Indoor Airflow Distribution Research Articles

Natural cross-ventilation configurations: Comparison using different evaluation parameters

The purpose of this research is to compare different natural cross-ventilation (NCV) configurations in a generic isolated building using various evaluation parameters. NCV is relevant for indoor air quality (IAQ) and thermal comfort in buildings. Validated computational fluid dynamic (CFD) simulations are employed. The NCV configurations are categorized into axial and corner, and four heights of windows (bottom, middle, top, and upward) were considered. Two kinds of evaluation parameters are employed: those that consider the indoor airflow distribution (IAD) and those that do not. Among the first group, a new parameter is proposed. It is found that the performance of each configuration improves by increasing the wind velocity and wall porosity. The evaluation parameters that consider IAD indicate that corner configurations outperform axial ones, contrary to what is indicated by the evaluation parameters that do not consider IAD. The corner upward configuration produces the highest average velocity and a high-velocity homogeneity in the living zone, which can improve thermal comfort in hot conditions. The bottom one produces the highest average velocity in the living zone for axial configurations. The window height has little influence on IAQ among corner configurations, while the middle one performs better for axial configurations. This research compares these configurations for the first time and reveals that corner configurations outperform axial configurations. The study results emphasize the importance of assessing natural ventilation by employing parameters that consider the IAD.

Convolutional neural networks-based surrogate model for fast computational fluid dynamics simulations of indoor airflow distribution

Computational fluid dynamics (CFD) is a powerful but time-consuming simulation tool for building indoor environment analysis. Artificial intelligence (AI)-based surrogate models, especially artificial neural networks (ANN)-based models which are the dominated ones, have demonstrated a great potential in accelerating CFD simulations. However, the published AI-based models are not good at capturing local spatial features in indoor airflow distribution, leading to poor local simulation accuracy. To overcome this challenge, this study proposes a convolutional neural networks (CNN)-based surrogate model for fast CFD simulations of indoor airflow distribution. Compared with other published AI-based models, this model can capture local spatial features in indoor airflow distribution datasets simulated by CFD, leading to higher accuracy. To enable this model to process room geometry information, a geometry representation strategy is proposed to convert room geometry information into inputs suitable for CNN models. Simulation data of 2000 indoor airflow velocity fields with various boundary conditions are generated using COMSOL Multiphysics. Five cases with various model training strategies are designed based on these simulation data to verify the performance of the CNN-based model by comparing this model with ANN-based and GNN-based models. The results show that the CNN-based model outperforms other models in all cases. The CNN-based and GNN-based models have significantly smaller local simulation errors than the ANN-based model. The simulation accuracy of the CNN-based model is improved by an average of 45.55 % and 32.90 % compared with the ANN-based and GNN-based models, respectively. Moreover, the computational time of the CNN-based model is reduced to about 0.05 % of the computational time of CFD simulations.

Wind tunnel measurements of cross-ventilation flow in a realistic building geometry: Influence of building partitions and wind direction

Wind tunnel measurements have widely been used for validation of computational fluid dynamics simulations of natural ventilation airflows. However, the majority of such measurements employed simple generic single-zone buildings, while there is a lack of studies on realistic buildings including flow-critical geometrical features (e.g. internal partitions). To assess the effect of internal partitions at different incident flow angles (α = 0° and α = 30°), wind tunnel measurements of velocities in and around a cross-ventilated realistic residential building (with and without internal partition) were performed. Measurements were conducted at a geometric scale 1:40, using laser Doppler anemometry. Results indicate a large impact of the internal partition on indoor airflow distribution and resulting ventilation flow rates. For instance, for α = 0°, on the partitioned building side, regions of velocity increase (from ∼0 m/s to ∼80% of the outdoor reference velocity, Uref), but also regions of velocity decrease (from ∼50% of Uref to ∼0 m/s) were observed. The ventilation flow rate through the windows at the partitioned side decreased by 23% and 32%, respectively. For the partitioned building, a change from α = 0° to α = 30° resulted in regions of velocity increase from 0 m/s to ∼60% of Uref.

