Doctoral Dissertations


"The primary objective of this study was to develop improved methodologies for efficient and accurate uncertainty quantification with stochastic expansions and apply them to problems in supersonic and hypersonic flows. Methods introduced included approaches for efficient dimension reduction, sensitivity analysis, and sparse approximations. These methods and procedures were demonstrated on multiple stochastic models of hypersonic, planetary entry flows, which included high-fidelity, computational fluid dynamics models of radiative heating on the surface of hypersonic inflatable aerodynamic decelerators during Mars and Titan entry. For these stochastic problems, construction of an accurate surrogate model was achieved with as few as 10% of the number of model evaluations needed to construct a full dimension, total order expansion. Another objective of this work was to introduce methodologies used for further advancement of a quantification of margins and uncertainties framework. First, the use of stochastic expansions was introduced to efficiently quantify the uncertainty in system design performance metrics and performance boundaries. Then, procedures were defined to measure margin and uncertainty metrics for systems subject to multiple types of uncertainty in operating conditions and physical models. To demonstrate the new quantification of margins and uncertainties methodologies, two multi-system, multi-physics stochastic models were investigated: (1) a model for reentry dynamics, control, and convective heating and (2) a model of ground noise prediction of low-boom, supersonic aircraft configurations. Overall the methods and results of this work have outlined many effective approaches to uncertainty quantification of large-scale, high-dimension, aerospace problems containing both epistemic and inherent uncertainty. The methods presented showed significant improvement in the efficiency and accuracy of uncertainty analysis capability when stochastic expansions were used for uncertainty quantification."--Abstract, page iii.


Hosder, Serhat

Committee Member(s)

Riggins, David W.
Du, Xiaoping
Isaac, Kakkattukuzhy M.
Maddalena, Luca


Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Aerospace Engineering


United States. National Aeronautics and Space Administration
Small Business Technology Transfer Program (U.S.)
M4 Engineering, Inc.
Missouri Space Grant Consortium


Partial funding for this work was provided by NASA STTR grant no. NNX11CC60C


Missouri University of Science and Technology

Publication Date

Spring 2015


xvi, 153 pages

Note about bibliography

Includes bibliographic references (pages 145-152).


© 2015 Thomas Kelsey West IV, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Subject Headings

Aerodynamics, Hypersonic -- Computer simulation
Aerodynamics, Supersonic -- Computer simulation
Stochastic analysis
Uncertainty -- Mathematics

Thesis Number

T 10730

Electronic OCLC #