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Integrated QFD-TRIZ Optimization Method for Satellite Solar Array Design in Cost-Sensitive Environment
Get PDFWith the rapid evolution of commercial space markets in the new space era, satellite design philosophy is transitioning from performance supremacy toward cost-effectiveness priority. This research concentrates on solar arrays, a critical component representing 15%-20% of satellite costs, addressing limitations in traditional design methodologies that inadequately coordinate user requirements and lack systematic frameworks for cost-performance collaborative optimization. We propose a solar array optimization methodology predicated on the integration of Quality Function Deployment (QFD) and the Theory of Inventive Problem Solving (TRIZ). This methodology incorporates cost dimensions through an extended QFD matrix, employs TRIZ to resolve technical contradictions, and develops a multi-constraint optimization model utilizing the NSGA-II multi-objective optimization algorithm. Through empirical validation with a commercial remote sensing satellite case study, results demonstrate that the optimized solar array design achieves a 12.5% cost reduction while simultaneously realizing a 20.8% improvement in specific power ratio, 0.04% enhancement in reliability, and 39.4% extension in operational lifespan. This cost-performance balanced optimization methodology provides practical engineering guidance for the commercial aerospace sector and offers valuable reference for promoting innovative satellite design in cost-sensitive environments
Project Anubis
Get PDFProject Anubis\u27 main objective is to design, fabricate, and test a cold gas thruster attitude control vehicle with the ability to maintain a stable orientation. This project will serve as a testbed to develop an active attitude control system for future Rocket Development Lab (RDL) rocket programs. The project is a cost-effective approach in building a vehicle that can control its orientation using ten cold gas thrusters for pitch, yaw, and roll attitude control. For attitude control, this project utilizes a deadband control. This control algorithm will be written in C++ for a Raspberry Pi. The Raspberry Pi will receive orientation data from an inertial measurement unit and automatically use thrusters to rotate the vehicle to stable orientation. Project Anubis will develop software to control the orientation along with designing a gyroscopic test stand. After software development, the program will be tested for its speed, accuracy, and duration until the sensors become inaccurate. Along with the software, the fluid system will be tested to determine its produced force, reaction time, and propellant consumption. As a project under the RDL, this project will help students put attitude control theory into practice and become experts within the field
IBM PS/55® 5545-T0B and IBM® PS/2 P70 Differences
No full textThe IBM PS/2® series, introduced in April 1987, was designed as the successor to the IBM PC XT®. It introduced the Micro Channel Architecture (MCA), which replaced the Industry Standard Architecture (ISA), along with innovations such as PS/2 ports, improved 3.5 DD to HD 1. 44MB floppy drives, UART . IBM Personal System/2 series are first released in April, 1 987 in U.S.A. Meanwhile in IBM Japan, they Introduced next personal computer series as IBM Personal System/55. IBM PS/55® is a personal computer series developed and manufactured by IBM® Japan. The IBM PS/ 55 was introduced to Japanese personal computer market a month after PS/2\u27s release. While externally similar to the PS/2, the PS/55 featured several unique hardware and software differences, such as Display Adapter I to V series. As regular users would not open the machine, people often overlooked PS/55 as PS/2 due to its close resemblance to its Western counterpart. Our Computer Restoration group is specialized in repairing non functional computers to function once again. Initially, our goal was to fix IBM PS/2® P70 computers. However, during repair, our team was able to acquire very unique IBM PS/55® 5545- T0B, which is a part of IBM PS/ 55 luggable series. This study aims to document the differences between the IBM PS/2 P70 and the Japanese IBM PS/ 55 5545-T0B, analyzing both hardware and software distinctions. By archiving, our group hopes to contribute to a better understanding of the IBM PS/ 55 series for future generations
Embry-Riddle VEX ROBOTICS
No full textThe Robotics Education and Competition (REC) Foundation is a worldwide organization that hosts robot-based sports and competition events for varying educational levels. This year’s game, High Stakes, challenges the team to complete a series of objectives to win points in competitive matches. The team has designed, built and tested robots that have fulfill these objectives, competing in local tournaments to qualify for the VEX World Championship in Dallas, Texas, which will soon be announced. An example in how participation in the club is useful, in addition to designing these robots, the team has utilized this opportunity to expand on student’s knowledge of additive manufacturing and materials with research into and the implementation of high strength and flexible filaments. Participating in the VEX U robotics competition has provided a platform for the ERAU VEX Robotics team to provide research into the use of stronger and flexible filaments. Team members modeled new parts for their robots and use the new filaments to make custom parts capable of handling more stress than normal filament. This creates an environment where members learn hands on material performance and cost-effective manufacturing in competitive robotics. The use of high extrusion temperature, high strength filaments has allowed us to produce more cost-effective reliable components
