Hutcheson, Ryan S. (2009-05). Function-based Design Tools for Analyzing the Behavior and Sensitivity of Complex Systems During Conceptual Design. Doctoral Dissertation. Thesis uri icon

abstract

  • Complex engineering systems involve large numbers of functional elements. Each functional element can exhibit complex behavior itself. Ensuring the ability of such systems to meet the customer's needs and requirements requires modeling the behavior of these systems. Behavioral modeling allows a quantitative assessment of the ability of a system to meet specific requirements. However, modeling the behavior of complex systems is difficult due to the complexity of the elements involved and more importantly the complexity of these elements' interactions. In prior work, formal functional modeling techniques have been applied as a means of performing a qualitative decomposition of systems to ensure that needs and requirements are addressed by the functional elements of the system. Extending this functional decomposition to a quantitative representation of the behavior of a system represents a significant opportunity to improve the design process of complex systems. To this end, a functionality-based behavioral modeling framework is proposed along with a sensitivity analysis method to support the design process of complex systems. These design tools have been implemented in a computational framework and have been used to model the behavior of various engineering systems to demonstrate their maturity, application and effectiveness. The most significant result is a multi-fidelity model of a hybrid internal combustion-electric racecar powertrain that enabled a comprehensive quantitative study of longitudinal vehicle performance during various stages in the design process. This model was developed using the functionality-based framework and allowed a thorough exploration of the design space at various levels of fidelity. The functionality-based sensitivity analysis implemented along with the behavioral modeling approach provides measures similar to a variance-based approach with a computation burden of a local approach. The use of a functional decomposition in both the behavioral modeling and sensitivity analysis significantly contributes to the flexibility of the models and their application in current and future design efforts. This contribution was demonstrated in the application of the model to the 2009 Texas A&M Formula Hybrid powertrain design.
  • Complex engineering systems involve large numbers of functional elements. Each
    functional element can exhibit complex behavior itself. Ensuring the ability of such
    systems to meet the customer's needs and requirements requires modeling the behavior
    of these systems. Behavioral modeling allows a quantitative assessment of the ability of
    a system to meet specific requirements. However, modeling the behavior of complex
    systems is difficult due to the complexity of the elements involved and more importantly
    the complexity of these elements' interactions.
    In prior work, formal functional modeling techniques have been applied as a means of
    performing a qualitative decomposition of systems to ensure that needs and requirements
    are addressed by the functional elements of the system. Extending this functional
    decomposition to a quantitative representation of the behavior of a system represents a
    significant opportunity to improve the design process of complex systems.
    To this end, a functionality-based behavioral modeling framework is proposed along
    with a sensitivity analysis method to support the design process of complex systems.
    These design tools have been implemented in a computational framework and have been
    used to model the behavior of various engineering systems to demonstrate their maturity,
    application and effectiveness. The most significant result is a multi-fidelity model of a
    hybrid internal combustion-electric racecar powertrain that enabled a comprehensive
    quantitative study of longitudinal vehicle performance during various stages in the design process. This model was developed using the functionality-based framework
    and allowed a thorough exploration of the design space at various levels of fidelity. The
    functionality-based sensitivity analysis implemented along with the behavioral modeling
    approach provides measures similar to a variance-based approach with a computation
    burden of a local approach. The use of a functional decomposition in both the
    behavioral modeling and sensitivity analysis significantly contributes to the flexibility of
    the models and their application in current and future design efforts. This contribution
    was demonstrated in the application of the model to the 2009 Texas A&M Formula
    Hybrid powertrain design.

publication date

  • May 2009