Air Conditioning Units Research Articles

Computational-Fluid-Dynamics-Based Optimization of Wavy-Slit Fin Geometry in Indoor Units of Air Conditioners Using Low-Global-Warming-Potential Refrigerants

This study explores the optimization of wavy-slit fins in the indoor units of air conditioners that use low-global-warming-potential refrigerants, with a focus on the interactions between slit length, width, and height. A response surface method was employed to analyze the trade-offs between thermal performance and pressure loss, and numerical optimization was performed using two objective functions: pumping power and volume goodness factor (Gv). The results demonstrated that optimizing the slits’ geometry significantly enhanced overall performance. For pumping power, a minimum point was observed near the design boundaries, which underscores the critical role of geometric interactions. The flow and temperature field analysis under fixed heat-duty conditions revealed substantial flow separation caused by the slits, enhanced mixing between the upper and lower surfaces, and a reduction of up to 2.05% in pumping power. In contrast, the Gv optimization model exhibited a more uniform flow, reducing flow separation beyond the pipe and improving the Gv by 1.85%, although it led to an increase in pumping power. These findings highlight the potential that tailored slit fin designs have to achieve a balanced enhancement in heat transfer and aerodynamic performance, offering valuable insights for the development of efficient, low-environmental-impact air conditioning systems.

Field Measurements of Building Air-Conditioning Heat Rejection and the Thermal Environment in Urban Areas

In recent years, the surge in air-conditioning ownership and usage has led to significant heat rejection, impacting the surrounding atmosphere. Despite this, studies examining the spatiotemporal effects of air-conditioning heat rejection at a block scale remain limited. Additionally, comparative studies on the role of building areas with air-conditioning systems versus natural underlying surfaces in the urban thermal environment are relatively scarce. This study employs field measurements and ArcGIS technology to investigate the local thermal and humidity environments, as well as the spatiotemporal distribution of heat rejection from air-conditioning systems in Wuyi Square, Changsha. Results show that cooling tower exhausts in commercial buildings maintain relative humidity levels of 95.2% to 99.8% during the day, enhancing surrounding humidity. At night, the humidity aligns with atmospheric levels (from 50.3% to 62.5%). The cooling tower exhaust temperature is approximately 2.2 °C lower during the day and 2.4 °C higher at night compared to the surrounding temperatures. In contrast, exhausts from split-type air-conditioning units in residential buildings have an average relative humidity about 14.2% lower than the atmosphere humidity, with temperature averages being 5.2 °C higher during the day and 6.5 °C higher at night, raising surrounding temperatures. The study also finds that natural surface areas are up to 3.1 °C cooler and 9.6% more humid compared to built environment surfaces. Furthermore, residential areas have air temperatures about 0.3 °C higher than commercial zones, with a humidity distribution approximately 0.5% lower. These findings offer a theoretical foundation for enhancing urban thermal environments and informing urban planning and design.

PARAMETRIC ANALYSIS OF A SAWDUST-POWERED BIOMASS CHP SYSTEM: EFFECTS ON SUSTAINABILITY AND PERFORMANCE

In this research study, we focused on a biomass-powered combined heat and power (CHP) system encompassing multigeneration purposes. The system was comprised of a combustion chamber, single-effect absorption cooling system, air conditioning unit, reheat steam Rankine cycle, organic Rankine cycle, and electrolyzer. Biofuel, namely, pine sawdust, was selected for the combustion process. Parametric analysis was also conducted for various parameters, including the ambient temperature, biofuel flow rate, and boiler outlet temperature. It was determined that the energy efficiency and overall utilization factor of the CHP plant were 2.096 and 24.03%, respectively. The analysis revealed an approximate sustainability index (SI) of 1.32 and specific carbon dioxide emissions of 354 kg/(MW·h). The results from the parametric studies indicated that the system's sustainability improved as the mass flow rate of the biofuel increased; specifically, increasing the biofuel mass flow rate by 0.097 kg/s increased the SI index by 0.233. However, any increase in the boiler and sink outlet temperatures led to a decrease in the SI, indicating a negative impact on the system's environmental performance.

