LOUIS University of Alabama in Huntsville
Not a member yet
8547 research outputs found
Sort by
Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network
Get PDFEnabling outer planet exploration : performance and feasibility of nuclear thermal propulsion for rendezvous missions
Get PDFNuclear Thermal Propulsion (NTP) provides the compelling alternative between the chemical and electric propulsion systems for outer planet robotic missions. High-thrust and high-specific impulse (over twice the best chemical propulsion engine) NTP systems can enable outer planet missions that have been limited due to the large ΔV requirements. This dissertation identifies an enabling mission architecture and NTP engine thrust class for rendezvous missions to the Gas giant and Ice giant systems. The work presented in the dissertation demonstrates the performance impact of the NTP system and its feasibility for robotic missions using a systems engineering model driven by the model based systems engineering (MBSE) which is coupled with the domain engineering analysis models and systems engineering architectural models. The performance metrics are chosen based on the finite maneuver analysis and design reference mission trade tree to determine the solution space of NTP for high energy missions. The developed spacecraft integrated system model allows rapid mission analysis for engine thrust class ranging from 5 klbf to 30 klbf and Isp from 850 s to 900 s for expendable and non-expendable architectures. The direct spacecraft injection analysis showed payload delivery of over 12% to Jupiter and over 30% to Saturn for NTP system on a commercial heavy lift launch vehicle when compared with standalone super heavy-lift launch vehicles. The point design studies demonstrated the enhanced capability of NTP system by reducing the trip times to Gas giant missions by a factor of two or more. Engine trade analysis shows 12.5 to 15 klbf NTP engines are optimum for rendezvous missions to the outer planets using expendable configuration, which consists of a spacecraft and NTP injection stage with injection stage being used for trans-planetary injection and plane change maneuver only and spacecraft’s storable propellant to be used for planetary orbit insertion. Payload mass delivery using an expendable configuration for a Jupiter rendezvous mission is shown to outperform non-expendable configuration (NTP system to be used for both trans-planetary injection and planetary orbit insertion) by 5.67% for Hohmann transfers. However, non-expendable configurations have shown similar payload delivery for high energy Type-I trajectory missions with trip times of 1.49 years
A novel dual-band outline elliptical dipole antenna for passive energy harvesting
Get PDFIn this thesis, a new dual-band antenna is presented for use in space-based passive energy harvesting. This antenna is based on elliptical dipole antennas, which are often used for their ultra-wideband (UWB) properties. The inner metallization is removed, leaving an outline antenna and room for a second set of antenna arms. This will in turn result in an interleaved structure to tune each set of dipole arms to two different frequencies. Due to the close proximity of the dipole arms there exists strong mutual coupling, which is lessened by adding decoupling elements to the design. The proposed antenna is supported by a partial ground plane to improve the front-to-back ratio of the radiation patterns. Different design iterations are full-wave analyzed in which the ground plane is extended with an exponential taper and additional parasitic elements are added to improve antenna performance. Sweeps are then carried out to show the effects of the eccentricity of the outer ellipse and the effects of the curvature of the extended ground plane. An additional method of reducing the board size is presented as well. Finally, the dual-band elliptical outline design was fabricated and measured and the results found to be in good agreement with simulation. This antenna design provides good impedance matching, peak gain, and radiation pattern for both of the bands of interest
Improving access to diabetes self-care education in underserved patients with poorly controlled diabetes
No full textDiabetes is a chronic health problem. When adequately controlled, the patient can live an active and high-quality life. Poorly controlled diabetes increases the risk of the development of diabetes-related complications. Despite evidence of the success of diabetes education programs in improving diabetes self-care skills and outcomes, many barriers limit patients’ access to these services. This quality improvement project implemented diabetes self-management education for adult patients aged 18 and older with poorly controlled diabetes. This project provided access to diabetes education among patients currently without access to these services. The diabetes education was based on the Association of Diabetes Care and Education Specialists (ADCES) curriculum called the ADCES 7 Self-Care Behaviors. The project’s outcomes were evaluated by measuring sample demographics, pre and post-education clinical outcomes, and self-care skills
Introduction to Computer Architecture and Operating Systems
Get PDFComputer architecture is a set of rules and methods that describe the functionality, organization, and implementation of computer systems. The architecture of a system refers to its structure in terms of separately specified components of that system and their interrelationships.
