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Comparative Life Cycle Assessment of Remote Potable Water Supply for the Department of Defense
No full textThe Department of Defense (DOD) and other agencies, including relief organizations, require potable water for remote missions around the globe. As part of recent initiative by the U.S. Federal government through Executive Order 14057, the DOD has been instructed to investigate the sustainability of operations and practices within the context of climate change. One such practice that needs to be addressed is the procurement of potable water, an essential requirement of any remote mission or location. Currently, there are three primary means of procuring potable water at remote locations: bottled water, on-site purification, or tie-in to existing, local infrastructure. The first two operations are often considered the most secure options, but have sustainability concerns. The purpose of this study is to compare the environmental impacts of bottled water procurement versus on-site treatment via a mobile Reverse Osmosis Water Purification Unit (ROWPU), which uses multiple levels of filtration to make potable water from a local source. A cradle-to-gate assessment was developed for both systems to compare different options for potable water supply. An in person inventory was paired with data taken from the Ecoinvent 3.8 database to directly compare the two systems. The two systems are compared on a 5-year timeline to analyze the environmental impact of repeated bottled water transport versus diesel generator-fueled on-site treatment. Across all impact categories, the results indicate that high energy costs of the reverse osmosis process have significantly less impact on the environment than the repetitive transport and procurement of bottled water. The results of the study have important implications for advancing sustainable operations for remote communities or temporary settlements
Enhancing Crossflow Dynamics through the Gas Injection from Multiple Cylinders
Get PDFWe investigate unsteady, two-dimensional laminar fluid flow around cylinders, focusing on understanding the impact of injecting methane gas through two diametrically opposite arcs on the cylinder in the crossflow of the second fluid. This study encompasses applications in mixing and dispersion, which are crucial in various technological and natural processes. Our analysis addresses velocity field’s contribution to the spatiotemporal distribution of transported quantities. We observed that mixing induces a transition from laminar to turbulent flow. Depending on the injected-to-crossflow velocity ratios, the wake vortices downstream of cylinder arrays separate from or connect to the injected gas. This phenomenon significantly impacts mixing efficiency caused by asymmetric vortical flow structures and their interactions. In this study, the effective Reynolds number was computed at the outlet to account for the increase of momentum due to the gas injection, and it showed a monotonic rise with increasing injected gas velocity. Particularly, we observed a linear increase in the effective Reynolds number with increasing ∊ (velocity ratio parameter) for the single cylinder case, however, it exhibits a non-linear behavior for the multiple cylinders case due to complex flow interactions and enhanced mixing. We further analyzed flow disturbance using a participation number, a statistical characterization of the system’s spatial distribution of kinetic energy. Surprisingly, the participation number decreases with the increase in ∊. This decrease indicates a highly localized and inhomogeneous distribution of kinetic energy in the system, increased tortuosity, and possibly the first sign of the inertial to turbulent transition in the system
Envisioning AI-powered Learning Stemming from Piloted Personalized Education
No full textLearning systems can potentially transform education and training by allowing educators to encourage behavioural changes and internalization of concepts in learners. This paper introduces such an AI-powered model for a personalized learning system, building on our success of hand-curated learning paths to support individualized education. We utilize a network science approach as a construct to create an environment that is supportive of an individualized learning process, presenting an AI-powered framework. This framework is informed by student and faculty interactions with two custom created learning systems, experiences that shaped goals and expectations of the proposed AI-powered method since 2018. This paper contributes to advancing the conversation around AI-powered learning systems and personalizing educational experiences. Our AI-powered model engages learners in their educational journey through individualized and adaptive learning paths, while meeting each learners’ specific learning outcomes. We conclude with open research problems that surfaced from this vision
Evaluating Installation Water Security Needs
No full textA study conducted by the Air Force Institute of Technology (AFIT) built a framework for enhancing common indicator-based methods to calculate the current and future relative water security at 30 installations across the continental United States, including both U.S. Air Force and U.S. Space Force locations. The analysis used 14 indicators that spanned climate, demand, and infrastructure categories obtained from the Air Force Civil Engineer Water Dashboard and other open-source data sets
Optimal codes in the Stiefel manifold
