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Aerospace Systems Design Laboratory (ASDL)

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

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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 18

A METHODOLOGY FOR THE MODULARIZATION OF OPERATIONAL SCENARIOS FOR MODELLING AND SIMULATION

(Georgia Institute of Technology, 2022-07-29) Muehlberg, Marc

As military operating environments and potential global threats rapidly evolve, military planning processes required to maintain international security and national defense increase in complexity and involve unavoidable uncertainties. The challenges in the field are diverse, including dealing with reemergence of long-term, strategic competition over destabilizing effects of rogue regimes, and the asymmetric non-state actors’ threats such as terrorism and international crime. The military forces are expected to handle increased multi-role, multi-mission demands because of the interconnected character of these threats. The objective of this thesis is to discuss enhancing system-of-systems analysis capabilities by considering diverse operational requirements and operational ways in a parameterized fashion within Capabilities Based Assessments process. These assessments require an open-ended exploratory approach of means and ways, situated in the early stages of planning and acquisition processes. In order to enhance the reflection of increased demands in the process, the integration of multi-scenario capabilities into a process with low-fidelity modelling and simulation is of particular interest. This allows the consideration of a high quantity of feasible alternatives in a timely manner, spanning across a diverse set of dimensions and its parameters. A methodology has been devised as an enhanced Capabilities Based Assessment approach to provide for a formalized process for the consideration and infusion of operational scenarios, and properly constrain the design space prior to computational analysis. In this context, operational scenarios are a representative set of statements and conditions that address a defined problem and include testable metrics to analyze performance and effectiveness. The scenario formalization uses an adjusted elementary definition approach to decompose, define, and recompose operational scenarios to create standardized architectures, allowing their rapid infusion into environments, and to enable the consideration of diverse operational requirements in a conjoint approach overall. Pursuant to this process, discrete event simulations as low-fidelity approach are employed to reflect the elementary structure of the scenarios. In addition, the exploration of the design and options space is formalized, including the collection of alternative approaches within different materiel and non-materiel dimensions and subsequent analysis of their relationship prior to the creation of combinatorial test cases. In the progress of this thesis, the devised methodology as a whole and the two developed augmentations to the Capabilities Based Assessment are tested and validated in a series of experiments. As an overall case study, the decision-making process surrounding the deployment of vertical airlift assets of varying type and quantity for Humanitarian Aid and Disaster Relief operations is utilized. A demonstration experiment is provided exercising the entire methodology to test specifically for its suitability to handle a variety of different scenarios through process, as well as a comprehensive set of materiel and non-materiel parameters. Based on a mission statement and performance targets, the status quo could be evaluated and alternative options for the required performance improvements could be presented. The methodology created in this thesis enables the Capabilities Based Assessment and general defense acquisition considerations to be initially approached in a more open and less constrained manner. This capability is provided through the use of low-fidelity modelling and simulation that enables the evaluation of a large amount of alternatives. In advances to the state of the art, the methodology presented removes subject-matter expert and operator driven constraints, allowing the discovery of solutions that would not be considered in a traditional process. It will support the work of not only defense acquisition analysts and decision-makers, but also provide benefits to policy planners through its ability to instantly revise and analyze cases in a rapid fashion.
Representative Data and Models for Complex Aerospace Systems Analysis

