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Daniel Guggenheim School of Aerospace Engineering

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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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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.
A multi-fidelity analysis selection method using a constrained discrete optimization formulation

(Georgia Institute of Technology, 2009-08-17) Stults, Ian Collier

The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method that will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results.
A modeling process to understand complex system architectures

(Georgia Institute of Technology, 2009-07-06) Balestrini Robinson, Santiago

Military analysis is becoming more reliant on constructive simulations for campaign modeling. Requirements for force-level capabilities, distributed command and control architectures, network centric operations, and increased levels of systems and operational integration are straining the analysis tools of choice. The models constructed are becoming more complex, both in terms of their composition and their behavior. They are complex in their composition because they are constituted from a large number of entities that interact nonlinearly through non-trivial networks and in their behavior because they display emergent characteristics. The modeling and simulation paradigm of choice for analyzing these systems of systems has been agent-based modeling and simulation. This construct is the most capable in terms of the characteristics of complex systems that it can capture, but it is the most demanding to construct, execute, verify and validate. This thesis is focused around two objectives. The first is to study the possibility of being able to compare two or more large-scale system architectures' capabilities without resorting to full-scale agent-based modeling and simulation. The second objective is to support the quantitative identification of the critical systems that compose the large-scale system architecture. The second objective will be crucial in the cases where a constructive simulation is the only option to capture the required behaviors of the complex system being studied. The enablers for this thesis are network modeling, graph theory, and in particular, spectral graph theory. The first hypothesis, stemmed from the first objective, states that if the capability of an architecture can be described as a series of functional cycles through the systems that compose them, then a simple network modeling construct can be employed to compare the different architectures' capabilities. The objective led to the second hypothesis, which states that a ranking based on the spectral characteristics of the network of functional interactions indicates the most critical systems. If modeling effort is focused on these systems, then the modeler can obtain the maximum fidelity model for the minimum effort.
An evolutionary method for synthesizing technological planning and architectural advance

(Georgia Institute of Technology, 2009-05-18) Cole, Bjorn Forstrom

There are many times in which a critical choice between proposed system architectures must be made. Two situations in particular motivate this dissertation: a "Cambrian explosion" when no dominant rchitecture has arisen, and times in which developments enable challenges to a dominant incumbent. In each situation, the advance of core technologies is key. This dissertation features a new computing technique to systematically explore the interaction of technological progress with architectural choices. This technique is founded upon a graph theoretic formulation of architecture, which enables the consideration of multifunctional components and modularity v. synergy trades. The technique utilizes a genetic algorithm formulated for graphs, and a solver that automatically constrains and optimizes component design variables. The use of quantitative technology models, graph theoretic formulation, and optimization algorithms together enables a systematic exploration of both time and combinatorial spaces. The quantitative results of this exploration enhance the strategic view of technology planners.
A design methodology for evolutionary air transportation networks

(Georgia Institute of Technology, 2009-05-18) Yang, Eunsuk

The air transportation demand at large hubs in the U.S. is anticipated to double in the near future. Current runway construction plans at selected airports can relieve some capacity and delay problems, but many are doubtful that this solution is sufficient to accommodate the anticipated demand growth in the National Airspace System (NAS). With the worsening congestion problem, it is imperative to seek alternative solutions other than costly runway constructions. In this respect, many researchers and organizations have been building models and performing analyses of the NAS. However, the complexity and size of the problem results in an overwhelming task for transportation system modelers. This research seeks to compose an active design algorithm for an evolutionary airline network model so as to include network specific control properties. An airline network designer, referred to as a network architect, can use this tool to assess the possibilities of gaining more capacity by changing the network configuration. Since the Airline Deregulation Act of 1978, the airline service network has evolved from a point-to-point into a distinct hub-and-spoke network. Enplanement demand on the H&S network is the sum of Origin-Destination (O-D) demand and transfer demand. Even though the flight or enplanement demand is a function of O-D demand and passenger routings on the airline network, the distinction between enplanement and O-D demand is not often made. Instead, many demand forecast practices in current days are based on scale-ups from the enplanements, which include the demand to and from transferring network hubs. Based on this research, it was found that the current demand prediction practice can be improved by dissecting enplanements further into smaller pieces of information. As a result, enplanement demand is decomposed into intrinsic and variable parts. The proposed intrinsic demand model is based on the concept of 'true' origin-destination demand which includes the direction of each round trip travel. The result from using true O-D concept reveals the socioeconomic functional roles of airports on the network. Linear trends are observed for both the produced and attracted demand from the data. Therefore, this approach is expected to provide more accurate prediction capability. With the intrinsic demand model in place, the variable part of the demand is modeled on an air transportation network model, which is built with accelerated evolution scheme. The accelerated evolution scheme was introduced to view the air transportation network as an evolutionary one instead of a parametric one. The network model takes in intrinsic demand data before undergoing an evolution path to generate a target network. The results from the network model suggests that air transportation networks can be modeled using evolutionary structure and it was possible to generate the emulated NAS. A dehubbing scenario study of Lambert-St. Louis International Airport demonstrated the prediction capability of the proposed network model. The overall process from intrinsic demand modeling and evolutionary network modeling is a unique and it is highly beneficial for simulating active control of the transportation networks.
A multi-objective stochastic approach to combinatorial technology space exploration

