Periodic Reporting for period 2 - CAELESTIS (Hyperconnected simulation ecosystem supporting probabilistic design and predictive manufacturing of next generation aircraft structures)
Période du rapport: 2023-11-01 au 2024-10-31
CAELESTIS will develop an end-to-end Interoperable Simulation Ecosystem (ISE) that will perform multidirectional dataflow across the aircraft value chain linking product design and distributed engineering teams´ CAD-CAE tools, to accelerate the design and engineering optimization of disruptive aircraft and engine configurations, ensuring their manufacturability from the design conceptualization.
CAELESTIS ecosystem will be developed to integrate, interoperate, and autonomously execute simulation workflows involving a variety of state-of-the-art simulation tools to support the multi-disciplinary design, optimization as well as uncertainty quantification and propagation across the design and manufacturing chain.
The ecosystem will be boosted by HPC infrastructures to massively execute predictions and deliver optimized design and engineering outputs at realistic-time scales. In this regard, high-fidelity model-based digital twins with unprecedented level of detail and covering several production stages and manufacturing deviations will be developed and interconnected. Those will be linked to machine learning tools adapted to HPC simulation outputs to improve detection of manufacturing flaws based on a probabilistic approach to quantify uncertainties and their propagation and influence on structural integrity, as well as identify design for manufacturing interdependencies to provide optimized product topologies.
From manufacturing point of view, HPC simulations will be made available at manufacturing shopfloor as reduced order models in online monitoring edge computing devices, to support i) product performance prediction based on detected defects on real time and ii) informed corrective actions to minimize and compensate their effect.
Overall, CAELESTIS ISE will contribute to generate optimized and reliable aircraft design configurations, widen the design space and optimize the manufacturing process window and improve production efficiency and inline quality assurance.
CAELESTIS ecosystem will be developed to integrate, interoperate, and autonomously execute simulation workflows involving a variety of state-of-the-art simulation tools to support the multi-disciplinary design, optimization as well as uncertainty quantification and propagation across the design and manufacturing chain.
The ecosystem will be boosted by HPC infrastructures to massively execute predictions and deliver optimized design and engineering outputs at realistic-time scales. In this regard, high-fidelity model-based digital twins with unprecedented level of detail and covering several production stages and manufacturing deviations will be developed and interconnected. Those will be linked to machine learning tools adapted to HPC simulation outputs to improve detection of manufacturing flaws based on a probabilistic approach to quantify uncertainties and their propagation and influence on structural integrity, as well as identify design for manufacturing interdependencies to provide optimized product topologies.
From manufacturing point of view, HPC simulations will be made available at manufacturing shopfloor as reduced order models in online monitoring edge computing devices, to support i) product performance prediction based on detected defects on real time and ii) informed corrective actions to minimize and compensate their effect.
Overall, CAELESTIS ISE will contribute to generate optimized and reliable aircraft design configurations, widen the design space and optimize the manufacturing process window and improve production efficiency and inline quality assurance.
In this second reporting period, main simulation and manufacturing activities on laboratory scale have been performed, being close to ending this phase.
Main simulation work had been performed on:
• Preforming on automated fibre placement
• Preform injection (simplified geometry and use case)
• Permeability and voids
• Curing and thermal distortions
• Design optimization
• Definition od reduced order models for quality verification.
Main manufacturing and characterization activities had been performed on:
• Sensitivity analysis
• Effects of defects
• Dissimilar materials (carbon fiber and titanium)
Main simulation work had been performed on:
• Preforming on automated fibre placement
• Preform injection (simplified geometry and use case)
• Permeability and voids
• Curing and thermal distortions
• Design optimization
• Definition od reduced order models for quality verification.
Main manufacturing and characterization activities had been performed on:
• Sensitivity analysis
• Effects of defects
• Dissimilar materials (carbon fiber and titanium)