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Daniel Guggenheim School of Aerospace Engineering

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https://hdl.handle.net/1853/70742

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College of Engineering

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Aerospace Design Group

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Aerospace Systems Design Laboratory (ASDL)

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Air Transportation Laboratory

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Cognitive Engineering Center

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Space Systems Design Laboratory (SSDL)

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Two studies in statistical data analysis for the space industry: cyclicality in the industry, and comparative satellite reliability analysis

(Georgia Institute of Technology, 2009-12) Hiriart, Thomas

This thesis brings statistical analyses techniques to bear on data derived from an extensive database of satellite launches and on-orbit anomalies and failures. The data collected is analyzed from two different perspectives and addresses, in two separate studies, two research objectives. The first study proposes to identify trends and cyclical patterns in the space industry, and to forecast the volume of launches for the next few years. Satellites have been rightfully described as the lifeblood of the entire space industry and the number of satellites ordered or launched per year is an important defining metric of the industry's level of activity. The structure of the space industry, its financial health and its workforce retention and development is dependent on the volume of satellites contracted. As such, trends and variability in this volume have significant strategic impact on the space industry. Over the past 40+ years, hundreds of satellites have been launched every year. Thus, an important data set is available for time series analysis and identification of trends and cycles in the various markets of the space industry. For the purpose of this first study, we collected data for over 6,000 satellites launched since 1960 on a yearly basis. We separated the satellites into three broad segments: 1) defense and intelligence satellites, 2) science satellites, and 3) commercial satellites. Several techniques are available for the analysis of time series data, both in the time domain and in the frequency domain. In this first study, we conducted spectral analysis of the time series for each of the three satellite populations and identified cycles contained in the data. In addition, once harmonic models were derived and fitted to the data, we built forecasting models of satellite launch volumes in the different market segments for the next few years. The potential implications of the results are discussed as a number of strategic matters for the space industry are contingent on the predictions or forecast of the volume of satellites contracted (the example of the U.S. auto industry is a solemn reminder of such possible strategic issues). The second study uses the previously collected launch data, confined to Earth-orbiting satellites launched between 1990 and 2008, and expanded with the failure information and retirement of each satellite to conduct a comparative analysis of satellite reliability in GEO, LEO, and MEO orbits. Reliability has long been recognized as an essential consideration in the design of space systems. However, there is limited statistical analysis of satellite reliability based on actual flight data. The objective of this second study is to conduct nonparametric satellite reliability analysis, with orbit type as a covariate, and to explore appropriate parametric fits (Weibull, lognormal, and mixture distributions). The results indicate for example that differences exist between the failure behaviors of satellites in different orbits, or that satellite infant mortality exists or dominates more clearly in a particular orbit type. The findings can be useful to satellite manufacturers as they would provide an empirical basis for reviewing and adjusting satellite testing and burn-in procedures.
Finding a representative day for simulation analyses

(Georgia Institute of Technology, 2009-11-23) Watson, Jebulan Ryan

Many models exist in the aerospace industry that attempt to replicate the National Airspace System (NAS). The complexity of the NAS makes it a system that can be modeled in a variety of ways. While some NAS models are very detailed and take many factors into account, runtime of these simulations can be on the magnitude of hours (to simulate a single day). Other models forgo details in order to decrease the runtime of their simulation. Most models are capable of simulating a 24 hour period in the NAS. An analysis of an entire year would mean running the simulation for every day in the year, which would result in a long run time. The following thesis work presents a tool that is capable of giving the user a day that can be used in a simulation and will produce results similar to simulating the entire year. Taking in parameters chosen by the user, the tool outputs a single day, multiple days, or a composite day (based on percentages of days). Statistical methods were then used to compare each day to the overall year. On top of finding a single representative day, the ability to find a composite day was added. After implementing a brute force search technique to find the composite day, the long runtime was deemed inconvenient for the user. To solve this problem, a heuristic search method was created that would search the solution space in a short time and still output a composite day that represented the year. With a short runtime, the user would be able to run the program multiple times. Once the heuristic method was implemented, it was found that it performed well enough to make it an option for the user to choose. The final version of this tool was used to find a representative day and the result was used in comparison with output data from a NAS simulation model. Because the tool found the representative day based on historical data, it could be used to validate the effectiveness of the simulation model. The following thesis will go into detail about how this tool, the Representative Day Finder, was created.
A strategic planning approach for the operational-environmental problem of air transportation system terminal areas

