Indoor Environment Of Buildings Research Articles

Photo‐ and Thermo‐Responsive Switchable Smart Windows Developed by the Control of 3D Molecular Orientation of Chiral Liquid Crystals with Azobenzene Molecular Engineering

AbstractThis study introduces a passive smart window whose state can be adjusted based on external stimuli, such as light and heat, by controlling the 3D molecular orientation of chiral liquid crystals (LCs). The 3D molecular orientation of chiral LCs is automatically controlled by azobenzene‐based molecular engineering of the surface (AZ‐COOH) and bulk molecular alignment (B1AZ) in the host LC (HLC). The resulting chiral LC (3D‐ALC) smart windows are transparent under ambient conditions because of the vertically aligned HLC in the chiral smectic A (SmA*) monodomain. When exposed to UV light at room temperature, the 3D‐ALC smart windows become opaque. This change occurs because the photoisomerization of the azobenzene‐based alignment molecules transforms the SmA* monodomains into randomly dispersed multidomains. Above 35 °C (TNI of HLC), the HLC phase transforms into a focal conic state, resulting in an opaque state, regardless of UV light irradiation. The newly developed photo‐ and thermo‐responsive smart window offers a new energy‐saving window for maintaining comfortable indoor environments in automobiles and buildings.

Study of the Optimal Control of the Central Air Conditioning Cooling Water System for a Deep Subway Station in Chongqing

Cooling water, a crucial component of the central air conditioning setup, exerts a relatively minor direct impact on the thermal comfort of building indoor environments while it has a great effect on the system’s energy efficiency. Numerous studies exist on the cooling water system, particularly focusing on the process by which the cooling tower system operates, but the linkage between the chiller and the cooling tower is typically overlooked. When the connection is long and the passage environment for the pipeline is not conventional, it cannot be neglected for the optimal control for system efficiency improvement and energy consumption reductions. Throughout this research, a control strategy of the cooling water system for deep subway stations with long pipelines is presented. This cooling system was connected with outdoor cooling towers through a corridor about one hundred meters long. In this process, the cooling water temperature is influenced by the corridor’s thermal environment. For this study, an online control strategy optimizes the cooling water temperature, and a simulation platform of the air conditioning cooling water system of the deep subway station was also developed to evaluate the energy-saving potential of the control strategy of this cooling water system. Atop this platform, a simplified heat transfer model of the pipe corridor was created to determine the cooling capacity provided by the cooling water pipe in the corridor. The outcomes suggest that, as opposed to the conventional control mode, the energy-saving ratio of the optimal control strategy during a typical day may reach 4.1%, and the cooling source system’s Coefficient of Performance (COP) might see an increase of about 4.2%. The energy consumption of the water system throughout the whole cooling season may decrease by 9778 kWh, and the energy-saving rate is 4.1%. The results also demonstrate that the cooling water pipes release heat to the air in the corridor most of the time, and the released heat is larger than the absorbed heat. The maximum heat dissipation to the air in the corridor from the cooling water supply and return pipe can be up to 24.3 kW. The cooling effect of the corridor of subway stations with large depths below the ground surface cannot be ignored when optimal control is considered for the cooling water system.

Study of the internal environment quality monitoring system for a laboratory model of a mining separator at key sensitive points of operation and process control using artificial intelligence

