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Advanced Lagrangian Optimization, Receptivity and Sensitivity analysis applied to industrial situations

Objective

In the last ten years there has been a surge of interest in non-modal analysis applied to canonical problems in fundamental fluid mechanics. Even in simple flows, the stability behaviour predicted by non-modal analysis can be completely different from and far more accurate than that predicted by conventional eigenvalue analysis.

As well as being more accurate, the tools of non-modal analysis, such as Lagrangian optimization, are very versatile. Furthermore, the outputs, such as receptivity and sensitivity maps of a flow, provide powerful insight for engineers. They describe where a flow is most receptive to forcing or where the flow is most sensitive to modification.

The application of non-modal analysis to canonical problems has set the scene for step changes in engineering practice in fluid mechanics and thermoacoustics. The technical objectives of this proposal are to apply non-modal analysis to high Reynolds number flows, reacting flows and thermoacoustic systems, to compare theoretical predictions with experimental measurements and to embed these techniques within an industrial design tool that has already been developed by the group.

This research group s vision is that future generations of engineering CFD tools will contain modules that can perform non-modal analysis. The generalized approach proposed here, combined with challenging scientific and engineering examples that are backed up by experimental evidence, will make this possible and demonstrate it to a wider engineering community.

Call for proposal

ERC-2010-StG_20091028
See other projects for this call

Host institution

THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE
EU contribution
€ 1 301 196,00
Address
TRINITY LANE THE OLD SCHOOLS
CB2 1TN Cambridge
United Kingdom

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Region
East of England East Anglia Cambridgeshire CC
Activity type
Higher or Secondary Education Establishments
Administrative Contact
Renata Schaeffer (Ms.)
Principal investigator
Matthew Pudan Juniper (Dr.)
Links
Total cost
No data

Beneficiaries (1)