Organizational Unit:
Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
Permanent Link
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Organizational Unit
Includes Organization(s)
Organizational Unit
Organizational Unit
Organizational Unit
Organizational Unit
Organizational Unit
ArchiveSpace Name Record
Publication Search Results
Now showing
1 - 7 of 7
-
ItemA strategic planning approach for the operational-environmental problem of air transportation system terminal areas(Georgia Institute of Technology, 2009-11-16) Jimenez, HernandoThe 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.
-
ItemModel predictive control (MPC) algorithm for tip-jet reaction drive systems(Georgia Institute of Technology, 2009-11-16) Kestner, BrianModern 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.
-
ItemA methodology for the robustness-based evaluation of systems-of-systems alternatives using regret analysis(Georgia Institute of Technology, 2008-07-01) Poole, Benjamin HancockAfter surveying the state-of-the-art in evaluation of alternatives in the defense acquisition process, a methodology for the evaluation of the robustness of systems-of-systems alternatives was proposed. In the methodology, robustness is defined as the integral of the alternative s regret over the likelihood-weighted plausible scenario space. Surrogate modeling techniques were used to overcome shortcomings associated with conventional regret analysis, including the discrete nature of scenario cases and static results. The new methodology, called Global Regret Analysis, was tested using an example problem based on the air campaign over Iraq in Operation Desert Storm. The results of the testing indicate that the methodology can provide a measure of the robustness of different system-of-systems alternatives against a wide range of possible scenarios. The methodology was then demonstrated on the US Air Force s persistent, precision strike mission. The demonstration showed the ability of Global Regret Analysis to overcome issues associated with using a single or other small number of scenarios to evaluate systems-of-systems alternatives. The methodology was then compared to a variety of existing methods and found to have strength for a wide range of evaluation applications. The possibility of applying Global Regret Analysis for military mission planning and opportunities for future work were also discussed.
-
ItemAn Intelligent, Knowledge-based Multiple Criteria Decision Making Advisor for Systems Design(Georgia Institute of Technology, 2007-01-16) Li, YongchangAerospace systems are complex systems with interacting disciplines and technologies. As a result, the Decision Makers (DMs) dealing with such problems are involved in balancing the multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. A variety of existing decision making methods are available to deal with this type of decision problems. The selection of a most appropriate decision making method is of particular importance since inappropriate decision methods are likely causes of misleading engineering design decisions. The research presented in this dissertation proposes a knowledge-based Multi-criteria Interactive Decision-making Advisor and Synthesis process (MIDAS), which can facilitate the selection of the most appropriate decision making method and which provides insight to the user for fulfilling different preferences. Once the most appropriate method is selected for the given problem, the advisor is also able to aid the DM to reach the final decision by following the rigorous problem solving procedure of the selected method. The MIDAS can also provide guidance as to the requirements needed to be fulfilled by a potentially new method for cases where no suitable method is found. In many other domains, such as complex system operation, decisions are often made in an environment with continuously changing situations. In addition, the decisions are usually completed based on uncertain or incomplete information due to the data availability and the environmental variation. This fact exacerbates the complexity of the decision making process because it results in the difficulties in perfectly and deterministically reasoning about the effects of the decisions and thus make it hard in determining the further decisions. In order to make proper decision and increase the system’s effectiveness, an advanced decision strategy is needed to capture the system’s dynamic characteristics and environmental uncertainty. An autonomous decision making advisor is developed to perform the real-time decision making under uncertainty. The development of the advisor system aims to solve a resource allocation problem to redistribute the limited resources to different agents under various scenarios and try to maximize the total rewards obtained from the resource allocation actions.
-
ItemA Systematic Process for Adaptive Concept Exploration(Georgia Institute of Technology, 2006-11-29) Nixon, Janel NicoleThis thesis presents a method for streamlining the process of obtaining and interpreting quantitative data for the purpose of creating a low-fidelity modeling and simulation environment. By providing a more efficient means for obtaining such information, quantitative analyses become much more practical for decision-making in the very early stages of design, where traditionally, quants are viewed as too expensive and cumbersome for concept evaluation. The method developed to address this need uses a Systematic Process for Adaptive Concept Exploration (SPACE). In the SPACE method, design space exploration occurs in a sequential fashion; as data is acquired, the sampling scheme adapts to the specific problem at hand. Previously gathered data is used to make inferences about the nature of the problem so that future samples can be taken from the more interesting portions of the design space. Furthermore, the SPACE method identifies those analyses that have significant impacts on the relationships being modeled, so that effort can be focused on acquiring only the most pertinent information. The results show that the combination of a tailored data set, and an informed model structure work together to provide a meaningful quantitative representation of the system while relying on only a small amount of resources to generate that information. In comparison to more traditional modeling and simulation approaches, the SPACE method provides a more accurate representation of the system using fewer resources to generate that representation. For this reason, the SPACE method acts as an enabler for decision making in the very early design stages, where the desire is to base design decisions on quantitative information while not wasting valuable resources obtaining unnecessary high fidelity information about all the candidate solutions. Thus, the approach enables concept selection to be based on parametric, quantitative data so that informed, unbiased decisions can be made.
-
ItemA Design Space Exploration Process for Large Scale, Multi-Objective Computer Simulations(Georgia Institute of Technology, 2006-07-07) Zentner, John MarcThe primary contributions of this thesis are associated with the development of a new method for exploring the relationships between inputs and outputs for large scale computer simulations. Primarily, the proposed design space exploration procedure uses a hierarchical partitioning method to help mitigate the curse of dimensionality often associated with the analysis of large scale systems. Closely coupled with the use of a partitioning approach, is the problem of how to partition the system. This thesis also introduces and discusses a quantitative method developed to aid the user in finding a set of good partitions for creating partitioned metamodels of large scale systems. The new hierarchically partitioned metamodeling scheme, the lumped parameter model (LPM), was developed to address two primary limitations to the current partitioning methods for large scale metamodeling. First the LPM was formulated to negate the need to rely on variable redundancies between partitions to account for potentially important interactions. By using a hierarchical structure, the LPM addresses the impact of neglected, direct interactions by indirectly accounting for these interactions via the interactions that occur between the lumped parameters in intermediate to top-level mappings. Secondly, the LPM was developed to allow for hierarchical modeling of black-box analyses that do not have available intermediaries with which to partition the system around. The second contribution of this thesis is a graph-based partitioning method for large scale, black-box systems. The graph-based partitioning method combines the graph and sparse matrix decomposition methods used by the electrical engineering community with the results of a screening test to create a quantitative method for partitioning large scale, black-box systems. An ANOVA analysis of the results of a screening test can be used to determine the sparse nature of the large scale system. With this information known, the sparse matrix and graph theoretic partitioning schemes can then be used to create potential sets of partitions to use with the lumped parameter model.
-
ItemA theoretical treatment of technical risk in modern propulsion system design(Georgia Institute of Technology, 2000-05) Roth, Bryce Alexander