Applying data-driven fault-diagnosis models to data center air-conditioning systems can significantly improve operational reliability. However， these models often lack diagnostic interpretability， which limits their application. This study develops a composite fault-diagnosis model based on typical machine-learning algorithms， compares the diagnostic performance of different models， and conducts interpretability research on the diagnostic models using the Shapley additive explanation method. The results demonstrate that the convolutional neural network （CNN）-based fault-diagnosis model achieves optimal performance in both the heat-pipe and vapor-compression modes， with F-1 scores exceeding 0.999 across all classifications. In the heat-pipe mode， the diagnosis of the CNN model primarily relies on the condenser-fan frequency， outdoor temperature， and refrigerant-pump power consumption as key features， whereas in the vapor-compression mode， the dominant features are the outdoor temperature， compressor frequency， and subcooling degree.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Seyed Hamidreza Nabaei, Ryan Lenfant, Viswajith Govinda Rajan
et al.
In the era of growing interest in healthy buildings and smart homes, the importance of sustainable, health conscious indoor environments is paramount. Smart tools, especially VOC sensors, are crucial for monitoring indoor air quality, yet interpreting signals from various VOC sources remains challenging. A promising approach involves understanding how indoor plants respond to environmental conditions. Plants produce terpenes, a type of VOC, when exposed to abiotic and biotic stressors - including pathogens, predators, light, and temperature - offering a novel pathway for monitoring indoor air quality. While prior work often relies on specialized laboratory sensors, our research leverages readily available commercial sensors to detect and classify plant emitted VOCs that signify changes in indoor conditions. We quantified the sensitivity of these sensors by measuring 16 terpenes in controlled experiments, then identified and tested the most promising terpenes in realistic environments. We also examined physics based models to map VOC responses but found them lacking for real world complexity. Consequently, we trained machine learning models to classify terpenes using commercial sensors and identified optimal sensor placement. To validate this approach, we analyzed emissions from a living basil plant, successfully detecting terpene output. Our findings establish a foundation for overcoming challenges in plant VOC detection, paving the way for advanced plant based sensors to enhance indoor environmental quality in future smart buildings.
Nicholas Bachand, Hesam Salehipour, Catherine Gorle
The urban canopy affects wind in complex ways, making it challenging to predict wind-driven natural ventilation and cooling in buildings. Using large eddy simulations of coupled outdoor and indoor airflow, we study how the surrounding urban canopy and wind angle influence ventilation rates through four ventilation configurations: cross, corner, dual-room, and single-sided. Flow visualizations demonstrate how both large-scale flow patterns and local interference effects can influence ventilation rates by 50-85%. In general, lower density canopies give higher ventilation rates and wind angles that align with a direct path between two openings also lead to higher ventilation rates. However, interference effects from surrounding buildings can significantly change the local wind speed and direction, thus also changing ventilation rates. The magnitude of these interference effects depends on both the wind angle and surrounding building geometries. The effect of wind angle is less pronounced in a higher density canopy, where the urban canopy geometry more strongly guides the flow. The results demonstrate that the canopy's effect on ventilation rates is much more complex than those suggested by existing natural ventilation parameterizations.
Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and air pollution data from three major cities in Norway. Findings from feature selection reveal a correlation between rising heating degree days and heightened air pollution levels, suggesting increased heating activities in Norway are a contributing factor to worsening air quality. PBDL demonstrates superior accuracy in air pollution predictions compared to LSTM. This paper contributes to the growing literature on PBDL methods for more accurate air pollution predictions using environmental variables, aiding policymakers in formulating effective data-driven climate policies.
We present and analyse observational data from a highly instrumented classroom computer laboratory and develop a multizone model to describe its mechanical ventilation and mixing regime. The laboratory houses 70 workstations that are used heterogeneously in time and space, in a manner similar to a generic office environment. Our model predicts CO2 concentration in the laboratory, accounting for air exchange between the occupied classroom and its ceiling plenum and by parametrising irreversible mixing in each zone. Applying the model to our measurements helps identify critical components in the ventilation network, as highlighted by a strong separation of the time scales characterising the flow response. On the one hand, this time scale separation leads to a simplified model describing the CO2 transport. On the other hand, it suggests that the forced exchange of volume between the room and the plenum is 'overdriven' in that reduced energy operation could be achieved without compromising air quality. More generally, our modelling approach offers a systematic method to enhance energy efficient ventilation of multi-zone systems.
