Performance Of Air Conditioning System Research Articles

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%.

A comparative study of DQN and D3QN for HVAC system optimization control

Ensuring the optimal performance of Heating, Ventilation, and Air Conditioning (HVAC) systems is paramount for achieving energy efficiency. This paper investigates the application of deep reinforcement learning algorithms in HVAC system control, aiming to identify the most suitable Q-network structure for optimizing HVAC systems and comparing the performance of Deep Q-learning (DQN) and Double Dueling Deep Q-learning (D3QN) algorithms. Initially, this paper evaluates and analyses existing literature to perform a normalization treatment on the state space. Through systematic simulation and rigorous data analysis, the impact of the Q-network structure on the efficacy of the DQN and D3QN algorithms is evaluated, resulting in the proposal of specific values for the Q-network structures within these two algorithms. Subsequently, comparisons are drawn on the optimization effectiveness, stability and reliability of these algorithms across diverse engineering projects. Results highlight the superiority of the D3QN algorithm over the DQN algorithm regarding both optimization effectiveness and stability across all evaluated projects. The proposed efficient Q-network structure comprises two hidden layers, with 64 and 12 neurons respectively in each layer. The findings of this paper are crucial in providing insights for HVAC systems control optimization using reinforcement learning and pave the way for advanced research and applications in the future.

Enhancing Air Conditioning System Performance via Dual Phase Change Materials Integration: Seasonal Efficiency and Capsulation Structure Impact

Enhancement of the cooling and heating capabilities of an air conditioning unit (ACU) coupled with a thermal energy storage system of dual phase change materials (PCM) is investigated. The dual PCM, namely SP24E and SP11_gel, are coupled with the ACU outdoor device (condenser/evaporator) during the summer/winter seasons, respectively. Moreover, ACU performance assisted with dual-PCM heat exchanger is compared with a single heat exchanger of SP24E in summer and single heat exchanger of SP11_gel in winter at different PCM capsulation structures (aligned and staggered cylinders). The system dynamic mathematical model is computationally solved using ANSYS software and experimentally validated. Results affirm that charging/discharging periods are minimal for the dual-PCM system and slower for PCM inline cylinder layouts than staggered ones. Inline design yields greater ACU average power savings. In summer, higher inlet air temperature to the PCM system reduces PCM discharging time and ACU power savings, with the opposite effect during winter. ACU COP with PCMs is improved by around 80 % in summer and 40 % in winter, respectively, compared to ACU without PCMs. The maximum average power saving over 4 h of ACU working in summer by single and dual-PCM systems is 21.4 % and 11.8 %, respectively, whereas the results in winter are 18.5 % and 12.8 %, respectively.

ANÁLISIS DEL COMPORTAMIENTO Y OPTIMIZACIÓN DE UN SISTEMA DE AIRE ACONDICIONADO SOLAR CON PRE-ENFRIAMIENTO DEL AIRE DE ENTRADA AL CONDENSADOR MEDIANTE ATOMIZACIÓN POR ULTRASONIDOS.

The most directly applicable strategies for improving the performance of air conditioning systems are the use of renewable energy sources and improving their efficiency by lowering their condensing temperature. An emerging area of study is the use of ultrasonic equipment to improve the efficiency of a wide variety of processes, with interesting results in the field of evaporative pre-cooling. The present study aims to analyse and optimise a conventional split-type solar cooling system driven by photovoltaic energy, which has a hybridisation of the condenser using ultrasonic atomisers to improve its efficiency. An experimental study has been carried out for different configurations of active atomisers, with the aim of modelling the behaviour of this system under different operating conditions. The annual energy results for different locations will be analysed and the control criteria of the ultrasonic pre-cooling system will be optimised according to the operating conditions.

IoT-driven monitoring and controlling to improve performance of thermoelectric air conditioning system

Thermoelectric module-based air conditioners (TEM-AC) are scalable, reliable, and have the potential to replace conventional air conditioners. However, the coefficient of performance (COP) of TEM-AC is still relatively low. Improving the COP requires a combination of temperature control, voltage control, heat recovery, airflow control, and material selection. The present study focuses on enhancing COP of the TEM-AC for building space cooling. To control and monitor the parameters of the TEM-AC, a low-cost, IoT-enabled monitoring and controlling system (MCS) is developed. The MCS helps in TEM-AC system designing as it can apply different current pulses to improve cooling capacity and control the temperature difference between both surfaces. MCS also measure all the required parameters of the TEM-AC to calculate the COP of the system and helps in reducing the calculation time for each experiment. Using a developed technique and TEM-AC design, a performance comparison of eight experimental cases has been done. Experiments have been performed in normal and control modes with different heat sinks and airflow of TEM-AC. The TEM-AC system is installed over a 16-inch × 12-inch enclosed space with a TEC-12706 module. The study indicates the COP of the system is enhanced by controlling the cold side temperature and air flow rate. For this study, maximum COP is achieved around 0.44 with control operation. The developed IoT-based monitoring and controlling system offers a promising solution for improving the performance of thermoelectric modules in building space conditioning applications.

