Abstract
Uncertainty-based optimisation techniques provide optimal airfoil designs that are less vulnerable to the presence of uncertainty in the operational conditions (i.e., Mach number, angle-of-attack, etc.) at which an airfoil is functioning. These uncertainty-based techniques typically require numerous function evaluations to accurately calculate the statistical measure of the quantity of interest. To render the computational burden down, the design optimisation of the airfoil is performed by a multi-fidelity surrogate-based technique. The high-fidelity aerodynamic performance is calculated with a compressible RANS solver using a fine grid. At the low-fidelity level, a coarser grid is used. The so-called far-field drag approximation is employed to obtain accurate drag predictions despite the lower grid resolution.
This research has been developed with the partial support of the H2020 MCSA ITN UTOPIAE grant agreement number 722734.
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Notes
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Program developed at the Italian Aerospace Research Centre (CIRA).
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Morales, E. et al. (2021). Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty Using Far-Field Drag Approximation. In: Vasile, M., Quagliarella, D. (eds) Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications. UQOP 2020. Space Technology Proceedings, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-030-80542-5_3
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