(Georgia Institute of Technology, 2013-11-06)
Mahzari, Milad
Thermal Protection System (TPS) is required to shield an atmospheric entry vehicle against the high surface heating environment experienced during hypersonic flight. There are significant uncertainties in the tools and models currently used for the prediction of entry aeroheating and TPS material thermal response. These uncertainties can be reduced using experimental data. Analysis of TPS ground and flight data has been traditionally performed in a direct fashion. Direct analyses center upon comparison of the computational model predictions to data. Qualitative conclusions about model validity may be drawn based on this comparison and a limited number of model parameters may be iteratively adjusted to obtain a better match between predictions and data. The goal of this thesis is to develop a more rigorous methodology for the estimation of surface heating and TPS material response using inverse estimation theory. Built on theoretical developments made in related fields, this methodology enables the estimation of uncertainties in both the aeroheating environment and material properties from experimental temperature data. Unlike direct methods, the methodology developed here is capable of estimating a large number of independent parameters simultaneously and reconstructing the time-dependent surface heating profile in an automated fashion. This methodology is applied to flight data obtained from thermocouples embedded in the Mars Pathfinder and Mars Science Laboratory entry vehicle heatshields.
(Georgia Institute of Technology, 2010-09-27)
Mahzari, Milad
There 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.