Evaluation of supervised machine learning regression models for CFD-based surrogate modelling in indoor airflow field reconstruction

Fast and reliable prediction of indoor airflow distribution is critical for indoor environment control. While neural networks (NN), often interchangeably referred to as Back Propagation Neural Networks (BPNNs), are popular for airflow predictions, optimizing these models is challenging due to their ”black box” nature and complex network structures. This study explores alternative robust regression models, including decision-tree-based models (e.g., XGBoost, LightGBM, Random Forest) and Support Vector Regression (SVR), for predicting indoor airflow. Two BPNN structures were initially developed to evaluate feasibility of NN models. BPNN A was trained using airflow velocities from two inlets as input neurons to directly predict the airflow velocity distribution within the domain. BPNN B was trained additionally with spatial information, including space samples and boundary wall data. Higher-dimensional training structures of BPNN B were applied to decision tree-based models and SVR to assess their capability in predicting non-linear airflow patterns. Results indicated that BPNN A achieved the highest accuracy, while the inclusion of higher-dimensional data in BPNN B led to decreased accuracy. Among all decision-tree-based models, XGBoost demonstrated the greatest potential, achieving an R2 above 99.5% and predictive errors below 10%. XGBoost also outperformed both BPNN models in speed, being 15.78 times faster than BPNN A and 252 times faster than BPNN B. The interpretability of XGBoost was further explored by analysing feature importance, which helps identify the most influential input variables while predicting the airflow velocity. This analysis is expected to offer an enhanced understanding of boundary conditions leading to optimised indoor environment strategy.

PM reduction performance according to filter grade and diffuser position in a school classroom

Ventilation performance for indoor air quality (IAQ) in schools has emerged as an important social issue due to the high concentrations of particulate matter (PM). The PM reduction performance is analyzed according to filter grade and the location of return diffusers, upper and floor return, selected based on indoor airflow distribution. Performance evaluation has been experimentally analyzed in the full-scale school environmental performance test-bed equipped with a fan filter unit (FFU). Through the experimental measurements in the classroom, it is found that the PM reduction time of the floor return is relatively shorter than that of the upper return. This is due to the increased ventilation efficiency by decreasing the stagnation flow inside the classroom. Although the supply air flowrate is relatively small at 800 cubic meter per hour (CMH), it has a PM reduction effect similar to 1200 CMH for all particle sizes due to the replacement of the conventional upper return with a floor return. Considering that the energy consumption of the Minimum Efficiency Reporting Value (MERV) 16 filter is approximately 1.7 times that of the MERV13 filter, a MERV13 filter is sufficient to demonstrate the performance of a MERV16 filter with the optimal installation of return diffusers.

Airflow and Pressure Design Review of Modular Negative Pressure Wards

In the aftermath of the COVID-19 pandemic, the urgent need for the rapid deployment of healthcare facilities propelled the rise of modular construction using an infill approach. In these modular, negative-pressure wards, the design of indoor airflow and pressure plays a crucial role in meeting the ventilation strategies required for isolation facilities. Accordingly, this paper focuses on modular negative-pressure wards employing an infill construction method and proposes an appropriate spatial pressure distribution to address the problem of air tightness degradation due to leakage. This study analyzed the indoor airflow and pressure distribution of a unit module corresponding to an infill. It aimed to examine whether the pressure difference with the adjacent room is maintained and to assess its effectiveness in isolating contaminated air. First, the airflow rate of the heating, ventilation, and air conditioning system in the unit module was calculated to ensure that it would meet the performance criteria of the negative-pressure ward. Afterward, based on the calculated rate, the study assessed the airflow and room-specific pressure within a typical floor, encompassing both the unit module and associated nursing support facilities. Here, the airflow in the external corridor of the typical floor was divided into two cases according to the pressure distribution: negative pressure and atmospheric pressure. The calculation results were compared using a computational fluid dynamics tool. The analysis results confirm that the air isolation performance is adequate as the pressure difference between adjacent rooms in the unit module and the typical floor was maintained at 2.5 Pa. Additionally, the indoor airflow in the negative-pressure isolation room formed a stable flow at a slow speed of 0.1–0.2 m/s, minimizing the possibility of air contamination from outside the isolation room. In particular, Case B of the typical floor design proposes a method to optimize the pressure distribution in the modular negative-pressure ward by designing the ventilation flow rate at atmospheric pressure level. Thus, this study emphasizes that atmospheric pressure design is appropriate when designing pressure in areas where negative-pressure control is difficult and can contribute to the design and improvement of similar medical facilities in the future.

