In the MORPHO project an innovative RTM manufacturing process that integrates sensors technology (as FBG or dielectrics sensors) will be developed. The aim in this PostDoc work, is to build a numerical tool (hybrid twin) that allows to simulate all the physics3 occurring in the mold in the presence of embedded sensors and to predict the final properties of the cured material that will be confronted and adapted on-fly using data provided by the sensors. Concerning the assessment of the structural health, the hybrid structure will be embedded, in addition to the FBG sensors, with printed sensors (PZT, temperature or humidity). The generated data will then be used for digitalization purposes and for feeding robust structural health monitoring algorithms4,5 that have to be elaborated within this thesis to predict the remaining useful life (RUL) of the structure.

The PostDoc candidate will be in charge of developing a hybrid twins platform merging physics-based and data-driven models for RTM manufacturing process. Model reduction and machine learning algorithms will be used to decrease computational complexity to quantify the smart blade performances within the aircraft engine.

Among the main objectives, we can highlight the following:

• To elaborate interfaced digital and hybrid twins representative of the RTM processes of the smart fan blade
• To provide an interactive hybrid twin platform allowing to provide feedback regarding specifications, to assess the smart fan blade performances.

Laboratory and/or research group: PIMM / DYSCO Team

Supervisors and contact:

Eric Monteiro
eric.monteiro@ensam.eu
Francisco Chinesta
Francisco.CHINESTA@ensam.eu
Marc Rébillat
marc.rebillat@ensam.eu
Nazih  Mechbal                      
nazih.mechbal@ensam.eu
 

Funding: EU H2020 MORPHO Project– Embedded Life-Cycle Management for Smart Multimaterials Structures: Application to Engine Components.

Starting date: September/October 2021

Job description