Modeling the quality of the indoor environment in buildings using neural networks, as an element supporting automatic process control, has become extremely popular nowadays. By analogy, attempts are being made to use the experience gained in construction and implement it in industry. The publication proposes a method of modeling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of the given quality criterion. This network was implemented in the control system of the mining separator operation as part of pilot studies. The research included testing a laboratory model of the separator placed in a sea container modified for the separator function, in which modern automation technologies and monitoring of environmental parameters were integrated. Among others, time, outside temperature, set temperature, temperature error and controller output were measured. The measurements were taken at the points of installation of devices sensitive to the working environment - controllers, I/O modules, X-ray (XRT-DE) and optical analysis (VIS-NIR), enabling precise examination of the composition and quality of mineral resources. The internal environmental conditions in the housings of the above-mentioned sensitive elements and in the server room were the basis for the analysis. The aim was to develop a performance model enabling effective improvement of the working environment of all electrical and mechanical devices affecting energy efficiency and the internal environment. Separators operate in a very diverse environment, such as: tropical forests, Canadian Tundra, or desert areas in Africa, as well as EU countries, the USA and Australia. These devices are used in both open pit and underground mines. The use of modern technologies and mobile solutions in the mining industry contributes to increased efficiency, operational safety and, consequently, minimizing the negative impact on the environment. The research results confirmed that precise monitoring and control to ensure environmental conditions at selected separator points is crucial to ensuring the continuity and quality of the separation process.

All the Way There and Back: Inertial-Based, Phone-in-Pocket Indoor Wayfinding and Backtracking Apps for Blind Travelers.

We describe two iOS apps designed to support blind travelers navigating in indoor building environments. The Wayfinding app provides guidance to a blind user while following a certain route. The Backtracking app records the route taken by the walker towards a certain destination, then provides guidance while re-tracing the same trajectory in the opposite direction. Our apps only use the inertial and magnetic sensors of the smartphone, and thus require no infrastructure modification (e.g., installation and support of BLE beacons). Unlike systems that use the phone's camera, users of our apps can conveniently keep their phone tucked inside their pocket, while interacting with the apps using a smartwatch. Routing directions are given via speech. Both apps were tested in a user study with seven blind participants who used them while navigating a campus building. Participants were able to successfully use the Wayfinding app to complete the prescribed paths (3 paths each), although the app had to be restarted for the first three participants in one path due to incorrect step length measurements (the app was later modified to track the users' step length). The Backtracking app worked well in most cases, although in 6 trials (out of 21) the app lost track of the participant's location.

Evaluation on effects of air inflow on indoor environment in an air-supported membrane coal-shed building

Evaluation on effects of air inflow on indoor environment in an air-supported membrane coal-shed building

Effects of thermal histories on human thermal adaptation to district heating environments: A cross-sectional investigation in the severe cold zone, China

Understanding personalized thermal comfort is critical for creating comfortable indoor environments and achieving energy efficiency. Thermal history has a significant impact on human thermal adaptation, but it has received insufficient attention. Combining field investigation and statistical analysis, this study introduces the thermal neutral rate (TNR) and thermal comfort and acceptability rate (TCAR) to analyze the differences in thermal and humidity responses and adaptability among groups with different thermal histories in northern China. Results show that as subjects acclimatize to indoor environments, their thermal neutral temperature rises, and humid sensation votes approach 'neutral' conditions. Participants with no prior northern thermal history require up to 1 year to adapt to district heating environments, while adaptation to humid environments may take 2–3 years. The thermal neutral temperature of the un-adapted subjects was 18.0 °C, and post-adaptation, it varied within the range of 20.2–21.4 °C. For engineers designing indoor environments, maintaining temperatures within the range of 19.1–22.3 °C meets the comfort and acceptability requirements of participants with different thermal histories in the office. These findings provide evidence for understanding human thermal adaptation mechanisms and give recommended heating temperature ranges for comfortable indoor environments in office buildings in the severe cold zone.

Multi-point temperature or humidity prediction for office building indoor environment based on CGC-BiLSTM deep neural network

The aim of this study is to predict the temperature or humidity changes at multiple relevant points in a building using a deep neural network architecture with multi-task learning to provide more reference information for the design and optimal operation of heating and ventilation systems. For this purpose, traditional multi-task prediction algorithm architecture is combined with Customized Gate Control and other neural networks to build deep neural network architectures for indoor environments with multi-point temperature or humidity prediction tasks. To test the prediction effectiveness of the architecture, a task of predicting temperature or humidity 24 h in advance was designed on a real office building indoor environment dataset, and the prediction results were compared with other single-task and multi-task prediction models. Two experimental conditions were designed for this study, one using the complete training set and the other reducing the training set at a certain point. Through the final prediction results, it is found that the multi-task prediction architecture used in this paper shows better or nearly optimal results compared to other prediction models under both working conditions. This study provides some reference value for the application of multi-task prediction algorithms to the task of predicting indoor environments in buildings.