Effectiveness of natural ventilation through single-sided window opening in air-conditioning rooms

Since many buildings are not equipped with fresh air systems, many people open windows to allow for natural ventilation while the air conditioner is running. However, prolonged window opening may cause unnecessary energy consumption. Additionally, the diverse ways of window opening lead to significant variations in indoor airflow distribution, consequently impacting ventilation efficiency. Therefore, this study used field experiments and computational fluid dynamics (CFD) to determine the air exchange efficiency and air change rate of single-sided natural ventilation in air-conditioning rooms by fitting the decay curves of tracer gas. CFD simulation was performed using the unsteady Reynolds-averaged Navier-Stokes (RANS) model to examine how window openings affect the room’s air distribution, and the computed indoor CO2 concentration was compared to the corresponding field experiment data. The results show that natural ventilation through the window opening induced constant heat and mass exchange between indoor and outdoor air, which caused extra cooling losses but was globally effective in eliminating indoor contaminants. The air exchange efficiency is influenced to varying degrees by factors such as the air supply register’s position, window opening percentage, and window opening position. The air supply register’s position exerted the most significant impact at 46.45%. Efficient indoor-outdoor air exchange can be achieved by opening windows intermittently. When the window is opened from one side and the opening percentage is 100%, the indoor environment can be improved by opening the window for about 10 min every 39 min for X-direction air supply and about 7 min every 39 min for Y-direction.

Opening windows as a supplementary ventilation strategy for large classrooms in a pandemic situation: Field measurements and simulation

The coronavirus disease 2019 (COVID-19) has led to significant global health emergencies in recent years, prompting widespread recommendations across various regions for opening windows in building codes. However, official guidelines have not adequately addressed the details of window opening arrangements related to the supporting evidence from existing literature. This study investigated the risk of disease outbreaks via respiratory transmission in large university studio classrooms. It examined the effectiveness of window-door opening strategies combined with mechanical ventilation in mitigating the infection risk of respiratory diseases in such spaces. A full-scale school ventilation performance test bed was used to measure indoor airflow and CO2 distribution patterns. Comparisons were made with the numerical simulation of exhaled viral emissions. A modified Wells-Riley equation was employed to calculate inhalation exposure and disease infection risks at the room level. Measurements showed that fresh air ventilation from the mechanical system was relatively low compared to existing standards, especially in pandemic situations. The results show that opening windows can lead to a decrease to low probabilities (below 5 %) of infection risk in 85.02 % area of a classroom over an 8-h period, which demonstrates that window-opening behavior is an effective supplementary strategy for large educational spaces with mechanically ventilated systems during emergencies when active measures are insufficient for diluting diseases. Furthermore, the study reveals that maximizing outdoor air rates by opening all windows may occasionally result in adverse indoor airflow redistribution, increasing virus load from 0.0382 to 0.8926 quanta/m3 near the virus source locations.

