3,080 research outputs found

    Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 171

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    This bibliography lists 186 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1977

    Airborne Contaminant Dispersal in Critical Built Environments

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    The Indoor Air Quality (IAQ), being one of the most significant exposures to human beings, encompasses the concepts of comfort and safety from unwanted contaminants. Whereas the thermal comfort is controlled through proper conditioning and distribution of ventilated air, controlling the airborne contaminants requires careful investigation of the flow characteristics. IAQ translates to different requirements, depending on the intended use of the indoor environment. In critical indoor spaces such as Operating Rooms and Cleanrooms, the principal focus of IAQ is to remove/contain/divert contaminants flowing with the airstream to maintain the required sterility, as contamination can lead to adverse patient/product outcomes. The airborne contaminants, generally submicron-sized particles, are controlled by directional airflow through differential pressure, depending on whether the space needs to exfiltrate (e.g., Operating Room – positive pressure) or contain (e.g., Isolation Room – negative pressure) the airborne contaminants. The current design paradigm that determines such pressure differential assumes steady-state conditions. Theoretically, during the steady-state, the rate of flow velocity change is zero, resulting in a constant flow field in time, and the distribution of contaminants in the space can be modeled using ordinary differential equations. Therefore, the steady-state assumption must hold to explain the contamination dispersal. However, in practice, transient occupant interventions like a door opening and walking through the steady-state flow fields alter the flow characteristics. In response, this dissertation examines how occupant-introduced transient events affect the steady-state flow. This study aims to quantify and identify patterns of the changes in the flow characteristics for different scenarios of realistic door openings and human walks under a range of ventilation rates through controlled experiments and numerical simulations. Through specifically designed experiments, the impacts of door operation and occupant walking were characterized and quantified based on different levels of supply flow rates from the ventilation system. The results of the experiments suggested that special considerations were required to control for the transient phenomena and the pressure differential. The walking and door opening experiments also found distinguishable changes in the flow characteristics under each separate interaction between the indoor environment and the occupant. It was interesting to note that even though the magnitude of the effects was different for different levels of initial condition and intervention types, the changes in the flow properties exhibited identical patterns that were possible to model and make predictions. Thus, this dissertation considers the sporadic transient interventions from the occupants (e.g., - door opening and walking) as events and discusses an approximation method called ‘Event-Based Modeling’ (EBM) using the collected data through these experiments. Two-dimensional numerical models were developed to obtain additional data on the changes in airflow characteristics and were used to model and test the accuracy of EBM’s prediction capabilities. The results demonstrated that the predictions from EBM were accurate, and the computational efficiency is improved compared to the traditional numerical simulation approach. This method can eliminate parallel modeling of the same phenomena, providing alternatives to simulate complex and computationally intensive transient events repeatedly. As a potential application, the changes in flow velocities from human-environment interactions in a critical indoor environment like an operating room can be predicted using the EBM method. This way, the ventilation systems can be designed as occupant-centric and energy-efficient by considering the impacts of the transient events instead of only considering the steady-state events

    Laser and 3-Dimensional Printers: Characterizing Emissions and Occupational Exposures

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    Introduction: Toxicology and epidemiology studies have observed an association between ultrafine particles (UFPs) and respiratory, cardiovascular, and neurological health effects. While there is a paucity of data in the literature on the potential toxicity and health effects from indoor UFP exposure, more exposure assessment studies and research evaluating the efficacy of controls is merited. An increased demand for efficiency, productivity, and manufacturing has led to conception of laser and 3- dimmensional (3-D) printers in various indoor workplaces. The indoor environment is one of the most important determinants of personal exposure. Introducing laser and 3-D printers to indoor workplaces, introduces a potential indoor source of UFP emissions. Given the current knowledge on the potential health effects from exposure to UFPs, Further research is needed to fully characterize occupational exposures to printer emissions and evaluate factors influencing exposures to better guide control strategies. Methods: The source-receptor model was used to identify relevant factors that may affect emissions and worker exposure. Mixed-effects regression modeling was used to identify sources of variability in exposure to laser printer emissions. UFP and copollutant emissions from laser printers were measured in a laboratory chamber to test the hypothesis that device-specific factors (e.g. make-model, technology, print speed, voltage) influence printer emission profiles. Results are described in Chapter 2. Realtime air samples for UFPs were collected at a laser printing facility. Emission rates for laboratory and real-world exposures were calculated using a one-box model and compared to emission rates calculated using the test method for hard copy devices to determine if results were significantly different. Results are described in Chapter 3. Real-time and time-integrated personal and area air sampling was performed to characterize indoor UFP and co-pollutant exposures to 3-D printer emissions during industrial printing. Personal and area air levels were characterized during industrial 3-D printing and post-processing tasks to determine if exposures were above occupational exposure limits. Results are described in Chapter 4. Conclusions: Device-specific factors such as, copy rate and printer voltage affect exposure. Laser printers evaluated in this study had higher between-device variance. Control strategies should focus on device-specific factors (e.g. copy rate). Future research will focus on other factors potentially influencing exposure (e.g. toner type, paper type). The test method for hard copy devices emission rates differed significantly differed from the one-box model emission rates. Continued research will use exposure and dose modeling to provide estimates and distributions that are meaningful or comparable to previously published data. Occupational exposures to metals and organic vapors during industrial 3-D printing were below respective occupational exposure limits. Further research is needed to fully characterize exposure and understand determinants (e.g. materials, tasks) of higher or lower exposure