Identification of Bacteria on Classroom Air Conditioner at Faculty of Medicine, Universitas Swadaya Gunung Jati, Cirebon, Indonesia

Background: Air conditioner became a significant factor in indoor air quality. Poorly maintained Air Conditioners can become breeding grounds for bacteria, leading to health problems and symptoms of Sick Building Syndrome, such as headaches, loss of concentration, dry throat, eye and skin irritation, and acute respiratory infections. Aims: Identify bacteria types and count bacterial colonies on Air Conditioning units in the classrooms of buildings A and B at Faculty of Medicine, Universitas Swadaya Gunung Jati, Cirebon, Indonesia. Methods: Cross-sectional study using 16 air conditioner samples from lecture rooms at Faculty of Medicine, Universitas Swadaya Gunung Jati. Direct swabs were taken from the air conditioner filters using cotton swabs, which were then identified using McConkey, Blood Agar, and Nutrient Agar media to count bacterial colonies followed by confirmatory bacterial tests using Triple Sugar Iron Agar (TSIA) and Indol, Methyl red, Vogues proskauer, Simmon Citrate (IMVIC) media. Results: Bacterial growth identification tests identified Bacillus sp., Micrococcus sp., Staphylococcus sp., Streptococcus sp., Coryneform/Diptheroid, Enterobacter sp., and Stenotrophomonas sp.. Highest colony count was 402 CFU/cm³ and lowest was 16 CFU/cm³. Conclusion: Bacterial identification on classroom the Air Conditioner revealed that the microbial community in the system was predominantly composed of Gram-positive strains, which were detected more frequently than Gram-negative strains. Received: 24 September 2024 | Reviewed: 02 November 2024 | Revised: 30 November 2024 | Accepted: 15 December 2024.

Research on the vibration analysis of the discharge pipe system in the outdoor unit of air conditioner

Research on the vibration analysis of the discharge pipe system in the outdoor unit of air conditioner

Experimental and numerical research on a C-type heat exchanger in duct air conditioner under non-uniform airflow

The indoor unit of duct air conditioner typically employs an A-type heat exchanger (HX), but non-uniform airflow within the duct significantly degrades heat transfer performance. The current study proposes a C-type HX to accommodate the non-uniform airflow better. Experiments are conducted to compare the C-type and A-type HXs under varying conditions, including air volume flow rate, air velocity non-uniformity, inlet air temperature, and condensing temperature. Results indicate that as the non-uniformity of inlet air velocity increases, the heat transfer capacity of A-type HX decreases obviously. In contrast, the C-type is insensitive to the non-uniform air velocity distribution, which consistently outperforms the A-type in terms of heat transfer capacity throughout various operating conditions. Additionally, a numerical model is developed to compare the local parameters of the two HX types. The C-type demonstrates a more uniform outlet air temperature and exhibits a heat transfer capacity that is 19.1% higher than the A-type, while maintaining the identical heat transfer area and size parameters.

Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion.

Guide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not meet the high demand for such skilled working animals. Towards optimizing the training process and to better understand the challenges these guide dogs may be experiencing in the field, we have created a multi-sensor smart collar system. In this study, we developed and compared two supervised machine learning methods to analyze the data acquired from these sensors. We found that the Convolutional Long Short-Term Memory (Conv-LSTM) network worked much more efficiently on subsampled data and Kernel Principal Component Analysis (KPCA) on interpolated data. Each attained approximately 40% accuracy on a 10-state system. Not needing training, KPCA is a much faster method, but not as efficient with larger datasets. Among various sensors on the collar system, we observed that the inertial measurement units account for the vast majority of predictability, and that the addition of environmental acoustic sensing data slightly improved performance in most datasets. We also created a lexicon of data patterns using an unsupervised autoencoder. We present several regions of relatively higher density in the latent variable space that correspond to more common patterns and our attempt to visualize these patterns. In this preliminary effort, we found that several test states could be combined into larger superstates to simplify the testing procedures. Additionally, environmental sensor data did not carry much weight, as air conditioning units maintained the testing room at standard conditions.

