrared spectrum is obtained. The ks of R152a with OH radicals are found to be (3.3 ± 0.1) × 10–14 cm3 /(molecule?s) at 298 K and (2.27 ± 0.04) × 10–14 cm3 /(molecule?s) at 272 K. The atmospheric lifetime and radiative efficiency (RE) are then estimated as 1.4 years and 0.099 W/(m2?ppb), respectively. The GWPs (time horizons of 20, 50, and 100 years) are estimated at 474, 129, and 37, respectively. Our GWP values are in good agreement with those from IPCC-AR5 (discrepancy is less than 7%), which indicates the high reliability of our experimental apparatus.

Payam Nejat, Fatemeh Jomehzadeh, Hasanen Mohammed Hussen et al.

Generally, two-third of a building’s energy is consumed by heating, ventilation and air-conditioning systems. One green alternative for conventional air conditioner systems is the implementation of passive cooling. Wing walls and windcatchers are two prominent passive cooling techniques which use wind as a renewable resource for cooling. However, in low wind speed regions and climates, the utilization of natural ventilation systems is accompanied by serious uncertainties. The performance of ventilation systems can be potentially enhanced by integrating windcatchers with wing walls. Since previous studies have not considered this integration, in the first part of this research the effect of this integration on the ventilation performance was assessed and the optimum angle was revealed. However, there is still gap of this combination; thus, in the second part, the impact of wing wall length on the indoor air quality factors was evaluated. This research implemented a Computational Fluid Dynamics (CFD) method to address the gap. The CFD simulation was successfully validated with experimental data from wind tunnel tests related to the previous part. Ten different lengths from 10 cm to 100 cm were analyzed and it was found that the increase in wing wall length leads to a gradual reduction in ventilation performance. Hence, the length does not have a considerable influence on the indoor air quality factors. However, the best performance was seen in 10 cm, that could provide 0.8 m/s for supply air velocity, 790 L/s for air flow rate, 39.5 1/h for air change rate, 107 s for mean age of air and 92% for air change effectiveness.