Modeling Damage Evolution and Damage Healing using Data Driven Method
No full textAdvanced aircraft use composite materials in over 50% of their airframe structures. Defects in composite materials frequently arise during manufacturing and processing phases, greatly affecting structural integrity and reliability. The intrinsic self-healing polymers are smart materials that have the capability to recuperate a crack partially or fully by intermolecular interaction such as disulfide, hydrogen bonding, hydrophobic interaction, ionic bonding. In this dissertation, we mainly have two contributions: (1) We used zero-bias deep neural networks to analyze a multisource dataset developed by Pragathi comprising grayscale images and strain contours during composite materials processing. We also utilized a Zero-Bias Deep Neural Network model to effectively detect defects in composite materials, thus enabling their repair or mitigation. In this approach, the last dense layer of a standard deep neural network is modified by incorporating a dimensionality reduction layer and a similarity-matching layer. The model identified multiple defects without individual training on any defective images. While grayscale images and strain data were successfully used independently for defect detection, the proposed approach achieved superior performance by combining both, resulting in a high anomaly detection accuracy of 99.09%. Our findings demonstrate a clear synergistic effect, where integrating strain profiles with grayscale images enhances defect detection effectiveness. (2) We also explored the study of self- healing polymer/carbon fiber composite, synthesized using a one-pot polycondensation method. This method combines amino-terminated poly(dimethylsiloxane) (Mn = 5000) with 4,4′- methylenebis (phenyl isocyanate) and isophorone diisocyanate, applied to carbon unidirectional fiber. We envisage applications where the self-healing polymer present in the composite interfaces heals the cracks on carbon fibers
Applying Software Engineering Black-Box Methods for Testing Machine Learning Models
Get PDFThis dissertation proposes researching an approach to incorporate and align Software black-box testing methods into Machine Learning (ML) applications, specifically in the context of computer vision models. Typically, testing methods within Software Engineering (SE) encompass a range of test types that assess levels of a software system, such as Unit, Integration, Functional, and System testing [1]. The testing spectrum offers two perspectives on the system: black-box, where the system’s code is hidden, and white-box, where the system\u27s code is exposed for testing. Software Quality pairs testing with requirements, in a many-to-one relationship, to ensure proper validation of the software system. Within ML, models are often tested in a black-box manner, with the focus on the input-output classification. However, the results are quantified by metrics such as, F-1 score, accuracy, precision, and recall, which do not capture the nuanced behavior of the ML model [2]. Interestingly, ML Non-Functional Requirements (NFR) are a growing research area with ML Functional Requirements (FR) remaining relatively unexplored [3]. This dissertation proposes using the growing ML FR domain to invigorate the testing of ML. The goal of this research is to leverage the expanding functional requirements research to introduce black-box testing methods of equivalence partition testing, decision table testing, and boundary value analysis, to uncover the behavioral qualities of an ML model. This dissertation accomplishes the exploration of ML testing through four case studies, which iteratively examine software engineering-based black-box testing methods and the adjustments required to accommodate the stochastic and non-deterministic nature of ML. The new SE-inspired ML testing strategies were evaluated on their abilities to fulfill requirements, which are a staple for traditional SE-based verification. The results showed that the strategies, by fulfilling requirements linked to specific behaviors, are effective in testing and identifying ML model behaviors in both industry-grade and academic object detection models
The Interaction of a Transient Forward Axial Disturbance Flow With a Rotor
Get PDFThis research presents an in-depth investigation of the aeroacoustic characteristics of a scaled-down Joby Aviation rotor, focusing on its performance under realistic conditions for urban air mobility (UAM). Utilizing a high-fidelity numerical simulation approach that combines Large-Eddy Simulation (LES) and Unsteady Reynolds-Averaged Navier-Stokes (URANS) models, the study effectively captures the complex aerodynamic and acoustic interactions inherent in rotor operations. Validation of the numerical framework was achieved through comprehensive comparisons with available experimental data, demonstrating a high level of accuracy in predicting both aerodynamic performance metrics, including thrust and torque, as well as acoustic emissions. The simulation revealed significant impacts resulting from transient disturbances designed to emulate realistic urban gust conditions. These disturbances notably affected the aerodynamic loading on the rotor blades, resulting in observable variations in thrust and torque.