An indoor airflow distribution predictor using machine learning for a real-time healthy building monitoring system in the tropics

Indoor air quality is the foundation of a good indoor environment. The COVID-19 pandemic further highlighted the importance of providing real-time airflow distribution information within the Building Environmental Monitoring System (BEMS) to minimize the risk of infectious airborne transmission. This paper discusses the process of developing a predictive model for indoor airflow distribution prediction with indoor and outdoor input parameters using machine learning and its implementation in healthy BEMS for a classroom in the tropical climate region of Yogyakarta, Indonesia. This paper encompassed field measurement and simulation involving outdoor climate conditions and the operational status of the classroom’s windows, Air Conditioning units, and fans. Three machine learning models were constructed using OLS, LASSO, and Ridge methods. Datasets for the modeling were generated from CFD model simulations in IES VE and were assessed for correlation. The mean temperature and velocity differences between the CFD model simulation and measurement results are 0.21°C and 0.083 m/s, respectively. Outdoor climate conditions and the operational status of the classroom’s utilities significantly influence the indoor airflow distribution characteristics. The three models indicate a relatively poor performance, where the classroom had a relatively low sensitivity to input changes. However, the best model performance was achieved using the LASSO method, with average values from post-normalization of [Formula: see text] and Root Mean Square Error (RMSE) of 0.336 and 0.077, respectively. The model was implemented in healthy BEMS on the “Platform for Healthy and Energy Efficient Building Management System.” Practical Application: This research proposed a machine learning model of indoor airflow characteristics of a classroom in Yogyakarta. The proposed model can be adapted to produce monitoring systems that best represent the related conditions. The method can be adopted to develop a relatively simple, low-cost sensor or model to monitor an indoor environment. Future studies may explore the results of the real-world implementation in a case study.

Data-driven prediction of indoor airflow distribution in naturally ventilated residential buildings using combined CFD simulation and machine learning (ML) approach

Predicting indoor airflow distribution in multi-storey residential buildings is essential for designing energy-efficient natural ventilation systems. The indoor environment significantly impacts human health and well-being, considering the substantial time spent indoors and the potential health and safety risks faced daily. To ensure occupants’ thermal comfort and indoor air quality, airflow simulations in the built environment must be efficient and precise. This study proposes a novel approach combining Computational Fluid Dynamics (CFD) simulations with machine learning techniques to predict indoor airflow. Specifically, we investigate the viability of employing a Deep Neural Network (DNN) model for accurately forecasting indoor airflow dispersion. The quantitative results reveal the DNN’s ability to faithfully reproduce indoor airflow patterns and temperature distributions. Furthermore, DNN approaches to investigate indoor airflow in the residential building achieved an 80% reduction in the time required to anticipate testing scenarios compared with CFD simulation, underscoring the potential for efficient indoor airflow prediction. This research underscores the feasibility and effectiveness of a data-driven approach, enabling swift and accurate indoor airflow predictions in naturally ventilated residential buildings. Such predictive models hold significant promise for optimizing indoor air quality, thermal comfort, and energy efficiency, thereby contributing to sustainable building design and operation.

Predicting indoor 3D airflow distribution using artificial neural networks with two different architectures

To meet the thermal comfort requirements of room occupants, a fast and accurate method for predicting indoor high-resolution 3D airflow distribution is necessary, which can be combined with heating, ventilation, and air conditioning (HVAC) systems to adjust the indoor environment. Artificial neural networks (ANN) can establish complex mappings between variables with nonlinear relationships. The aim of this study was to verify the feasibility of an ANN for the fast and accurate prediction of indoor 3D airflow distribution. Considering two prediction strategies, ANN A and B were constructed. The inputs of both ANNs were the air supply velocity, air supply direction, and section location. ANN A output the airflow distribution of the entire room directly, whereas ANN B output the airflow distribution of the characteristic sections first and then the airflow distribution of other sections according to the contribution of the characteristic sections. The two ANNs’ performances on training and testing datasets were compared. Both ANNs performed well in the training and interpolation cases. However, their performances decreased rapidly in the extrapolation cases. In the case of a large gap between the input and output dimensions of the ANNs, the two-step prediction strategy improved the prediction accuracy, accelerated convergence, and alleviated data imbalance problems. The prediction time of ANN A and B was 0.89 s and 12.38 s, respectively. The ANN can predict indoor high-resolution 3D airflow distribution quickly and accurately, and can add functions that include identifying internal heat sources and air supply inlet position changes. ANN combined with HVAC could allow real-time control to regulate indoor airflow.

