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Abstract: Abstract Determining required sample size is one of the critical pathways to reproducible, reliable and robust results in human-related studies. This paper aims to answer a fundamental but often overlooked question: what sample size is required in surveys of occupant responses to indoor environmental quality (IEQ). The statistical models are introduced in order to promote determining required sample size for various types of data analysis methods commonly used in IEQ field studies. The Monte Carlo simulations are performed to verify the statistical methods and to illustrate the impact of sample size on the study accuracy and reliability. Several examples are presented to illustrate how to determine the value of the parameters in the statistical models based on previous similar research or existing databases. The required sample size including “worst” and “optimal” cases in each condition is obtained by this method and references. It is indicated that 385 is a “worst case” sample size to be adequate for a subgroup analysis, while if the researcher has an estimate of the study design and outcome, the “optimal case” sample size can potentially be reduced. When the required sample size is not achievable, the uncertainty in the result can properly interpret via a confidence interval. It is hoped that this paper would fill in the gap between statistical analysis of sample size and IEQ field research, and it can provide a useful reference for researchers when planning their own studies. PubDate: 2023-04-01
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Abstract: Abstract As an important component of the heating, ventilating and air conditioning (HVAC) systems, air handling units (AHUs) are responsible for regulating indoor temperature and humidity. In this paper, a multivariable nonlinear dynamic model of the AHUs with unknown strength of the humidity source is considered, and an improved backstepping controller is proposed to realize the tracking objective of the indoor temperature, relative humidity and carbon dioxide concentration. Firstly, the original system is represented in simplified state space form, and then the state transformation is introduced with a gain to overcome the difficulty caused by the unknown strength of the humidity source. Then, the improved backstepping controller is designed in a step-by-step way. Moreover, the stability of the closed-loop system is analyzed in detail. Finally, we consider the case that the AHUs work in summer of Jinan, China, as an example. The simulation results show the effectiveness of the controller. Meanwhile, the performance of the improved backstepping controller are compared with that of the decoupled sliding mode and PID controllers. PubDate: 2023-04-01
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Abstract: Abstract Building energy efficiency is a key factor in reducing CO2 emissions. For this reason, European Union (EU) member states have developed thermal regulations to ensure building thermal performance. These results are often based on results achieved with building simulation software during the design stage. However, the actual thermal performance can deviate significantly from the predicted one, and this difference is known as the energy performance gap. Accurate indicators of the actual thermal performance are a valuable tool to guarantee building quality. These indicators, including the heat transfer coefficient (HTC) and the heat loss coefficient (HLC), can be estimated by the application of in situ methods. As multi-family housing and tertiary sector buildings are an important part of the building stock, mature methods to measure their thermal performance are needed. This paper presents a short-duration method for assessing the HTC in large building typologies using a sampling approach. The method was applied in a four-storey building model under different conditions to study the limits of the method and to improve indicator bias and uncertainty. Indicator quality was strongly influenced by the external weather conditions, the temperature variation during the protocol and the heat exchange with the adjacent apartments. Under winter conditions and with stable indoor temperatures, the method had a high accuracy when the protocol was applied for half a day. It is recommended that the protocol be used over two days to improve indicator quality under less favorable test conditions. PubDate: 2023-04-01
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Abstract: Abstract The large-scale application of renewable energy is an important strategy to achieve the goal of carbon neutrality in the building sector. Energy flexibility is essential for ensuring balance between energy demand and supply when targeting the maximum penetration rate of renewable energy during the operation of regional integrated energy systems. Revealing the energy flexibility characteristics of centralized hot water systems, which are an important source of such flexibility, is of great significance to the optimal operation of regional integrated energy systems. Hence, in this study, based on the annual real-time monitoring data, the energy flexibility of the centralized hot water system in university dormitories is evaluated from the perspective of available storage capacity (CADR), recovery time (trecovery), and storage efficiency (ηADR), by the data-driven simulation method. The factors influencing the energy flexibility of the centralized hot water system are also analyzed. Available storage capacity has a strong positive correlation with daily water consumption and a strong negative correlation with daily mean outdoor temperature. These associations indicate that increased water use on the energy flexibility of the centralized hot water system is conducive to optimal dispatching. In contrast, higher outdoor temperature is unfavorable. The hourly mean value of the available storage capacity in spring and winter is found to be around 80 kWh in the daytime, and about twice that in summer and autumn. Recovery time is evenly distributed throughout the year, while trecovery/CADR in spring and winter is about half that in summer. The storage efficiency was significantly higher in spring, summer, and winter than in autumn. The hourly mean storage efficiency was found to be about 40% in the daytime. The benefits of activating energy flexibility in spring and winter are the best, because these two seasons have higher available storage capacity and storage efficiency, while the benefit of activating energy flexibility is the highest at 6:00 a.m., and very low from midnight to 3:00 a.m. PubDate: 2023-04-01