Cost-Effectiveness Analysis of Nearly Zero-Carbon Office Buildings in Taiwan

The formulation of an affordable and economically-feasible policy for nearly zero-carbon building design is a challenge that countries encounter in promoting net-zero building. In 2022, Taiwan promulgated Building Energy Rating System (BERS), the world's first building energy efficiency assessment system tailored for subtropical climates, as a basis for evaluating its goal of achieving nearly zero-carbon buildings by 2050. However, whether this nearly zero-carbon building policy is financially feasible has remained unknown to the government and the industry. To address the issue, the objective of the paper is to conduct a cost-benefit and payback period analysis of newly constructed nearly zero-carbon office buildings in accordance with the Building Energy-efficiency Rating System for New Buildings (BERSn) assessment framework, and thereby examines the financial affordability and feasibility of Taiwan's net-zero building policies.Firstly, this study introduces existing building energy performance evaluation methods in Europe, America, and Japan, and explains the methodology of Taiwan's Building Energy Rating System (BERS). The paper then reviews international Cost-Benefit Analyses of Nearly Zero Energy Office Buildings and parallelly conducts a Cost-Benefit Analysis of Nearly Zero-Carbon Office Buildings in Taiwan. Following the BERS methodology, this paper identifies the baseline efficiency level and the Nearly Zero-Carbon Building (NZCB) efficiency level, along with their associated solutions and investment costs, for air conditioning and lighting systems. In this study, a questionnaire survey of 28 experts identifies the air conditioning unit costs of Basic Mechanical Equipment Efficiency (BME) and energy-saving control technologies (R) for energy-efficient design of Fan Coil Unit (FCU), Air Handling Unit (AHU), and Variable Refrigerant Flow (VRF) system. Accordingly, 33 air conditioning design solutions and their corresponding construction costs complying with the BERSn requirements of EAC≦0.5 for nearly-zero carbon office buildings are identified. Regarding the nearly zero-carbon lighting design, the simulations reveal that the use of LED panel luminaires or T8 LED luminaires (including those with energy-saving labels) can achieve an average operational lighting illuminance of 500 LUX and the BERSn requirements of EL ≤ 0.5 for lighting. Finally, the study further analyzes the payback period and premium cost ratio of NZCB by employing the budget data of seven building projects. The analysis shows that the premium cost ratio and the payback period of NZCB range from 1.33%-1.50% and 6.4-7.7 years, respectively. The results demonstrate that while a higher budget is commonly associated with NZCB by the public, it is in fact economically and technologically feasible to realize NZCB with prevailing energy-efficient measures.

Experimental investigation on thermodynamic and environmental performance of a novel ocean thermal energy conversion (OTEC)-Air conditioning (AC) system

In the field of isolated island energy supply, the OTEC system is considered promising due to its large storage capacity, and pollution-free characteristics. To improve energy efficiency, polygeneration systems have gradually become a research hotspot for their low cost and high return features. In this context, a novel Ocean Thermal Energy Conversion system integrated with an air conditioning unit (OTEC-AC) is introduced, demonstrating capabilities in electricity, cooling capacity, and fresh water production by experiments. The effects of various flow rates and temperatures of the heat and cold sources, along with the air-conditioning system water flow rate on the system performance is explored. Besides, the overall performance of the OTEC-AC is compared with four representative OTEC models. The results indicate that there is a threshold for the influence of cold and heat source flow rate on the performance of power generation system. The OTEC-AC system shows a highest net power of 132.6 W, cooling capacity of 2.14 kW, condensate production of 3.24 kg/h, and achieves a system exergy efficiency and CO2 reduction of 34.7 % and 5801 kg/year, respectively. The annual CO2 reduction per kilowatt of installed capacity of the system is increased by 135.6 %, compared with the conventional OTEC system.

