HVAC Systems Research Articles

Investigating the Reliability of Heating, Ventilation, and Air Conditioning Systems Utilized in Passenger Vehicles

A Heating, Ventilation, and Air Conditioning (HVAC) system is often utilized in passenger vehicles to enhance the comfort of both the driver and the passengers. The reliability of an HVAC system refers to the probability that a component within the system will fulfil its intended function during a specified timeframe while operating according to the predefined operational and environmental conditions. Conducting a reliability analysis for the HVAC system of a passenger vehicle is crucial to ensure safety, comfort, cost-effectiveness, and a positive standing. A methodology for analyzing the reliability analysis of a HVAC system using field failure data were developed to identify the critical failure modes, components, and subsystems. A detailed Pareto analysis was applied at subsystem and failure mode levels in order to prioritize them accordingly to their failure frequency. The analysis showed that the A/C evaporator and blower front sides were observed to be the most critical subsystems, contributing to approximately 50% of all failures. Furthermore, the leakages at the joints and vibrations are the primary failure modes of the HVAC system. The Weibull++ software package (version 2021) was used to estimate the best-fit probability distributions for each subsystem and system reliability modelling using a Reliability Block Diagram. The results show that the exponential distribution fits well for several subsystem’s Time-To-Failure (TTF) data and show that the failures were random and due to external reasons.

Cooling peak load reduction potential of ventilated slabs: a numerical study

Thermally activated building systems provide an effective method to combine thermal inertia and HVAC systems, and can be used to promote comfortable indoor conditions in summer. Ventilated slabs (VS) use supply air flow directly for heating or cooling, without the need for separate auxiliary systems. This paper proposes a detailed model of an office building equipped with VS under three different climatic conditions during one month in the summer. A Model Predictive Controller and a Genetic Algorithm, developed in MATLAB, are subsequently implemented to reduce energy consumption of the system while maintaining the room temperature set point. The outcomes of both short and extended sequences are presented after conducting modelling considerations. The dynamic behaviour of the room is highlighted, while the cooling demand is further evaluated using statistical indexes. The reduction in peak cooling load was noted in all instances, including during outdoor conditions that are unfavourable to free cooling strategies.

Heat Exchanger Improvement of a Counter-Flow Dew Point Evaporative Cooler Through COMSOL Simulations

Due to modern comfort demands and global warming, heating, ventilation, and air conditioning (HVAC) systems are widely used in many homes and buildings. However, HVAC based on the Vapor Compression System (VCS) is a major energy consumer, accounting for 20–50% of a building’s energy consumption and responsible for 29% of the world’s CO2 emissions. Dew-point evaporative coolers offer a sustainable alternative yet face challenges, e.g., dew point and wet bulb effectiveness. Given the above, dew point evaporative cooling systems may find a place to dethrone conventional air conditioning systems. This research aims to design a dew point evaporative cooler system with better performance in terms of dew point and wet bulb effectiveness. In terms of methodology, a heat exchanger as part of a counter-flow dew point cooling system was designed and analyzed using COMSOL simulations under different representative climatic, geometric, and dimensional conditions, taking into account turbulent flow. Next, our model was compared with other cooling systems. The results show that our model performs similarly to other cooling systems, with an error of around 6.89% in the output temperature at low relative humidity (0–21%). In comparison, our system is more sensitive to humidity in the climate, whereas heat pumps can operate in high humidity. The average dew point and wet bulb effectiveness were also higher than reported in the literature, at 91.38% and 147.84%, respectively. In addition, there are some potential limitations of the simulations in terms of the assumptions made about atmospheric conditions. For this reason, the results cannot be generalized but must be considered as a starting point for future research and technology development projects.

Erratum to “Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm”

Erratum to “Active noise control of vibroacoustic noise from HVAC system in autonomous bus using conjugate gradient-based algorithm”

Active fault-tolerant control based on DDQN architecture applied to HVAC system

Active fault-tolerant control based on DDQN architecture applied to HVAC system

The HVAC system engineering method for a new family of unified cabins of combine harvesters