An efficient thermal comfort prediction method for indoor airflow environment using a CFD-based deep learning model

Thermal comfort in indoor environments significantly affects human health and productivity, while there remains room for improvement in enhancing thermal comfort around individuals. This study proposed an efficient thermal comfort prediction method based on the Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model to rapidly and accurately assess indoor thermal comfort. As demonstrated with a high-speed train, the computational fluid dynamics (CFD) technology is combined to establish the dataset. Five design parameters (the ratio and angle of the upper inlets, supply air temperature and humidity, and external temperature) and four evaluation indices (air velocity, air temperature, Predicted Mean Vote, and Draft Rate) are considered in assessing the accuracy of the method on the test dataset. The results indicate that CNN-LSTM achieves consistent and accurate predictive performance, with average mean absolute error (MAE) close to 0.01 m/s, 0.2 °C, 0.1, and 1.0. On the generalization test set, the predictive performance of CNN-LSTM decreases slightly, but the average of the determination coefficients (R2) still approaches 0.89. The thermal comfort prediction method developed in this study demonstrates significant advantages in predictive performance, showing great potential for application in the construction of healthy and comfortable indoor environments in buildings, aircraft, subways, etc.

Research on the regulation of emotions in elderly people through bedroom audio-visual environments

The indoor environment of buildings has a long-term impact on the psychological and physiological state of people, and a good indoor environment can help the physical and mental health of the elderly. As the mobility of the elderly weakens, the indoor visual and auditory environments are important channels for them to acquire external perceptions. In order to investigate the effects of visual and auditory environments on the psychological (positive and negative emotions) and physiological (heart rate) states of older adults in their bedrooms, the study designed an audio-visual interaction experiment, selecting three common sounds preferred by older adults and six visual scenes that included two types of visual factors , to measure the emotions and heart rate changes under different sound and visual interaction conditions. The study revealed that natural sounds were more effective in positively modulating mood in warm-toned rooms, while artificial sounds were more appropriate for cool-toned rooms. Regardless of the type of therapeutic sound environment, the addition of greenery significantly contributes to mood regulation in older adults. Combining sound environments with visual factors can be an effective design strategy to promote the physical and mental health of occupants.

Indoor environment and user perceptions in offices in Greenland compared to Denmark

Most of the Greenlandic building stock comprises residential houses and apartments. Consequently, the indoor environment in Greenlandic office buildings has not been subject to wide-ranging investigations. The purpose of this study was to investigate the indoor environment in office buildings in Greenland and to characterise the buildings, their indoor environment, and occupant well-being, health and self-assessed performance. This was done through a post occupancy evaluation (POE), where 22 office buildings, including 70 offices in Nuuk and Sisimiut, were evaluated based on indoor environment measurements and questionnaires of occupants' perceived indoor environment. This was compared with data and results from a related study in office buildings in Denmark (26 office buildings, including 83 offices) collected in the same period. The Danish office buildings performed slightly better than the Greenlandic office buildings regarding the perceived indoor environment. The measured indoor environment differed in performance between the countries when benchmarking towards the requirements and guidelines of the Danish Working Environment Authority (DWEA). It was concluded that the office buildings in Greenland did not differ much from the office buildings in Denmark regarding the measured and perceived indoor environment.