(Georgia Institute of Technology, 2022-04-28) Gao, Zhenyu

Catalyzed by advances in data quantity and quality, effective and scalable algorithms, and high-performance computation, the data-intensive transformation is rapidly reframing the aerospace industry. The integration of data-driven methods brings many new opportunities, such as (1) streamlining the aerospace design, testing, certification, and manufacturing process, (2) driving fundamental advancements in the traditional aerospace fields, and (3) enhancing the business and operations side of the industry. However, modern aerospace datasets collected from real-world operations, simulations, and scientific observations can be massive, high-dimensional, heterogeneous, and noisy. While sometimes being beyond people's capacity to store, analyze, and archive, these large datasets almost always contain redundant, trivial, and irrelevant information. Because the design and analysis of complex aerospace systems value computational efficiency, robustness, and interpretation, an additional procedure is required to process large datasets and extract/refine a small amount of representative information for in-depth analysis. This dissertation utilizes improved representations of operations data and aircraft models for efficient, accurate, and interpretable air transportation system analysis. Under the overall scope of representative data and models, this dissertation consists of three main parts. Part I, representative operations data, considers the problem of selecting a small subset of the operations data from a large population for more efficient yet accurate probabilistic analyses. This is tackled by a novel distributional data reduction method called Probabilistic REpresentatives Mining (PREM), which is consistent in generating small samples with the same data distribution. Part II, representative aircraft model portfolios, considers the problem of selecting a small proportion of representative aircraft models to sufficiently cover the richness and complexity of a large population when the modeling of every participant in the complex system is infeasible. This is tackled through a clustering framework which optimizes for the minimax criterion and can conduct a trade-off between multiple criteria of the selected portfolio's representativeness. Part III, representative aircraft model features, considers the problem of obtaining improved aircraft model representations for environmental impacts modeling. This is accomplished through using a combination of large-scale computer experiment and multi-level feature representation and selection. The proposed methodologies are demonstrated and tested on four selected experiments through data visualization and quantitative metrics. Overall, this dissertation aims to contribute to both the general methodologies and the solutions to specific aerospace applications.
A robust methodology for strategically designing environments subject to unpredictable and evolving conditions

(Georgia Institute of Technology, 2019-01-15) Minier, Ethan T.

The layout design process, a lean technique, has the potential to provide a manufacturer with significant cost reductions. The major challenge for layout designers is then ensuring this reduction can be maximized, but more so realized when implemented in practice. Guaranteeing this realization requires both the real-life behavior and characteristics of the environment as well as the market and business model conditions to adequately be captured. Unfortunately, current methods fail to accurately capture real-life considerations such as flow path feasibility, they neglect continuous detailed representations of evolving layouts subject to financial restrictions and uncertainty, and moreover they tend to provide insufficient insight into the problem. The objective of this dissertation was then to establish an improved methodology for exploring the design space of a detailed evolving environment, enabling more informed and collaborative design decisions to be made in the presence of evolving and uncertain conditions. In the process of achieving this goal, a three-step methodology (problem initialization, solution, analysis), titled LIVE, is formed. Along with its formation an extensive array of novel methods, revolutionary optimization techniques, and a detailed performance model are developed; all to facilitate effective solution to the uniquely complex and arduous layout problem formulation considered in this dissertation. It is then postulated, that if the problem of designing an environment subject to evolving and uncertain conditions was to be solved with this LIVE methodology, then designers would be capable of making more informed and collaborative design decisions. Substantiation of this is then pursued by systematically testing the methodology and the various models, methods, and solution approaches deployed by it. A series of compounding experiments are performed. During this testing, developed methods are proven to outperform existing approaches, consideration of flow path feasibility is proven imperative, and the novel bimodel multi-stage solution approach deployed by the LIVE methodology is well exercised whereby the best optimization parameter settings to ensure effective solution are identified. Finally, while applying the LIVE methodology to a real-world layout design problem, complete substantiation to the postulated hypothesis is achieved. It is shown that the methodology effectively facilitates improved insight and potential collaboration into the layout design process. The developed performance model proves significant in enabling new insights to be drawn and further for a richer understanding of the operations and layout design to be gained. Overall, the methodology demonstrates its ability to provide an improved layout design process that can effectively handle design problems subject to uncertain and evolving conditions; enabling strategic business decisions to be considered in parallel to the design of the layout.
A methodology for risk-informed launch vehicle architecture selection

