Organizational Unit:
School of Computational Science and Engineering

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Now showing 1 - 2 of 2
  • Item
    Cost benefit analysis of adding technologies to commercial aircraft to increase the survivability against surface to air threats
    (Georgia Institute of Technology, 2018-07-27) Patterson, Anthony
    Flying internationally is an integral part of people's everyday lives. Most United States airlines fly internationally on a daily basis. The world continues to become a more dangerous place, due to improvements to technology and the willingness of some nations to sell older technology to rebel groups. In the military realm, there have been countermeasures to combat surface to air threats and thus increase the survivability of military aircraft. Survivability is defined as the ability to remain mission capable after a single engagement. Existing commercial aircraft currently do not have any countermeasure systems or missile warning systems integrated into their onboard systems. Better understanding of the interaction between countermeasure systems and commercial aircraft will help bring additional knowledge to support a cost benefit analysis. The scope of this research is to perform a cost benefit analysis on the addition of these technologies that are currently available on military aircraft, and to study the adding of these same technologies to commercial aircraft. The research will include a cost benefit analysis along with a size, weight, and power analysis. Additionally, a simulation will be included that will analyze the success rates of different countermeasures versus different surface to air threats in hopes of bridging the gap between a cost benefit analysis and a survivability simulation. The research will explore whether or not adding countermeasure systems to commercial aircraft is technically feasible and economically viable.
  • Item
    Geometric feature extraction in support of the single digital thread approach to detailed design
    (Georgia Institute of Technology, 2016-12-08) Gharbi, Aroua
    Aircraft design is a multi-disciplinary and complicated process that takes a long time and requires a large number of trade-offs between customer requirements, various types of constraints and market competition. Particularly detailed design is the phase that takes most of the time due to the high number of iterations between the component design and the structural analysis that need to be run before reaching an optimal design. In this thesis, an innovative approach for detailed design is suggested. It promotes a collaborative framework in which knowledge from the small scale level of components is shared and transferred to the subsystems and systems level leading to more robust and real time decisions that speed up the design time. This approach is called the Single Digital Thread Approach to Detailed Design or shortly STAnDD. The implementation of this approach is laid over a bottom-up plan, starting from the component level up to the aircraft level. In the component level and from a detailed design perspective, three major operations need to be executed in order to deploy the Single Digital Thread approach. The first one is the automatic geometric extraction of component features from a solid with no design history, the second phase is building an optimizer around the design and analysis iterations and the third one is the automatic update of the solid. This thesis suggests a methodology to implement the first phase. Extracting geometric features automatically from a solid with no history(also called dumb solid) is not an easy process especially in aircraft industry where most of the components have very complex shapes. Innovative techniques from Machine Learning were used allowing a consistent and robust extraction of the data.