Optimising indoor environmental quality: Development of a benchmarking tool for valuing performance in office buildings

A novel benchmarking tool, the Building Office Benchmark (BOB) tool, has been developed to evaluate the economic benefit of increased work performance by upgrading the indoor environmental quality (IEQ) in office buildings. Utilising post occupancy evaluations in Danish and Greenlandic office buildings (26 buildings in Denmark and 22 buildings in Greenland comprising a total of 153 offices), a database comprising registration of building properties, measurements of IEQ parameters and occupant responses has been established. This may be used not only to benchmark the indoor environment in office buildings but also to identify suboptimal conditions and, based on this, quantify the economic benefits of indoor environment upgrades.Informed by literature review findings, the tool's IEQ domains and weightings of different IEQ domains and parameters align with existing certification schemes. The tool may be used to outline the economic advantages of IEQ improvements, presented straightforwardly to building owners and tenants. Overall, the tool presents a framework for assessing and improving IEQ in office environments.

Multi-sensor fault detection and correction for automated IAQ monitoring in smart buildings through attention-aware autoencoders with spatial prediction module

The integration of interconnected monitoring and control devices has significantly enhanced the management of indoor building environments, notably in underground subway stations. In these sophisticated settings, the accuracy of monitoring systems is critical, as data-driven control systems rely extensively on sensor feedback. However, sensor faults arising from hardware damage, wear, and cyberattacks compromise the reliability of these complex systems. Despite extensive efforts, existing methods still face challenges in accurately detecting and reconstructing faults, particularly when dealing with multiple sensor failures. This study introduces a hybrid sensor validation framework that combines a multi-head attention-based denoising autoencoder (AE) with a deep learning prediction module. The performance assessment of the proposed gated recurrent unit (GRU) integrated attention-based fault detection, and reconstruction network (GADRN) shows a significant 33 % and 21 % improvement in critical success index compared to Independent Component Analysis and GRU-AE frameworks. GADRN consistently identified faulty sensors across various fault periods, achieving the lowest false alarm rate of 11 % against several conventional and machine learning-based models. A ventilation control analysis highlights the significance of incorporating a sensor validation framework in modern buildings. The outputs by the GADRN led to a 22.5 % decrease in energy consumption by preventing unnecessary resource use attributed to sensor faults.

Using Indoor and Outdoor Measurements to Understand Building Protectiveness against Wildfire, Atmospheric Inversion, and Firework PM2.5 Pollution Events

The world has seen an increase in the frequency and severity of elevated outdoor pollution events exacerbated by the rise in distant polluting events (i.e., wildfires). We examined the intersection between indoor and outdoor air quality in an urban area using research-grade sensors to explore PM2.5 infiltration across a variety of pollution events by testing two separate indoor environments within the same building. We confirmed prior work suggesting that indoor environments in buildings are most protective during wintertime inversion events and less so during fireworks and wildfire events. The building indoor environment protectiveness varies notably during different pollution episodes, especially those that have traveled longer distances (e.g., wildfires), and we found evidence of varied infiltration rates across PM2.5 types. Inversion events have the lowest infiltration rates (13–22%), followed by fireworks (53–58%), and wildfires have the highest infiltration rates (62–70%), with distant wildfire events persisting longer and, therefore, infiltrating for greater durations than local-wildfire-related particle matter. The differences in PM infiltration rates were likely due to the combined effects of several factors, including varying particle size, concentration, and chemistry. Subsequently, the local wildfires had different temporal air quality impacts than distant wildfire pollution in this case. Based on these findings, indoor air quality appears more conducive to protective action and policies than outdoor air quality because the built environment may serve to shield individuals from outdoor air.

Measurement of the indoor environment and heating energy consumption of a passive office building in severely cold region, China

Passive ultra-low energy buildings represent an effective strategy for energy conservation and emission reduction within the global building industry. The prolonged and cold winters in severely cold regions of China necessitate substantial heating energy consumption. The study utilized a combination of overall surveys and long-term tracking surveys to evaluate the indoor environment and energy consumption in a passive office building situated in severely cold region under different heating modes over 2 years. The results show that the average temperature and indoor air quality (IAQ) can meet the standards at most moments. However, the average relative humidity tends to fall below the specified lower limit. Nevertheless, the Predicted Mean Vote (PMV) index suggests that the indoor environment provides a comfortable thermal experience for humans. The heat pump air conditioning (HPAC) system operation revealed that when the outdoor air temperature fell below −7°C, the coefficient of performance (COP) of the air source heat pump units would deteriorate. Implementing intermittent heating during the second year can reduce heating energy consumption by 13.4 kWh/(m2·a), resulting in 38.9% energy savings. These findings will serve as a valuable reference for the design and operation of heating systems in passive buildings in severely cold regions.