BACKGROUND: Designing a combine harvester cabin climate system is a complex and multi-stage process that includes solving a set of tasks. To solve them, a design engineer must have knowledge and skills in various fields, starting with thermal engineering calculations and ending with experimental research methods and computer modeling, which requires a large amount of intellectual and time resources. Therefore, the task of creating a unified method of engineering of combine harvester cabin HVAC system is highly relevant. AIM: Development of the HVAC system for a unified cabin of a grain harvester and a forage harvester. The designed HVAC system is purposed to create a comfortable microclimate in the cabin of the combine for 2 people in summer and winter operating conditions. METHODS: The methodology of calculation and selection of the combine harvester cabin HVAC system, which includes the development of engineering calculation methods and mathematical modeling of thermodynamic and ventilation processes in the cabin, is considered. RESULTS: The main parameters were determined: heat intakes and heat losses for the grain harvester and forage harvester cabins, which amounted to 2.8 and 2.2 kW for the grain harvester, 2.9 and 2.35 kW for the forage harvester; the required air flow rate supplied to the cabin to ensure a comfortable temperature — 740 m3/h; the percentage of air recirculation regarding the conditions of absence of fogging and creation of excessive pressure in the cabin — 75%; the cooling and heating capacity of the HVAC system, taking into account the operating conditions of the combine, are 7.8 and 6.3 kW respectively. The selection of the main equipment of the HVAC system for a unified cabin for a new family of unified cabins of combines — the BUHLER 1000 MFWD evaporator-heater and the Valeo TM16 compressor are chosen. CONCLUSION: The HVAC system designed in accordance with the presented methodology is capable of ensuring a comfortable cabin air temperature in the range of 22–24 °C at various operating modes of the combine harvester in different regions. In addition, the HVAC system parameters eliminate fogging of the cabin windows and the penetration of dusty air inside due to the created excessive pressure.

Enhancing Energy Efficiency in HVAC Systems through Intelligent Control Strategies

Enhancing Energy Efficiency in HVAC Systems through Intelligent Control Strategies

Comprehensive energy footprint of electrified fleets: School bus fleet case study

This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel under the average price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.

Seasonal performance comparison of R-410A and R-454B in a variable-speed air-cooled scroll chiller

In recent years, there has been increasing concern about the impact of air conditioning and refrigeration on global warming. This is particularly related to the emissions of refrigerants with high global warming potentials (GWP), such as R-410A, which is used in air conditioning and chiller systems. There has been a concerted effort within the HVAC industry to find lower GWP refrigerants to replace R-410A in HVAC systems. In this paper, a 10.5 kW (3 TR) air-cooled variable-speed scroll chiller has been utilized to conduct an experimental comparison of R-410A (GWP of 2088) and its low GWP A2L alternative R-454B (GWP of 466) according to AHRI 551/591 testing condition at rating and part load condition with optimized charge. The compressor speed and suction superheat were matched for both refrigerants at all the test conditions. R-454B shows 98 % capacity and 102 % efficiency compared to R-410A at rating conditions of 35 °C outdoors and water return temperature of 12 °C. The IPLV of the R-454B chiller was just 1 % higher than R-410A. The discharge temperature of R-454B and compressor isentropic efficiency is 8 % higher and almost like R-410A, respectively. The optimized charge of R-454B was 5 % lower refrigerant charge compared to R-410A. The LCCP analysis for major Indian cities over a 15-year operational span demonstrates a notable reduction, ranging from 6.6 % to 7.3 %, in overall R-454B emissions compared to R-410A. The study demonstrates that R-454B is a drop-in replacement to R-410A designs and reduces the direct GHG emission from the chiller by 76 %.

Building performance optimization through sensitivity Analysis, and economic insights using AI

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

Data-enhanced convolutional network based on air conditioning system start/stop time prediction

Most enterprise workshop operators frequently adjust the start/stop time of air conditioning systems based on indoor and outdoor temperatures and humidity to accommodate changing demand and weather conditions. However, relying on personal subjective experience for these adjustments often leads to operational delays or energy waste due to the lack of precision in determining optimal timing. Predicting air conditioning system start and stop times is crucial for energy consumption and savings in HVAC systems. Traditional data-driven methods have been insufficient in this regard, as they mainly focus on feature mapping and overlook the dynamic coupling relationships of process variables, resulting in subpar predictions. In response to this challenge, the paper introduces a novel approach known as the Periodicity and Long-Term Convolutional Neural Network (PLCNN). This method converts one-dimensional regression prediction data into two-dimensional data containing time series features to capture the dynamic coupling characteristics of the air conditioning system while maintaining the independent variation relationships of features. Experimental results using real factory floor data have demonstrated the superior performance of the PLCNN method. Specifically, this method achieved a 14.96% lower error rate compared to the traditional method and an 8.18% improvement compared to the deep learning method. Moreover, the implementation of the PLCNN method in the optimal control of air conditioning systems led to a significant 19.43% reduction in total monthly energy consumption. In conclusion, the proposed method offers a promising alternative to traditional approaches to forecasting and provides a solution to the common challenges encountered in traditional prediction tasks.