Carolanne V. M. Vouriot, Maarten van Reeuwijk, Henry C. Burridge
Indoor air quality in schools and classrooms is paramount for the health and well-being of pupils and staff. CO2 monitors offer a cost-effective way to assess and manage ventilation provision. However, often only a single point measurement is available which might not be representative of the CO2 distribution within the room. A relatively generic UK classroom in wintertime is simulated using CFD. The natural ventilation provision is driven by buoyancy through high- and low-level openings in both an opposite-ended or single-ended configuration, in which only the horizontal location of the high-level vent is modified. CO2 is modelled as a passive scalar and is shown not to be `well-mixed' within the space. Perhaps surprisingly, the single-ended configuration leads to a `more efficient' ventilation, with lower average CO2 concentration. Measurements taken near the walls, often the location of CO2 monitors, are compared with those made throughout the classroom and found to be more representative of the ventilation rate if made above the breathing zone. These findings are robust with respect to ventilation flow rates and to the flow patterns observed, which were tested by varying the effective vent areas and the ratio of the vent areas.
Niima Es-sakali, Moha Cherkaoui, Mohamed Oualid Mghazli
et al.
Predictive maintenance is a preventive maintenance approach that is performed based on an online health assessment and allows for timely pre-failure interventions. It can diminish the cost of maintenance by reducing the frequency of maintenance as much as possible to avoid unplanned reactive maintenance, without incurring the costs associated with too frequent preventive maintenance. The main objective of predictive maintenance of heating, ventilation, and air conditioning (HVAC) systems is to predict when the HVAC equipment failure may occur. The benefits are numerous: planning of maintenance before the failure occurs, reduction of maintenance costs, and increased reliability. For this, the predictive maintenance of the HVAC systems is based on the historical data of the system for predicting the state of health of the system. The process of predictive maintenance application is composed of the Internet of Things (IoT) sensors that are installed inside the HVAC system, then the IoT platforms that help in collecting the signals coming from the sensors and converting them to existing databases. Afterward, the algorithms of application of predictive maintenance could be either knowledge-based approaches, physics-based approaches, or even data-driven-based approaches. A systematic literature review on the existing algorithms of HVAC predictive maintenance application is conducted in this paper to summarize the most used approach for predicting future failures in HVAC systems and to explain the benefits and limits of these algorithms.
Background: While increasing the ventilation rate is an important measure to remove inhalable virus-laden respiratory particles and lower the risk of infection, direct validation in schools with population-based studies is far from definitive. Methods: We investigated the strength of association between ventilation and SARS-CoV-2 transmission reported among the students of Italy's Marche region in more than 10,000 classrooms, of which 316 were equipped with mechanical ventilation. We used ordinary and logistic regression models to explore the relative risk associated with the exposure of students in classrooms. Findings: For classrooms equipped with mechanical ventilation systems, the relative risk of infection decreased with the increase in ventilation: ventilation ranging from 10 to 14 L s-1 student-1 reduced the likelihood of infection for students by 80% compared with a classroom with only natural ventilation. From the regression analysis, as confirmed by the predictive theoretical approach, we obtained a relative risk reduction in the range 12%-15% for each additional unit of ventilation rate per person. Interpretation: We need high ventilation rates (> 10 L s$^{-1}$ student$^{-1}$) to protect students in classrooms from airborne transmission; this is higher than the rate needed to ensure indoor air quality. The excellent agreement between the results from the retrospective cohort study and the outcomes of the predictive theoretical approach makes it possible to assess the risk of airborne transmission for any indoor environment.
Natural ventilation can play an important role towards preventing the spread of airborne diseases in indoor environments. However, quantifying natural ventilation flow rates is a challenging task due to significant variability in the boundary conditions that drive the flow. In the current study, we propose and validate an efficient strategy for using computational fluid dynamics (CFD) to assess natural ventilation flow rates under variable conditions, considering the test case of a single-room home in a dense urban slum. The method characterizes the dimensionless ventilation rate as a function of the dimensionless ventilation Richardson number and the wind direction. First, the high-fidelity large-eddy simulation predictions are validated against full-scale ventilation rate measurements. Next, simulations with identical Richardson numbers, but varying dimensional wind speeds and temperatures, are compared to verify the proposed similarity relationship. Last, the functional form of the similarity relationship is determined based on 32 LES. Validation of the surrogate model against full-scale measurements demonstrates that the proposed strategy can efficiently inform accurate building-specific similarity relationships for natural ventilation flow rates in complex urban environments.
