Hypersonic vehicle design is a challenging problem. The main obstacle that makes this work challenging is the non-linear interdependency between each of the vehicle’s systems. Thus, when generating a vehicle design model for a hypersonic system, a large number of parameters must be considered. The addition of a single parameter into the design space leads to an exponential increase in the number of necessary single point solutions.This thesis will examine hypersonic vehicle design in a unique way that decreases the computational power required to achieve a more accurate solution. This was done through the examination of error vectors between multi-fidelity design spaces using Kriging surrogate models and superimposing the computed difference onto a low fidelity solver. By adding this empirical correction onto the low fidelity data, the accuracy of the computed metrics was improved by an order of magnitude in some cases.In proving this theory a simplified airfoil test case and a generic cavity scramjet, that included combustion chemistry, were investigated. Examination of both of these geometries required a sizable amount of computing resources. To meet this requirement a custom cloud controller that could dynamically load processor cores depending on individual computer workloads was developed. This computational cloud, as well as an automated case generation algorithm, were developed in order to process the hundreds of computational runs necessary.
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