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Aerospace Systems Design Laboratory (ASDL)
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ItemA METHODOLOGY FOR THE MODULARIZATION OF OPERATIONAL SCENARIOS FOR MODELLING AND SIMULATION(Georgia Institute of Technology, 2022-07-29) Muehlberg, MarcAs 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.
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ItemRepresentative Data and Models for Complex Aerospace Systems Analysis(Georgia Institute of Technology, 2022-04-28) Gao, ZhenyuCatalyzed 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.