Date of Award


Document Type


Degree Name

Master of Science


Department of Aeronautics and Astronautics

First Advisor

Ramana Grandhi, PhD


In the field of multidisciplinary hypersonic vehicle design, striking the balance between the accuracy and efficiency of a predictive aerodynamic response model is a significant challenge. In response to this challenge, the objective of this thesis is to evaluate the aerodynamic performance of a Generic Hypersonic Vehicle (GHV) using the technique of surrogate modeling Computational Fluid Dynamic data points across a large range of flight conditions. To accomplish this, the full CFD process was conducted by preparing the vehicle geometry, generating a grid, computing the flow, and post-processing the data. A three-dimensional, quasi-random distribution of 1000 points defined the design space of the study which consisted of varied Mach number, angle of attack, and flight altitude. Using inviscid CFD training data from the design space, surrogate models of integrated forces and critical surface pressures were generated using the Kriging method, and the suitability of these models was evaluated using additional validation CFD data. Additional studies were conducted to evaluate the optimal correlation and regression functions for the Kriging models and to determine the optimal number of training points needed for a sufficiently accurate model.

AFIT Designator


DTIC Accession Number