(Georgia Institute of Technology, 2009-11-16) Jimenez, Hernando

The air transportation system plays a crucial role in modern society, comprising a major industrial sector as well as a key driver for adjacent economies. Moreover, it is a prime enabler of the modern way of life, characterized by access to products and services from around the world, and access to remote locations. Therefore there is a strong incentive to maintain the system and promote its growth. None the less, important challenges have plagued civil aviation, particularly the commercial aviation sector. On one hand, demand for air travel has grown dramatically and at an accelerated pace, in part due to the deregulation of airlines in 1978, providing airlines with the freedom to arrange their operational schedule freely and compete for markets. The dynamic nature of demand and its fast-paced growth contrasts with the relative rigidity of air transportation infrastructure development and the sluggish evolution of its operational architecture. The supply-demand mismatch that results has led to degradation in system efficiency, excessive delays, and substantial economic losses. This phenomenon is particularly exacerbated in the terminal area of major airports which have inevitably become operational choke points. On the other hand the environmental impact of air transportation, embodied primarily by the emissions and noise caused by aircraft operations, has also grown as a result of the increase in aviation activity, and has therefore become a major issue of public interest. Airport communities experience said environmental impact most intensely, particularly those associated with bottleneck airports, and thus represent a uniquely strong force opposing further expansion of air transportation in these areas where it is most needed. Past efforts to address these challenges have been notably stovepiped and have failed to recognize the importance of the relationship between the operational nature of the system and its environmental impact. Only recently have research efforts begun to incorporate a joint view of the operational-environmental problem that attempts to formulate solutions accordingly. However, the state of the art has yet to answer some of the most fundamental questions. First, the relationship between operational and environmental elements has not been quantified conclusively. Doing so is vital to understand the operational-environmental nature of terminal areas before any solutions can be considered. Secondly, many different types of solution alternatives have been proposed, such as the construction of new runways, redesign of operational procedures, introduction of advanced aircraft concepts, and transformation of airspace capabilities. However, a direct comparison between dissimilar alternatives that accounts for operational and environmental issues is rarely found, and yet remains crucial in the formulation of a solution portfolio. More importantly, the additive and countervailing interactions that different solutions have on each other are widely recognized but remain, for the most part, unknown. Because all solutions under consideration require an extended period of time to develop and represent very large economic commitments, the selection of a portfolio demands a careful look at the future to determine the adequate measures that should be pursued in the present. In response to this methodological need, this thesis proposes a strategic planning approach to investigate the operational-environmental nature of the air transportation system, as well as the adequacy of solution alternatives for terminal areas in the formulation of a portfolio. The state of the art currently incorporates elements of strategic planning, but has yet to address two important methodological gaps. First, the inherent systemic complexity of airport performance obfuscates its quantitative characterization, which is paramount in attaining adequate insight and understanding to support informed strategic decision-making in the selection of terminal area solutions. Second, there is significant uncertainty about the evolution of the aviation demand and its operational context, making the use of forecasts grossly inadequate for this application. A scenario-based approach is used in its place, but the current frameworks for the generation, evaluation, and selection of an adequate scenario set currently lack traceability and methodological rigor. To address the first gap, this thesis proposes the use of well established statistical analysis techniques, leveraging on recent developments in interactive data visualization capabilities, to quantitatively characterize the interactions, sensitivities, and tradeoffs prevalent in the complex behavior of airport operational and environmental performance. Within the strategic airport planning process, this approach is used in the assessment of airport performance under current/reference conditions, as well as in the evaluation of terminal area solutions under projected demand conditions. More specifically, customized designs of experiments are utilized to guide the intelligent selection and definition of modeling and simulation runs that will yield greater understanding, insight, and information about the inherent systemic complexity of a terminal area, with minimal computational expense. Regression analysis leverages the creation of response surface equations that explicitly and quantitatively capture the behavior of system metrics of interest as functions of factors or terminal area solutions. This explicit mathematical characterization enables a variety of interactive visualization schemes that allow analysts and decision makers to confirm or rectify expected patterns of behavior, and to discover the unknown and the unexpected. Said visualization schemes are also instrumental in communicating, in a very direct and succinct fashion, complex relationships, sensitivities, tradeoffs, and interactions, that would be otherwise too complex to explain or communicate transparently. More importantly, this approach provides a rigorous and formalized mathematical framework within which the statistical significance of different factors or terminal area solutions can be quantitatively and explicitly assessed, primarily by means of statistical hypotheses testing of regression parameter estimates, such as the analysis of variance, or the t-statistic test. This proposed approach does not suggest a new strategic planning process, but rather improves specific steps pertaining to performance assessments, and builds upon established practices and the recommended planning process for airports to leverage on the decades of experience supporting