(Georgia Institute of Technology, 2017-11-13) Edwards, Stephen James

Modern society in the 21st century has become inseparably dependent on human mastery of the near-Earth regions of space. Billions of dollars in on-orbit assets provide a set of fundamental, requisite services to such diverse domains as telecom, military, banking, and transportation. While orbiting satellites provide these services, launch vehicles (LVs) are unquestionably the most critical piece of infrastructure in the space economy value chain. The past decade has seen a significant level of activity in LV development, including some fundamental changes to the industry landscape. Every space-faring nation is engaged in new program developments; most notable, however, is the surge in commercial investments and development efforts, which has been spurred by a combination of private investments by wealthy individuals, new government policies and acquisition strategies, and the increased competition that has resulted from both. In all the LV programs of today, affordability is acknowledged as the single biggest objective. Governments seek assured access to space that can be realized within constrained budgets, and commercial entities vie for survival, profitability, and market-share. From literature, it is clear that the biggest opportunity for affecting affordability resides in improving decision-making early on in the design process. However, a review of historical LV architecture studies shows that very little has changed over the past 50 years in how early architecting decisions are analyzed. In particular, architecture analyses of alternatives are still conducted deterministically, despite uncertainty being at its highest in the very early stages of design. This thesis argues that the ``design freedom'' that exists early on manifests itself as volitional uncertainty during the LV architect's deliberation, motivating the objective statement ``to develop a methodology for enabling risk-informed decision making during the architecture selection phase of LV programs.'' NASA's Risk-Informed Decision Making process is analyzed with respect to the particulars of the LV architecture selection problem. The most significant challenge is found to be LV performance modeling via trajectory optimization, which is not well suited to probabilistic analysis. To overcome this challenge, an empirical modeling approach is proposed. However, this in turn introduces the challenge of generalizing the empirical model, as creating distinct performance models for every architecture concept under consideration is considered infeasible. A review of the main drivers in LV trajectory performance observes T/W not only to be one of the parameters with most sensitivity, but also reveals it to be a functional in its true form. Based on the performance-driving nature of the T/W profile, and the fact that in its infinite-dimensional form it offers a common basis for representing diverse architectures, functional regression techniques are proposed as a potential means of constructing an architecture-spanning empirical performance model. A number of techniques are formulated and tested, and prove capable of supporting the LV performance modeling in support of risk-informed architecture selection.
A risk-value-based methodology for enterprise-level decision-making

