Air Conditioning System Research Articles

Numerical and Performance Analysis of R290 Heat Pump Driven Air Conditioning System for Heating and Cooling

The demand for heating and air conditioning is rising due to rapidly changing climatic conditions. The market for heat pumps and air conditioning systems is seeing a steady increase in low global warming potential. There is a growing need to use environment-friendly refrigerants. Contemporary Refrigerants (HCFCs) such as those found in heat pump air conditioning systems have the potential to cause global warming and ozone depletion. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower Global Warming Potential (GWP) and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22 and R290. To examine the performance of R290, theoretical and simulation results are compared for various refrigerants while taking into account several performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity and compressor work for One Ton of Refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of R290 are significantly better than those of other materials, according to theoretical and simulation studies utilising Cool Pack software, hence it is a viable replacement.

A novel dual-wheel air-conditioning system operating strategy in different seasons

The application of solid desiccant air-conditioning systems can significantly reduce building energy consumption. Previous studies have focused on innovating the integration of solid desiccant air-conditioning systems (DACS) to improve dehumidification performance in summer. In this paper, a novel DACS is proposed, which can humidify the supply air in winter based on satisfying the dehumidification demands in summer. It can satisfy the year-round humidity requirements of domestic housing with significant savings in humidification costs. Furthermore, the operating strategy of the novel DACS is proposed, particularly for winter humidification conditions. It is adaptable as well as cost-effective compared to traditional operating strategies. The dehumidification/humidification capacity of the core component, polymer desiccant wheel, and the variation of temperature rise/temperature drop are investigated under different variations of parameters, including air temperature, air humidity, air volume flow rate, and wheel speed. The results show that with the increase of supply air mass flow from 0.6 kg/s to 1.9 kg/s, the power consumption of the system only increases by 9.5 kW. In winter, when the supply air temperature decreases from 8 °C to 1 °C, the power consumption increases by 7.2 kW, but in summer, when the temperature of the supply air increases by 7 °C, the power consumption of the system decreases by 7.0 kW. The power consumption of the system varies greatly when the humidity of the supply air is less than 17 g/kg, conversely, the power consumption of the system showed a negative trend. The analysis demonstrates the effective performance of the proposed system under varying climatic conditions.

Microclimate Effect on Cooling Energy for Buildings in Hot, Humid Climates: A Comparative Analysis of Shaded and Unshaded Environments

This paper explores the influence of microclimates on changes in air temperature and the often-overlooked aspect of their effect on energy savings across varying microclimatic conditions. The study compares the cooling energy requirements of two identical single-story buildings in distinct microclimates: one characterized by concrete ground devoid of shade and the other featuring soil ground with tree shade. Climatic environmental data were collected over 15 days in the concrete-exposed field and shaded area beneath the trees to conduct the investigation. These datasets were input into EnergyPlus 9.6 to model the energy demands and consumption of buildings subject to the specified climatic conditions. The validation of the simulated model against actual energy demand data from a classroom building demonstrated agreement. The findings reveal notable differences in air temperature, with the shaded area experiencing temperatures 0.8°C to 8.0°C lower than the concrete-exposed monitoring location. The building in the tree-shaded microclimate exhibited a lower peak cooling load than its concrete-exposed counterpart, resulting in a 35% reduction in the electrical energy requirements for the air-conditioning system. The study recommends implementing 0.08-m polyurethane insulation for the building walls and roof to equalize the energy demand and consumption of the concrete-exposed building with that of its shaded counterpart. Furthermore, building design in shaded areas can maximize the window glass area while consuming less energy than buildings on concrete-exposed grounds. The study advocates leveraging the microclimate associated with surrounding buildings in the design process to enhance the overall energy savings.