Experimental assessment of using phase change materials in vapor compression refrigeration systems for condenser pre-cooling

The primary objective of this investigation is to empirically assess a new vapor compression cycle while employing phase change material (PCM) energy storage. During off-peak periods, the PCM undergoes charging, and during on-peak hours, it is discharged to cool the refrigerant entering the condenser, thereby enhancing the condenser's overall performance. In contrast to previous studies that exclusively examined the charging or discharging processes of air conditioning (AC) units, this research delves into the entire 24-h charge and discharge cycle. The proposed system is subjected to testing under identical conditions, both without PCM (conventional mode) and with a PCM storage tank, throughout a 24-h period. The PCM storage tank, which utilizes water as its phase change medium, possesses a volume of approximately 300 L and starts at an initial temperature of 25 °C. The outcomes of the study demonstrate notable improvements when incorporating the PCM storage tank. Specifically, the daily coefficient of performance (COP) increases by approximately 7 %, rising from 2.17 to 2.33. Additionally, it leads to a reduction in both the daily available cooling load and daily compressor energy consumption, with decreases of approximately 3.7 % (from 58.4 to 56.2 kWh) and 10.3 % (from 26.9 to 24.1 kWh), respectively.

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

Banana slice drying using waste heat from an air conditioner for enhancing performance and sustainability

AbstractAs waste heat dryers (WHD) exhibit conservation of energy with environmental and economic benefits, the direct use of waste hot air rejected from the outdoor unit of a window air conditioning (AC) unit for drying banana slices is demonstrated experimentally in the present research work. For this purpose, a drying chamber was directly coupled to an AC unit (AC‐WHD) and analyzed its performance during banana slice drying by revisiting the concepts of generalized drying curve and drying performance index (DPI). The experimental data set of moisture content ratio obtained with AC‐WHD matched well with the generalized drying curve and the DPI value was found to be 0.999. It was found that the waste air supplied by the AC unit to the drying chamber was hot (≈ 50°C) and 28% dry, which aided the drying process. The thermodynamic performance of AC‐WHD was also evaluated using warm and wet exergy to provide insight into the mechanism of AC‐WHD drying. The exergy based sustainability index was higher while the exergetic destruction coefficient and environmental impact factor were lower for AC‐WHD when compared to literature values of the solar drying method. The findings of this research work indicate the practicality of coupling AC units with dryer units to address sustainable development goals (SDG).

A convolutional neural network to control sound level for air conditioning units in four different classroom conditions

Air conditioning units (ACUs) are widely used in educational areas like classrooms in order to ensure the occupant’s well-being and to control CO2 concentration (by ventilating using fresh outdoor air). However, the noise level of these ACUs can disrupt the learning environment. Consequently, we propose an approach based on machine learning that is able to distinguish between the different acoustic situations in a classroom, and to dynamically adjust the air volume flow accordingly. To this end, the present algorithm was trained with sound recordings in four different scenarios in a classroom. Both Mel spectrogram and Cochleagram are considered and applied for the task of training the convolutional neural networks (CNN) model, thus enhancing published works in the literature, which only considered the Mel spectrogram. Results show how the Cochleagram is pre-eminent to handle the CNN model training over the Mel spectrogram. Accordingly, we use the Cochleagram for the CNN model training, which is subsequently used for detecting the current situation in the room and adapting the ACU operation to this situation. The results show that running ACUs in high mode provides a learning environment with low CO2 concentration and high noise. Instead, controlling ACU based scenario predictions made by the CNN model provides a good learning environment with adequate CO2 concentration and acceptable noise level. Our results demonstrate the effectiveness of using sound classification as a trigger for ventilation control, with the CNN model achieving a good accuracy rate in sound recognition. This underscores the potential of integrating advanced machine learning techniques into building management systems to foster environments that adapt to the needs of their inhabitants automatically. The results of the present work are useful for improving the comfort of the occupants through dynamic ACUs adjustments based on acoustical situations.