(Georgia Institute of Technology, 2017-07-31) Burgaud, Frederic

Despite its long lasting existence, aerospace remains a non-commoditized field. To sustain their market domination, the major companies need to commit to large capital investments and constant innovation, in spite of multiple sources of risk and uncertainty, and significant chances of failure. This makes aerospace programs particularly risky. However, successful programs more than compensate the costs of disappointing ones. In order to maximize the chances of a favorable outcome, a business-driven, multi-objective, and multi-risk approach is needed to ensure success, with particular attention to financial aspects. Additionally, aerospace programs involve multiple divisions within a company. Besides vehicle design, finance, sales, and production are crucial disciplines with decision power and influence on the outcome of the program. They are also tightly coupled, and the interdependencies existing between these disciplines should be exploited to unlock as much program-level value potential as possible. An enterprise-level approach should, therefore, be used. Finally, suborbital tourism programs are well suited as a case study for this research. Indeed, they are usually small companies starting their projects from scratch. Using a full enterprise-level analysis is thus necessary, but also more easily feasible than for larger groups. These motivations lead to the formulation of the research objective: to establish a methodology that enables informed enterprise-level decision-making under uncertainty and provides higher-value compromise solutions. The research objective can be decomposed into two main directions of study. First, current approaches are usually limited to the design aspect of the program and do not provide the optimization of other disciplines. This ultimately results in a de-facto sequential optimization, where principal-agent problems arise. Instead, a holistic implementation is proposed, which will enable an integrated enterprise-level optimization. The second part of this problem deals with decision-making with multiple objectives and multiple risks. Current methods of design under uncertainty are insufficient for this problem. First, they do not provide compelling results when several metrics are targeted. Additionally, variance does not properly fit the definition of risk, as it captures both the upside and downside uncertainty. Instead, the deviation of the Conditional Value at Risk (called here downside deviation) is used as a measure of value risk. Furthermore, objectives are categorized and aggregated into risk and value scores to facilitate convergence, visualization, and decisionmaking. As suborbital vehicles are complex non-linear systems, with many infeasible concepts and computationally expensive M&S environments, a time-efficient way to estimate the downside deviation needs to be used. As such, a new uncertainty propagation structure is used that involves regression and classification neural networks, as well as a Second-Order Third-Moment (SOTM) technique to compute statistical moments. The proposed process elements are combined, and integrated into a method following a modified Integrated Product and Process Development (IPPD) approach, using five main steps: establishing value, generating alternatives, evaluating alternatives, and making decisions. A new M&S environment is implemented and involves a design framework to which several business disciplines are added. A bottom-up approach is used to study the four research questions of this dissertation. At the lowest level of the implementation, an enhanced financial analysis is evaluated. Common financial valuation methods used in aerospace have heavy limitations: all of them rely on a very arbitrary discount rate despite its critical impact on the final value of the NPV. The proposed method provides detailed analysis capabilities and helps capture more value by enabling the optimization of the company’s capital structure. A sensitivity analysis also verifies the importance of the added factors in the proposed method. The second implementation step is to time-efficiently evaluate downside deviation. As such, regression and classification neural networks are implemented to estimate the base costs of the vehicle and speed up the vehicle sizing process. Business analyses are already time-efficient and therefore maintained. These neural networks ultimately show good validation prediction root-mean-square error (RMSE), which confirms their accuracy. The SOTM method is also checked and shows a downside deviation prediction accuracy equivalent to a 750-point Monte Carlo method. From a computation time standpoint, the use of neural networks is required for a reasonable convergence time, and the SOTM used jointly with neural networks results in an optimization time below 1 hour. The proposed approach for making risk/value trade-offs in the presence of multiple risks and objectives is then tested. First, the importance of using downside deviation is demonstrated by showing the risk estimation error made when using the standard deviation rather than the actual downside deviation. Additionally, the use of risk and value scores also helps decision-making from a qualitative and quantitative point of view. Indeed, it facilitates visualization by supplying a two-dimensional Pareto frontier, while still being able to color it to observe program features and cluster patterns. Furthermore, the problem with risk and value scores provides more optimal solutions, compared to the non-aggregated case, unless very large errors in weightings are committed. Finally, the proposed method provides good capabilities for identifying, ranking, and selecting optimal concepts. The last research question presents the following interrogation: does an enterpriselevel approach help improve the optimality of the overall program, and does it result in significantly different decision-making? Two elements of the enterprise-level approach are tested: the integrated optimization, and the use of additional enterprise-level objectives. In both cases, the resulting Pareto frontiers are significantly dominating their counterparts, demonstrating the usefulness of the enterprise-level approach from a quantitative point of view. It also shows that the enterprise-level approach results in significantly different decisions, and should, therefore, be applied early in the design process. Hence, the method provided the capabilities sought in the research objective. This research resulted in contributions in the financial analysis of aerospace programs, in design under multiple sources of uncertainty with multiple objectives, and in design optimization by proposing the adoption of an enterprise-level approach.
Formulation of control strategies for requirement definition of multi-agent surveillance systems

(Georgia Institute of Technology, 2014-08-21) Aksaray, Derya

In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses. In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.
A reliability-based measurement of interoperability for conceptual-level systems of systems