(Georgia Institute of Technology, 2009-05-18) Patel, Chirag B.

Several techniques were studied to select and prioritize technologies for a complex system. Based on the findings, a method called Pareto Optimization and Selection of Technologies (POST) was formulated to efficiently explore the combinatorial technology space. A knapsack problem was selected as a benchmark problem to test-run various algorithms and techniques of POST. A Monte Carlo simulation using the surrogate models was used for uncertainty quantification. The concepts of graph theory were used to model and analyze compatibility constraints among technologies. A probabilistic Pareto optimization, based on the concepts of Strength Pareto Evolutionary Algorithm II (SPEA2), was formulated for Pareto optimization in an uncertain objective space. As a result, multiple Pareto hyper-surfaces were obtained in a multi-dimensional objective space; each hyper-surface representing a specific probability level. These Pareto layers enabled the probabilistic comparison of various non-dominated technology combinations. POST was implemented on a technology exploration problem for a 300 passenger commercial aircraft. The problem had 29 identified technologies with uncertainties in their impacts on the system. The distributions for these uncertainties were defined using beta distributions. Surrogate system models in the form of Response Surface Equations (RSE) were used to map the technology impacts on the system responses. Computational complexity of technology graph was evaluated and it was decided to use evolutionary algorithm for probabilistic Pareto optimization. The dimensionality of the objective space was reduced using a dominance structure preserving approach. Probabilistic Pareto optimization was implemented with reduced number of objectives. Most of the technologies were found to be active on the Pareto layers. These layers were exported to a dynamic visualization environment enabled by a statistical analysis and visualization software called JMP. The technology combinations on these Pareto layers are explored using various visualization tools and one combination is selected. The main outcome of this research is a method based on consistent analytical foundation to create a dynamic tradeoff environment in which decision makers can interactively explore and select technology combinations.
A strategic planning methodology for aircraft redesign

(Georgia Institute of Technology, 2009-04-06) Romli, Fairuz Izzuddin

Due to a progressive market shift to a customer-driven environment, the influence of engineering changes on the product's market success is becoming more prominent. This situation affects many long lead-time product industries including aircraft manufacturing. Derivative development has been the key strategy for many aircraft manufacturers to survive the competitive market and this trend is expected to continue in the future. Within this environment of design adaptation and variation, the main market advantages are often gained by the fastest aircraft manufacturers to develop and produce their range of market offerings without any costly mistakes. This realization creates an emphasis on the efficiency of the redesign process, particularly on the handling of engineering changes. However, most activities involved in the redesign process are supported either inefficiently or not at all by the current design methods and tools, primarily because they have been mostly developed to improve original product development. In view of this, the main goal of this research is to propose an aircraft redesign methodology that will act as a decision-making aid for aircraft designers in the change implementation planning of derivative developments. The proposed method, known as Strategic Planning of Engineering Changes (SPEC), combines the key elements of the product redesign planning and change management processes. Its application is aimed at reducing the redesign risks of derivative aircraft development, improving the detection of possible change effects propagation, increasing the efficiency of the change implementation planning and also reducing the costs and the time delays due to the redesign process. To address these challenges, four research areas have been identified: baseline assessment, change propagation prediction, change impact analysis and change implementation planning. Based on the established requirements for the redesign planning process, several methods and tools that are identified within these research areas have been abstracted and adapted into the proposed SPEC method to meet the research goals. The proposed SPEC method is shown to be promising in improving the overall efficiency of the derivative aircraft planning process through two notional aircraft system redesign case studies that are presented in this study.
A methodology for the quantification of doctrine and materiel approaches in a capability-based assessment