Assessment of thermal performance of energy-active window systems in hot climates

Window systems, particularly in hot climates, are the source of considerable heat transfer that affects the buildings’ indoor environment and energy consumption. Proper design and optimization of its thermal performance significantly contribute to the overall envelope energy performance. Using a multi-layer glazing system, Energy Active Window (EAW) adapts window insulation technology to reuse low-grade air from the HVAC system, keeping the temperature at the internal surface of the window close to the indoor air temperature, minimizing heat exchange between indoor and outdoor. This study aims to reduce energy consumption by optimizing EAW to increase its thermal resistance by channeling the return air (RA) from the HVAC system into the curtain frame, thereby lowering the temperature of the air gap and air film layer. The study examined the window system’s exhaust/return air behavior using the Computational Fluid Dynamics (CFD) tool (Fluent) to simulate the heat exchange between outdoor and indoor building environments. The CFD simulation was validated using experimental measurements. Results show a surface temperature decrease of 4 to 7 °C based on the inner and outer pane airflow conditions when the outdoor temperature is 45 °C with an 8 % margin of error. Various EAW components were tested, including air velocity, air–gap width, volume, and outdoor temperatures, examining both triple and double-glazing layers.

Analytical model and comprehensive index construction of indoor air purification effect based on dynamic characteristics and energy efficiency

Meeting environmental regulation requirements while improving energy usage efficiency are crucial endeavors to achieve decarbonization and indoor environmental regulation. In this study, experimental testing, data analysis, and modeling are performed for the dynamic purification process of indoor PM2.5 (fine particulate matter) in the context of regulating the indoor environment of buildings. The results show that indoor PM2.5 levels are dynamically matched to the outdoor environment state and the requirements of environmental control. An analytical model of the effect of PM2.5 purification is constructed, which can accurately reflect the dynamic changes in the PM2.5 mass concentration during the indoor air purification process considering the outdoor PM2.5 concentration and the purification efficiency of the purification filters and purification systems, thus supporting the prediction of purification outcomes and accurately constraining the actual indoor air quality purification process. Meanwhile, an indoor air quality control index (IAQCI) is established based on the purification effect and purification energy efficiency under various combinations of air filters and purification systems. This approach is used to quantitatively assess the dual control of energy efficiency and environmental regulation, as well as the purification effect and efficiency of indoor purification schemes based on outdoor dynamic PM2.5 conditions.This study provides a set of innovative and feasible research methods for realizing comprehensive indoor environmental performance evaluations of buildings.

An experimental methodology for the calibration of indoor building environment models using thermal point clouds and CFD simulation

ABSTRACT This paper presents an experimental methodology employing thermal point clouds (TPCs) of indoor spaces to develop and calibrate Computational Fluid Dynamics (CFD) models for simulating indoor building environments. TPCs, captured at various time intervals, provide geometry and surface temperatures of the space envelope are crucial for initial and final contrast parameters in model calibration. Segments from TPCs of interior surfaces serve as ground truth for calibrating theoretical CFD models. This involves adjusting boundary conditions to approximate real surface temperature distributions on selected walls. The innovative calibration approach incorporates automatic wall segmentation processes, enabling comparison between in-situ measured and simulated values. A case study assesses the similarity between real thermal orthoimages and CFD simulations, employing qualitative and quantitative analysis with a convergence criterion. Results validate the methodology, highlighting the need for further automation of manual processes.