the existing strategic airport planning paradigm. On the other hand, the proposed approach recognizes the methodological limitations and constraints that lead to the lack of terminal area performance characterization within the strategic planning process, embodied primarily by computational constraints and unmanageable systemic complexity, and directly addresses these shortcomings by incorporating mature statistical analysis techniques into key steps of said process. In turn, the proposed approach represents a novel adaptation of the strategic airport planning process that results in greater knowledge, insight, and understanding, at a resource cost comparable to current airport planning practices. As such, this proposed approach is demonstrated using the Atlanta Hartsfield-Jackson International Airport as a representative test case, and constitutes a contribution to strategic airport planning given that it supports strategic decision making by revealing, at an acceptable analysis and computational expense, the various sensitivities, interactions, and tradeoffs of interest in operational-environmental performance that would otherwise remain implicit and obfuscated by systemic complexity. For the research documented in this thesis, a modeling and simulation environment was created featuring three primary components. First, a generator of schedules of operations, based primarily on previous work on aviation demand characterization, whereby growth factors and scheduling adjustment algorithms are applied on appropriate baseline schedules so as to generate notional operational sets representative of consistent future demand conditions. The second component pertains to the modeling and simulation of aircraft operations, defined by a schedule of operations, on the airport surface and within its terminal airspace. This component is a discrete event simulator for multiple queuing models that captures the operational architecture of the entire terminal area along with all the necessary operational logic pertaining to simulated ATC functions, rules, and standard practices. The third and final component is comprised of legacy aircraft performance, emissions and dispersion, and noise exposure modeling tools, that use the simulation history of aircraft movements to generate estimates of fuel burn, emissions, and noise. A set of designed modeling and simulation experiments were conducted to examine the interactions between exogenous and endogenous factors, as well as their main and quadratic effect, on operational metrics such as delay, and on fuel burn as the primary environmental metrics. Results show that for a gate-hold scheme used to manage surface traffic density, the departure queue threshold features a statistically significant interaction with the increasing number of operations, but that otherwise the relative percent change in the number of operations remains as the predominant exogenous factor driving operational and environmental performance. A separate design of modeling and simulation experiments was conducted to test the statistical significance of proposed geographical regional categories that could potentially be used to classify operations and capture operational demand characteristics such as fleet mix, time of day distribution, and arrival/departure route distribution. Results show that whereas the proposed categorization is statistically significant for a few metric of interest, marginally significant for others, and not statistically significant for most metrics, the proposed regional classification scheme is not appropriate for operational demand characterization. The implementation of the proposed approach for the assessment of terminal area solutions incorporates the use of discrete response surface equations, and eliminates the use of quadratic terms that have no practical significance in this context. Rather, attention is entire placed on the main effects of different terminal area solutions, namely additional airport infrastructure, operational improvements, and advanced aircraft concepts, modeled as discrete independent variables for the regression model. Results reveal that an additional runway and a new international terminal, as well as reduced aircraft separation, have a major effect on all operational metrics of interest. In particular, the additional runway has a dominant effect for departure delay metrics and gate hold periods, with moderate interactions with respect to separation reduction. On the other hand, operational metrics for arrivals are co-dependent on additional infrastructure and separation reduction, featuring marginal improvements whenever these two solutions are implemented in isolation, but featuring a dramatic compounding effect when implemented in combination. The magnitude of these main effects for departures and of the interaction between these solutions for arrivals is confirmed through appropriate statistical significance testing. Finally, the inclusion o advanced aircraft concepts is shown to be most beneficial for airborne arrival operations and to a lesser extent for arrival ground movements. More specifically, advanced aircraft concepts were found to be primarily responsible for reductions in volatile organic compounds, unburned hydrocarbons, and particulate matter in this flight regime, but featured relevant interactions with separation reduction and additional airport infrastructure. To address the second gap, pertaining to the selection of scenarios for strategic airport planning, a technique for risk-based scenario construction, evaluation, and selection is proposed, incorporating n-dimensional dependence tree probability approximations into a morphological analysis approach. This approach to scenario construction and downselection is a distinct and novel contribution to the scenario planning field as it provides a mathematically and explicitly testable definition for an H parameter, contrasting with the qualitative alternatives in the current state of the art, which can be used in morphological analysis for scenario construction and downselection. By demonstrating that dependence tree probability product approximations are an adequate aggregation function, probability can be used for scenario construction and downselection without any mathematical or methodological restriction on the resolution of the probability scale or the number of morphological alternatives that have previously plagued probabilization and scenario downselection approaches. In addition, this approach requires expert input elicitation that is comparable or less than the current state of the art practices.
Model predictive control (MPC) algorithm for tip-jet reaction drive systems