(Georgia Institute of Technology, 2014-07-01) Jones Wyatt, Elizabeth Ann

The increasing complexity of net-centric warfare requires assets to cooperate to achieve mission success. Such cooperation requires the integration of many heterogeneous systems into an interoperable system-of-systems (SoS). Interoperability can be considered a metric of an architecture, and must be understood as early as the conceptual design phase. This thesis approaches interoperability by first creating a general definition of interoperability, identifying factors that affect it, surveying existing models of interoperability, and identifying fields that can be leveraged to perform a measurement, including reliability theory and graph theory. The main contribution of this thesis is the development of the Architectural Resource Transfer and Exchange Measurement of Interoperability for Systems of Systems, or ARTEMIS methodology. ARTEMIS first outlines a quantitative measurement of system pair interoperability using reliability in series and in parallel. This step incorporates operational requirements and the capabilities of the system pair. Next, a matrix of interoperability values for each resource exchange in an operational process is constructed. These matrices can be used to calculate the interoperability of a single resource exchange, IResource, and layered to generate a weighted adjacency matrix of the entire SoS. This matrix can be plugged in to a separate model to link interoperability with the mission performance of the system of systems. One output of the M&S is a single value ISoS that can be used to rank architecture alternatives based on their interoperability. This allows decision makers to narrow down a large design space quickly using interoperability as one of several criteria, such as cost, complexity, or risk. A canonical problem was used to test the methodology. A discrete event simulation was constructed to model a small unmanned aircraft system performing a search and rescue mission. Experiments were performed to understand how changing the systems' interoperability affected the overall interoperability; how the resource transfer matrices were layered; and if the outputs could be calculated without time- and computationally-intensive stochastic modeling. It was found that although a series model of reliability could predict a range of IResource, M&S is required to provide exact values useful for ranking. Overall interoperability ISoS can be predicted using a weighted average of IResource, but the weights must be determined by M&S. Because a single interoperability value based on performance is not unique to an architecture configuration, network analysis was conducted to assess further properties of a system of systems that may affect cost or vulnerability of the network. The eigenvalue-based Coefficient of Networked Effects (CNE) was assessed and found to be an appropriate measure of network complexity. Using the outputs of the discrete event simulation, it was found that networks with higher interoperability tended to have more networked effects. However, there was not enough correlation between the two metrics to use them interchangeably. ARTEMIS recommends that both metrics be used to assess a networked SoS. This methodology is of extreme value to decision-makers by enabling trade studies at the SoS level that were not possible previously. It can provide decision-makers with information about an architecture and allow them to compare existing and potential systems of systems during the early phases of acquisition. This method is unique because it does not rely on qualitative assessments of technology maturity or adherence to standards. By enabling a rigorous, objective mathematical measurement of interoperability, decision-makers will better be able to select architecture alternatives that meet interoperability goals and fulfill future capability requirements.
A representation method for large and complex engineering design datasets with sequential outputs

(Georgia Institute of Technology, 2013-08-22) Iwata, Curtis

This research addresses the problem of creating surrogate models of high-level operations and sustainment (O&S) simulations with time sequential (TS) outputs. O&S is a continuous process of using and maintaining assets such as a fleet of aircraft, and the infrastructure to support this process is the O&S system. To track the performance of the O&S system, metrics such as operational availability are recorded and reported as a time history. Modeling and simulation (M&S) is often used as a preliminary tool to study the impact of implementing changes to O&S systems such as investing in new technologies and changing the inventory policies. A visual analytics (VA) interface is useful to navigate the data from the M&S process so that these options can be compared, and surrogate modeling enables some key features of the VA interface such as interpolation and interactivity. Fitting a surrogate model is difficult to TS data because of its size and nonlinear behavior. The Surrogate Modeling and Regression of Time Sequences (SMARTS) methodology was proposed to address this problem. An intermediate domain Z was calculated from the simulation output data in a way that a point in Z corresponds to a unique TS shape or pattern. A regression was then fit to capture the entire range of possible TS shapes using Z as the inputs, and a separate regression was fit to transform the inputs into the Z. The method was tested on output data from an O&S simulation model and compared against other regression methods for statistical accuracy and visual consistency. The proposed methodology was shown to be conditionally better than the other methodologies.
A multidisciplinary framework for mission effectiveness quantification and assessment of micro autonomous systems and technologies