In a similar manner to other uses of the word architecture, computer architecture is focused on determining the needs of the user/system/technology and creating a logical design and standards based on those requirements.
The goals for this course include: To learn how to write advanced ARM Assembly Language programs for the Raspberry Pi and the relationship of these instructions to the hardware that implements them. To learn how to calculate the different performance metrics with CPUs so that different CPUs performance can be compared. To learn how pipelines work and calculate the effects on CPU performance associated with branches. To learn the different cache organizations and calculate the performance of cache and effects on overall CPU performance. To learn the different Input/Output strategies used for modern computers and the advantages and disadvantages of each. To learn the organization of modern computer main memory. To learn security defense mechanism implemented by CPUS.https://louis.uah.edu/oer/1000/thumbnail.jp
Quasi-static and dynamic tension testing of as-built and heat-treated additively manufactured 316L stainless steel
Get PDFAdditive manufacturing (AM) has several advantages over conventional subtractive manufacturing techniques, including the ability to create parts of highly complex geometries in one process thus reducing time and cost for each part. However, AM parts show different mechanical behavior compared to wrought parts due to the differences in the manufacturing processes. In particular, lack-of-fusion defects, voids, and keyhole defects can act as crack initiation sites leading to the possibility of more brittle behavior. This may be exacerbated by high rate loads, which tends to induce more brittle behavior, higher flow stress, and higher yield stress in materials. Therefore, it is critically important to characterize the material at the strain rate it will experience during use, for example in the automotive industry where the AM parts will experience dynamic loading during crash events. In this study, tension tests are performed on AM 316L stainless steel at strain rates of 10^−3 ^-1, 1000 ^-1, 2500 ^-1, and 5000 ^-1, and results are compared to conventional wrought 316L. The experimental results are used to develop a material model for finite element analysis using LS-DYNA. The microstructure in the samples are then examined
The effectiveness of online video-based training methods
Get PDFEmployee training is considered critical for organization success legitimacy. The present study investigated the effects of four different online training methods on post- test recall. Student participants from UAH viewed one of four types of videos covering diversity, equity, and inclusion concepts--generative scenarios in which participants generated a label for what the scenarios covered, descriptive scenarios that included a label, a control with scenarios, and a control with no scenarios. Scenarios involved school-based and work-based examples to assess how the relevance of context for student non-workers versus student-workers impacted recall. A repeated measures ANOVA revealed that across all four conditions participants showed improved performance after watching the DEI training videos; however, there were no differences between training methods and there were no significant differences in the recall performance of work-and school-based scenarios between student workers and student non-workers. We can conclude from our research that video-based training methods enhanced learning
Deep learning approach for robust structural health monitoring using guided Lamb wave responses
Get PDFGuided Lamb waves offer a promising solution for the early detection of internal damages in structures due to their high sensitivity to small damages. However, noise can adversely impact the development of data-driven models for damage detection, leading to inaccurate monitoring systems. This thesis explores deep learning techniques to robustly predict the location and severity of damage in cantilevered beams using noisy guided wave responses. Initially, Multi-Layer Perceptron (MLP) is trained with frequency domain features to achieve robust performance against noisy data. Further performance improvement is achieved using end-to-end learning models, which include autoencoder and one-dimensional Convolutional Neural Network (CNN). The autoencoder demonstrates better dimensionality reduction compared to frequency-based feature extraction while also exhibiting better performance. The one-dimensional CNN model outperforms other techniques, achieving an R2 score of 0.9908 in the highest noise level settings. These results facilitate the development of robust structural health monitoring using deep learning techniques