On-site evaluation of indoor thermal comfort and energy performance of a liquid-desiccant assisted air-conditioning system in a high-latent-load building

Indoor humidity control is increasingly important because indoor sensible heat ratio decreases to reduce building energy consumption. However, reference vapor compression cooling systems exhibit energy inefficiency and limitations in maintaining indoor thermal comfort. Therefore, this study proposes an alternative heat-pump-driven liquid-desiccant air-conditioning system, independently controlling air temperature and humidity. The reference and proposed systems are applied to a high-latent-load building to investigate their onsite indoor thermal comfort and energy performance empirically and simultaneously under various outdoor summer conditions. The reference system exhibits a thermal comfort satisfaction ratio of 97% only under limited hot and dry weather. Conversely, the thermal comfort satisfaction ratio sharply drops to 2% or less under humid weather. The proposed system consistently achieves a high thermal comfort satisfaction ratio exceeding 90% under various outdoor summer conditions. In empirical comparisons, the proposed system can maintain a thermally comfortable room for an hour while achieving energy savings exceeding 92.4% and 12.1% under warm and humid (rainy season) and hot and humid outdoor conditions, respectively. The proposed system is concluded to consistently maintain indoor thermal comfort while using energy efficiently, demonstrating its widespread applicability for general high-latent-load buildings characterized by low indoor sensible heat ratio values of 0.56 on average.

A novel evaluation method of measurement sensitivities on common faults in VAV HVAC systems

Today, a high volume of operation interval data can be efficiently captured by a diverse range of measurements including sensors, control signals and meters, which are deployed in building automation systems (BAS). Hence, advanced data analytics tools such as fault detection and diagnostic (FDD) can be developed to analyze the operational performance of heating, ventilation and air conditioning (HVAC) systems. In the past, enormous efforts have been made to develop various FDD approaches, assuming interval data contains essential information for identifying fault signatures. However, a “data rich, but information poor” phenomenon exists due to the fact that not all measurements are sensitive to faults in HVAC systems. This highlights a significant research gap, the lack of systematic analysis of measurement sensitivity to different HVAC system faults, which is vital for FDD development, measurement deployment optimization, and control system design. To address this gap, this study introduces a novel approach to assess the sensitivity of BAS measurements in relation to various HVAC fault types. We propose two sensitivity indices (SI), the SI of fault (SI_fault) and the global measurement SI (SI_measurement_global) to quantify measurement sensitivities. The SI_fault quantifies the measurement's sensitivity to a particular fault, while the SI_measurement_global assesses its sensitivity across all fault types. These indices integrate probability distributions, enhancing the interpretability and scalability. Utilizing the HVACSIM+ fault simulation dataset, which includes 15 common faults at varying severity levels and 89 different measurements within an HVAC system, we conducted an extensive analysis of measurement sensitivities by looking at the proposed SIs.

Deep reinforcement learning for PID parameter tuning in greenhouse HVAC system energy Optimization: A TRNSYS-Python cosimulation approach

The control of indoor temperature in greenhouses is crucial as it directly impacts the crop's thermal comfort and the performance of heating, ventilation, and air-conditioning (HVAC) systems. Conventional feedback controllers, like on/off, can sometimes make HVAC system work at full capacity when only half that capacity is needed. In contrast, the proportional-integral-derivative (PID) controller, provides precise control based on its P, I, and D parameters. However, it lacks a formal design procedure for optimizing a specified objective function. Previous studies have utilized conventional PID tuning approaches to track room setpoint temperature for residential buildings, data centers, and office buildings, with limited research in greenhouse applications. To address this gap, this study proposes a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters, by tracking the setpoints and energy consumption of a greenhouse planted with tomatoes. This approach is different from the typical method of using the trained RL agent directly in HVAC controls. Through a self-made TRNSYS-Python cosimulation framework, the DRL agent interacts directly and in real time with the greenhouse and its plants. Consequently, optimized PID parameters were established and tested in the simulated environment. The resulting performance, in terms of both energy consumption and its ability to maintain the crop’s comfort temperature, was compared with the simulated on/off and manually tuned PID controllers. Compared to the on/off baseline control, the proposed PID optimized parameters reduce energy use by 8.81% to 12.99% and the manually tuned PID parameters with the Ziegler-Nichols tuning method reduce energy use by 7.17 %. Additionally, the proposed method had a deviation of 2.07% to 3.13%, while the manually tuned PID controller and the on/off controller had deviations of 7.27% and 3.27%, respectively, from the minimum comfortable temperature. This study serves as a framework for improving the energy efficiency of greenhouse HVAC system operations.