Additionally, a detailed analysis of wake structures revealed substantial alterations, characterized by intensified turbulence and increased vortex interactions. The research identified a marked increase in broadband noise attributable to these turbulent phenomena and the subsequent ingestion of vortical structures, underscoring the critical importance of accurately modeling transient effects for the development of UAM vehicles. The findings from this study underscore the importance of considering non-uniform inflow conditions and the dynamic interactions between rotor blades and turbulent wakes. These insights not only contribute to a deeper understanding of rotorcraft aeroacoustics but also provide valuable guidance for the design, optimization, and operational planning of advanced aerial mobility solutions aimed at minimizing noise pollution and enhancing public acceptance
Machine Learning Methods for Hypervelocity Fragment Flyout Characterization
Get PDFResulting from breakup events, such as collisions and explosions, hypervelocity fragments create potential hazards for both terrestrial and on-orbit environments, such as terrestrial weapons explosions and satellite breakup events, respectively. To avoid unnecessary damage, an accurate understanding or characterization of hypervelocity fragmentation events is vital. Currently, publicly available two-line elements collected from on-orbit breakup events are limited, excluding pre-detonation parent body conditions, such as orientation, and information of smaller fragments. The uncertainty of these datasets varies between each collected set. Therefore, the overall goal of this work is to employ machine learning to estimate distribution characteristics of a space debris cloud resulting from on-orbit breakup events by supplementing existing space debris data with simulated fragmentation events and augmented datasets.
The first proposed machine learning technique uses Gaussian mixture models and K-nearest neighbors regression to estimate the spatial distribution and number of fragments resulting from an on-orbit collision breakup event. The technique is applied to real satellite debris information in the form of two-line element datasets.
Neural networks are also proposed to predict the location of satellite debris fragments following an explosion. Here, realistic terrestrial explosions are used to model the initial conditions of the satellite debris fragments relative to a parent body directly after an explosion occurs. Parent body detonation locations are randomly selected around the final orbit of NOAA-16, a weather satellite that experienced an explosion event in 2015. The debris is propagated under the influence of J2 through J6 zonal gravitational harmonics and atmospheric drag perturbations using analytic continuation, a highly precise and efficient semi-analytic integration method. The collected simulation data is used to train a neural network, from which one can predict the orbital elements of each of the fragments individually.
After initial tests with this simulation method, another fragmentation training dataset was propagated in the same manner with the addition of the final conditions of NOAA-16. Then, two-line element information for the debris resulting from the explosion of NOAA-16 were propagated using the SGP4 model and filtered for accuracy. A final dataset is formed by strategically up-sampling a limited filtered two-line element dataset at the initial timestep of the explosion and then propagating all fragments. Distributions are utilized again, this time in the form of generalized extreme value distributions, to model the orbital elements of the debris cloud at each timestep for all datasets. Deep neural networks can then be used to estimate the overall behavior of the debris cloud. By combining the real two-line element data with the synthetic simulation data and the augmented dataset, the training data contains realistic dynamics with consideration of new information that may be missed by the two-line element dataset, better generalizing the model.
A validation dataset is used to test the complete augmented machine learning approach, proving to successfully estimate orbital element distributions for semi-major axis, eccentricity, inclination, and right ascension of the ascending node for the NOAA-16 breakup event, which produces fragments in near-circular orbits. This approach is also validated by demonstrating an improvement in estimations when compared to a set of inaccurate two-line elements filtered from the training and testing dataset. In terms of novelties, machine learning successfully enables a fast space debris orbital element characterization tool with reduced computational costs and improved accuracy to bridge the gap between simulations and realistic data, capturing aspects excluded from the real two-line element data such as possible missed fragments. Overall, this dissertation successfully presents an approach contributing to space debris estimation methods