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Daniel Guggenheim School of Aerospace Engineering

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    Bayesian networks for uncertainty estimation in the response of dynamic structures
    (Georgia Institute of Technology, 2008-07-07) Calanni Fraccone, Giorgio M.
    The dissertation focuses on estimating the uncertainty associated with stress/strain prediction procedures from dynamic test data used in turbine blade analysis. An accurate prediction of the maximum response levels for physical components during in-field operating conditions is essential for evaluating their performance and life characteristics, as well as for investigating how their behavior critically impacts system design and reliability assessment. Currently, stress/strain inference for a dynamic system is based on the combination of experimental data and results from the analytical/numerical model of the component under consideration. Both modeling challenges and testing limitations, however, contribute to the introduction of various sources of uncertainty within the given estimation procedure, and lead ultimately to diminished accuracy and reduced confidence in the predicted response. The objective of this work is to characterize the uncertainties present in the current response estimation process and provide a means to assess them quantitatively. More specifically, proposed in this research is a statistical methodology based on a Bayesian-network representation of the modeling process which allows for a statistically rigorous synthesis of modeling assumptions and information from experimental data. Such a framework addresses the problem of multi-directional uncertainty propagation, where standard techniques for unidirectional propagation from inputs' uncertainty to outputs' variability are not suited. Furthermore, it allows for the inclusion within the analysis of newly available test data that can provide indirect evidence on the parameters of the structure's analytical model, as well as lead to a reduction of the residual uncertainty in the estimated quantities. As part of this work, key uncertainty sources (i.e., material and geometric properties, sensor measurement and placement, as well as noise due data processing limitations) are investigated, and their impact upon the system response estimates is assessed through sensitivity studies. The results are utilized for the identification of the most significant contributors to uncertainty to be modeled within the developed Bayesian inference scheme. Simulated experimentation, statistically equivalent to specified real tests, is also constructed to generate the data necessary to build the appropriate Bayesian network, which is then infused with actual experimental information for the purpose of explaining the uncertainty embedded in the response predictions and quantifying their inherent accuracy.
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    An integrated approach to the design of supercavitating underwater vehicles
    (Georgia Institute of Technology, 2007-05-09) Ahn, Seong Sik
    A supercavitating vehicle, a next-generation underwater vehicle capable of changing the paradigm of modern marine warfare, exploits supercavitation as a means to reduce drag and achieve extremely high submerged speeds. In supercavitating flows, a low-density gaseous cavity entirely envelops the vehicle and as a result the vehicle is in contact with liquid water only at its nose and partially over the afterbody. Hence, the vehicle experiences a substantially reduced skin drag and can achieve much higher speed than conventional vehicles. The development of a controllable and maneuvering supercavitating vehicle has been confronted with various challenging problems such as the potential instability of the vehicle, the unsteady nature of cavity dynamics, the complex and non-linear nature of the interaction between vehicle and cavity. Furthermore, major questions still need to be resolved regarding the basic configuration of the vehicle itself, including its control surfaces, the control system, and the cavity dynamics. In order to answer these fundamental questions, together with many similar ones, this dissertation develops an integrated simulation-based design tool to optimize the vehicle configuration subjected to operational design requirements, while predicting the complex coupled behavior of the vehicle for each design configuration. Particularly, this research attempts to include maneuvering flight as well as various operating trim conditions directly in the vehicle configurational optimization. This integrated approach provides significant improvement in performance in the preliminary design phase and indicates that trade-offs between various performance indexes are required due to their conflicting requirements. This dissertation also investigates trim conditions and dynamic characteristics of supercavitating vehicles through a full 6 DOF model. The influence of operating conditions, and cavity models and their memory effects on trim is analyzed and discussed. Unique characteristics are identified, e.g. the cavity memory effects introduce a favorable stabilizing effect by providing restoring fins and planing forces. Furthermore, this research investigates the flight envelope of a supercavitating vehicle, which is significantly different from that of a conventional vehicle due to different hydrodynamic coefficients as well as unique operational conditions.