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Abstract: Abstract In the simulation of building overheating risks, the use of typical meteorological years (TMY) can greatly reduce the simulation workload and accurately reflect the distribution of simulation results according to the weather conditions over a given period. However, all meteorological parameters in most current TMY methods use a uniform weighting factor which may make the simulation results against the actual simulation results of the period and negatively affect the accuracy of the evaluation results. In addition to differences in climate characteristics between climate zones, the sensitivity of different simulation results to external parameters will also be different. Therefore, a TMY method based on the Finkelstein-Schafer statistical method is proposed, which considers the climatic characteristics of different regions and the correlation with the output parameters of indoor simulation to select the typical month. The proposed method is demonstrated in the three future scenarios for the three cities in different climate zones in China. The results show that the traditional TMY method has an overestimated weight of solar radiation and wind speed and an undervalued weight of dry bulb temperature when indoor temperature-related indicators are the output target. Compared with the traditional TMY method, the TMY generated by the improved method is closer to the distribution characteristics of the long-term outdoor weather data. Furthermore, when using the improved TMY data to evaluate the overheating performance of the passive residential buildings, the difference of the results of the unmet degree hours, indoor overheating degree, and the overheating escalation factor between the long-term projected data and the TMY data can be reduced by 63%–67% compared with the traditional TMY data. PubDate: 2023-04-01
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Abstract: Abstract In the metropolises, it is unlikely to use merely solar and wind energy to pursue zero carbon building design. However, it would become possible if biofuel-driven trigeneration systems (BDTS) are adopted. It is thus essential to assess the application opportunity of BDTS in a holistic way. In this study, BDTS offered definite primary energy saving of up to 15% and carbon emissions reduction of at least 86% in different types of non-residential buildings as compared to the conventional systems. With 24/7 operation for the hotel and hospital buildings, the corresponding BDTS could even achieve zero carbon emissions. All the BDTS primed with compression-ignition internal combustion engine were not economically viable even in running cost due to the high local biodiesel price level. The BDTS primed with spark-ignition engine and fueled by biogas, however, would have economic merit when carbon price was considered for the conventional systems that fully utilize fossil fuels. Adoption of carbon tax and social cost could have the payback ceilings of 8 years and 2 years respectively for most of building types. Consequently, the results could reflect the application potential of BDTS for non-residential buildings, leading the pathway to carbon neutrality for sustainable sub-tropical cities. PubDate: 2023-04-01
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Abstract: Abstract Parameter estimation methods can be classified into (1) manual (trial-and-error), (2) numerical optimization (optimization, sampling), (3) Bayesian inference (Bayes filter, Bayesian calibration), and (4) machine learning (generative model). Bayesian calibration has been widely used because it can capture stochastic nature of uncertain parameters. However, the results of Bayesian calibration could be biased by (1) the prior distribution assumed by the expert’s subjective judgment; (2) the likelihood function that cannot always describe the true likelihood; and (3) the posterior distribution approximation method, such as the Markov Chain Monte Carlo, which requires significant computation time. To overcome this, a new approach using a generator-regularized continuous conditional generative adversarial network (GRcGAN) is presented in this paper. Five target parameters of the DOE reference building model were selected. GRcGAN was trained to estimate uncertain parameters using simulated monthly electricity and gas use. GRcGAN can successfully estimate five uncertain parameters based on 1,000 training data points. The proposed approach presents a potential for stochastic parameter estimation. PubDate: 2023-04-01
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Abstract: Abstract Noise exposure is becoming extremely common in urban area, but its specific impact on sleep remains controversial. Considering the limitations of previous researches, a field study which can conduct both horizontal and longitudinal analysis was designed. Urban participants were tested during two weeks in their homes, and the noise level of bedroom was artificially regulated by changing the status of window and door. During the 1050 test nights in 75 households, noise exposure was reflected from both instrument monitoring at night and perception questionnaire in the morning, and sleep quality was accessed from actigraphy and questionnaire. The analysis results showed that, 92.3% of the bedroom acoustic environment did not meet the minimum requirements of Chinese standards, and 87.9% of subjects had ever experienced harmful noise during the test period. Furthermore, sleep quality was affected by noise exposure from the perspective of both physiological and psychological; the duration of rapid eye movement (REM) sleep was significantly (p < 0.05) shortened with the increase of sound intensity, the duration of deep sleep shortened and subjective sleep quality worsened significantly (p < 0.05) with the increase of acoustic sensation vote. In addition, females were more sensitive to noise exposure and their subjective sleep quality was more likely to be influenced by emotions. This study has important implications for acoustic environment design of bedrooms in cities, and suggested more attention should be paid to the anxiety caused by noise exposure. PubDate: 2023-04-01
