In this project, I assigned us in 2 basic groups. Maruyama, D., Liu, D., Görtz, S.: An efficient aerodynamic shape optimization framework for robust design of airfoils using surrogate models.I am currently working on a personal single seat, piston aircraft that I would fly myself and I am currently in a bit of conundrum when it comes to my wing design. (eds.), Uncertainty Management for Robust Industrial Design in Aeronautics Chapter 43 (2017) Maruyama, D., Liu, D., Görtz, S.: Surrogate model based approaches to UQ and their range of applicability. (eds.), Uncertainty Management for Robust Industrial Design in Aeronautics Chapter 13 (2017) Maruyama, D., Liu, D., Görtz, S.: Comparing surrogates for estimating aerodynamic uncertainties of airfoils. Han, Z.H., Görtz, S., Zimmermann, R.: Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function. In: Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics, volume 122 of the Series Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Liu, D.: Efficient quantification of aerodynamic uncertainies using gradient-employing surrogate methods. Han, Z.H., Görtz, S., Zimmermann, R.: Improving variable-fidelity surrogate modeling via gradient-enhanced Kriging and a generalized hybrid bridge function. thesis, Department of Computer Sciences, University of Texas at Austin (1990) Rowan, T.: Functional Stability analysis of numerical algorithms, Ph.D. In: Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol. In: RTO AVT-189 Specialists’ Meeting on Assessment of Stability and Control Prediction Methods for Air and Sea Vehicles, Portsdown West, Oct 12–14, 2011īrezillon, J., Abu-Zurayk, M.: Aerodynamic inverse design framework using discrete adjoint method. Heinrich, R., Reimer, K., Michler, A.: Multidisciplinary simulation of maneuvering aircraft interacting with atmospheric effects using the DLR TAU code. In: Seventh International Conference on Computational Fluid Dynamics (ICCFD7), ICCFD7-1902, Hawaii, July 2012 (Eds.) Proceedings of the European Conference on Computational Fluid Dynamics (ECCOMAS CFD 2006), The Netherlands (2006)Īllmaras, S.R., Johnson, F.T., Spalart, P.R.: Modifications and clarifications for the implementation of the Spalart-Allmaras turbulence model. Schwamborn, D., Gerhold, T., Heinrich, R.: The DLR TAU-code: recent applications in research and industry, invited lecture. (Eds.) New Results in Numerical and Experimental Fluid Mechanics. In: Nitsche, W., Heinemann, H.-J., Hilbig, R. Gerhold, T., Hannemann, V., Schwamborn, D.: On the validation of the DLR-TAU code. (Eds.) Proceedings of 11th Parallel CFD Conference Williamsburg, VA, North-Holland, 23– Galle, M., Gerhold, T., Evans, J.: Parallel computation of turbulent flows around complex geometries on hybrid grids with the DLR-TAU code. Liu, D., Litvinenko, A., Schillings, C., Schulz, V.: Quantification of airfoil geometry-induced aerodynamic uncertainties-comparison of approaches. In: New Results in Numerical and Experimental Fluid Mechanics IX, volume 124 of the Series Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Liu, D., Görtz, S.: Efficient quantification of aerodynamic uncertainty due to random geometry perturbations. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. Deterministic Design Optimization (DDO).Reliability-based Design Optimization (RBDO). The strength and location of the shock wave of the robustly designed airfoils are shown to be less sensitive to random geometrical perturbations than the initial and deterministically designed airfoils. The airfoil obtained by optimizing the two robustness measures has similar geometrical features and shows better performance in terms of the robustness measures than the initial and the deterministically designed airfoils. Two operational parameters are also considered uncertain. The nominal airfoil geometry is assumed to be perturbed by a Gaussian random field which is parameterized by 10 independent variables through a truncated Karhunen–Loève expansion. The airfoil is parameterized with 10 deterministic design variables, which are optimized by a gradient-free Subplex algorithm. Both robustness measures are efficiently evaluated by using efficient sampling techniques assisted by a gradient-enhanced Kriging model. Reliability-based design optimization (RBDO) targets minimizing the maximum drag coefficient. Robust design optimization (RDO) aims at minimizing the mean and standard deviation of the drag coefficient. Two kinds of robustness measures are introduced and applied to design optimization of the UMRIDA BC-02 transonic airfoil test case under uncertainty.
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