Multi-fidelity reduced order modelling approach for the inverse identification of acoustic material properties (VAMOR DC10)
Offer DescriptionThis doctoral project is part of a larger, multidisciplinary and international project VAMOR: “Vibro-Acoustic Model Order Reduction” (GA 101119903) funded under the Marie-Sklodowska-Curie Actions Doctoral Networks within the Horizon Europe Programme of the European Commission.VAMOR contributes to a more sustainable and quieter future for Europe. Noise pollution has arisen as one of the key factors towards the degradation of the quality of life in European societies. In that context, efficient physics-based sound modelling is a key enabler towards not only optimized and sustainable acoustic profiles through efficient design procedures, but also affordable so-called digital twins that monitor product performance in real time. To this end, the overarching goal of VAMOR is to provide high level scientific and transferable skills training on a new generation of efficient vibro-acoustic modelling techniques, so-called model order reduction (MOR) strategies, to a group of high achieving, competent doctoral candidates to promote a quieter and more sustainable environment. VAMOR brings together a remarkable consortium, which combines research leading academic institutions – KU Leuven, Technische Universitaet Munchen (TUM), Technical University of Denmark (DTU), Kungliga Tekniska Hoegskolan (KTH), Universite du Mans, Conservatoire National des Arts et Metiers (CNAM) – with a constantly innovating, wide variety of industrial partners working on software, material, testing, design and sound enhancement (Siemens Industry Software NV, Müller BBM, Trèves, Phononic Vibes, Saint-Gobain Ecophon, Tyréns, Purifi ApS).Doctoral Candidate 10 (DC10) within VAMOR will develop novel inverse identification approaches for the characterisation of various acoustic materials by combining multi-fidelity approaches and reduced-order models. It will lead to the development of efficient machine learning models that will be used in a global optimization framework. The first step is to determine the most appropriate material models and reduced-order models for the low-fidelity and high-fidelity models with the aim of efficiently evaluating the vibro-acoustic response. The second step would be to test this approach on an established test case to estimate its robustness and the speed-up as compared to other inverse techniques. Experimental tests may be considered for the validation of the approaches and/or the enrichment of the surrogate models. Finally, the proposed methodology will be applied to more challenging inverse problem identification: stochastic inverse identification, simultaneous identification of acoustic and/or elastic and/or viscoelastic properties of foams/composites/multilayer structures, or the multilevel optimization of material parameters. Ultimately, the developed techniques will be deployed to identify the properties of degraded materials on existing structures, such as the acoustic treatment of operating vehicles.RequirementsResearch Field Engineering Education Level Master Degree or equivalentResearch Field Physics Education Level Master Degree or equivalentResearch Field Mathematics » Applied mathematics Education Level Master Degree or equivalentLanguages ENGLISH Level GoodResearch Field Engineering » Mechanical engineeringAdditional InformationBenefits
Eligibility criteria
Work Location(s)Number of offers available 1 Company/Institute Le Cnam Country France City Paris Postal Code 75003 Street 2 Rue Conté GeofieldWhere to apply WebsiteContact CityParis WebsiteStreet2 rue Conté Postal Code75003 E-Maillucie.rouleau@lecnam.netSTATUS: EXPIRED
€2800 per month
Paris
Sat, 04 May 2024 22:22:29 GMT
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