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Abstract: Abstract Deep Q Network (DQN) is an efficient model-free optimization method, and has the potential to be used in building cooling water systems. However, due to the high dimension of actions, this method requires a complex neural network. Therefore, both the required number of training samples and the length of convergence period are barriers for real application. Furthermore, penalty function based exploration may lead to unsafe actions, causing the application of this optimization method even more difficult. To solve these problems, an approach to limit the action space within a safe area is proposed in this paper. First of all, the action space for cooling towers and pumps are separated into two sub-regions. Secondly, for each type of equipment, the action space is further divided into safe and unsafe regions. As a result, the convergence speed is significantly improved. Compared with the traditional DQN method in a simulation environment validated by real data, the proposed method is able to save the convergence time by 1 episode (one cooling season). The results in this paper suggest that, the proposed DQN method can achieve a much quicker learning speed without any undesired consequences, and therefore is more suitable to be used in projects without pre-learning stage. PubDate: 2023-04-01
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Abstract: Abstract Digital twin is regarded as the next-generation technology for the effective operation of heating, ventilation and air conditioning (HVAC) systems. It is essential to calibrate the digital twin models to match them closely with real physical systems. Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances. To address this challenge, this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems. This method utilizes clustering algorithms to remove redundant data for achieving data compression. Moreover, a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models. Its basic idea is that once a component model is calibrated by heuristic methods, its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components. By doing so, the calibration process can be guided, so that fewer iterations would be used. The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building. Results show that the proposed clustering compression-based method can reduce computation loads by 97%, compared to the conventional calibration method. And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6% of the time costs. PubDate: 2023-03-23
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Abstract: Abstract The design and potential application analysis of the novel solar-absorbing integrated facade module and its corresponding building-integrated solar facade water heating system are presented in this study. Compared with the conventional building envelope, the main novities of the proposed facade module lie in its contributions towards the supplied water preheating to loads and the internal heat gain reduction. Besides, the proposed building-integrated solar facade water heating system broadens the combination modes of the solar thermal system and the building envelope. A dynamic model is introduced first for system design and performance prediction. To evaluate the energy-saving potential and feasibility of the implementation of the proposed facade module, this paper carried out a suitable case study by replacing the conventional facade module in the ongoing retrofitting project of a kitchen, part of the canteen of a graduate school. The detailed thermal performances of three system design options are compared in the typical winter and summer weeks and throughout the year, and then, with the preferred system design, the economic, energy, and environmental effects of the proposed system are evaluated. It was found that the system with a high flow rate of the circulating water is suggested. The annual electricity saved reaches 4175.3 kWh with yearly average thermal efficiency at 46.9%, and its corresponding cost payback time, energy payback time, and greenhouse gas payback time are 3.8, 1.7, 1.7 years, respectively. This study confirms the feasibility and long-term benefits of the proposed building-integrated solar facade water heating system in buildings. PubDate: 2023-03-21
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Abstract: Abstract Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. This paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities. PubDate: 2023-03-14
