71-80 of 201 results
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Implementing Active Learning Techniques in an Undergraduate Aviation Meteorology Course
PI Daniel Halperin
PI Joseph Keebler
CO-I Robert Eicher
CO-I Thomas Guinn
CO-I Kim Chambers
Student feedback from end-of-course evaluations repeatedly indicated a desire to change the format of the course by de-emphasizing the PowerPoint-based lectures. The goal of the present study was to determine whether including a set of new active-learning techniques in an Aviation Weather course would result in better student understanding (as measured by exam scores) and make the course more engaging (as measured by end-of-course evaluations). During 2018-19, three instructors implemented five different active-learning techniques into their classes (i.e., the experimental group), while two instructors continued to use the unrevised course materials (i.e., the control group). The new active-learning techniques, described below, included daily quizzes, polling questions, flipped classroom sessions, in-class activities, and assertion-evidence-based lectures. All sections used the same assignments and exams, allowing for direct assessment of the effectiveness of the active-learning techniques. Analyses of Variance (ANOVA) tables were used to determine the statistical significance of the differences in exam scores. Indirect assessments in the form of end-of-course evaluations were also examined.
Categories: Faculty-Staff
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Adding Tropical Cyclone Verification Capabilities to the Model Evaluation Tools – Tropical Cyclone (MET-TC) Software
PI Daniel Halperin
Producing reliable tropical cyclone (TC) genesis forecasts is an operational priority. The National Hurricane Center uses several TC genesis guidance products for their Tropical Weather Outlook. Furthermore, global model output is used in many TC genesis guidance products and is considered an important source of deterministic TC genesis forecast guidance. This project creates a standard framework for verifying deterministic and probabilistic TC genesis forecasts using the TC-Gen tool in the Model Evaluation Tools software package.
Accurately predicting tropical cyclone (TC) genesis is an important component of providing the public with the information they need to protect life and property in the event of a landfalling storm. The National Hurricane Center (NHC) issues probabilistic forecasts of TC genesis within two and five days in their Tropical Weather Outlook product. There are several guidance products available to the forecasters, many of which rely at least in part on global forecast models. It is important to understand how accurate these guidance products and the global models are at forecasting TC genesis.
This project seeks to create a standard framework for verifying TC genesis forecasts from various sources. This will allow a straightforward comparison between official forecasts, the experimental guidance products, and global model forecasts. These verification capabilities are developed within the existing Model Evaluation Tools (MET) software package from the National Center for Atmospheric Research (NCAR). As such, the user will have considerable flexibility when setting up a verification task. They will also be provided numerous output statistics for deterministic and probabilistic forecasts.
Categories: Faculty-Staff
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Demonstration of an Electrostatic Dust Shield on the Lunar Surface
PI Troy Henderson
This project will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface.
This project, which is funded by NASA Kennedy Space Center, will demonstrate the capability of an electrostatic dust shield, developed by NASA/KSC engineers, to remove dust from the lens of a camera after impact on the lunar surface. Laboratory tests will confirm the experiment design, followed by a flight to the lunar surface in early 2022
Categories: Faculty-Staff
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Hazard Detection and Avoidance for Lunar Landing
PI Troy Henderson
This project develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander
This project, funded by Intuitive Machines, develops and demonstrates algorithms for detecting and avoiding areas of large rocks and high slopes for a lunar lander. Preliminary work uses an optical camera and future work will include a lidar sensor. These algorithms will be tested in simulation, tested in laboratory experiments and demonstrated on a lunar lander flight mission.
Categories: Faculty-Staff
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Improved Image Processing for Orbit Estimation
PI Troy Henderson
This project seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes.
This project, funded by Air Force Research Laboratory, seeks to improve orbit estimation methods using advanced image processing techniques applied to images from ground and space-based telescopes. Additional work uses RF signals to estimate orbits of transmitting spacecraft.
Categories: Faculty-Staff
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Using Machine Learning to Improve Forecasting of Deep Convection
PI Christopher Hennon
CO-I Ronny Schroeder
CO-I Curtis James
CO-I Abd AlRahman AlMomani
We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).