A market-based load regulation method for heterogeneous residential air-conditioning loads under cloud-edge collaboration

Residential air-conditioning load on the power demand side has gradually increased. The air-conditioning loads have thermal storage characteristics, and adjusting their temperature settings will not cause significant negative impacts to users, making them well-suited for demand response. In existing studies, load aggregators usually serve as demand response implementors, and reducing the energy consumption levels of air conditioning loads directly. However, this method often lacks a clear definition of the flexibility of individual air-conditioning units and fails to account for their variations. The absence of well-defined commodity characteristics for the flexible regulation potential complicates pricing, making it difficult to safeguard the interests of the load aggregator and each air-conditioning user. Furthermore, the diversity among residential air-conditioning loads introduces high-dimensional variables into demand response optimization, making it challenging to balance data security with computational efficiency. To address these challenges, this paper proposes a market-based load reduction method for heterogeneous residential air-conditioning loads using cloud-edge collaboration. First, the flexible adjustment capability of air-conditioning loads is defined as a tradable commodity. Then, a cloud-edge collaborative trading scheme is proposed to guarantee the benefits of the load aggregator and each air-conditioning user in a data-neutrality manner. Finally, a hybrid method based on encrypted clustering is developed to compute the optimal trading strategy, ensuring data security and computational efficiency. Simulation results based on data from the Austin area show that Pareto improvement is achieved compared to other load reduction schemes, and the proposed encrypted clustering method reduces computational complexity and data security risks compared to traditional analytical and iterative approaches.

ENERGY AUTOMATION SUPPORTS ENERGY CONSUMPTION INTENSITY IN CLASSROOMS

Automation of energy use to control energy consumption intensity (IKE) in classrooms is carried out using an Arduino Nano-based system. This system utilizes an IR transmitter to send signals to the air conditioning unit, a servo motor to position the IR transmitter to the appropriate air conditioning unit, a relay to control the lights, and a Real-Time Clock (RTC) module for scheduling based on a specified schedule. The main goal is to prevent energy waste and ensure comfort by turning on and off the air conditioning unit and lights according to a preset schedule. This automatic scheduling includes time setting, transmitter IR control, servo motor, relay control, and LCD. Programmed scheduling ensures that the air conditioner and lights only turn on when needed, while the LCD provides real-time information about the status of the air conditioner and lights. With this automation, the system helps reduce excessive energy consumption, drives significant energy savings, and creates a comfortable and energy-efficient learning environment. The automatic monitoring and regulation system also allows users to focus more on teaching and learning activities without having to worry about manual settings for air conditioning and lights.

An automatic and efficient fault diagnosis strategy for air conditioning units by combining attention mechanisms

The air conditioning system is a very important energy-consuming device in the field of construction. Timely troubleshooting of air conditioning chillers is an important aspect of reducing building energy consumption. Aiming at the problems of many model parameters, large amount of calculation and strong dependence of diagnostic performance on input features in data-driven diagnostic methods, this paper proposes an efficient fault diagnosis strategy combining RepVGG network and SENet attention mechanism. The multi-parameter feature signal is converted into a two-dimensional matrix to obtain multiple feature images. Based on the SENet attention mechanism, the multi-channel weights of the images are extracted, and the weighted feature image is input into the RepVGG network for feature depth mining and classification. Therefore, the method proposed in this paper greatly reduces the calculation amount of parameters, improves the representativeness of signal features to faults and the efficiency of model training. The effectiveness of the method is validated using the ASHRAE RP-1043 chiller unit dataset. The results show that the accuracy of this method reaches 96.55%, which is significantly better than the comparison methods PSO-ELM(96.4%), DT(95.1%), RepVGG(91.6%), ELM(87.6%) and BP(80.8%).