(Georgia Institute of Technology, 2009-11-16) Kestner, Brian

Modern technologies coupled with advanced research have allowed model predictive control (MPC) to be applied to new and often experimental systems. The purpose of this research is to develop a model predictive control algorithm for tip-jet reaction drive system. This system's faster dynamics require an extremely short sampling rate, on the order of 20ms, and its slower dynamics require a longer prediction horizon. This coupled with the fact that the tip-jet reaction drive system has multiple control inputs makes the integration of an online MPC algorithm challenging. In order to apply a model predictive control to the system in question, an algorithm is proposed that combines multiplexed inputs and a feasible cooperative MPC algorithm. In the proposed algorithm, it is hypothesized that the computational burden will be reduced from approximately Hp(Nu + Nx)3 to pHp(Nx+1)3 while maintaining control performance similar to that of a centralized MPC algorithm. To capture the performance capability of the proposed controller, a comparison its performance to that of a multivariable proportional-integral (PI) controller and a centralized MPC is executed. The sensitivity of the proposed MPC to various design variables is also explored. In terms of bandwidth, interactions, and disturbance rejection, the proposed MPC was very similar to that of a centralized MPC or PI controller. Additionally in regards to sensitivity to modeling error, there is not a noticeable difference between the two MPC controllers. Although the constraints are handled adequately for the proposed controller, adjustments can be made in the design and sizing process to improve the constraint handling, so that it is more comparable to that of the centralized MPC. Given these observations, the hypothesis of the dissertation has been confirmed. The proposed MPC does in fact reduce computational burden while maintaining close to centralized MPC performance.
Design space pruning heuristics and global optimization method for conceptual design of low-thrust asteroid tour missions

(Georgia Institute of Technology, 2009-11-13) Alemany, Kristina

Electric propulsion has recently become a viable technology for spacecraft, enabling shorter flight times, fewer required planetary gravity assists, larger payloads, and/or smaller launch vehicles. With the maturation of this technology, however, comes a new set of challenges in the area of trajectory design. Because low-thrust trajectory optimization has historically required long run-times and significant user-manipulation, mission design has relied on expert-based knowledge for selecting departure and arrival dates, times of flight, and/or target bodies and gravitational swing-bys. These choices are generally based on known configurations that have worked well in previous analyses or simply on trial and error. At the conceptual design level, however, the ability to explore the full extent of the design space is imperative to locating the best solutions in terms of mass and/or flight times. Beginning in 2005, the Global Trajectory Optimization Competition posed a series of difficult mission design problems, all requiring low-thrust propulsion and visiting one or more asteroids. These problems all had large ranges on the continuous variables - launch date, time of flight, and asteroid stay times (when applicable) - as well as being characterized by millions or even billions of possible asteroid sequences. Even with recent advances in low-thrust trajectory optimization, full enumeration of these problems was not possible within the stringent time limits of the competition. This investigation develops a systematic methodology for determining a broad suite of good solutions to the combinatorial, low-thrust, asteroid tour problem. The target application is for conceptual design, where broad exploration of the design space is critical, with the goal being to rapidly identify a reasonable number of promising solutions for future analysis. The proposed methodology has two steps. The first step applies a three-level heuristic sequence developed from the physics of the problem, which allows for efficient pruning of the design space. The second phase applies a global optimization scheme to locate a broad suite of good solutions to the reduced problem. The global optimization scheme developed combines a novel branch-and-bound algorithm with a genetic algorithm and an industry-standard low-thrust trajectory optimization program to solve for the following design variables: asteroid sequence, launch date, times of flight, and asteroid stay times. The methodology is developed based on a small sample problem, which is enumerated and solved so that all possible discretized solutions are known. The methodology is then validated by applying it to a larger intermediate sample problem, which also has a known solution. Next, the methodology is applied to several larger combinatorial asteroid rendezvous problems, using previously identified good solutions as validation benchmarks. These problems include the 2nd and 3rd Global Trajectory Optimization Competition problems. The methodology is shown to be capable of achieving a reduction in the number of asteroid sequences of 6-7 orders of magnitude, in terms of the number of sequences that require low-thrust optimization as compared to the number of sequences in the original problem. More than 70% of the previously known good solutions are identified, along with several new solutions that were not previously reported by any of the competitors. Overall, the methodology developed in this investigation provides an organized search technique for the low-thrust mission design of asteroid rendezvous problems.
Exploring the F6 Fractionated Spacecraft Trade Space with GT-FAST

(Georgia Institute of Technology, 2009-11-12) Lafleur, Jarret M.