    A review of the meteorological parameters which affect aerial application

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    The ambient wind field and temperature gradient were found to be the most important parameters. Investigation results indicated that the majority of meteorological parameters affecting dispersion were interdependent and the exact mechanism by which these factors influence the particle dispersion was largely unknown. The types and approximately ranges of instrumented capabilities for a systematic study of the significant meteorological parameters influencing aerial applications were defined. Current mathematical dispersion models were also briefly reviewed. Unfortunately, a rigorous dispersion model which could be applied to aerial application was not available

    Bridging the Divide between Air Quality Monitoring, Management and Policy in the Sea-to-Sky Airshed: A method for analyzing and interpreting large volume air quality data for management and policy guidance

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    Air pollution has increasingly been the focus of management and policy efforts since the early 1950s. Networks of monitoring stations for data to inform, create, focus, assess and improve air pollution management and policy. However, monitoring systems can become disconnected from air quality management and policy without analysis and interpretation to bridge the divide. This thesis develops a method of analyzing and interpreting large volume air quality data into key air pollutant trends and characteristics to guide air quality management and policy. The method is applied to air quality data between 2002 and 2013 in the Sea-to-Sky Airshed, located in south-western British Columbia, Canada. At the time of study, this airshed contained a monitoring system that had been growing increasingly disconnected from the airshed’s air quality management and policy. Applying this method uncovered significant instances of inaccurate and missing air quality data, and identified the airshed’s key pollutant trends and characteristics. These findings were then used to create recommendations for improving the resource efficiency and quality of the airshed’s monitoring, management and policy. Also identified were applications of R and R’s OpenAir package which are estimated to significantly reduce analysis time and offer additional analysis options

    Turbofan Engine Behaviour Forecasting using Flight Data and Machine Learning Methods

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    The modern gas turbine engine widely used for aircraft propulsion is a complex integrated system which undergoes deterioration during operation due to the degradation of its gas path components. This dissertation outlines the importance of Engine Condition Monitoring (ECM) for a more efficient maintenance planning. Different ML approaches are compared with the application of predicting engine behaviour aiming at finding the optimal time for engine removal. The selected models were OLS, ARIMA, NeuralProphet, and Cond-LSTM. Long operating and maintenance history of two mature CF6-80C2 turbofan engines were used for the analysis, which allowed for the identification of the impact of different factors on engine performance. These factors were also considered when training the ML models, which resulted in models capable of performing prediction under specified operation and flight conditions. The Machine Learning (ML) models provided forecasting of the Exhaust Gas Temperature (EGT) parameter at take-off phase. Cond-LSTM is shown to be a reliable tool for forecasting engine EGT with a Mean Absolute Error (MAE) of 7.64?, allowing for gradual performance deterioration under specific operation type. In addition, forecasting engine performance parameters has shown to be useful for identifying the optimal time for performing important maintenance action, such as engine gas path cleaning. This thesis has shown that engine removal forecast can be more precise by using sophisticated trend monitoring and advanced ML methods.O moderno motor de turbina a gás amplamente utilizado para propulsão de aeronaves é um sistema integrado complexo que sofre deterioração durante a operação devido à degradação de seus componentes do percurso do gás. Esta dissertação destaca a importância da monitorização da condição do motor para um planejamento de manutenção mais eficiente. Diferentes abordagens de Machine Learning (ML) são comparadas visando a aplicação de previsão do comportamento do motor com o objetivo de encontrar o momento ideal para a remoção do motor. Os modelos selecionados foram OLS, ARIMA, NeuralProphet e Cond-LSTM. O longo histórico de operação e manutenção de dois motores turbofan CF6-80C2 maduros foi usado para a análise, o que permitiu a identificação do impacto de diferentes fatores no desempenho do motor. Esses fatores também foram considerados no treinamento dos modelos de ML, o que resultou em modelos capazes de realizar a previsão em operação e condições de voo especificadas. Os modelos ML forneceram previsão do parâmetro Exhaust Gas Temperature (EGT) na fase de decolagem. O Cond-LSTM demonstrou ser uma ferramenta confiável para previsão do EGT do motor com um erro absoluto médio de 7,64 ?, permitindo a deterioração gradual do desempenho sob um tipo específico de operação. Além disso, a previsão dos parâmetros de desempenho do motor tem se mostrado útil para identificar o momento ideal para realizar ações de manutenção importantes, como a limpeza do percurso do gás do motor. Esta tese mostrou que a previsão de remoção do motor pode ser mais precisa usando um monitoramento sofisticado de tendências e métodos avançados de ML

    Vegetation Cover Analysis of Hazardous Waste Sites in Utah and Arizona Using Hyperspectral Remote Sensing

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    This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 x 2.3 m spatial resolution), collected over the U. S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R-2 > 0.80. The use of REPs failed to accurately predict LAI (R-2 < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (<1 m) found on the sites.open111

    Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

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    In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers' probability safety requiremen

    Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

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    Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS
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