20 research outputs found
Characteristics of bubble behavior in microgravity conditions
No full textThe main objective of this thesis is to study the characteristics of bubbles in pool boiling under microgravity conditions with and without the influence of a magnetic field. In order to create microgravity conditions, the parabolic path of an aircraft was used for a period of 20-30 seconds, during which time the experiment was conducted. Two cylindrical tanks were used for the boiling process in the experiment. One tank had a permanent magnet attached at the bottom, and the other one was kept as the control. MnCl2 was dissolved in distilled water to make a paramagnetic liquid, and which used in the pool boiling process. The experiment was captured on video simultaneously from two different angles, and later analyzed frame by frame using image processing techniques to obtain the bubble characteristics during the different stages of the boiling process. During this investigation of bubble motion, two main studies were conducted. First, the characteristics of a single bubble were studied, such as: center of the bubble, shape of the bubble (maximum radius and minimum radius in vertical and horizontal direction), vertical and horizontal displacement of the center of the single bubble, rate of change of the position of the bubble coordinates and the vertical component of the selected single bubble velocity. Second, the motion of the bubbles was studied, including the directionality, the velocity and the fluctuation of the size and shape of the bubble was studied. The results from the study revealed that as the strength of the magnetic field reduces, the influence of the magnetic field on bubbles was weakened along the vertical axis of the tank. This phenomenon was visualized graphically with various bubble metrics
Utilization of Classical and Quantum Machine Learning-Based Models to Study the Human Migration Dynamics
No full textSocioeconomic, demographic, and climate-related extreme weather events have significantly influenced human dynamics such as short-term mobility and migration across the world. This dissertation investigates the interplay between environmental and socioeconomic-demographic factors and human migration using a data-driven approach. The study focused on implementing machine learning-based predictive models for county-to-county residential migration in the state of Texas, in the United States of America, leveraging both classical and quantum machine learning techniques. The research is structured into two primary objectives: (1) to integrate and analyze environmental, socioeconomic, and migration datasets to understand what could influence human migration, and (2) to evaluate the feasibility of quantum machine learning models for data analytics compared to traditional classical machine learning approaches. Datasets include county-to-county residential mobility data from the US Census Bureau’s socioeconomic and demographic indicator data, and Earth observational environmental and weather data from NASA. Classical machine learning models, including Support Vector Machines (SVM), Logistic Regression, Extreme Gradient Boosting (XGBoost), and Huber regression method, were employed to conduct data analytics for county-to-county migration. This study explored the feasibility of quantum machine learning-based classification models, such as the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC), implemented within the IBM Qiskit quantum computing framework. However, current quantum hardware limitations pose practical challenges when using a large number of features to train the model compared to classical machine learning approaches. Experimental results indicate that classical machine learn- ing models offer strong predictive power compared to their quantum counterparts, which utilize current quantum computing technology. Quantum machine learning models are still in the early stages of development as of writing this dissertation. Our study revealed the practical limitations of using a larger number of features developed using real-world data on a quantum computing environment to study real-world phenomena of human migration. Quantum computers have a path to exhibit potential advantages as technology develops. The findings from our exploratory analysis and machine learning model development exercises contribute to the understanding of county-to-county residential migration, which is shaped by socioeconomic indicators and environmental factors. Furthermore, this study demonstrates the applicability and limitations of both classical and quantum machine learning in data analytics for environmental and social systems, providing insights on how to develop quantum machine learning applications in similar domains
Studying the characteristics of bubble motion in pool boiling in microgravity conditions under the influence of a magnetic field
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IDENTIFYING VULNERABLE FACILITIES DUE TO THE EXTREME WEATHER EVENTS
No full textIdentifying Vulnerable Facilities (Hospitals, Power Plants, Airports) Due to Extreme Weather Effects (Precipitation, High Wind, Landslides). This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.</div
Exploring Image Segmentation Techniques to Detect High Latitude Dust on Satellite Images
No full textExploratory Data Analysis to Understand Social Determinants Important to Global Neonatal Mortality Rate
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