Enhancing Diesel Generators Power and Fuel Consumption using Thermoelectric Generators and Exhaust air of HVAC Systems

Enhancing Diesel Generators Power and Fuel Consumption using Thermoelectric Generators and Exhaust air of HVAC Systems

Performance analysis of air conditioner system integrated with thermal energy storage using enhanced machine learning modelling coupled with fire hawk optimizer

Integrating air conditioning (AC) systems with thermal energy storage (TES) offers a promising solution for managing large buildings' peak load demands and energy efficiency. Predicting the performance of the AC-TES is a significant index in ensuring optimal cooling load and energy consumption. However, conventional approaches encounter challenges in achieving high levels of accuracy, reliability, and scalability. Therefore, this study introduces leveraging machine learning techniques and in-situ measurements for precise predicting the energy performance of AC-TES system in a semi-arid climate building. The study proposes a hybrid prediction strategy leveraging the benefits of machine learning and meta-heuristic optimization algorithms. The proposed approach integrates a Radial Basis Function Neural Network (RBFNN) with the Fire Hawk Optimizer (FHO) for predicting the performance parameters of the AC-TES system; including, energy consumption, cooling load, air room temperature and performance coefficient (COP). The RBFNN framework in the developed strategy is trained to identify intricate patterns and correlations within the data, while the FHO is utilized to explore the optimal hyperparameters of the RBFNN for maximizing the prediction accuracy. The proposed strategy was modelled in MATLAB software and validated using a publicly available measurements for the AC-TES system. The system maintained improved cooling performance in which the average daily values of energy consumption, cooling load, air room temperature and COP are 1100 kWh/hr, 1.30 kW, 23.97 oC, and 3.12, respectively. The statistical outcomes depict that the developed RBFNN-FHO strategy achieved an impressive prediction accuracy of 95.81% accuracy, a 0.94 correlation coefficient, a 0.4193 mean absolute error (MAE), and a 0.5200 root mean square error (RMSE), respectively. Furthermore, the performance of the proposed RBFNN-FHO is model, is compared with the existing machine-learning approaches utilized for predicting the dynamic performances of HVAC systems. The comparative analysis with existing techniques highlights the robustness and effectiveness of the proposed RBFNN-FHO strategy in predicting AC-TES performances.

Short cellulose acetate nanofibers: A novel and scalable coating for enhancing nanoparticles filtration efficiency of filter media

This study introduces a novel and scalable approach to significantly enhance the filtration efficiency of automotive cabin air filters using short cellulose acetate nanofibers applied via a spray coating method. The short nanofibers, produced through the mechanical fragmentation of electrospun mats, enable uniform dispersion and application onto microfibrous filter media, addressing a key limitation in nanofiber technology. This innovative process enhances the capture of ultrafine particles (7.4 to 250 nm), which are critical for improving air quality in confined environments such as vehicle cabins. The filters, coated with nanofiber weights ranging from 1 to 13 mg, showed a prominent improvement in filtration efficiency, achieving quality factors (Qf) from 0.01 to 0.0221 Pa−1, outperforming both uncoated filters (0.0025 Pa−1) and commercial HEPA filters (0.0212 Pa−1). Filtration efficiency increased from 18 % to 99.8 %, with corresponding pressure drops between 44.5 and 620 Pa. Remarkably, a filter with 9 mg of nanofibers achieved a superior quality factor of 0.0221 Pa−1, effectively optimizing both efficiency and pressure drop while outperforming HEPA filters. This method addresses the common challenge in conventional filters of balancing filtration efficiency with pressure drop. The scalable spray coating process, combined with the advantages of electrospinning, offers a practical, industrially viable solution to improve air filtration in automotive HVAC systems and other environments requiring high filtration efficiency and low-pressure drop.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Hybrid membranes incorporating microencapsulated PCM for enhanced hygrothermal performance in enthalpy recovery ventilation systems