Applying Natural Ventilation for Commercial Buildings With Atrium: Indoor Environment Prediction and Outdoor Pollutant Impact

Abstract The use of natural ventilation for commercial buildings becomes ever attractive due to the potential for economic savings and increased occupant satisfaction. However, it has proven to be particularly challenging to predict the indoor air temperature and airflow distribution from natural ventilation in more complex building geometries such as those with an atrium. This study used the energy-simulation-coupled computational fluid dynamics (CFD) method to predict the indoor temperatures of a typical multi-story, open-floorplan office building with a central atrium. The prediction accuracy using CFD was slightly improved for the periods with extreme outdoor conditions, where large temperature disparities often occur between simulation and experiment. For the tested cases, adjustment of window opening sizes seems to have marginal impacts on the simulation results. This paper further explores the impacts of outdoor gas-phase pollutants on indoor air quality of such a naturally ventilated commercial building with an atrium. A few architectural features such as window blockers and double skin façade (DSF) designs were numerically investigated for their performance to lower the indoor pollution levels while still maintaining adequate building ventilation rates. The results reveal that the features affecting the wind patterns around and above the building have a strong influence on the contamination rates on each floor of the building. DSF can not only reduce indoor pollution levels but also reduce the ventilation rate. When a pollutant source is not close to the building, a conventional central atrium design is preferred for better ventilation rates.

A Study on Establishing Thermal Output Conditions of Radiant Ceiling Heating Panels for Improving Thermal Comfort of Perimeter Zone in Buildings

Amid concerns over airflow-induced transmission of the COVID-19 virus in buildings frequented by large numbers of people, such as offices, the necessity for radiant ceiling heating panels has increased. This is due to the concern that the airflows emitted from the convection heating systems installed near the ceiling or windows for winter heating may be a major cause of COVID-19 transmission. In this study, we aim to evaluate thermal comfort under various indoor and outdoor environmental conditions of a building and present the thermal output conditions of the radiant ceiling heating panel that can replace the convection heating system while ensuring comfort in the perimeter zone and handling the heating load. As a result, we were able to present, in a chart format, the thermal output conditions that can secure thermal comfort by analyzing the indoor airflow distribution depending on the surface temperature of the radiant ceiling heating panel, the interior surface temperature of the window, and the influence of internal heat generation. Moreover, through derived empirical formulas, we were able to determine the heating conditions of the panel that can secure the necessary heat dissipation while minimizing discomfort, such as downdrafts, even for indoor and outdoor conditions that were not evaluated in this study.

Enhancing IAQ, thermal comfort, and energy efficiency through an adaptive multi-objective particle swarm optimizer-grey wolf optimization algorithm for smart environmental control

People in modern societies spend most of their time in buildings, and a considerable amount of energy is used to maintain indoor air quality (IAQ) and thermal comfort levels to ensure people's health and productivity. Thus, to ensure the energy efficiency of buildings, smart environmental control must be implemented through prediction and optimization based on artificial intelligence (AI) algorithms. However, rapid and accurate environmental control based on comprehensive consideration of IAQ, thermal comfort, and energy savings remains to be accomplished. Therefore, this paper describes the development and application of a multi-objective AI algorithm that can be deployed in practical environmental control to rapidly and accurately optimize the IAQ, thermal comfort levels, and energy efficiency of buildings. First, a database of indoor airflow and temperature distribution is established through computational fluid dynamics (CFD) simulations and chamber experiments. Then, with the database, the back-propagation neural network (BPNN) model is created to rapidly predict air pollution concentrations and thermal comfort levels. If these predictions using the BPNN model do not satisfy the setup requirements, an adaptive multi-objective particle swarm optimizer-grey wolf optimization (AMOPSO-GWO) algorithm is used to identify the optimal strategy for maximizing indoor air quality, thermal comfort levels, and energy saving. The results demonstrate that the BPNN-based AMOPSO-GWO algorithm has high predictive accuracy (>90%). The mean reduction in air pollution concentrations, increase in thermal comfort levels, and average energy savings are 31%, 45%, and 35%, respectively.

Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence.

Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by CFD data can be used for fast and accurate prediction of indoor airflow, but current methods have limitations, such as only predicting limited outputs rather than the entire flow field. Furthermore, conventional AI models are not always designed to predict different outputs based on a continuous input range, and instead make predictions for one or a few discrete inputs. This work addresses these gaps using a conditional generative adversarial network (CGAN) model approach, which is inspired by current state-of-the-art AI for synthetic image generation. We create a new Boundary Condition CGAN (BC-CGAN) model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter, such as a boundary condition. Additionally, we design a novel feature-driven algorithm to strategically generate training data, with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the AI model. The BC-CGAN model is evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box. We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria. The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5% relative error and up to about 75,000 times faster when compared to reference CFD simulations. The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the AI models while maintaining prediction accuracy, particularly when the flow changes non-linearly with respect to an input.

Quantification of the impact of RANS turbulence models on airflow distribution in horizontal planes of a generic building under cross-ventilation for prediction of indoor thermal comfort

Computational fluid dynamics (CFD) simulations based on the steady Reynolds-Averaged Navier-Stokes (RANS) approach are widely employed to predict airflow field and thermal comfort in cross-ventilated buildings. RANS models are highly sensitive to the selected turbulence parametrization. Although the impact of turbulence models on airflow distribution in the vertical center plane of a building has been studied, limited studies have focused on detailed analysis of horizontal planes.The current study investigated the impact of RANS turbulence models (Standard k-ε, Standard k-ω, Realizable k-ε, Renormalization group k-ε model) on indoor airflow distribution of horizontal planes in an isolated generic building. CFD simulations were validated with wind tunnel measurements. Airflow distribution in each turbulence model was quantitatively assessed in terms of the percentage area of a horizontal plane which holds a certain airflow speed (Au). The indoor thermal comfort in the horizontal planes was also evaluated with predicted mean vote (PMV). Moreover, the surface area, volume, angle, spreading width of incoming jets were studied.Best overall performance in validation was demonstrated by standard k-ε model. Turbulence models exhibited distinct airflow patterns and significant differences in Au, even in horizontal planes which showed similar results for validation metrics. Similar differences were observed in PMV distribution. This study provides new insight on the prediction of airflow distribution and thermal comfort in horizontal planes, by showcasing the necessity of acquiring wind tunnel data in carefully selected points in the horizontal planes (i.e. not limited to the vertical center plane) for CFD model validation.

Solar-assisted naturally ventilated double skin façade for buildings: Room impacts and indoor air quality

Although the performance of naturally ventilation double-skin facade (NVDSF) assisted by solar radiation has been widely demonstrated for building energy saving, there are still several unsolved issues towards its application regarding the user space's side and its air quality. This study focuses on two aspects through validated CFD simulations: firstly, a theoretical model is calibrated for the use of an NVDSF connected to a room, with its validity examined by different solar intensities and applicable scenarios clarified; secondly, the age of air and ventilation efficiency under each scenario are evaluated. Results reveal the impacts due to air supply vent sizes, installation heights and aspect ratios, identify discounts in ventilation rates up to 14.8%, 15.3% and 2.9%, respectively. With a fixed air supply vent, different NVDSFs locations deliver almost identical natural ventilation rates (no more than 1.7% differences between cases), but significantly different indoor air quality – the difference in ventilation efficiencies can be up to 18%. An NVDSF located furthest to the air supply vent delivers the best indoor airflow distribution, with more fresh air coverage across the room and reduced bad circulation. The solar irradiation's (I) impact on natural ventilation rate (V) has been identified as V=0.004I0.33 (new theoretical model) and V=0.002I0.42 (CFD), which shows a good agreement with an average discrepancy of 6% for solar intensities 100–1000W/m2. This study highlights the importance of evaluating indoor air quality in addition to the ventilation rates for an NVDSF with a room, and provides a more accurate theoretical model for transition seasons' ventilation that can reflect impacts from solar intensities to assist in future strategy-making for mixed-mode operations in buildings.

