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Fibrous air filters are common devices used to remove airborne particles. Their performance is typically measured through their resistance to airflow and captured particle mass. Models describing the evolution of filter performance have been heavily researched; however, the need for improvement remains. Experimental work is irreplaceable in the development of high-fidelity models, yet the estimation of necessary variables is not trivial and may be influenced by selected measurement instruments and analysis methodologies. Therefore, the purpose of this work is to propose a framework to investigate the response of common aerosol measurement instruments, their corresponding analysis methodologies, and the application of their data. A Scanning Mobility Particle Sizer (SMPS) and Laser Aerosol Spectrometer (LAS) were selected for consideration, and their recorded data were compared against baseline measurements. The results of the experiments indicated that the SMPS and LAS yielded a ratio of estimated mass concentrations to the baseline mass concentrations of approximately 1.175 and 0.749, respectively. Regarding the SMPS, it was suggested that the measurable size range, application of a coverage factor, dynamic shape factor, and association between the curve fits and histograms were influential in the final estimates. For the LAS, the application of a curve fit, its association to the histograms, and the selection of the sampling periods were influential. Considering the results, the impact of these factors may not be considered negligible and may skew reproducibility between studies and fossilize confounding factors. Therefore, the proposed methodologies are useful in addressing potential errors in data collection and analysis. [ABSTRACT FROM AUTHOR]

The distributed energy system (DES) represents an innovative approach to energy generation and distribution that promotes decentralization and diversification of energy sources. DESs can offer numerous benefits, including increased resiliency, reduced transmission losses, improved efficiency, and lower carbon emissions. The optimal design of a DES requires careful consideration of various factors such as geographical location, climate conditions, and energy demand patterns. This paper utilizes a multi-objective genetic algorithm to optimize the combination of technologies and their corresponding sizes in a distributed energy system for three types of commercial buildings—hospitals, large offices, and large hotels across eight different climate zones in the U.S. A range of technologies are considered for integration into the DES. These technologies include photovoltaic systems, wind turbines, combined heat and power systems, solar thermal collectors, and electrical and thermal energy storage. The two objectives considered are maximizing the reduction in carbon dioxide emissions and minimizing the life cycle costs for the DES. The purpose of this study is to optimize and evaluate the multi-objective design of distributed energy systems aimed at decentralizing and diversifying energy sources. The analysis of optimized DES designs across all 24 case scenarios shows that a balance between cost saving and emission reduction has been achieved. Although this study primarily focuses on specific buildings and climate zones, the methods and findings can be adapted for a wider variety of building types across different geographical locations, thus paving the way for more widespread adoption of optimized distributed energy systems. [ABSTRACT FROM AUTHOR]

Particulate matter air pollution and volatile organic compounds released into the air from the incomplete combustion of fossil fuels and wildfires creates significant damage to human health and the environment. Advances in air filtration and purification technology are needed to mitigate aerosol hazards. This article details an effort to explore the potential benefits of new materials and methods for the production of nonwoven air filtration media through electrospinning and stabilizing polyacrylonitrile fibers. The investigated production methods include electrospinning fibrous matting onto a stainless steel wire mesh and stabilizing the nonwoven media in a chamber furnace. The media is then tested for air filtration penetration and airflow resistance, and the fiber size distribution is measured using scanning electron microscopy. The experimental results show that the electrospun media approaches the performance criteria for airflow resistance and particle capture efficiency of high-efficiency particulate air (HEPA) filter media. Furthermore, performance estimations for electrospun media of increased thickness and for a decreased filtration velocity show potential to exceed the HEPA media resistance and efficiency criteria. Thus, it is suggested that electrospun and stabilized nonwoven fibrous media are candidates as alternatives to traditionally manufactured HEPA media and may potentially benefit modern air filtration technology and reduce hazards associated with particulate matter. Additionally, the authors recommend future exploration into the carbonization and activation of electrospun filter media for the adsorption and mitigation of volatile organic compounds as a secondary benefit, while maintaining high efficiency and low airflow resistance in the removal of particulate matter from aerosol streams. [ABSTRACT FROM AUTHOR]