Numerical investigation of high temperature heat pump integrated hybrid cooling system

Over 67 % of the energy demand within the pharmaceutical and semiconductor industries is for their cleanrooms, driven by stringent temperature and humidity control requirements. Despite high energy usage, heating, ventilation, and air-conditioning (HVAC) units consisting of conventional vapor compression systems (CVCS) are favoured for their simplicity. Hybrid cooling systems (HCS) offer potential efficiency gains by separating latent and sensible loads. Still, reliance on fossil-fuel-fired or electric heaters to regenerate the desiccant makes it energy intensive and reduces CO2 mitigation benefits. This study explores high-temperature heat pumps to simultaneously cool process air and heat regeneration air, thereby using multiple benefits. Heat pumps with condensing temperatures of 80 °C, 90 °C, and 120 °C and an ejector-enhanced trans-critical CO2 heat pump were compared with CVCS and HCS. The 120-bu and CO2HP configurations demonstrated a system COP of 2.94 and 3.22, respectively, surpassing CVCS, which has a system COP of 1.44. These setups achieved CO2 emission reduction of 51 % and 55.2 %, compared to CVCS, respectively.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

A gray-box model for unitary air conditioners developed with symbolic regression

In this paper, we present the development of a gray-box model for unitary air conditioning equipment that can be trained with as little as 5 data points with higher accuracy on test data. The model utilizes the same model inputs as is typical in building energy simulation, and is accurate. While black-box models require large data sets to deliver accurate results, white-box models require higher computational and engineering efforts along with detailed knowledge of the system, and are often difficult to obtain. The model presented here addresses a hybrid solution that is a steady-state, component-based, gray-box model that requires inputs from the source and sink fluids and rated performance of the specific piece of equipment, only. The basic physics of a vapor compression cycle are captured in individual component models for the heat exchangers, compressor, and expansion valve. These components are generalized to eliminate refrigerant-side inputs. A key addition is the development of correlations for the overall heat transfer coefficient times surface area (UA) obtained from Symbolic Regression (SR). The model successfully predicts the cooling capacity, coefficient of performance (COP), and sensible heat ratio (SHR) for three state-of-the-art variable speed, split-system, air conditioning systems with capacities of 12.3(3.5), 14(4), and 17.6(5) kW(tons), achieving a mean absolute percentage error (MAPE) of less than 3.4%. These results suggest that the gray-box model can be useful in predicting the performance of similar systems in the future, which could be valuable for energy management and optimization purposes.

Improvement of thermal environment in the outdoor atrium by employing the spray system

Outdoor atriums have recently been applied with increasing frequency for natural illumination, but they produce a harsh thermal environment easily in summer. Moreover, overheating of the outdoor atrium necessitates air-conditioning to moderate indoor thermal comfort. Simultaneously, the substantial heat emissions from air-conditioning outdoor units worsen the outdoor thermal environment, creating a vicious cycle. Traditional passive evaporative methods involving water and greenery, while capable of regulating the thermal environment, suffer from low evaporative efficiency and pose significant challenges. To improve thermal environment in outdoor atriums, the spray system was employed due to its high cooling efficiency, especially in open or semi-open spaces. In this study, a comparative experiment was conducted to evaluate the effectiveness of using a spray system for evaporative cooling in open outdoor spaces. Furthermore, employing high-efficiency evaporative cooling through spraying to disrupt the vicious cycle of indoor and outdoor thermal environments. The dual goals include regulating indoor and outdoor thermal conditions while also mitigating the local heat island effect. Temperature and humidity distribution within the atrium and adjacent hallways were monitored, along with the impact on air-conditioning operation consumption in neighboring offices. Results showed that the spray system significantly improved the thermal environment in the outdoor atrium, reducing the average and peak air temperatures by 0.94–2.83 °C and 2.92–5.21 °C, respectively. It also resulted in a drop in the average temperature by 0.56–1.62 °C and the peak temperature by 2.31–3.25 °C in adjacent hallways. This effectively eased the issue of overheating in these areas while raising the comfort level in adjacent office spaces. The predicted mean vote decreased from 1.46 to 0.87, indicating a significant improvement in thermal environment in neighboring offices. Furthermore, the daily energy consumption was reduced by 10.6–12.4% in neighboring offices. This study provided the valuable guidance for improving thermal environments within outdoor atrium.