Thesis Type: Postgraduate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Aerospace Engineering, Turkey
Approval Date: 2018
Student: HALİT KUTKAN
Supervisor: SİNAN EYİAbstract:
This thesis study presents a hybrid method based on Proper Orthogonal Decomposition (POD) with Radial Basis Function (RBF), on Direct Simulation Monte Carlo (DSMC) solutions for aerothermodynamic front surface optimization of Stardust re-entry. Gaussian and multiquadric RBFs are implemented for comparison, and multiquadric functions are chosen due to their insensitivity to diverse shape parameters. Cubic uniform B-spline curves are used innovatively for parameterization of the geometry change, instead of curve fitting the geometry itself. This makes possible to reduce the number of design variables. Gradient based optimization strategy is implemented by regarding the distributions of pressure, shear stress and heat flux along the surface of the geometries. DS2V two dimensional axisymmetric DSMC solver is used as the physics solver, and 11 species air model are chosen with 41 chemical reactions according to atmospheric conditions of the re-entry. Different geometries are obtained via deviating the design variables arbitrarily to form a snapshot pool. In this manner, the approximation success of the POD-RBF methodology is tested on highly nonlinear flow conditions with arbitrarily chosen design of experiment. Finally, the optimized geometries are simulated via DSMC code and the solutions are compared with the solutions of POD-RBF Reduced Order Model (ROM). Method lowered the optimization time extraordinarily and provided satisfactory results.