Building energy consumption accounts for about 40% of global primary energy use and 30% of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present in buildings, heating, cooling, and air-conditioning (HVAC) systems are considered major contributors to whole-building energy use. To improve the energy efficiency of HVAC systems and mitigate whole-building energy consumption, accurately predicting the building energy consumption can play a significant role. Although many prediction approaches are available for building energy use, a machine learning-based modeling approach (i.e., black box models) has recently been considered to be one of the most promising building energy modeling techniques due to its simplicity and flexibility compared to physics-based modeling techniques (i.e., white box models). This study presents a building energy load forecasting method based on long-term short-term memory (LSTM) and transfer learning (TL) strategies. To implement this approach, this study first conducted raw data pre-processing analysis to generate input datasets. A hospital building type was considered for a case study in the first stage. The hospital prototype building model, developed by the U.S. department of energy (DOE), was used to generate an initial input training and testing dataset for source domain tasks before the transfer learning process. For the transfer learning process in a target domain, a simulation-based analysis was also conducted to obtain target datasets by assuming limited data lengths in different weather conditions. The training and testing procedures were performed using separate cooling and heating periods with and without the transfer learning process for source and target domain tasks, respectively. Lastly, a comparative analysis was carried out to investigate how the accuracy of LSTM prediction can be enhanced with the help of transfer learning strategies. The results from this study show that the developed LSTM-TL model can achieve better performance than the prediction model, which only uses LSTM under different weather conditions. In addition, accurate performance can vary according to different transfer learning methods with frozen and fine-tuning layers and locations. [ABSTRACT FROM AUTHOR]

Mean radiant temperature (MRT) is important for indoor thermal comfort determination. Several good ways to practically obtain accurate MRT include measuring all indoor surface temperatures for MRT calculation or using a black globe thermometer. Still, it can be hard to apply in practice because using such experimental measurements increases the efforts of data management times and acquisition costs. In this regard, there is a practical advantage in reducing the number of measured surfaces by grouping similar surfaces rather than measuring all indoor surface temperatures individually to obtain MRT. However, since even those similar surfaces are not the same, it can lead to erroneous MRT estimation, which needs to be investigated. This study analyzes the uncertainty of MRT estimates by categorizing the surfaces with similar temperature behaviors to examine the risk of such inaccuracy. In this study, the input data required for the MRT calculation are generated using a measurement data-based simulation model, and the uncertainty of the MRT is quantified using the Monte Carlo method. As a result of the study, it is observed that excluding surfaces with similar temperatures for MRT estimation does not significantly affect the uncertainty. When the appropriate number of input surfaces is satisfied, its MRT shows a difference of less than 1% compared to the results calculated with all surfaces. [ABSTRACT FROM AUTHOR]