Released in July 2007, the Broad Agency Announcement for DARPA’s System F6 outlined goals for flight demonstration of an architecture in which the functionality of a traditional monolithic satellite is fulfilled with a fractionated cluster of free-flying, wirelessly interconnected modules. Given the large number of possible architectural options, two challenges facing systems analysis of F6 are (1) the ability to enumerate the many potential candidate fractionated architectures and (2) the ability to analyze and quantify the cost and benefits of each architecture. This paper applies the recently developed Georgia Tech F6 Architecture Synthesis Tool (GT-FAST) to the exploration of the System F6 trade space. GT-FAST is described in detail, after which a combinatorial analysis of the architectural trade space is presented to provide a theoretical contribution applicable to future analyses clearly showing the explosion of the trade space as the number of fractionatable components increases. Several output metrics of interest are defined, and Pareto fronts are used to visualize the trade space. The first set of these Pareto fronts allows direct visualization of one output against another, and the second set presents cost plotted against a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) score aggregating performance objectives. These techniques allow for the identification of a handful of Pareto-optimal designs from an original pool of over 3,000 potential designs. Conclusions are drawn on salient features of the resulting Pareto fronts, important competing objectives which have been captured, and the potential suitability of a particularly interesting design designated PF0248. A variety of potential avenues for future work are also identified.
Dynamics of longitudinally forced bluff body flames with varying dilatation ratios

(Georgia Institute of Technology, 2009-11-09) Plaks, Dmitriy Vital

This thesis focuses on experimentally measuring the response of varying dilatation ratio bluff body flames under harmonic excitation. Such flames are often encountered in jet engine afterburners and are susceptible to combustion instabilities. Previous work has been done modeling such flames, however, only limited experimental data has been obtained at these conditions and is the motivation for this thesis. The focus of this work is to measure the transfer function of longitudinally forced, varying dilatation ratio bluff body flames. The transfer function is obtained by measuring flame position and flame luminosity fluctuations at the forcing frequency. Specifically, the amplitude and phase of the fluctuations are characterized as a function of flow velocity, axial location, and perturbation amplitude. These measurements are also compared to available theoretical predictions, showing that qualitative measured trends are consistent with theory. In addition, a detailed quantitative comparison is performed at one condition, showing good agreement between predictions and measurements in the near and mid-field of the flame response. However, agreement is not obtained in the far-field, indicating that continued theoretical work is needed to understand the flame response characteristics in this region.
A comparative study and application of continuously variable transmission to a single main rotor heavy lift helicopter

(Georgia Institute of Technology, 2009-10-19) Hameer, Sameer Hameer Jaffer

Rotorcraft transmission design is limited by empirical weight trends that are proportional to the power/torque raised to the two-thirds coupled with the relative inexperience industry has with the employment of variable speed transmission to heavy lift helicopters of the order of 100,000 lbs gross weight and 30,000 installed horsepower. The advanced rotorcraft transmission program objectives are to reduce transmission weight by at least 25%, reduce sound pressure levels by at least 10 dB, have a 5000 hr mean time between removal, and also incorporate the use of split torque technology in rotorcraft drivetrains of the future. The major obstacle that challenges rotorcraft drivetrain design is the selection, design, and optimization of a variable speed transmission in the goal of achieving a 50% reduction in rotor speed and its ability to handle high torque with light weight gears, as opposed to using a two-speed transmission which has inherent structural problems and is highly unreliable due to the embodiment of the traction type transmission, complex clutch and brake system. This thesis selects a nontraction pericyclic continuously variable transmission (P-CVT) as the best approach for a single main rotor heavy lift helicopter to target the above mentioned obstacle for drivetrain design and provides advancement in the state of the art of drivetrain design over existing planetary and split torque transmissions currently used in helicopters. The goal of the optimization process was to decrease weight, decrease noise, increase efficiency, and increase safety and reliability. The objective function utilized the minimization of the weight and the constraint is the tooth bending stress of the facegears. The most important parameters of the optimization process are weight, maintainability, and reliability which are cross-functionally related to each other, and these parameters are related to the torques and operating speeds. The analysis of the split torque type P-CVT achieved a weight reduction of 42.5% and 40.7% over planetary and split torque transmissions respectively. In addition, a 19.5 dB sound pressure level reduction was achieved using active gear struts, and also the use of fabricated steel truss like housing provided a higher maintainability and reliability, low cost, and low weight over cast magnesium housing currently employed in helicopters. The static finite element analysis of the split torque type P-CVT, both 2-D and 3-D, yielded stresses below the allowable bending stress of the material. The goal of the finite element analysis is to see if the designed product has met its functional requirements. The safety assessment of the split torque type P-CVT yielded a 99% probability of mission success based on a Monte Carlo simulation using stochastic- petri net analysis and a failure hazard analysis. This was followed by an FTA/RBD analysis which yielded an overall system failure rate of 140.35 failures per million hours, and a preliminary certification and time line of certification was performed. The use of spherical facegears and pericyclic kinematics has advanced the state of the art in drivetrain design primarily in the reduction of weight and noise coupled with high safety, reliability, and efficiency.
Mission-based guidance system design for autonomous UAVs

