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Reduced Order Modeling & Optimization: (Constrained) Proper Orthogonal Decomposition (C)POD based surrogates for aircraft design (ROM&O)
Start date: Jan 18, 2010, End date: Dec 17, 2010 PROJECT  FINISHED 

"To meet the ACARE 2020 objectives, new green innovative A/C configurations are to be investigated. Following preparation of preliminary layout configuration for each green aircraft, optimization will need to be performed so as to help reaching improvements in the design solution, but also assess key factors and trades and point out innovative design options. In this framework, the conjunction of mature high-fidelity CFD analysis, HPC facilities, and the most advanced optimization algorithms is mandatory so as to maintain the computational effort within feasible limits. In this respect, the present proposal answers topic JTI-CS-2009-1-GRA-05-004, dedicated to the development of the high performance tools that will allow the efficient aerodynamic design of new configurations. An adequate and general answer to optimization based on computationally expensive analysis lies in the exploitation of surrogate models in lieu of the expensive analysis results, i.e. Surrogate-Based Optimization. However, the performance of such methods is known to be largely dependent on the following key elements: • the underlying optimization algorithm(s), • the surrogate model(s), • the training, • the surrogate model(s) management scheme. The present topic focuses on the second element to be handled by Proper Orthogonal Decomposition (POD). Cenaero possesses the expertise and critical technologies to tackle all these aspects in a coherent way to take a leap forward and make POD techniques truly efficient in an industrial setting. Cenaero proposes to implement and demonstrate on a GRA representative case an online (i.e. dynamic) non intrusive (Constrained) POD to perform the space reduction of the high-fidelity aerodynamic model, tackling the challenge of snapshot selection through innovative capture/recapture Design of Experiments and based on the most efficient generic interpolators for the reconstruction in the low-dimensional space."
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