(Georgia Institute of Technology, 2013-11-06)
Dutta, Soumyo
There is significant uncertainty in our knowledge of the Martian atmosphere and the aerodynamics of the Mars entry, descent, and landing (EDL) systems. These uncertainties result in conservatism in the design of the EDL vehicles leading to higher system masses and a broad range of performance predictions. Data from flight instrumentation onboard Mars EDL systems can be used to quantify these uncertainties, but the existing dataset is sparse and many parameters of interest have not been previously observable. Many past EDL reconstructions neither utilize statistical information about the uncertainty of the measured data nor quantify the uncertainty of the estimated parameters. Statistical estimation methods can blend together disparate data types to improve the reconstruction of parameters of interest for the vehicle. For example, integrating data obtained from aeroshell-mounted pressure transducers, inertial measurement unit, and radar altimeter can improve the estimates of the trajectory, atmospheric profile, and aerodynamic coefficients, while also quantifying the uncertainty in these estimates. These same statistical methods can be leveraged to improve current engineering models in order to reduce conservatism in future EDL vehicle design. The work in this thesis presents a comprehensive methodology for parameter reconstruction and uncertainty quantification while blending dissimilar Mars EDL datasets. Statistical estimation methods applied include the Extended Kalman Filter, Unscented Kalman Filter, and Adaptive Filter. The estimators are applied in a manner in which the observability of the parameters of interest is maximized while using the sparse, disparate EDL dataset. The methodology is validated with simulated data and then applied to estimate the EDL performance of the 2012 Mars Science Laboratory. The reconstruction methodology is also utilized as a tool for improving vehicle design and reducing design conservatism. A novel method of optimizing the design of future EDL atmospheric data systems is presented by leveraging the reconstruction methodology. The methodology identifies important design trends and the point of diminishing returns of atmospheric data sensors that are critical in improving the reconstruction performance for future EDL vehicles. The impact of the estimation methodology on aerodynamic and atmospheric engineering models is also studied and suggestions are made for future EDL instrumentation.
(Georgia Institute of Technology, 2010-05-05)
Dutta, Soumyo
Flight data from an entry, descent, and landing (EDL) sequence can be used to reconstruct the vehicle’s trajectory
as well as compute the associated uncertainty. The atmospheric profile encountered by the vehicle can
similarly be estimated from the flight data. Past Mars missions have contained instruments, such as accelerometers,
gyroscopes, and radar altimeters that do not provide direct measurement of the free-stream atmospheric
conditions. Thus, uncertainties in the atmospheric reconstruction and the aerodynamic database knowledge
cannot be separated. However, the upcoming Mars Science Laboratory (MSL) will take measurements of the
pressure on the aeroshell forebody during entry. These measurements will provide means to determine the
free-stream conditions and to separate the atmospheric and aerodynamic uncertainties. In this paper, analytical
methods to statistically estimate trajectories and free-stream conditions from flight data and to quantify
uncertainties in these parameters are discussed. A sample data set from a ballistic range test of an Orion Crew
Exploration Vehicle (CEV) model is then used to demonstrate results from applying these procedures. This
approach utilizes the same techniques and toolset planned for subsequent application for the reconstruction of
MSL’s EDL sequence in 2012.