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Daniel Guggenheim School of Aerospace Engineering
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ItemProblem decomposition by mutual information and force-based clustering(Georgia Institute of Technology, 2012-03-28) Otero, Richard EdwardThe scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter-dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.
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ItemOscillation of Supersonic Inflatable Aerodynamic Decelerators at Mars(Georgia Institute of Technology, 2010-12-01) Smith, Brandon P.This analysis considers the dynamic stability of a notional Mars 2018 entry probe augmented with an attached supersonic inflatable aerodynamic decelerator (SIAD) deployed at Mach 5. Dynamics of the attached isotensoid and tension cone SIAD configurations are compared using an explicit solution to the planar equations of motion. A current experimental database of flexible isotensoid and tension cone static aerodynamics is employed in the simulation. Pitch-damping data from the Mars Science Laboratory (MSL) ballistic range tests is parameterized and applied to the SIAD-augmented portion of flight. The Mach number at which safe parachute deployment can occur depends on the amplitude of pitch oscillation, so the sensitivity of this metric to the parameterized pitch-damping behavior is determined. Pitch dynamics yielding unacceptable parachute staging conditions are quantified to inform SIAD configuration selection and design. These exploratory results are used to recommend a general strategy for measuring the pitch dynamics of SIAD augmented blunt vehicles in ground testing facilities.
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ItemAn Inverse Parameter Estimation Methodology for the Analysis of Aeroheating and Thermal Protection System Experimental Data(Georgia Institute of Technology, 2010-09-27) Mahzari, MiladThere are substantial uncertainties in the computational models currently used to predict a spacecraft’s heating environment and the Thermal Protection System (TPS) material response during Mars entry. Flight data will help reduce these uncertainties and improve the current computational tools. The Mars Science Laboratory (MSL) Entry, Descent and Landing Instrumentation (MEDLI) suite will provide more aeroheating data than all the previous Mars missions combined. Motivated by this future data, a comprehensive inverse parameter estimation methodology is presented in this paper for the analysis of aeroheating and TPS experimental data. The proposed methodology is applied to an MSL relevant Arcjet test dataset to investigate the feasibility of the proposed approach. The first step is the Nominal Analysis where the quality of the experimental data is examined and a comparison to the nominal predictions is presented. The second step is the Monte Carlo Analysis where a Monte Carlo study is performed to identify the model input parameters that contribute the most to the measurement uncertainty. The third step is the Sensitivity Analysis where the correlation between the different input parameters is investigated in order to determine what parameters can be estimated simultaneously. Finally the last step is the Inverse Analysis where an inverse parameter estimation code is developed to estimate heating and material parameters from the Arcjet data. Solution existence, uniqueness and stability were identified as the main challenges faced in the inverse analysis. Some strategies were suggested in order to deal with these challenges. Finally, in order to show how the different steps of this methodology come together a test problem was solved.