Numerical-experimental study of the thermal behavior of a green facade in a warm climate in Mexico

Demographic growth entails an increase in the construction of buildings and streets, whose materials significantly absorb heat and, upon release, create discomfort for the inhabitants of large urban areas. Vegetation emerges as a crucial element to enhance both the indoor environment of buildings and the outdoor surroundings. Green facades present themselves as a highly efficient alternative to integrate vegetation in densely urbanized areas. This study focuses on evaluating the thermal performance of an indirect-type green facade system in a warm climate in Mexico. The research encompasses experimental measurements and numerical simulations of the thermal effect of the green facade and also a parametric study of the vertical leaf area index (LAIV) and air gap size. In the experimental test, temperatures were recorded in two identical test cells, one of which was equipped with a green facade with Pyrostegia Venusta plants on one of its surfaces. The numerical study validated the results obtained through experimental tests, using simulations of Computational Fluid Dynamics (CFD) considering a typical day for each season throughout the year. Validation results revealed Mean Percentage Errors (MPE) of −2.86 % and 0.25 %, Mean Bias Errors (MBE) of −0.49 °C and 0.08 °C, and Root Mean Square Errors (RMSE) of 1.34 °C and 1.29 °C for the bare facade and the green facade, respectively. In the numerical analysis, it was observed that the green facade succeeded in reducing the indoor air temperature in a range of 4.57 to 5.64 °C, while decreasing the heat flux in an interval of 7.84 to 16.79 W/m2. Moreover, a parametric study revealed that the LAIV ceases to be a significant parameter beyond 2.5, while the air gap size proves to be a considerably less influential factor compared to the LAIV. These results confirm the relevance of using vegetation in buildings to mitigate the effects of heat and improve the thermal comfort of inhabitants.

Optimizing a courtyard microclimate with adaptable shading and evaporative cooling in a hot mediterranean climate

While in recent years significant improvements have been made in the thermal adaptation of indoor building environments, less progress has been made in improving thermal comfort in transitional spaces next to buildings. This makes it imperative to investigate the thermodynamics of such outdoor urban spaces, particularly in warm locations such as southern Spain. The application of suitable passive systems for the regulation of thermal exchanges and the creation of temperate conditions is explored. In order to determine the thermal comfort of users this study focuses on the assessment of two key parameters, mean radiant temperature and relative humidity, and how these are impacted by shading and misting systems in a courtyard. Field measurements analysed the extent to which shading and misting systems enhance user comfort levels in a Seville courtyard. The results indicate a considerable decrease in temperature, with thermal mitigation of up to 11.7 °C below reference points outside the courtyard. The percentage of hours within comfort ranges during heat waves increased by 100% by optimizing the results of an additional experimental campaign and considering results from previous ones. The main novelty of this research lies in the comparative study of standalone and integrated passive cooling strategies aiming to provide optimal results in high temperature and heat wave scenarios. The study findings help understand and estimate the effectiveness of low-cost and easily applicable strategies for maintaining comfort conditions during diurnal and nocturnal cycles in both hot seasons and heat waves.

Evaluation of the Effects of Window Films on the Indoor Environment and Air-Conditioning Electricity Consumption of Buildings

The objective of this study was to evaluate the effects of window films on indoor environmental conditions and electricity consumption of air conditioning. The research focused on the performance of different window films (HAG, RG), taking into account variations from different building orientations. The findings of this research indicated that building orientation could significantly influence the duration of direct sunlight entering the interior, with the areas closer to the glass being more susceptible to the effects of outdoor temperature and solar radiation. The clear glass with heat-absorbing film (HAG) and reflective film (RG) both reduced the indoor temperature and indoor illuminance while increasing indoor comfort. The RG could accumulate less heat on the glass surface compared with the HAG. The glass temperature of the RG will be lower than the HAG. The electricity-saving ratios of the HAG were 1.4%, 1.9%, 1.4%, and 1.2%, respectively, when facing the east, south, west, and northwest orientations compared with the clear glass (OG). The electricity-saving ratios of the RG were 3%, 4.2%, 4.2%, and 10.3%, respectively.