It is important to study their reliability in order to secure their use for more electric and or hybrid aircraft.
The IRT Saint Exupéy therefore invites you, via this call for expression of interest, with the aim of integrating into a consortium, “End users”, manufacturers of test equipment (SMEs, mid-cap companies, and large groups), certification organization, normative committees as well as academics, to participate in the co-construction of the PREDICAT project and its financing.
These variations, to varying degrees, can occur at the same time, not individually. In order to meet this need, it is important to develop a multi-stress testing platform corresponding to this need.
The development of a multi-stress chamber combining electrical, thermal, vacuum, temperature and vibration will make it possible to define a new method for testing accelerated aging of integrated components and equipment (electrical, electrochemical, materials, etc.) in future more electric or hybrid aircraft, and to validate their robustness before their integration into the systems.
However, beyond aircraft, the current transport market aims to increase on-board electrical power, and move towards more electric, less polluting transport. This platform will harmonize the components, and their reliability, between these different applications.
- Electronic Components (SiC, Gan, digital, memory, microprocessor…)
- Storage solution and energy sources: Battery Cells, fuel pack, super capacitors,
- Technological bricks: Energy converters, Cooling system, new filtering system, switching and protection devices, and electrical distribution solutions,
- Electrical components: High Voltage wires, busbar, connectors, printed circuit board…
- New materials: electrical insulators, composite, parts produced by additive manufacturing, or new surface treatment
To adapt to the system tested, it must be possible to make this platform must be removable and modular. To optimize the tests, it is important to develop data management and processing tools in order to develop models for predicting the evolution of the tested product, and finally study the possibility to establish virtual tests, by using deep learning.
This platform will allow optimizing the tests by defining test profile according the test profile and the requirements of the products tested, environmental constraints, and the mission profile. Through this project, the qualification and certification will be optimized to reduce the development costs of the tested products.