Advancements in building technologies are increasingly centered on improving energy efficiency, particularly within ventilation systems. Integrating Microencapsulated Phase Change Materials (MPCMs) into paper-based membranes enhances hygrothermal performance in enthalpy recovery ventilation systems. MPCMs, known for their latent heat storage capabilities, were incorporated at concentrations of 0 %, 5 %, 10 %, and 20 % by weight. The hybrid membranes exhibited a significant latent heat storage capacity, achieving up to 34.38 J/g at MPCM 20 wt% content. Although the latent heat decreased by approximately 10 % after 1000 cycles of extreme temperature fluctuations between 15 °C and 60 °C, the membranes still exhibited durability sufficient to endure a replacement cycle of over three years, maintaining their effectiveness in long-term applications. The addition of MPCMs also led to improvements in moisture resistance, with the moisture resistance coefficient increasing from 8.88 to 19.19 at MPCM 20 wt%, while still preserving sufficient moisture permeability for efficient enthalpy exchange. These improvements in thermal and hygrothermal properties underscore the potential of MPCM-integrated membranes to enhance energy efficiency and thermal comfort in HVAC systems, providing a sustainable solution for modern building designs. The findings suggest further exploration into optimizing MPCM content for varying climate conditions and assessing the long-term performance of these membranes in diverse practical applications.

Examining the Role of Surface Temperature on Boundary Layer Stability and Transition to Turbulence

This study investigates the influence of varying surface temperature on the critical Reynolds number to understand the onset of turbulence in the boundary layer of a flat plate. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent have been utilized for the purpose of this study. The study systematically varies surface temperature to measure its impact on flow stability. The research demonstrates that an increase in surface temperature results in a lower critical Reynolds number (leading to an earlier transition from laminar to turbulent flow) by modeling air as the working fluid and applying the k-ω SST turbulence model. The results align with theoretical predictions which highlight the destabilizing effect of temperature-induced viscosity changes. This finding has practical implications for aerospace engineering, HVAC systems, and environmental modeling where controlling turbulence can optimize performance and energy efficiency. The study acknowledges limitations due to the use of a 2D model and suggests future work using 3D simulations and other flow conditions to expand the applicability of the findings.

Improvement of heat transfer in microchannels using nanofluids by numerical contribution on a sinusoidal shaped dissipator

This research conducts an extensive numerical investigation into the enhancement of heat transfer in wavy-walled channels using aluminum oxide-water nanofluids. The study examines the impact of key parameters, including geometric aspects (wave spacing ratio: 0-1), operational conditions (Reynolds number: 5,000-20,000), and varying nanofluid concentrations (1-6% by volume). The results indicate that zero wave spacing combined with a nanofluid concentration of 4% provides the most favorable thermal performance, resulting in a heat transfer enhancement of up to 2.5 times when compared to conventional smooth channels. While the wavy channel geometries significantly improve heat transfer, they also impose additional pressure drops, revealing a critical trade-off between enhanced thermal performance and the increased pumping power required. The study highlights the balance necessary between maximizing heat transfer and maintaining operational efficiency, particularly in systems where space is limited. These findings contribute valuable insights to the design of heat exchange systems that demand both high thermal efficiency and compact dimensions, such as those used in electronics cooling or energy-efficient HVAC systems. Additionally, the study explores the influence of the Reynolds number on both thermal and hydrodynamic behavior within the wavy channels, providing a thorough performance evaluation across different operational ranges. The conclusions drawn from this investigation offer practical guidance for optimizing heat exchanger designs, taking into account both thermal resistance and overall system performance under varying conditions.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models

The design process of heating, ventilation, and air conditioning (HVAC) systems is complex and time consuming due to the need to follow design codes. Since the design standards are not fixed, the final outcome often depends on the designer’s experience. The development of building information modeling (BIM) technology has made information throughout the building lifecycle more integrated. BIM-based forward design is now widely used, providing a data foundation for combining HVAC system design with machine learning. This paper proposes an unsupervised learning method based on deep graph generative models to uncover hidden design patterns and optimization strategies from the design results. We trained and validated four deep graph generative models—GAE, GNF, GAN, and diffusion—using HVAC system terminal pipeline layout data. Accuracy and precision metrics were used to compare the generated designs with automated forward design solutions, assessing the models’ ability to capture both local variations and broader changes in design logic. A graph-neural-network-based evaluation method was employed to measure the models’ capacity to detect changes. The results indicate that all four models achieved prediction accuracies exceeding 90% and precision rates above 75%. The models effectively captured both local modifications made by designers and global design changes, showing greater sensitivity to global layout adjustments than to local updates. When comparing the results generated by deep graph generative models and the actual design, it is obvious that the accuracy of the predictions varies significantly due to the complexity of the test buildings.