Buildings use up to 40% of the global primary energy and 30% of global greenhouse gas emissions, which may significantly impact climate change. Heating, ventilation, and air-conditioning (HVAC) systems are among the most significant contributors to global primary energy consumption and carbon gas emissions. Furthermore, HVAC energy demand is expected to rise in the future. Therefore, advancements in HVAC systems' performance and design would be critical for mitigating worldwide energy and environmental concerns. To make such advancements, energy modeling and model predictive control (MPC) play an imperative role in designing and operating HVAC systems effectively. Building energy simulations and analysis techniques effectively implement HVAC control schemes in the building system design and operation phases, and thus provide quantitative insights into the behaviors of the HVAC energy flow for architects and engineers. Extensive research and advanced HVAC modeling/control techniques have emerged to provide better solutions in response to the issues. This study reviews building energy modeling techniques and state-of-the-art updates of MPC in HVAC applications based on the most recent research articles (e.g., from MDPI's and Elsevier's databases). For the review process, the investigation of relevant keywords and context-based collected data is first carried out to overview their frequency and distribution comprehensively. Then, this review study narrows the topic selection and search scopes to focus on relevant research papers and extract relevant information and outcomes. Finally, a systematic review approach is adopted based on the collected review and research papers to overview the advancements in building system modeling and MPC technologies. This study reveals that advanced building energy modeling is crucial in implementing the MPC-based control and operation design to reduce building energy consumption and cost. This paper presents the details of major modeling techniques, including white-box, grey-box, and black-box modeling approaches. This paper also provides future insights into the advanced HVAC control and operation design for researchers in relevant research and practical fields. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) infection remains a threat to global public health. Serum hepatitis B surface antigen (HBsAg) has been used in screening for HBV infection. Quantitative HBsAg assays are useful for monitoring the natural history of HBV infection and its response to therapy. The aim of this study was to determine the relationship between quantitative (qHBsAg; IU/mL) and semi-quantitative (sqHBsAg; signal-to-cutoff ratio [S/Co]) HBsAg titers in patients with chronic hepatitis B (CHB). Methods: We retrospectively included 284 samples with HBV DNA < 20 IU/mL from patients who had simultaneous qHBsAg (using electrochemiluminescence assay) and sqHBsAg tests. Patients were grouped according to their serum HBV-envelope antigen (HBeAg) status (HBeAg-negative, n = 239 and HBeAg-positive, n = 45). The Spearman test was used to analyze the correlation between the quantitative and semi-quantitative assays. Results: There was a significant linear correlation between sqHBsAg and qHBsAg in the HBeAg-negative patients (qHBsAg [IU/mL] = 0.0094 × sqHBsAg [S/Co]1.323; adjusted R2 = 0.8445; p < 0.001). There was a substantial hook effect in the assays from the HBeAg-positive patients, so we performed a stratified analysis according to qHBsAg <1000 IU/mL or ≥1000 IU/mL and found a significant positive linear correlation between sqHBsAg S/Co and qHBsAg (qHBsAg [IU/mL] = 0.072 × sqHBsAg [S/Co]1.331; adjusted R2 = 0.7878; p < 0.001) in HBeAg-positive patients with qHBsAg titers of <1000 IU/mL and a significant negative correlation in HBeAg-positive patients with qHBsAg titers of ≥1000 IU/mL (qHBsAg [IU/mL] = 8.987 × 1014 × sqHBsAg [S/Co]−3.175; adjusted R2 = 0.6350; p < 0.001). Conclusions: There was a highly linear, positive correlation between qHBsAg and sqHBsAg in HBeAg-negative CHB patients. The hook effect led to a negative correlation in HBeAg-positive CHB patients with qHBsAg titers ≥1000 IU/mL. [ABSTRACT FROM AUTHOR]

The building sector is one of the largest contributors to the world's total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people's daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields. [ABSTRACT FROM AUTHOR]

Computational modeling of air filtration is possible by replicating nonwoven nanofibrous meltblown or electrospun filter media with digital representative geometry. This article presents a methodology to create and modify randomly generated fiber geometry intended as a digital twin replica of fibrous filtration media. Digital twin replicas of meltblown and electrospun filter media are created using Python scripting and Ansys SpaceClaim. The effect of fiber stiffness, represented by a fiber relaxation slope, is analyzed in relation to resulting filter solid volume fraction and thickness. Contemporary air filtration media may also be effectively modeled analytically and tested experimentally in order to yield valuable information on critical characteristics, such as overall resistance to airflow and particle capture efficiency. An application of the Single Fiber Efficiency model is incorporated in this work to illustrate the estimation of performance for the generated media with an analytical model. The resulting digital twin fibrous geometry compares well with SEM imagery of fibrous filter materials. This article concludes by suggesting adaptation of the methodology to replicate digital twins of other nonwoven fiber mesh applications for computational modeling, such as fiber reinforced additive manufacturing and composite materials. [ABSTRACT FROM AUTHOR]

In this paper, we present a performance-based approach to building configuration design to improve the urban ventilation potential at the conceptual design stage, and we demonstrate its application through a case study. The target performance optimized was the ventilation potential of a district, including a region of interest at a spatial scale of hundreds of meters. To estimate this performance, we used computational fluid dynamics (CFD), coupled with an evolutionary algorithm, to optimize the design alternatives to produce the building configuration most suitable for a given set of site conditions. Three calculation components must be assembled for a CFD-based design optimization: an optimizer, a geometry/mesh generator, and a CFD solver. To provide links between the calculation components, we utilized an in-house parametric design program. A case study was conducted to test the applicability of the proposed design method to identify the optimal solutions that minimize adverse effects on the ventilation potential of the surrounding area. For a configuration of buildings in a dense urban area, the proposed design method successfully improved the design alternatives. The results show that the urban ventilation potential in the case of the optimized building configuration is 16% greater than that of the initial building configuration. [ABSTRACT FROM AUTHOR]