Comprehensive Evaluation to a Prefabricated Building for Indoor Environment and Energy Consumption

Prefabricated building, as a convenient and burgeoning building form, is worth popularizing. The research is aimed at comprehensively evaluating and analyzing the indoor thermal environment, air quality, and energy consumption of prefabricated buildings. Therefore, indoor temperature distribution, airflow distribution, and energy system of prefabricated buildings are considered. Different building envelopes proposed accord with average temperature standard (18°C) of indoor thermal environment in different climate regions based on simulation results of DeST software. Take the hot-summer and cold-winter region as an example, the distribution of the indoor temperature, air velocity, and the emission characteristics of indoor polluted particles are explored via ANSYS Fluent software. And the energy supply system for the prefabricated auxiliary building, as well as system operation performances and cost components, is studied. Results show the average indoor temperature on typical days in winter (4.1°C) and summer (31.1°C); air-source heat pump needs to activate for the needs of human thermal comfort. Moreover, the indoor air quality can meet the cleanliness standard after polluted particles release 40 s. And the annual cost of system 1 (photovoltaic system, air-source heat pump, and the state grid) is 839.53 $, 269.83 $ lower than system 2 (photovoltaic system, electrical storage, air-source heat pump, and the state grid), whereas the increase of electricity price can significantly reduce the payback period and thus improve the economy of system 2. These findings provide suggestions in terms of indoor thermal environment, cleanliness, and suitable energy supply system to evaluate and facilitate the widespread application of buildings.

Optimization research on indoor ventilation mode of cave dwellings in northern Shaanxi

Cave dwelling is a characteristic residential building in northern Shaanxi. It is not only a masterpiece created by local residents in accordance with nature and local cases, but also meets the requirements of green ecology and sustainable development. In order to understand the influence of ventilation on the indoor environment of cave dwellings in northern Shaanxi, a representative traditional cave dwelling was selected as the research object. Based on the meteorological data in Yan'an and the actual measurement of indoor and outdoor thermal parameters, the methods of theoretical analysis, field testing and numerical simulation were adopted to study the indoor airflow distribution of cave dwellings under different ventilation methods, the results show that: the cave dwellings in northern Shaanxi have good thermal insulation performance, and the indoor temperature fluctuation is small; the relative humidity in the cave dwelling is relatively high, which is opposite to the trend of temperature change; reducing the humidity is the ventilation of the cave dwelling. The main task; the indoor wind speed is too small to form effective ventilation. Adjusting the size of the skylight can improve the natural ventilation in summer to optimize indoor ventilation. Through optimization analysis, when the size of the skylight is 0.5m*0.5m, the indoor airflow velocity changes between 0 and 0.3m/s, the ventilation efficiency is high, and the ventilation effect is high. Good, there will be no eddy current in the room, which can effectively remove moisture and enhance lighting, which can provide a basis for the opening of ventilation skylights in new local caves. Effectively improve indoor heat and humidity environment.

Indoor air quality evaluation in naturally cross-ventilated buildings for education using age of air

Natural ventilation (NV) is a strategy of bioclimatic design to promote hygrothermal comfort and indoor air quality (IAQ). Nowadays, COVID-19 pandemic highlights the review of ventilation standards. In Mexico, the IAQ standard states a minimum of 6 ACH for educational buildings. ACH considers NV as an ideal piston flow and does not provide information of indoor airflow distribution. In this work, new age of air associated parameters are proposed, considering the indoor airflow distribution: the air renovation per hour (ARH) and the renovation parameter R. An isolated educational building located in a rural region is studied. Four window configurations of cross-ventilation are considered. All configurations have one windward window located at bottom. The configurations axial and upward have one leeward window at bottom and top, respectively. While, configurations corner and upward corner have one lateral side window at bottom and top, respectively. A CFD model of the educational building is validated with experiments. The axial configuration has the best performance according to ACH, nevertheless has the worst performance according to ARH and R. The results show that NV evaluation using ACH can lead to wrong decisions. An improvement of NV standard with the age of air associated parameters is recommended.