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M/W Post-Doc Multi-scale design of Ceramic Matrix Composites using a hybrid digital modelling/machine learning approach - Pessac, France - CNRS
Description
25 Nov 2023
Job Information
Organisation/Company
CNRS
Department
Laboratoire des Composites ThermoStructuraux
Research Field
Engineering » Materials engineering
Physics » Acoustics
Researcher Profile
First Stage Researcher (R1)
Country
France
Application Deadline
15 Dec 2023 – 23:59 (UTC)
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
35
Offer Starting Date
1 Feb 2024
Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme
Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
Context & problematic
Ceramic matrix composites (CMC) have remarkable mechanical properties up to very high temperatures, which makes them materials of choice for many thermostructural applications in the aeronautics, space and defense fields. These are structured, multi-scale, anisotropic and heterogeneous materials, which makes the dimensioning of parts with these materials particularly complex. Among their specificities, the lack of separability between the "material" scales (micro-meso) and that of the part (macro) strongly constrains the direct use of conventional methods of homogenization, in particular outside common areas and all the more so in presence of strong loading gradients. CMCs also have an intrinsic variability, inherent in their manufacturing process, which must be integrated into the design to anticipate the potentially deleterious effects of material health defects. These particularities make the direct use of the usual scaling methods difficult and require the development of suitable numerical methods.
Different approaches can be considered to tackle this problem. The first is to process the part completely at the scale of the material (micro or meso), which involves the resolution of very large numerical problems and the use of domain decomposition type approaches (BDD, FETI, etc.) or of advanced multi-scale techniques (MS-(G)FEM, CMCM, etc.). These approaches require the use of HPC computing resources. Besides these constraints, their systematic extension to highly non-linear behaviors is not immediately possible in terms of computational cost. The second approach consists in processing at the fine scale only the zones of interest of the part and in connecting the solution obtained to a coarse calculation at the macro scale. However, this is only possible if the areas of interest can be determined a priori and are few in number. Finally, a last approach supposes building a priori stochastic micro-meso and meso-macro bridges, on characteristic volumes compatible with an approximate description of the microstructure, but sufficiently representative to include the variability of the material and the loading gradients. This last approach makes it possible to naturally include non-linear behaviors, only if the damage remains limited to the elementary volumes — which can pose a problem in the case of delamination for example.
The objective of this work is to propose a numerical methodology reconciling the different previous approaches and allowing, in the long term, to design a CMC part by integrating, as needed, the description of the singular zones and the variability of the material at any point of the part.
To do this, we will rely on the one hand on digital twins, either virtual or produced from 3D imaging of CMC parts, and on the other hand we will use machine learning techniques both to accelerate the resolution of any calculations in subdomains6 as the integration of local nonlinear laws
LCTS is a joint lab of CNRS, Bordeaux University, CEA and Safran, located on the Bordeaux Campus. It has 34 years experience in basic science research on refractory composites. These high-performance materials are used in aviation, space, energy & industrial applications. LCTS is single research team working in project-oriented mode, in strong relation with its non-academic stakeholders. Currently it includes 34 permanent staff members, plus some 15 PhD candidates and 4 post-doc researchers.
Requirements
Research Field
Engineering
Education Level
PhD or equivalent
Research Field
Physics
Education Level
PhD or equivalent
Languages
FRENCH
Level
Basic
Research Field
Engineering » Materials engineering
Years of Research Experience
None
Research Field
Physics » Acoustics
Years of Research Experience
None
Additional Information
Eligibility criteria
Mechanics of materials; Finite Element Modeling; Machine Learning Methods
Website for additional job details
Work Location(s)
Number of offers available
1
Company/Institute
Laboratoire des Composites ThermoStructuraux
Country
France
City
PESSAC
Where to apply
Website
Contact
City
PESSAC
Website
STATUS: EXPIRED
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