Development of water-based micro-particle enhanced phase change material for cool thermal energy storage systems

ABSTRACT Building accounts over one-third of global energy consumption, mainly for cooling/heating the space, contributing to enormous emission of greenhouse gases. Extensive usage of air-conditioning system in tropical countries demands cool thermal energy storage system for energy management that essentially demands development of novel energy storage materials. In this study, micro-particle enhanced phase change materials (MePCMs) were prepared through two-step method with deionized water as base PCM and natural graphite flakes (NGF) as additive. Heat transfer characteristics of the MePCMs were experimentally analyzed during solidification in a spherical capsule. High specific surface area of the NGF creates more nucleation sites that lead to elimination of subcooling in the MePCM. As a result of enhanced thermal conductivity of MePCM, solidification duration was reduced to 40% with 5 wt.% NGF, compared to base PCM. Accelerated mode of charging was seen in MePCMs, storing 96% of energy during 70% of corresponding solidification duration. In addition to the above fascinating advantages, abundant availability and low cost of the NGF make the MePCM as potential energy storage material for space cooling applications.

Simulation and optimization design of air distribution in a cigarette production factory

Air-conditioning energy consumption accounts for a significant proportion of the total energy usage in large commercial buildings. This study utilizes computational fluid dynamics (CFD) to examine the airflow distribution in a cigarette factory with a high ceiling. Numerical simulations were performed to assess the effectiveness of various air supply methods. Using FLUENT, the researchers analyzed the uniformity of airflow, temperature and humidity fields under summer conditions. The findings indicate that employing an upper supply and lower return air method can decrease air-conditioning energy consumption by 7.25%. Field measurements were carried out to validate the numerical simulations, with the measured values used as initial parameters. The average deviation between the measured and simulated temperature and humidity in critical work areas was within 2%. The comparative analysis of the simulation and measurement results confirms the reliability and effectiveness of airflow organization simulations in large commercial buildings. These results offer a scientific foundation and computational guidance for designing and implementing energy-efficient upgrades to air-conditioning systems in similar industrial facilities.

Awareness-guided incremental control optimization for chilled water system with deep learning model under cold-start scenarios

For heating, ventilation, and air conditioning (HVAC) systems, the technology of optimal control can achieve significant energy savings by resetting the control setpoint in responding to the change of user load and weather condition. However, traditional method will fail in cold-start scenarios, in which the diversity of history data is limited. Under this case, significant prediction error will occur in extrapolation space, and the optimized control strategy cannot fully achieve energy saving potential or even increase energy consumption. To solve this problem, here we propose a novel awareness-guided incremental control optimization method. Its basic idea is to explicitly consider the prediction error of energy model during the control optimization process. The deep ensemble algorithm is firstly adopted to capture the prediction error. And the optimization process will be aware of such potential error, and make a tradeoff between conservative state and exploratory state. The control optimization process will start with the known optimal setpoint (conservative state) to achieve stable energy saving, and then gradually explores other unknown candidates (exploratory state) to achieve further energy saving. To validate our method, a virtual test bed of chilled water system is developed by Modelica language to compare fixed setpoint strategy, traditional method, and proposed method. The results demonstrate that the proposed method can avoid negative energy saving ratio during the early stage of control optimization. And it can also find optimal control strategy faster. Comparing with traditional method, the awareness-guided method can achieve further 2.9 % energy saving ratio, and the overall energy saving is between 6.1% and 13.9 %.

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

Simulation of diffusion of combustible refrigerants R1234yf and R290 leakage in automotive air conditioning

Electric vehicles that utilize a heat pump system have a refrigerant charge increase of at least 400 g compared to traditional fuel vehicle air conditioning systems. If combustible refrigerants are used, the risk of combustion increases when the refrigerant leaks and spreads to the passenger compartment. This paper dynamically monitored the volume concentrations of combustible refrigerants R1234yf and R290 as they leaked and entered the passenger compartment accompanied by air supply by numerical simulation. The results indicated that refrigerants are more prone to accumulate in the rear row than in the front. After a leak, the average volume concentration of R1234yf at the four air outlets was 1.58 %, and 3.36 % for R290. at the breathing points of four passengers, the average volume concentration was 0.99 % for R1234yf and 2.39 % for R290. Near the feet of the passengers, the average volume concentration was 0.95 % for R1234yf and 2.27 % for R290. The highest volume concentration of R1234yf in the passenger compartment was below its LFL, whereas all monitoring points for R290 exceeded its LFL. Compared to experimental data, the difference in maximum refrigerant volume concentration was approximately 1.2 %.