Abstract Despite the advantages of building enclosure systems for improving ventilation efficiency and reducing energy consumption, there is a large difference in performance from the central heating, ventilating, and air-conditioning (HVAC) system. This study investigated the performance of a smart window-integrated ventilation (SWV) system in conjunction with a centralized HVAC system for enhancing indoor air quality (IAQ) and thermal comfort in a typical single-occupancy office. To evaluate the performance of two coupled systems, IAQ and thermal comfort in an office were analyzed for satisfying seasonally optimal ventilation scenarios using computational fluid dynamics simulations. A set of scenarios for case study was determined by outdoor temperature, supply airflow rate and temperature, and operation strategy of the two systems. Numerical results showed that using the SWV system as an aid to the HVAC system was more effective in winter than in summer for improving IAQ and thermal comfort. Only in the summer, the SWV led to poor IAQ. In spring and autumn, there were scenarios in which the SWV system performed similar to the HVAC system. Therefore, depending on indoor and outdoor conditions, the SWV system can be a good choice as an auxiliary means of the HVAC system or for single operation.
Francesco Barreca, Pasquale Praticò, Giuseppe Davide Cardinali
In 2018, the food, beverages, and tobacco sectors within the EU-27 consumed approximately 27,500 ktoe of energy. The food facilities and the food production plants are responsible for a large part of this energy consumption. Current global strategies focus on energy conservation and natural environmental protection, ascribing a lot of importance to building-related analyses. Areas for food storage are essential within the food production chain, as the indoor thermal parameters determine the characteristics of the final products. In this paper, a low-energy storage container is proposed. The envelope of the container is made from sandwich panels with a polyurethane layer paired with two phase change material (PCM) layers. The container is designed to store perishable materials, such as extra virgin olive oil. A storage container prototype, equipped with a mini-split heating, ventilation, and air conditioning electric system, was built to analyse and assess the energy spent during its use. Moreover, the achievable yearly energy savings with respect to a container without the PCM layers was calculated. The results showed that the PCM layers improve the energy performance of the container at an indoor temperature of 20°C with an energy saving of about 27%, and at an indoor temperature of 17°C with an energy saving of over 22%.
Geothermal energy is widely used in building heating owing to its stability, large reserves, and wide distribution. Beginning from the classification of geothermal energy heating technology, this paper elaborates on the basic concepts, development history, and application status of shallow ground source heat pump technology, hydrothermal heating technology, and medium-deep borehole heat exchanger heating technology. Based on the reported research, future directions for investigation of geothermal energy heating technology are summarized, from the perspective of the operation mechanism and application practice. These future research directions mainly include heat balance analysis of large-scale shallow borehole heat exchanger arrays, high-efficiency water recharge technology of hydrothermal heating, and evaluation of the heat transfer performance of medium and deep borehole heat exchanger arrays.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Pau Ferrer-Cid, Julio Garcia-Calvete, Aina Main-Nadal
et al.
The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.
This work presents the derivation of a model for the heating process of the air of a glass dome, where an indoor swimming pool is located in the bottom of the dome. The problem can be reduced from a three dimensional to a two dimensional one. The main goal is the formulation of a proper optimization problem for computing the optimal heating of the air after a given time. For that, the model of the heating process as a partial differential equation is formulated as well as the optimization problem subject to the time-dependent partial differential equation. This yields the optimal heating of the air under the glass dome such that the desired temperature distribution is attained after a given time. The discrete formulation of the optimization problem and a proper numerical method for it, the projected gradient method, are discussed. Finally, numerical experiments are presented which show the practical performance of the optimal control problem and its numerical solution method discussed.
Heating and ventilation air conditioning systems in hospitals (cleanroom HVAC systems) are used to control the transmission/spreading of airborne diseases such as COVID-19. Air exiting from these systems may contribute to the spreading of coronavirus droplets outside of hospitals. Some research studies indicate that the shortest time of survival of SARS-CoV-2 in aerosol form (as droplets in the air) is four hours and the virus becomes inactive above 60 °C air temperature. Therefore, SARS-CoV-2 droplets cannot exit from the exhaust duct if the temperature is above 60 °C. At the condenser, heat is dissipated in the form of hot air which could be utilized to warm the exhaust air. The objective of this paper is to establish a novel technique for eliminating SARS-CoV-2 from cleanroom HVAC systems using the recovered heat of exhaust air. This can eliminate SARS-CoV-2 and reduce the greenhouse effect.
S. Cosman, Iulia Văscan, Cristina-Adina Bilațiu
et al.
This paper deals with design and analysis of a heating, ventilation and air conditioning system used in electric vehicles. The study was developed in two parts. In the first part, were performed measurements of a heating, ventilation and air conditioning system from a battery electric vehicle. The second part of the study consists in the analysis of this system, using Amesim software. Also, the results of the Amesim study were compared with the measurements obtained in the first part of the study. Following the comparison, the performances of Amesim software and its ability to simulate the entire heating, ventilation and air conditioning system, both for electric or conventional vehicles, were highlighted. In fact, the performance of a heating, ventilation and air conditioning system is closely related to the performance of the compressor and the electric machine that drives it.