|Name:||Duane L. Knill|
|Title:||Implementing Aerodynamic Predictions from Computational Fluid Dynamics in Multidisciplinary Design Optimization of a High-Speed Civil Transport|
|Degree:||Doctor of Philosophy|
|Department:||Aerospace and Ocean Engineering|
|Committee Chair:||Bernard Grossman|
|Committee Members:||William H. Mason|
|Raphael T. Haftka|
|Layne T. Watson|
|Robert W. Walters|
|Joseph A. Schetz|
|Keywords:||MDO CFD HSCT|
|Date of defense:||December 1, 1997|
|Availability:||Release the entire work for Virginia Tech access only.
After one year release worldwide only with written permission of the student and the advisory committee chair.
A method to efficiently introduce supersonic drag predictions from computational fluid dynamics (CFD) calculations in a combined aerodynamic-structural optimization of a High-Speed Civil Transport (HSCT) is presented. To achieve this goal, the method must alleviate the large computational burden associated with performing CFD analyses and reduce the numerical noise present in the analyses. This is accomplished through the use of response surface (RS) methodologies, a variation of the variable-complexity modeling (VCM) technique, and coarse grained parallel computing. Variable-complexity modeling allows one to take advantage of the information gained from inexpensive lower fidelity models while maintaining the accuracy of the more expensive high fidelity methods. The utility of the method is demonstrated on HSCT design problems of five, ten, fifteen, and twenty design variables. Motivation for including CFD predictions into the HSCT optimization comes from studies detailing the differences in supersonic aerodynamic predictions from linear theory, Euler, and parabolized Navier-Stokes (PNS) calculations for HSCT configurations. The effects of these differences in integrated forces and distributed loads on the aircraft performance and structural weight are investigated. These studies indicate that CFD drag solutions are required for accurate HSCT performance and weight estimates. Response surface models are also used to provide useful information to the designer with minimal computational effort. Investigations into design trade-offs and sensitivities to certain design variables, available at the cost of evaluating a simple quadratic polynomial, are presented. In addition, a novel and effective approach to visualizing high dimensional, highly constrained design spaces is enabled through the use of RS models.
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