Characterization and prediction of demand response potential of air conditioning systems integrated with chilled water storage

Chilled water storage offers a cost-effective and convenient solution for load flexibility of air-conditioning systems. However, its impacts on system flexibility and energy efficiency have not been comprehensively explored. In this paper, demand response (DR) potential delivered by chilled water storage was explored and predicted through both experimental and simulation studies. The initial experimental investigation focused on the DR potential at various cold charging and discharging proportions of chilled water storage. Subsequently, a virtual platform, validated through experimental data, was utilized to analyze load flexibility and energy efficiency of the system across different baseline loads, storage characteristics, and heat pump characteristics. Prediction models for DR potential in different cold charging and discharging scenarios were further developed based on the simulation results. The experimental and simulation results demonstrated effective load manage performance through adjusting charging and discharging proportions, even though the flexibility may sacrifice energy efficiency in cold discharging scenarios. In addition, the developed prediction models confirmed their feasibility with the coefficient of determination (R2) exceeding 0.80 in charging scenarios and 0.90 in discharging scenarios. The analysis also revealed significant benefits from storing supply water at lower temperatures and selecting heat pumps with partial-load efficiency curves characterized by higher Pearson Correlation Coefficients.

Exergy analysis of hybrid air-conditioning systems with different evaporative-cooling condensers under hot-humid climates

Evaporative cooling (EC) technology can effectively improve the energy efficiency of the mechanical vapor compression (MVC) system by enhancing the heat exchange capacity of its condenser. However, the current evaporative-cooling condenser system is usually only equipped with a single-stage evaporative cooler as the precooling unit, and these designs are mainly suitable for hot-dry conditions. In this study, three hybrid air-conditioning systems with different two-stage evaporative-cooling condenser configurations were proposed, one is single-condenser type and the other two are dual-condenser type, which are used to improve the exergy performance and adaptability of the evaporative condenser system under hot-humid climates. Then, the all systems were modeled via the distributed parametric method and analyzed comparatively based on the exergy method. A suitable exergy reference temperature at air saturation was discussed. Finally, the effect of four key parameters (ambient temperature and relative humidity, outdoor airflow rate and room heat load) was studied. The results showed that under the high temperature, relative humidity or room heat load conditions, the hybrid systems have a low exergy efficiency ratio (EXR) and high exergy efficiency (ηx,OEC). Among them, the newly countercurrent dual-condenser configuration (MVC-TSEC(C)) exhibits the best exergy performance under most hot-humid conditions. Compared to the conventional MVC system, the EXR and ηx,OEC of MVC-TSEC(C) increased by at most 35.0 % and 78.2 %, respectively. Moreover, its components like compressor, condenser and expansion valve have the maximum reduction rates of exergy destruction of 43.5 %, 17.2 % and 55.3 %, respectively.

The indoor thermal environment performance of various air-conditioning system configurations and airflow modes in a large space museum building

Optimizing air distribution in site museums is crucial for the long-term protection of heritage sites and the thermal comfort of visitors and staff. This study examined a site museum, analyzing the traditional suspended ceiling air-conditioning system and proposing a more rational renovation. Using computational fluid dynamics (CFD) numerical simulations, the study evaluated the effects of both systems on site protection and thermal comfort. The results indicated that the retrofit scheme could significantly reduce the indoor air velocity and temperature with the same air supply parameters, which would improve human thermal comfort and also reduce the negative impact of temperature and wind speed on the site. Additionally, the retrofit scheme cut air-conditioning energy consumption by 40% and reduced initial investment costs for a unit with a rated airflow of 62,000 m³/h. The optimization of the air-conditioning system ends was also carefully considered, resulting in a safer and easier installation and maintenance process, as well as enhancing the visual appeal of the museum’s interior. The proposed retrofit scheme can provide a reference for the design of air-conditioning systems in similar types of buildings.

