Organizational Unit:

Aerospace Systems Design Laboratory (ASDL)

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https://hdl.handle.net/1853/70744

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Organizational Unit

Daniel Guggenheim School of Aerospace Engineering

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1135

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Publication Search Results

Now showing 1 - 10 of 61

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.
A systematic approach to design for lifelong aircraft evolution

(Georgia Institute of Technology, 2009-04-06) Lim, Dongwook

Modern aerospace systems rely heavily on legacy platforms and their derivatives. Historical examples show that after a vehicle design is frozen and delivered to a customer, successive upgrades are often made to fulfill changing requirements. Current practices of adapting to emerging needs with derivative designs, retrofits, and upgrades are often reactive and ad-hoc, resulting in performance and cost penalties. Recent DoD acquisition policies have addressed this problem by establishing a general paradigm for design for lifelong evolution. However, there is a need for a unified, practical design approach that considers the lifetime evolution of an aircraft concept by incorporating future requirements and technologies. This research proposes a systematic approach with which the decision makers can evaluate the value and risk of a new aircraft development program, including potential derivative development opportunities. The proposed Evaluation of Lifelong Vehicle Evolution (EvoLVE) method is a two- or multi-stage representation of the aircraft design process that accommodates initial development phases as well as follow-on phases. One of the key elements of this method is the Stochastic Programming with Recourse (SPR) technique, which accounts for uncertainties associated with future requirements. The remedial approach of SPR in its two distinctive problem-solving steps is well suited to aircraft design problems where derivatives, retrofits, and upgrades have been used to fix designs that were once but no longer optimal. The solution approach of SPR is complemented by the Risk-Averse Strategy Selection (RASS) technique to gauge risk associated with vehicle evolution options. In the absence of a full description of the random space, a scenario-based approach captures the randomness with a few probable scenarios and reveals implications of different future events. Last, an interactive framework for decision-making support allows simultaneous navigation of the current and future design space with a greater degree of freedom. A cantilevered beam design problem was set up and solved using the SPR technique to showcase its application to an engineering design setting. The full EvoLVE method was conducted on a notional multi-role fighter based on the F/A-18 Hornet.
A small perturbation based optimization approach for the frequency placement of high aspect ratio wings

(Georgia Institute of Technology, 2009-03-26) Goltsch, Mandy

Design denotes the transformation of an identified need to its physical embodiment in a traditionally iterative approach of trial and error. Conceptual design plays a prominent role but an almost infinite number of possible solutions at the outset of design necessitates fast evaluations. The traditional practice of empirical databases loses adequacy for novel concepts and an ever increasing system complexity and resource scarsity mandate new approaches to adequately capture system characteristics. Contemporary concerns in atmospheric science and homeland security created an operational need for unconventional configurations. Unmanned long endurance flight at high altitudes offers a unique showcase for the exploration of new design spaces and the incidental deficit of conceptual modeling and simulation capabilities. The present research effort evolves around the development of an efficient and accurate optimization algorithm for high aspect ratio wings subject to natural frequency constraints. Foundational corner stones are beam dimensional reduction and modal perturbation redesign. Local and global analyses inherent to the former suggest corresponding levels of local and global optimization. The present approach departs from this suggestion. It introduces local level surrogate models to capacitate a methodology that consists of multi level analyses feeding into a single level optimization. The innovative heart of the new algorithm originates in small perturbation theory. A sequence of small perturbation solutions allows the optimizer to make incremental movements within the design space. It enables a directed search that is free of costly gradients. System matrices are decomposed based on a Timoshenko stiffness effect separation. The formulation of respective linear changes falls back on surrogate models that approximate cross sectional properties. Corresponding functional responses are readily available. Their direct use by the small perturbation based optimizer ensures constitutive laws and eliminates a previously necessary optimization at the local level. The great economy of the developed algorithm makes it suitable for the conceptual phase of aircraft design.