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

In addition to the FBG sensors that will be included during the RTM manufacturing process, the hybrid structure will be embedded with printed sensors (PZT, temperature or humidity). The generated data will then be used for digitalization purposes and for feeding robust structural health monitoring algorithms3,4 that have to be elaborated within this thesis to predict the remaining useful life (RUL) of the structure.

Among the main objectives, we can highlight the following:

• To elaborate an interactive hybrid twin platform allowing to provide feedback regarding specifications, to assess the smart fan blade performances.
• To quantify smart fan blade performances within the aircraft engine and its environment by developing a reduced parametric smart fan blade model that will be included in complex system simulation tools.

Laboratory and/or research group: PIMM / DYSCO Team

Supervisors and contact:

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

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

Starting date: Autumn 2021

Job description