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Abstract: Abstract Origin of differently sized respiratory droplets is fundamental for clarifying their viral loads and the sequential transmission mechanism of SARS-CoV-2 in indoor environments. Transient talking activities characterized by low (0.2 L/s), medium (0.9 L/s), and high (1.6 L/s) airflow rates of monosyllabic and successive syllabic vocalizations were investigated by computational fluid dynamics (CFD) simulations based on a real human airway model. SST k−ω model was chosen to predict the airflow field, and the discrete phase model (DPM) was used to calculate the trajectories of droplets within the respiratory tract. The results showed that flow field in the respiratory tract during speech is characterized by a significant laryngeal jet, and bronchi, larynx, and pharynx-larynx junction were main deposition sites for droplets released from the lower respiratory tract or around the vocal cords, and among which, over 90% of droplets over 5 µm released from vocal cords deposited at the larynx and pharynx-larynx junction. Generally, droplets’ deposition fraction increased with their size, and the maximum size of droplets that were able to escape into external environment decreased with the airflow rate. This threshold size for droplets released from the vocal folds was 10–20 µm, while that for droplets released from the bronchi was 5–20 µm under various airflow rates. Besides, successive syllables pronounced at low airflow rates promoted the escape of small droplets, but do not significantly affect the droplet threshold diameter. This study indicates that droplets larger than 20 µm may entirely originate from the oral cavity, where viral loads are lower; it provides a reference for evaluating the relative importance of large-droplet spray and airborne transmission route of COVID-19 and other respiratory infections. PubDate: 2023-03-13
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Abstract: Abstract 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. PubDate: 2023-03-13
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Abstract: Abstract Indoor air quality becomes increasingly important, partly because the COVID-19 pandemic increases the time people spend indoors. Research into the prediction of indoor volatile organic compounds (VOCs) is traditionally confined to building materials and furniture. Relatively little research focuses on estimation of human-related VOCs, which have been shown to contribute significantly to indoor air quality, especially in densely-occupied environments. This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom. The time-resolved concentrations of two typical human-related (ozone-related) VOCs in the classroom over a five-day period were analyzed, i.e., 6-methyl-5-hepten-2-one (6-MHO), 4-oxopentanal (4-OPA). By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression (RFR), adaptive boosting (Adaboost), gradient boosting regression tree (GBRT), extreme gradient boosting (XGboost), and least squares support vector machine (LSSVM), we find that the LSSVM approach achieves the best performance, by using multi-feature parameters (number of occupants, ozone concentration, temperature, relative humidity) as the input. The LSSVM approach is then used to predict the 4-OPA concentration, with mean absolute percentage error (MAPE) less than 5%, indicating high accuracy. By combining the LSSVM with a kernel density estimation (KDE) method, we further establish an interval prediction model, which can provide uncertainty information and viable option for decision-makers. The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors, making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings. PubDate: 2023-03-13
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Abstract: Abstract Studies on urban energy have been growing in interest, and past research has mostly been focused on studies of urban solar potential or urban building energy consumption independently. However, holistic research on the combination of urban building energy consumption and solar potential at the urban block-scale is required in order to minimize energy use and maximize solar power generation simultaneously. The aim of this study is to comprehensively evaluate the impact of urban morphological factors on photovoltaic (PV) potential and building energy consumption. Firstly, 58 residential blocks were classified into 6 categories by k-means clustering. Secondly, 3 energy performance factors, which include the energy use intensity (EUI), the energy use intensity combined with PV potential (EUI-PV), and photovoltaic substitution rate (PSR) were calculated for these blocks. The study found that the EUI of the Small Length & High Height blocks was the lowest at around 30 kWh/(m2·y), while the EUI-PV of the Small Length & Low Height blocks was the lowest at around 4.45 kWh/(m2·y), and their PSR was the highest at 87%. Regression modelling was carried out, and the study concluded that the EUI of residential blocks was mainly affected by shape factor, building density and floor area ratio, while EUI-PV and PSR were mainly affected by height and sky view factor. In this study, the results and developed methodology are helpful to provide recommendations and strategies for sustainable planning of residential blocks in central China. PubDate: 2023-03-07
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Abstract: Abstract Well-mixed zone models are often employed to compute indoor air quality and occupant exposures. While effective, a potential downside to assuming instantaneous, perfect mixing is underpredicting exposures to high intermittent concentrations within a room. When such cases are of concern, more spatially resolved models, like computational-fluid dynamics methods, are used for some or all of the zones. But, these models have higher computational costs and require more input information. A preferred compromise would be to continue with a multi-zone modeling approach for all rooms, but with a better assessment of the spatial variability within a room. To do so, we present a quantitative method for estimating a room’s spatiotemporal variability, based on influential room parameters. Our proposed method disaggregates variability into the variability in a room’s average concentration, and the spatial variability within the room relative to that average. This enables a detailed assessment of how variability in particular room parameters impacts the uncertain occupant exposures. To demonstrate the utility of this method, we simulate contaminant dispersion for a variety of possible source locations. We compute breathing-zone exposure during the releasing (source is active) and decaying (source is removed) periods. Using CFD methods, we found after a 30 minutes release the average standard deviation in the spatial distribution of exposure was approximately 28% of the source average exposure, whereas variability in the different average exposures was lower, only 10% of the total average. We also find that although uncertainty in the source location leads to variability in the average magnitude of transient exposure, it does not have a particularly large influence on the spatial distribution during the decaying period, or on the average contaminant removal rate. By systematically characterizing a room’s average concentration, its variability, and the spatial variability within the room important insights can be gained as to how much uncertainty is introduced into occupant exposure predictions by assuming a uniform in-room contaminant concentration. We discuss how the results of these characterizations can improve our understanding of the uncertainty in occupant exposures relative to well-mixed models. PubDate: 2023-03-05
