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Reduced Order Modeling Optimization Constrained Proper Orthogonal Decomposi...

Reduced Order Modeling Optimization Constrained Proper Orthogonal Decomposition C POD based surrogates for aircraft design 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... ver más

17/12/2010

CENAERO

60K€

Presupuesto del proyecto: 60K€

Líder del proyecto

CENTRE DE RECHERCHE EN AERONAUTIQUE ASBL CEN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2010-12-17 No tenemos la información de la convocatoria

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CENAERO

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Líder del proyecto

CENTRE DE RECHERCHE EN AERONAUTIQUE ASBL CEN... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 60K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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