Advancements in computer and mobile technologies have driven transformations of classroom activities in engineering education. This evolution provides instructors more opportunities to introduce computational tools that can be effectively used and promoted in engineering education to advance students' learning process when the tools are appropriately utilized in the classroom activities. This paper presents a methodology to improve student learning of energy systems through a class assignment implementing a self-developed computational tool using Microsoft Excel and utilizing the tool to enhance their learning experience. The proposed method, a student-centered learning approach, was applied in a technical elective course called "Power Generation Systems" within a mechanical engineering curriculum. In the course, students were guided to develop a computational tool by themselves based on their learning of the fundamental principles and governing equations of a thermodynamics cycle. The self-developed computational tool allows the students to focus on more design-oriented problems, instead of the calculation process. Using the self-developed tool, students can have an enhanced understanding of the energy system performance in varying design and operational conditions and can perform the parametric analysis and visualization of essential parameters. Feedback from the students and class instructors proves that the self-development and use of the tool can significantly improve the students' learning experience in the implemented course, make the course more dynamic, and motivate the students to learn the material more iteratively. In addition, students feel confident using computational tools to perform analysis, and are willing to develop more tools for other energy-related engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents an analysis to foresee renewable design requirement changes of net- zero carbon buildings (NZCBs) under different scenarios of potential future climate scenarios in the U.S. Northeast and Midwest regions. A climate change model is developed in this study using the Gaussian random distribution method with monthly temperature changes over the whole Northeast and Midwest regions, which are predicted based on a high greenhouse gas (GHG) emission scenario (i.e., the representative concentration pathways (RCP) 8.5). To reflect the adoption of NZCBs potential in future, this study also considers two representative future climate scenarios in the 2050s and 2080s of climate change years in the U.S. Northeast and Midwest regions. An office prototype building model integrates with an on-site photovoltaics (PV) power generation system to evaluate NZCB performance under the climate change scenarios with an assumption of a net-metering electricity purchase agreement. Appropriate capacities of the on-site PV system needed to reach NZCB balances are determined based on the building energy consumption impacted by the simulated climate scenarios. Results from this study demonstrated the emission by electricity consumption increases as moving toward the future scenarios of up to about 25 tons of CO2-eq (i.e., about 14% of the total CO2-eq produced by the electricity energy source) and the PV installation capacity to offset the emission account for the electricity consumption increases significantly up to about 40 kWp (i.e., up to more than 10% of total PV installation capacities) as the different climate scenarios are applied. It is concluded that the cooling energy consumption of office building models would significantly impact GHG emission as future climate scenarios are considered. Consequently, designers of NZCBs should consider high performance cooling energy systems in their designs to reduce the renewable energy generation system capacity to achieve net-zero carbon emission goals. [ABSTRACT FROM AUTHOR]

This study simulates an increased thermal capacitance (ITC) and thermal storage management (TSM) system to reduce the energy consumed by air conditioning and heating systems. The ITC/TSM is coupled with phase change materials (PCM), which enable tank volume reduction. The transient energy modeling software, the Transient System Simulation Tool (TRNSYS), is used to simulate the buildings’ thermal response and energy consumption, as well as the ITC/TSM system and controls. Four temperature-controlled operating regimes are used for the tank: building shell circulation, heat exchanger circulation, solar panel circulation, and storage. This study also explores possible energy-saving benefits from tank volume reduction such as losses associated with the environment temperature due to tank location. Three different tank locations are considered in this paper: outdoor, buried, and indoor. The smallest tank size (five gallons) is used for indoor placement, while the large tank (50 gallons) is used either for outdoor placement or buried at a depth of 1 m. Results for Atlanta, Georgia show an average 48% required energy decrease for cold months (October–April) and a 3% decrease for warm months (May–September) for the ITC/TSM system with PCM when compared with the reference case. A system with PCM reduces the tank size by 90% while maintaining similar energy savings. [ABSTRACT FROM AUTHOR]