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Abstract: Abstract Building ventilation is essential to discharge indoor pollutants and improve indoor air quality for occupant health. Tracer gas method is an efficient way in the field of building ventilation to measure ventilation rate and to evaluate the ventilation performance. Literature shows notable deviation of measured ventilation rate using different tracer gases. In the present study, CFD simulations are carried out to analyze He-, CO2- and SF6- based tracer gas methods. The effects of tracer gas density and release rate on the concentration distribution and ventilation effectiveness are studied. Various application scenarios of different ventilation rates and airflow distribution forms are compared. The results show that the deviation of ventilation effectiveness evaluated by different tracer gases can be above 2–4 times, and the error is introduced by non-passive dispersion. Whether tracer gas dispersion is passive or not depends on the relative importance of density difference driven mass transfer to forced convection mass transfer, which is due to the combined effects of density difference, release rate, and indoor airflow velocity, and can be judged by a dimensionless number θ. Under the geometry and ventilation settings in the present study, the critical value of θ is 1.0 for the error range of 5%, and 2.0 for the error range of 10%. When θ is below the critical value, the gas transport is passive and dominated by the indoor ventilation airflow. A release of tracer gas with smaller release rate and smaller density difference into a stronger indoor airflow behaves more passive. Heavier tracer gas tends to significantly overestimate the performance of upper supply and lower exhaust ventilation, and lighter tracer gas aggravates the overestimation of the performance for lower supply and upper exhaust ventilation. In mechanical ventilation rooms with air change rate of 3.0–6.0 h−1, a continuous release of tracer gas SF6, CO2 or He with release rate above 8 mg/s or source concentration above 8–75 ppm should not be considered as passive. This work clarified the passive and non-passive transport characteristics and mechanisms of various tracer gases, which is helpful for the engineering applications of tracer gas method in building ventilation studies. PubDate: 2023-03-01 DOI: 10.1007/s12273-022-0947-3
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Abstract: Abstract A significant feature of the indoor environment is the heterogeneity of airflow and pollutant distributions, which are primarily dependent on ventilation systems. In the case of short- and high-concentration exposures to hazardous chemical pollutants, it may be necessary to precisely determine the concentration in the breathing zone or, more directly, the inhalation exposure concentration in the respiratory tract, rather than the representative room average concentration in an indoor environment, because of the non-uniformity of pollutant concentration distributions. In this study, we developed a computer-simulated person with a detailed respiratory system to predict inhalation exposure concentration and inhalation dose via transient breathing and reported a demonstrative numerical simulation for analyzing acetone concentration distributions in a simplified model room. Our numerical analysis revealed that the ventilation efficiency distribution in a room could change significantly by changing the design of the ventilation system, and that the inhalation exposure concentration estimated by a computer-simulated person could differ from the representative concentration, such as perfect-mixing or volume-averaged acetone concentration, by a factor of two or more. PubDate: 2023-03-01 DOI: 10.1007/s12273-022-0954-4
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Abstract: Abstract Fast and accurate identification of the pollutant source location and release rate is important for improving indoor air quality. From the perspective of public health, identification of the airborne pathogen source in public buildings is particularly important for ensuring people’s safety and health. The existing adjoint probability method has difficulty in distinguishing the temporal source, and the optimization algorithm can only analyze a few potential sources in space. This study proposed an algorithm combining the adjoint-pulse and regularization methods to identify the spatiotemporal information of the point pollutant source in an entire room space. We first obtained a series of source-receptor response matrices using the adjoint-pulse method in the room based on the validated CFD model, and then used the regularization method and composite Bayesian inference to identify the release rate and location of the dynamic pollutant source. The results showed that the MAPEs (mean absolute percentage errors) of estimated source intensities were almost less than 15%, and the source localization success rates were above 25/30 in this study. This method has the potential to be used to identify the airborne pathogen source in public buildings combined with sensors for disease-specific biomarkers. PubDate: 2023-02-10
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