(Georgia Institute of Technology, 2009-04-06) Tangen, Steven Anthony

Due to the complexities of modern military operations and the technologies employed on today's military systems, acquisition costs and development times are becoming increasingly large. Meanwhile, the transformation of the global security environment is driving the U.S. military's own transformation. In order to meet the required capabilities of the next generation without buying prohibitively costly new systems, it is necessary for the military to evolve across the spectrum of doctrine, organization, training, materiel, leadership and education, personnel, and facilities (DOTMLPF). However, the methods for analyzing DOTMLPF approaches within the early acquisition phase of a capability-based assessment (CBA) are not as well established as the traditional technology design techniques. This makes it difficult for decision makers to decide if investments should be made in materiel or non-materiel solutions. This research develops an agent-based constructive simulation to quantitatively assess doctrine alongside materiel approaches. Additionally, life-cycle cost techniques are provided to enable a cost-effectiveness trade. These techniques are wrapped together in a decision-making environment that brings crucial information forward so informed and appropriate acquisition choices can be made. The methodology is tested on a future unmanned aerial vehicle design problem. Through the implementation of this quantitative methodology on the proof-of-concept study, it is shown that doctrinal changes including fleet composition, asset allocation, and patrol pattern were capable of dramatic improvements in system effectiveness at a much lower cost than the incorporation of candidate technologies. Additionally, this methodology was able to quantify the precise nature of strong doctrine-doctrine and doctrine-technology interactions which have been observed only qualitatively throughout military history. This dissertation outlines the methodology and demonstrates how potential approaches to capability-gaps can be identified with respect to effectiveness, cost, and time. When implemented, this methodology offers the opportunity to achieve system capabilities in a new way, improve the design of acquisition programs, and field the right combination of ways and means to address future challenges to national security.
Simultaneous multi-design point approach to gas turbine on-design cycle analysis for aircraft engines

(Georgia Institute of Technology, 2009-04-06) Schutte, Jeffrey Scott

Gas turbine engines for aircraft applications are required to meet multiple performance and sizing requirements, subject to constraints established by the best available technology level. The performance requirements and limiting values of constraints that are considered by the cycle analyst conducting an engine cycle design occur at multiple operating conditions. The traditional approach to cycle analysis chooses a single design point with which to perform the on-design analysis. Additional requirements and constraints not transpiring at the design point must be evaluated in off-design analysis and therefore do not influence the cycle design. Such an approach makes it difficult to design the cycle to meet more than a few requirements and limits the number of different aerothermodynamic cycle designs that can reasonably be evaluated. Engine manufacturers have developed computational methods to create aerothermodynamic cycles that meet multiple requirements, but such methods are closely held secrets of their design process. This thesis presents a transparent and publicly available on-design cycle analysis method for gas turbine engines which generates aerothermodynamic cycles that simultaneously meet performance requirements and constraints at numerous design points. Such a method provides the cycle analyst the means to control all aspects of the aerothermodynamic cycle and provides the ability to parametrically create candidate engine cycles in greater numbers to comprehensively populate the cycle design space from which a "best" engine can be selected. This thesis develops the multi-design point on-design cycle analysis method labeled simultaneous MDP. The method is divided into three different phases resulting in an 11 step process to generate a cycle design space for a particular application. Through implementation of simultaneous MDP, a comprehensive cycle design space can be created quickly for the most complex of cycle design problems. Furthermore, the process documents the creation of each candidate engine providing transparency as to how each engine cycle was designed to meet all of the requirements. The simultaneous MDP method is demonstrated in this thesis on a high bypass ratio, separate flow turbofan with up to 25 requirements and constraints and 9 design points derived from a notional 300 passenger aircraft.