This research, funded through an NSF EAGER grant, seeks to improve forecast accuracy of monsoon thunderstorm activity and precipitation amounts in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service tto help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.
The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research.
Categories: Faculty-Staff
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Data-enabled Science & Engineering Education (IUSE 1626602, 2016-2020),
PI Matthew Ikle
CO-I Hong Liu
CO-I Michael Wolyniak
CO-I Raphael Isokpehi
This project will develop a virtual department across four partner campuses to provide computer science education to students at campuses that are individually too small to support this kind of department. The new department will focus on the analysis of "big data" - large sets of computational and observational data - that are becoming increasingly prevalent in STEM. Cyber-learning techniques such as recorded lectures, archived materials, blog participation, and active learning approaches will be combined to offer a set of classes in big data science spanning meteorology, environmental science, biology and chemistry. By combining students from different campuses into the same courses, problems with minimal resources and limited potential enrollments on the individual campuses can be overcome. In particular, the project will focus on developing courses in biology and earth science, areas where students are not attracted by traditional computer science classes.
The project will develop a flexible, blended learning model and effective learning assessment tools that can be implemented across multiple disciplines and institutions. The major goals and corresponding objectives of the project are to:
1) Develop and implement high quality and relevant Computational and Data-Enabled Science and Engineering (CDSE) courses in mathematical modeling, data mining, genomics and bioinformatics, and problems in atmospheric and hydrospheric science using active learning and research-based teaching methodologies that promote inter-institutional and interdisciplinary collaboration.
2) Use innovative web-based technologies, to develop and implement learning assessment tools to gauge achievement of students from diverse backgrounds and contexts.
3) Develop, implement, and test an expanded CDSE pedagogical network in which resource sharing allows institutions of all sizes and types to consistently and sustainably offer CDSE coursework.
Instructors from different campuses will be paired in a peer teaching/peer review model for course design and implementation. Including pairs of instructors from different institutions ensures that (1) each instructor will gain the knowledge and experience to teach a new course that is originally developed by the other instructor; and (2) the courses are thoroughly reviewed and revised by peers. The coalition will share its discoveries in building inter-institutional teaching efficiency, undergraduate research opportunities, and learning assessment via online networks, new coalition partners, conferences, and publications.Categories: Faculty-Staff
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The Covariant Stone-von Neumann Theorem for Actions of Abelian Groups on C*-algebras of Compact Operators
PI Lara Ismert
This is a collaborative work with Leonard Huang, Ph.D. at the University of Nevada, Reno.
In this paper, we formulate and prove a version of the Stone-von Neumann Theorem for every C*-dynamical system of the form (G,K(H),α), where G is a locally compact Hausdorff abelian group and H is a Hilbert space. The novelty of our work stems from our representation of the Weyl Commutation Relation on Hilbert K(H)-modules, instead of just Hilbert spaces, and our introduction of two additional commutation relations, which are necessary to obtain a uniqueness theorem. Along the way, we apply one of our basic results on Hilbert C*-modules to significantly shorten the length of Iain Raeburn's well-known proof of Takai-Takesaki Duality.Categories: Faculty-Staff
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CONVECT (CONvection and water Vapor Exchange in Complex Terrain)
PI Curtis James
CO-I Ronny Schroeder
CONVECT is a major meteorological field research project being proposed for July - August 2027 in north-central Arizona. The project is aimed at improving our understanding and ability to predict the convective development, propagation, and intensification of thunderstorms during the North American Monsoon (NAM)
CONVECT is focused in north-central Arizona between the cities of Prescott and Flagstaff. This targeted region, encompassing the Bradshaw Mountains, Black Hills, Verde and Prescott Valleys, and Mogollon Rim, provides an ideal laboratory for investigating processes connecting complex terrain to boundary-layer and convective processes. During the summer monsoon season, this region experiences frequent deep, precipitating convection. These storms typically initiate over the most prominent terrain features and sometimes propagate into the populated lower lying areas or send out density currents or buoyancy bores that subsequently initiate new convection. The thunderstorms are generally spatially localized, forming over a deep convective boundary layer, but are often associated with pulse-severe conditions (damaging wind gusts or large hail). Some cells may become terrain-locked or exhibit back-building behavior, leading to intense rainfall and flash flooding.