Comprehensive analysis of waste heat recovery and thermal energy storage integration in air conditioning systems

The proposed work aims to address the challenge of effectively recovering and storing wasted heat in air conditioning (AC) systems, which is crucial for improving energy efficiency and system stability. This study focuses on the comprehensive analysis of a Waste Heat Recovery (WHR) system integrated with Thermal Energy Storage (TES) tanks. A lumped-dynamic thermal model was developed and validated against literature data to accurately simulate the system’s performance. Through a detailed parametric study, the research explores how factors like WHR effectiveness, TES type, PCM type, and TES volume influence the system. The results demonstrate that recovering and storing wasted heat from AC systems significantly enhances operational stability and performance. Notably, increasing WHR effectiveness from 0.55 to 0.85 extends the duration of constant thermal power recovery and also results in higher recovered water temperatures with reduced water pump energy consumption. Furthermore, latent heat storage (PCM tank) extends the duration of stable thermal power recovery by over 61 % compared to sensible storage. Using PCM RT 44HC is more effective for WHR in AC units compared to RT 50 and RT 54HC. Finally, it was shown that increasing PCM tank volume from 2 to 4 m3 improves the duration of constant thermal power recovery by up to 52 % while stabilizing the recovered water temperature.

Anti-Frosting and Defrosting Fins with Hierarchical Interlocking Structure for Enhancing Energy Utilization Efficiency of Heat Exchanger.

Air conditioners, being an indispensable component of contemporary living, consume a significant amount of electricity every year. The accumulation of frost, dust, and water on the fins surface hinders the efficiency of the heat exchange process, thereby reducing the effectiveness of the air conditioning system. To address these limitations, this paper proposes a large-scale and cost-effective method combining compression molding, chemical etching, and spray coating to fabricate aluminum fins (HMNA) with hierarchical interlocking structures. The HMNA exhibits outstanding durability, passive and active anti-icing, anti-frosting and defrosting, and self-cleaning capabilities associated with the robust super-hydrophobicity. The hierarchical interlocking structure effectively enhances the physical and environmental durability of the HMNA. Most significantly, the frost time of the HMNA fins assembled heat exchanger is significantly delayed by ≈700% compared to the traditional Al fins heat exchanger, while the frost layer thickness is reduced by ≈75%. This greatly reduces the frequency with which the defrosting cycle is started, thus effectively improving the efficiency of the air conditioning system. The proposed method for economical and mass production of the HMNA fins can be an excellent candidate for the development of low energy consumption air conditioning system.

Investigation into the Operating Performance of a Novel Direct Expansion-Based Air Conditioning System

This study introduces a novel direct expansion air conditioning (DX AC) system with three evaporators (DX-TE) to enhance indoor temperature and humidity control. Operating in two modes, the DX-TE system provides variable cooling output, adapting to fluctuating indoor cooling loads while maintaining uniform air supply. Experimental and simulation studies were conducted to investigate the system’s operational characteristics. An experimental setup was established to obtain preliminary steady-state data, followed by the development and validation of a steady-state mathematical model. Simulation studies were then performed to optimize the evaporator sizes. The results indicate that the DX-TE system delivers variable cooling capacities at a constant compressor speed and airflow rate, outperforming conventional variable frequency DX AC systems in cooling and dehumidification. The evaporator area ratio significantly impacts the system’s performance, with smaller ratios yielding a larger output range. As the area ratio increases from 1:1 to 1:3, the cooling capacity range in Modes 1 and 2 increases by 33.6% and 14.3%, respectively, while the dehumidification range expands by 58.6% and 51.69%.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.