(Georgia Institute of Technology, 2013-08-21) Mian, Zohaib Tariq

Micro Autonomous Systems and Technologies (MAST) is an Army Research Laboratory (ARL) sponsored project based on a consortium of revolutionary academic and industrial research institutions working together to develop new technologies in the field of microelectronics, autonomy, micromechanics and integration. The overarching goal of the MAST consortium is to develop autonomous, multifunctional, and collaborative ensembles of microsystems to enhance small unit tactical situational awareness in urban and complex terrain. Unmanned systems are used to obtain intelligence at the macro level, but there is no real-time intelligence asset at the squad level. MAST seeks to provide that asset. Consequently, multiple integrated MAST heterogeneous platforms (e.g. crawlers, flyers, etc.) working together synergistically as an ensemble shall be capable of autonomously performing a wide spectrum of operational functions based on the latest developments in micro-mechanics, micro-electronics, and power technologies to achieve the desired operational objectives. The design of such vehicles is, by nature, highly constrained in terms of size, weight and power. Technologists are trying to understand the impacts of developing state-of-the-art technologies on the MAST systems while the operators are trying to define strategies and tactics on how to use these systems. These two different perspectives create an integration gap. The operators understand the capabilities needed on the field of deployment but not necessarily the technologies, while the technologists understand the physics of the technologies but not necessarily how they will be deployed, utilized, and operated during a mission. This not only results in a major requirements disconnect, representing the difference of perspectives between soldiers and the researchers, but also demonstrates the lack of quantified means to assess the technology gap in terms of mission requirements. This necessitates the quantification and resolution of the requirements disconnect and technology gap leading to re-definitions of the requirements based on mission scenarios. A research plan, built on a technical approach based on the simultaneous application of decomposition and re-composition or 'Top-down' and 'Bottom-up' approaches, was used for development of a structured and traceable methodology. The developed methodology is implemented through an integrated framework consisting of various decision-making tools, modeling and simulation, and experimental data farming and validation. The major obstacles in the development of the presented framework stemmed from the fact that all MAST technologies are revolutionary in nature, with no available historical data, sizing and synthesis codes or reliable physics-based models. The inherently multidisciplinary, multi-objective and uncertain nature of MAST technologies makes it very difficult to map mission level objectives to measurable engineering metrics. It involves the optimization of multiple disciplines such as Aero, CS/CE, ME, EE, Biology, etc., and of multiple objectives such as mission performance, tactics, vehicle attributes, etc. Furthermore, the concept space is enormous with hundreds of billions of alternatives, and largely includes future technologies with low Technology Readiness Level (TRL) resulting in high uncertainty. The presented framework is a cyber-physical design and analysis suite that combines Warfighter mission needs and expert technologist knowledge with a set of design and optimization tools, models, and experiments in order to provide a quantitative measure of the requirements disconnect and technology gap mentioned above. This quantification provides the basis for re-definitions of the requirements that are realistic in nature and ensure mission success. The research presents the development of this methodology and framework to address the core research objectives. The developed framework was then implemented on two mission scenarios that are of interest to the MAST consortium and Army Research Laboratory, namely, Joppa Urban Dwelling and Black Hawk Down Interior Building Reconnaissance. Results demonstrate the framework’s validity and serve as proof of concept for bridging the requirements disconnect between the Warfighter and the technologists. Billions of alternative MAST vehicles, composed of current and future technologies, were modeled and simulated, as part of a swarm, to evaluate their mission performance. In-depth analyses of the experiments, conducted as part of the research, presents quantitative technology gaps that needs to be addressed by technologist for successful mission completion. Quantitative values for vehicle specifications and systems' Measures of Performance were determined for acceptable level of performance for the given missions. The consolidated results were used for defining mission based requirements of MAST systems.
Conceptual design methodology of distributed intelligence large scale systems

(Georgia Institute of Technology, 2013-06-28) Nairouz, Bassem R.

Distributed intelligence systems are starting to gain dominance in the field of large-scale complex systems. These systems are characterized by nonlinear behavior patterns that are only predicted through simulation-based engineering. In addition, the autonomy, intelligence, and reconfiguration capabilities required by certain systems introduce obstacles adding another layer of complexity. However, there exists no standard process for the design of such systems. This research presents a design methodology focusing on distributed control architectures while concurrently considering the systems design process. The methodology has two major components. First, it introduces a hybrid design process, based on the infusion of the control architecture and conceptual system design processes. The second component is the development of control architectures metamodel, placing a distinction between control configuration and control methods. This enables a standard representation of a wide spectrum of control architectures frameworks.