Research on refrigerant charge determination under different compressor speed and its effects on the performance of transcritical CO2 air-conditioning heat pump system in electric vehicle

CO2 is assumed to be one of the most potential refrigerant alternatives for electric vehicles for its excellent properties. However, the charge determination of CO2 in current studies remain controversial. In this study, a transcritical CO2 air-conditioning heat pump system was established and experimentally tested to analyze the optimal charge amount. Based on two conflicting methods of charge determining proposed by the preceding research, this paper substantiated the existing controversy and subsequently proposed a more comprehensive method. The effects of different refrigerant charge on the system characteristics were investigated. The influence of the compressor speed on the optimal refrigerant charge and system characteristics was also analyzed. It was found that the optimal charge plateau occurred from refrigerant of 500 g–580 g at the compressor speed of 3000 r·min−1. However, the optimal charge declined with the increment of compressor speed from 3000 r·min−1 to 4500 r·min−1. Among three models, Hughmark's model was proved to be the most appropriate for the theoretical calculation of optimal charge within an error of 6.09%. Further study illustrates that refrigerant mass in high-pressure pipe and intermediate heat exchanger/accumulator (IHX/A) accounted for the main proportion about 52.6%–55.14%.

Field test study on thermal performance of a novel embankment using solar refrigeration technology

The embankment construction in permafrost regions affects the energy exchange process between the original ground and the surrounding environment, accelerates the degradation of the underlying permafrost layer, and endangers the stability of the embankment. To prevent permafrost degradation, a solar air conditioning system based on solar refrigeration technology was proposed. In this study, a field test of cooling embankment was carried out in permafrost regions, and the cooling performance of the new technology was analyzed. The solar air conditioning system shows good cooling performance, with a horizontal influence range of about 1.77–2.28 m and a vertical influence depth of more than 3 m. By active cooling, the temperature of the permafrost layer beneath the embankment decreased significantly, and the permafrost table rose by about 0.3 m. In addition, the influence mechanism of complex environmental factors on the cooling performance of the solar air conditioning system was revealed by correlation analysis and machine learning methods. Compared with existing technologies, solar refrigeration technology achieves active refrigeration during the whole season and presents a significant price advantage. The research results help promote the application of solar refrigeration technology in transportation geotechnics and provide a new approach for actively cooling embankments.

Simulation on two-phase refrigerant compression in the cylinder of rotary compressors using CFD method

The two-phase compression process in the rotary compressor often occurs, such as defrosting and startup processes, which has a significant impact on the performance and reliability of air conditioning systems. In this paper, the CFD simulations predicting the two-phase refrigerant compression process in the compressor cylinder are conducted using the commercial software ANSYS Fluent. The dynamic mesh for the fluid domain and phase change model for the refrigerant are considered in the simulation. Effects of initial liquid volume fraction, refrigerant type and compressor type on the two-phase compression characteristics using R290 as refrigerant are carried out. Variations of the pressure, temperature, gas fraction distribution and evaporation rate in the cylinder are discussed. The results show that most liquid accumulates near the leakage gap and the bottom of the compression chamber during the two-phase compression process. The peak pressure during the two-phase compression decreases with the increase of the liquid volume fraction. The evaporation rate of R32 in the cylinder is much higher than that of R290. The maximum pressure of the reciprocating compressor is 2.26 times higher than that of the rotary compressor.

Impact of Ambient Temperature and Humidity on the Performance of Vapour Compression Air Conditioning System - Experimental and Numerical Investigation

Due to global warming, population growth, and urbanisation, demands for space cooling in buildings have significantly increased. Mechanical vapour compression cooling technology is relatively mature, well-understood and most commonly used globally. However, they suffer from high energy consumption and adverse environmental impact due to the refrigerants used. As ambient temperature and humidity have significant effects on the performance of vapour compression systems, this paper experimentally and numerically investigates their impact on the performance of vapour compression air conditioning system in terms of power consumption and cooling capacity. It was concluded that at ambient humidity of 55%, as the ambient temperature increases from 22 °C to 40 °C, the compressor temperature has increased from 46 °C to 59 °C which increased its power consumption from 751.2 to 935.52 Watts, giving an increase of 10.24 Watts for every one degree of ambient temperature increase. Also, as ambient humidity in-creased from 32% to 88%, the power consumption increased from 739.2 Watts to 853.2 Watts, an increase of 114 Watts. A validated CFD (Computational Fluid Dynamics) model was developed and predicted a loss in the enthalpy change from 11.7kJ/kg.d.a to 3.6kJ/kg.d.a. due to ambient temperature increasing from 22 °C to 40 °C and a loss of enthalpy change from 12.857kJ/kg.d.a to 5.524kJ/kg.d.a. due to increasing the ambient air humidity from 32% to 88%. This work high-lighted the impacts of ambient conditions on the performance of vapour compression cooling system in terms of increased power consumption and loss of cooling capacity.