Optimization strategy of property energy management based on artificial intelligence

This study focuses on the design and optimization of property energy management systems, aiming to improve energy efficiency, reduce waste, and enhance user comfort and satisfaction through intelligent means. The research background is based on the urgency of energy conservation and emission reduction, and the rise of smart property management models on a global scale, especially the increasing demand for energy efficiency monitoring, predictive analysis, automated control, and user engagement. To address the urgent need for energy conservation and emission reduction, particularly in the realm of property management, this study designed and optimized a property energy management system. The core of the research is a systematic energy management framework that encompasses efficient monitoring, intelligent predictive analytics using techniques such as Long Short-Term Memory (LSTM) networks for energy consumption forecasting, automated control, user-friendly interfaces, and system safety. An empirical case study was conducted at a large-scale commercial complex, confirming the effectiveness of the system. Through intelligent transformation, specifically the optimization of air conditioning and lighting systems using advanced technologies like frequency modulation and LED lighting, a total energy saving rate of 25% was achieved. The annual economic savings exceeded 1.25 million yuan, and user satisfaction was significantly improved. During the research process, several limitations and challenges were encountered, including data quality issues and scalability concerns. These limitations were addressed through rigorous data preprocessing and validation, ensuring the robustness of the findings and their applicability to similar environments. The results demonstrate the potential of integrating artificial intelligence and machine learning techniques into property energy management systems, paving the way for more sustainable and efficient buildings. This revised abstract includes more specific details about the technologies used, such as LSTM networks, and mentions the limitations and challenges faced during the research. It also emphasizes the practical application and scalability of the system.

Building energy consumption analysis and measures: a case study from an administration building in Chengdu, China

With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality.

Monitoring airborne pathogen transmission for enhanced safety at food processing facilities

AbstractFood manufacturing and processing are part of the nation's critical infrastructure. Due to the recent global spread of the SARS-CoV-2 virus, the potential contamination of the food chain and the resulting public health implications are of high consequence to society. The current primary food manufacturing and processing facilities already have various mechanisms such as hazard analysis and critical control point (HACCP) system in place. However, the widespread microbial infections in these facilities raise concerns that they will not only threaten the welfare of food processing workers, but also have a potentially greater consequence on the public if the food is contaminated with an infectious agent.Despite the increasingly recognised role of the environment in the spread of microbes, the effect of air properties remains poorly understood. Heating, ventilation, and air conditioning (HVAC) systems in meat processing facilities not only provide a means of transport for viruses and bacteria but may also deposit them on surfaces where they can survive for days. To maintain a stable and safe food chain supply during the pandemic, the challenges to ensure safe food supply and protect the workers' health must be quickly addressed through sustainable, safe and economic approaches. With these two imminent challenges in mind, the overall goal of this review article is to provide a comprehensive overview of the role of the environment in the impaction and resuspension of bioaerosols, focusing on airborne bacteria and viruses. The review includes the latest results of modeling the spread of microbial aerosols in the airflow and the development of preventive measures to mitigate virus contamination in the unique environment of meat processing operations. By understanding how the environmental factors and seasonality affect the infectivity and spread of airborne pathogens, mitigation measures can be designed to minimise future infections within and beyond these facilities.

Comparative analysis of radiant air-conditioning systems combined with two different types of solar-powered dehumidification methods

Abstract This paper proposes a solar liquid desiccant radiation air-conditioning system(SLDRS) and a solar desiccant wheel radiant air-conditioning system(SDWRS) that combine with a phase change energy storage radiation terminal, solar energy, and heat pump system. The models of the two systems are simulated with the transient system tool (TRNSYS) to compare the refrigeration and dehumidification effects and energy saving of the two systems. The results show that the total refrigeration capacity of the SLDRS is reduced by 20.45% compared with the solar desiccant wheel radiation air-conditioning system, the monthly average dehumidification capacity is increased by 37.09%, and the total energy consumption is reduced by 712.9 KW·h. It is evident that the cooling and dehumidifying effect and energy efficiency of the SLDRS are superior to those of the SDWRS.