(Georgia Institute of Technology, 2009-10-01) Moon, Jongki

The advantages of UAVs in the aviation arena have led to extensive research activities on autonomous technology of UAVs to achieve specific mission objectives. This thesis mainly focuses on the development of a mission-based guidance system. Among various missions expected of UAVs for future needs, autonomous formation flight (AFF) and obstacle avoidance within safe operation limits are investigated. In the design of an adaptive guidance system for AFF, the leader information except position is assumed to be unknown to a follower. Thus, the only measured information related to the leader is the line-of-sight range and angle. Adding an adaptive element with neural networks into the guidance system provides a capability to effectively handle leader's velocity changes. Therefore, this method can be applied to the AFF control systems that use passive sensing methods. The simulation and flight test results clearly show that the adaptive guidance control system is a promising solution for autonomous formation flight of UAVs. The successful flight evaluations using the GTMax rotary wing UAV also demonstrate unique maneuvering aspects associated with rotary wing UAVs in formation flight. In the design of an autonomous obstacle avoidance system, an integrated approach is proposed to resolve the conflict between aggressive maneuvering needed for obstacle avoidance and the constrained maneuvering needed for envelope protection. A time-optimal problem with obstacle and envelope constraints is used for an integrated approach for obstacle avoidance and envelope protection. The Nonlinear trajectory generator (NTG) is used as a real-time optimization solver. The computational complexity arising from the obstacle constraints is reduced by converting the obstacle constraints into a safe waypoint constraint along with an implicit requirement that the horizontal velocity during the avoidance maneuver must be non-negative. The issue of when to initiate a time-optimal avoidance maneuver is addressed by including a requirement that the vehicle must maintain its original flight path to the maximum extent possible. The simulation results using a rotary wing UAV demonstrate the feasibility of the proposed approach for obstacle avoidance with envelope protection.
Comparative Reliability of GEO, LEO, and MEO Satellites

(Georgia Institute of Technology, 2009-10) Hiriart, Thomas ; Castet, Jean-Francois ; Lafleur, Jarret M. ; Saleh, Joseph H.

Reliability has long been a major consideration in the design of space systems, and in recent years it has become an essential metric in spacecraft design trade-space exploration and optimization. The purpose of this paper is to statistically derive and compare reliability results of Earth-orbiting satellites as a function of orbit type, namely geosynchronous orbits (GEO), low Earth orbits (LEO) and medium Earth orbits (MEO). Using an extensive database of satellite launches and failures/anomalies, life data analyses are conducted over three samples of satellites within each orbit type and successfully launched between 1990 and 2008. Because the dataset is censored, the Kaplan-Meier estimator is used to estimate the reliability functions. Plots of satellite reliability as a function of orbit altitude are provided for each orbit type, as well as confidence bounds on these estimates. Using analytical techniques such as maximum likelihood estimation (MLE), parametric fits are conducted on the previous nonparametric reliability results using single Weibull and mixture distributions. Based on these parametric fits, a comparative reliability analysis is provided identifying similarities and differences in the reliability behaviors of satellites in these three types of orbits. Finally, beyond the statistical analysis, this work concludes with several hypotheses for structural/causal explanations of these trends and difference in on-orbit failure behavior.