CONVECT will also examine the water vapor sources and land-atmosphere interactions favoring convective initiation. Our previous work has shown that soil moisture is an important predictor of monsoon convection. We will therefore examine the effects of soil moisture variability on surface and PBL energy and moisture exchange over heterogeneous, sloping surfaces within a thermally driven planetary boundary layer (PBL). The proposed deployment includes a dense network of surface flux and energy balance probes, lower-tropospheric thermodynamic and kinematic profiling systems, mobile radars, and crewed and uncrewed aircraft with in-situ and remote sensors. The campaign will be carefully guided by multi-scale modeling and our machine learning model, and in turn, experimental observations will be assimilated to evaluate their impact on multi-scale predictability and the validity of surface layer and PBL parameterizations in complex terrain. The CONVECT science team of instrument scientists and numerical modelers contains the necessary, complementary expertise in the surface layer, the boundary layer, and deep convection to substantially advance understanding of water vapor exchanges between the surface and free troposphere, as well as extreme precipitation. The broader impacts will be improved forecast accuracy during the North American Monsoon by identifying improvements in operational instrumentation networks and forecast model parameterizations.Categories: Faculty-Staff
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3D Printing of Continuous Carbon Fiber Composites with Programmable Thermal Behaviors: A Proactive Safety Design for Advanced Thermal Management
PI Yizhou Jiang
CO-I Leitao Chen
CO-I Yanbing Chen
This study aimed to fabricate composite materials, i.e. continuous carbon fibers reinforced thermoset composites (CCFRTC), in a way that makes heat transfer predictable, enabling effective control measures. The ability to control thermal transfer through 3D-printing can lead to significant improvements in preventing thermal-related accidents.
Findings: Final report submitted 9/24. This study demonstrated the adaptability and precision of the team’s 3D printing method but also underscored its potential in advancing the field of thermosetting composite material manufacturing, paving the way for innovative applications, including fire suppression systems.
Scholarly products: Three external proposals were submitted, one $38k award was received from the Florida Space Research Program. Two journal articles were submitted, one has been accepted and one is under second round review. Two conference presentations have been accepted.
- Zhuoyuan Yang, Evan Medora, Zefu Ren, Meng Cheng*, Sirish Namilae*, and Yizhou Jiang*. "Coaxial direct ink writing of ZnO functionalized continuous carbon fiber-reinforced thermosetting composites." Composites Science and Technology (Acceptance: 7/30/2024): Impact Factor: 8.3 DOI: https://doi.org/10.1016/j.compscitech.2024.110782
- Zhuoyuan Yang, Kehao Tang, Wenjun Song, Zefu Ren, Yuxuan Wu, Daewon Kim, Sirish Namilae, Yifei Yuan*, Meng Cheng*, and Yizhou Jiang*. " Coaxial direct writing of ultrastrong supercapacitors with braided continuous carbon fiber based electrodes" Chemical Engineering Journal. Under journal’s 2nd round review. Impact Factor: 13.3 18
- Conference papers
- Myles Brussels, Theodore Bernold, Patrick McGuinness, Casey Troxler, Sandra Boetcher, Zhuoyuan Yang, Zefu Ren, Daewon Kim, Yanbing Chen, Yizhou Jiang, Leitao Chen. 3D Printing of Continuous Carbon Fiber Composites with Programmable Thermal Behaviors: A Proactive Safety Design for Advanced Thermal Management. 2025 AIAA SciTech.
- Zefu Ren, Zhuoyuan Yang, Rishikesh Srinivasaraghavan Govindarajan, Nicholas Reed, Daewon Kim, Yizhou Jiang. Flame Retardancy of Additively Manufactured Continuous Carbon Fiber Reinforced PEKK Composites with Expandable Graphite Coating. 2025 AIAA SciTech.
Categories: Faculty-Staff
71-80 of 201 results