No full textWe consider the coding problem in the Stiefel manifold with chordal distance. After considering various low-dimensional instances of this problem, we use Rankin\u27s bounds on spherical codes to prove upper bounds on the minimum distance of a Stiefel code, and then we construct several examples of codes that achieve equality in these bounds
Porosity Effects on Oxidation of Ultra-High Temperature Ceramics
No full textUltra-high-temperature ceramics (UHTCs) are the materials of choice in aerospace applications involving extreme environmental conditions. A major challenge concerning these materials performance is oxidation. Understanding the underlying oxidation mechanisms is essential for predicting applicability in specific aerospace circumstances. Utilizing a combined experiment-modeling approach, this work analyzes the effects of one of the major factors, i.e., porosity, on oxidation of a typical UHTC, namely hafnium diboride (HfB2). Systematic assessments of porosity characteristics in the micrographs depicting oxide layer emergence from parent boride result in the quantitative measures needed for modeling the oxidation process. The combination of experimental and modeling results indicates that the pore fraction and radii independently affect the oxidation outcome. The current study provides evidence for each of these effects, and provides a basis for the modification of the utilized model in conjunction with the experimental results. The inclusion of tortuosity for narrow pores results in reasonable agreement with experiments. Analysis of pores roughness and orientation provides justification for this possible modification. Results for ZrB2 and TiB2 supply comparisons with those for HfB2. The outcomes therefore present insight into the fundamentals of oxidation that can help with materials processing and selection in extreme-environment aerospace applications
Disk Engine with Circumferential Swirl Radial Combustor
No full textA disk engine and system configured to provide high power at a reduced axial length is disclosed herein. The disk engine includes a radial compressor, a compressor discharge manifold positioned circumferentially about compressor, a combustion chamber positioned within the discharge manifold and a radial turbine positioned radially inward of the combustion chamber
An Investigation of Torso Muscle Fatigue and Its Relation to Discomfort during Vertical Vibration Exposure
Get PDFMilitary and civilian helicopter pilots often experience back pain attributed to vibration exposure during flight and sustained poor posture, known as the helicopter hunch. This study investigated torso muscle activation using surface electromyography (EMG) to assess the relationship between discomfort and muscle fatigue. Eight subjects were exposed to different vibration frequencies over two-hour periods, with EMG data collected every five minutes. Significant differences in muscle activation were observed before and after vibration exposure, suggesting increased muscle activation during vibration compared to prolonged seating alone. Discomfort surveys indicated higher discomfort during vibration
Human Performance Modeling Architecture with HC-130J Mission Application
Get PDFThe DOD emphasizes digital engineering using Model-Based Systems Engineering (MBSE), where MBSE includes SysML-based models of systems. Analysis of system impacts on the human operators and their performance typically occurs through non-MBSE approaches, if at all. One current process, considered the As-Is process for this research, evaluates human performance and workload using IMPRINT, a discrete event simulation (DES) tool. IMPRINT primarily exists as a standalone tool with limited built-in functionality to integrate with an MBSE tool. Using the HC-130J and its crew during a CSAR mission as the system, this research develops a generalizable and updateable human performance modeling architecture that combines SysML-based model development with a new To-Be process. The To-Be process creates a traceable process from the SysML-based model to IMPRINT, using a custom Excel tool that automatically organizes the data from the SysML-based model into an importable format for IMPRINT. Comparisons of the As-Is and To-Be process for both baseline and alternative models determined that the To-Be process is faster at creating the same baseline model than the As-Is process and also supports alternative model development with minimal manual rework by the modeler, while maintaining the SysML-based model as the authoritative source of truth
Forecasting Stock Prices Using ARIMA Models And Technical Analysis
Get PDFThis thesis explores the integration of Autoregressive Integrated Moving Average (ARIMA) models and technical analysis to forecast stock prices, with a focus on Coca-Cola\u27s (KO) and Netflix’s (NFLX) stocks. It examines the effectiveness of combining ARIMA models, known for their predictive accuracy in time-series analysis, with technical indicators, particularly moving averages. The study evaluates whether this integrated approach can enhance the predictive capability of stocks prices beyond traditional methods. The predictive capability is evaluated using error metrics from the ARIMA models, as well as by assessing the return earned using simple rules based on the technical indicators. Utilizing data spanning 2003 to 2023, the research applies a rolling forecast method to predict future prices over multiple periods. It also introduces novel investment strategies based on moving average cross over technique signals. The analysis includes a comprehensive literature review on forecasting, time series analysis, ARIMA models, and technical analysis, alongside related works demonstrating the practical application of these methods in diverse market conditions