Research
Interoperable Simulation Ecosystem (ISE)
The CAELESTIS Interoperable Simulation Ecosystem (ISE) will be built to support virtual prototyping, encompassing several facets of the project into one system. It will bring together an interoperable digital framework enabled by high performance computing (HPC), digital twins, uncertainty quantification, and in-line quality assessment. This ISE will be able to address current needs of aeronautics, as of yet unmet by available solutions – and it will even enable the industry to better respond to future challenges.
During the design and engineering phases of next generation aerostructures, HPC will carry out complex large-scale simulation workflows to deliver fast and accurate predictive insights for a large number of design and manufacturing scenarios. This will provide a wide range of outputs on mechanical performance, manufacturability, and uncertainty quantification.
CAELESTIS will focus on the most promising manufacturing technologies envisaged to widening the design space, such as automated fibre placement (AFP), powder bed fusion metal additive manufacturing (PBF) and resin transfer moulding (RTM). These technologies can provide new levels of flexibility, adaptability, and capability in the manufacturing of high-quality complex-shape products.
Overall, CAELESTIS ISE will address the following functionalities within the aeronautics design/manufacturing process:
During the design phase, it will include:
- Connectivity between simulation softwares and HPC
- Execution of large-scale simulations
- Identification of defects generation probability and their effects
- Automated proposition of new optimised designs
- Development of suitable models to be integrated into manufacturing lines
During the manufacturing phase, it will include:
- The connection of simulation data to edge devices for online monitoring and manufacturing control
Digital Twins
virtual models of components used to optimize production.
Machine Learning
AI tools working towards accurate generative design.
Defects Prediction
using simulation to predict defects in aircraft manufacturing.
Multi-Actor Approach for Collaboration
ensuring industry experts are involved in the project.
Digital
Twins
Digital twins are virtual models designed to accurately portray an object in the physical world. The object in question, e.g., a jet engine, is fitted with sensors to produce essential data on performance. This data is then applied to a detailed digital copy, which can be used to run simulations and study performance issues in search of possible improvements.
CAELESTIS will develop a simulation-assisted framework to interoperate product and manufacturing processes with digital twins (CAD-CAE software and surrogate models). These twins will enable the assessment of the possible defects and structural performance of respective components.
New multiscale surrogate models of key aircraft components will be developed to predict process deviations as well as the generation and propagation of defects across the manufacturing chain. Particular focus will be placed on quantifying uncertainties towards the geometric tolerances of final assembly. By taking advantage of HPC capabilities, large simulations can be carried out to thoroughly understand and optimise design and manufacturing parameters.
Predicting
Manufacturing
Defects
The formation of defects during the manufacturing of key components for aircraft can occur during processes such as Automated Fibre Placement (AFP) and can subsequently propagate into new defects during Resin Transfer Moulding (RTM). This introduces large uncertainties into the process with potentially huge knock-on effects over the final structural properties of components. Additionally, metal parts added by Power Bed Fusion (PBF) can show shape distortion which may affect the final product’s tolerance and assembly process after joining the composite and metal parts. Thus, both defect and uncertainty propagation need to be thoroughly analysed along the entire manufacturing chain.
CAELESTIS will undertake analysis of these potential defects by use of digital twin technology and methods. The project will simulate the ways in which defects are generated and propagated during various stages of the manufacturing processes. The results of this AFP-PBF-RTM simulation chain will yield data on the distribution of defects and final dimensions of components, used for predicting assembly tolerances in the engine structure and operational performance.
Machine
Learning
Machine learning is the artificial intelligence process through which software can become more accurate at predicting outcomes without a human programming it to do so. The algorithms behind this process use historical data as inputs to predict new output values.
In CAELESTIS, machine learning tools will be developed to extract dependencies between design, manufacturing performance, defects generation and mechanical properties. These tools will create accurate and efficient models to quantify uncertainties, in turn enabling the acceleration of the simulations required to obtain new structural designs for aircraft through generative design algorithms.
The machine learning model will be material-agnostic and open-source. Additionally, machine learning will be used to obtain a comparative fatigue model that implicitly captures the effects of defects.
Multi-Actor Approach
for Collaboration (MAC)
Ultimately securing widespread uptake for the CAELESTIS prototype ecosystem requires concerted efforts to ensure the participation of end-users and key stakeholders during (and after) the project. This strategy takes the form of a so-called Multi-Actor Approach for Collaboration, facilitating strengthening the scientific, societal, technological, and economic alignment of project results with the needs and expectations of stakeholders to improve exploitation of results.
The bottom-up approach will create the CAELESTIS Network of invited industry experts and experienced stakeholders. This network will be consulted in workshops and activities to improve and refine aspects of CAELESTIS’ main outputs. These MAC activities will be based on responsible R&I methods such as webinar consultations, in-depth surveys, interviews, workshops, and other knowledge exchanges. MAC is a key tool for CAELESTIS, and will enable the project to address technological expectations and needs in the aviation and manufacturing sectors, and to discuss and workshop the various barriers and socio-economic aspects of relevance to CAELESTIS’ innovation.