Z-Freq Hybrid: Signal Analysis Based Car Air Conditioning Compressor Monitoring Technique Using Accelerometer and Piezo-Film Sensor

The efficiency and performance of car air conditioning systems rely heavily on adequately functioning the compressor and its associated components. There are many ways to detect an unfunctioning compressor and one of them is through vibration. Disfunctioning compressors could lead to discomfort, fatigue, stress and fogging windows, especially in long-distance driving. Thus, researchers focus on detecting the nonfunctioning car air conditioning compressor early. This paper introduced a new enhanced statistical method, namely Z-freq Hybrid. Z-freq Hybrid was based on a Z-notch frequency domain filter with a combination input of two different types of sensors introduced to detect functioning compressors. Data were recorded at various compressor speeds using an accelerometer and piezo film sensor with Signal Express 2015 software. The acceleration (m/s2) and voltage (mV) data were analyzed to find the combination degree of scattering data in a Z-freq Hybrid chart. The analyzed data show that the Z-freq Hybrid coefficient increases as the compressor’s speed increases. Then, the value dropped significantly when the compressor was dysfunctional. In conclusion, a Z-freq Hybrid can be employed to detect abnormalities and irregular vibration patterns, which may indicate the impending failure of a compressor.

Enhancement of an air conditioning system performance utilizing a PCM-based heat exchanger: Impact of aspect ratio

This research emphasizes enhancing air conditioning (AC) system performance through the integration of a phase change material (PCM)-based annulus heat exchanger (HEX) within the condenser unit. The present study delves into how aspect ratio (AR), inlet velocities, and inlet air temperature impact the coefficient of performance (COP) and melting rate. Moreover, it considers the variable specific heat of the PCM, a less-explored aspect in AC-integrated PCM systems. Results have revealed that the AR notably affects the complete melting time. Specifically, an increase in the AR from 15 to 21 has led to a noteworthy reduction of 30.07% in the total melting time under fixed velocity conditions at 2.55 m/sec and θ = 0.429. The present study has also established a correlation to demonstrate how total melting time is influenced by AR, inlet air temperature, and inlet velocity. Also, modifying AR and inlet velocity to the HEX could lead to lower outlet temperatures from the HEX. Initially, at a given velocity of 2.55 m/sec and θ of 0.429, increasing the AR from 15 to 18 leads to a 1.61% decrease in the outlet air temperature from the HEX, while increasing AR from 15 to 21 results in a 3.12% decrement. PCM-enhanced AC systems consistently exhibit improved COP values, especially in low-velocity scenarios, outperforming systems without PCM. At the onset of melting, for a given velocity of 2.55 m/sec and θ of 0.429, the percentage increase in the COP for the altered system compared to a system without the PCM is found to be 11.28%, 12.84%, and 14.01% for ARs of 15, 18, and 21, respectively. Configurations with higher ARs are found to be more suitable for higher cooling loads with shorter working periods.

Impacts of Using AlO Nano Particle to Compressor Oil on Performance of Automobile Air Conditioning System

Impacts of Using AlO Nano Particle to Compressor Oil on Performance of Automobile Air Conditioning System

Reliability analysis of CRAC system of a modern Data centre via Kriging algorithm

In the modern era of digital growth, Data centres serve as the important infrastructure of the interconnected global network. The Data centres perform as central hubs where immense volumes of data are processed, stored, and distributed. Reliable performance of data centres and Computer Room Air-Conditioning (CRAC) system is of critical importance, through which optimal environmental conditions are achieved. This paper proposes an interesting approach to evaluate the CRAC system’s ability to function properly under various uncertainties. Heat transfer model is developed using various environmental parameters, data server rack parameters, and evolving capacity of data servers. The uncertainties are modelled as random variables. One of the challenging issues in estimating the reliability of a system under uncertainties is to reduce the computational requirements while maintaining the accuracy. To overcome the issue, Kriging model is developed using adaptive sampling. The proposed approach is compared with the available approaches. The method shows better computational efficiency and accuracy.

