Date

2018-01-01

Author

Savas, Ozgun
Topbas, Eren
Unal, Kenan
Karaca, H. Deniz
Kutay, Ali Türker

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.Wind tunnel testing is an essential procedure to investigate the aerodynamics forces and moments. In this paper, a methodology is presented to perform such test in an efficient way. Experimental design process is carried out before the testing in order to cover the flight regime as much as possible with the least possible number of tests. After the determination of the test matrix and conducting the wind-on tests, the modeling of the data is the next step. On that matter, two nonlinear modeling techniques are presented and compared with each other. First technique is the well-known Artificial Neural Networks (ANN) and the second one is relatively lesser known but a powerful modeling algorithm Multivariate Adaptive Regression Splines (MARS). After the data is modeled using both approaches, their statistical metrics are compared and the models are integrated into a high fidelity 6DoF equations of motion model. Since all this effort is to improve the simulation accuracy and performance, 6DoF simulations are performed using three different flight scenarios that cover the flight regime of the missile. Afterwards, obtained trajectory and flight parameters are compared. Finally, various flight conditions are produced and the models are evaluated in batch mode to see their performances in terms of computational speed.

URI

https://hdl.handle.net/11511/57710

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Department of Aerospace Engineering, Conference / Seminar