Impact of Building Automation on Indoor Air Quality and HVAC Performance

In response to the growing emphasis on healthier indoor environments and sustainable building practices, this research delves into the intricate relationship between building automation and key facets of indoor environmental quality. Specifically, the study focuses on the impact of building automation systems on Indoor Air Quality (IAQ) and the overall performance of Heating, Ventilation, and Air Conditioning (HVAC) systems. With the increasing demand for enhanced IAQ, building automation emerges as a crucial tool for real-time monitoring and optimization of various IAQ parameters. The research explores the comprehensive integration of automation technologies within building systems, elucidating their multifaceted effects on IAQ. By scrutinizing the intricate interplay between automation systems and HVAC components, the study unveils the ways in which automation technology influences and contributes to IAQ management. The investigation encompasses a thorough examination of sensor technologies, control algorithms, and data analytics employed in building automation to ensure a nuanced understanding of their impact on IAQ. Furthermore, the study elucidates the broader implications of building automation on HVAC system performance. It investigates how automated control mechanisms, coupled with real-time data analytics, contribute to the efficient operation of HVAC systems in maintaining optimal IAQ. The research contemplates the synergies between automation technologies and HVAC components, emphasizing their role in not only ensuring healthier indoor environments but also in achieving energy-efficient and sustainable HVAC operation.

Research on heat pump air conditioner compressor speed control strategy based on whale algorithm

Accurate control of compressor speed is crucial for enhancing the heating performance of heat pump air conditioning systems and extending the driving range of electric vehicles. Based on traditional PID control and fuzzy PID control strategies, a control system utilizing the WOA-PID (Whale Optimization Algorithm)fuzzy control strategy is proposed to regulate the compressor speed and achieve optimal passenger compartment temperature control.The results demonstrate that, compared to traditional PID control and fuzzy PID control, the WOA-PID fuzzy control strategy results in significantly reduced overshooting of the passenger compartment temperature, as well as increased COP (Coefficient Of Performance)values. Specifically, when the target temperature is set at 18 °C, the WOA-PID fuzzy control strategy achieves a temperature overshoot amount of only 0.01 °C, and increases the COP by 0.10 and 0.01 compared to traditional PID control and fuzzy PID control, respectively. Similarly, when the target temperature is set at 25 °C, the WOA-PID fuzzy control strategy achieves a temperature overshoot amount of only 0.08 °C, and increases the COP by 0.14 and 0.07 compared to traditional PID control and fuzzy PID control, respectively. These improvements optimize the maximum overshooting amount and adjustment time of the passenger compartment temperature, effectively enhancing the heating performance of the system.

A data-driven performance analysis and prediction method for electric vehicle cabin thermal management system

Urban conditions and passenger behaviors primarily affect the performance of the electric vehicle (EV) cabin thermal management system (CTMS). The systematic analysis and accurate prediction of performance variations are crucial for the holistic optimization of the system. This study evaluated the effect of different ambient temperatures, solar radiations, driving patterns, and preset temperatures in Beijing, China. Energy analysis, exergy analysis, and the adaptive predictive mean vote method were employed to comprehensively analyze the performance of air conditioning (AC) system and cabin thermal comfort. A support vector machine model based on the multi-improved snake optimizer (MISO-SVM) was proposed to predict the CTMS performance. Chaotic mapping, subtraction-average-based optimizer, and lens imaging reverse learning strategy were applied to enhance the optimization capabilities of the traditional snake optimizer. A CTMS model was developed and validated to facilitate analysis and dataset acquisition. The results indicate that the energy consumption increases dramatically with elevated ambient temperatures (9.27 times increase from 26 °C to 40 °C) and solar radiation (11.42 times increase from 0 to 1200 W/m2). Conversely, high-speed driving patterns and higher preset temperatures (16 °C to 26 °C) are associated with improved AC system performance, leading to a reduction in energy consumption by up to 79.59 %. Moreover, the MISO-SVM model outperforms the traditional prediction models regarding CTMS performance prediction efficiency. The optimized MISO algorithm reduces the average convergence iterations by 20.59 % and achieves an average reduction of 18.92 % in fitness values. Across diverse prediction tasks, the MISO-SVM consistently maintains R2 above 0.99 and the lowest mean squared error. This study may provide new insights into the performance analysis and prediction of CTMS in EVs.

Real-time energy and economic performance of the multi-zone photovoltaic-drive air conditioning system for an office building in a tropical climate

Real-time energy and economic performance of the multi-zone photovoltaic-drive air conditioning system for an office building in a tropical climate