Reduced-order models for aeronautics based on the LATIN-PGD method within non-linear industrial Simcenter Samcef software
Creators: name:  R. SCANFF 
name:   B. CAUVILLE 
name:   D. NERON 
name:   P. LADEVEZE 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: ADVANCED PERFORMANCE ENGINEERING
MECHANICAL

description: As the complexity of systems explodes, due to the desire of representing more ?nely the physics, increase performance of products and foster innovation, simulations are becoming increasingly expensive in terms of computational time. Carrying out parametric studies on multi-scale or multi-physics problems with complex materials on millions of unknowns and over long periods of time remains still challenging or even sometimes unreachable, despite a tremendous growth in computing resources recently. Therefore, the reduced-order modeling (ROM) methods are being looked at more and more by the industrial sector. These methods make it possible to e?ectively reduce computational times by taking advantage of the redundancy of information while breaking the so-called curse of dimensionality. However, to be widely used in design o?ces by users not necessarily experts of these methods, the latter must ?t into industrial ?nite element software. But due to the nature of the associated algorithms involving many unconventional operations and data storage, reduced-order modeling methods are unfortunately still not commonly applied in industrial software. The proposed work provides a methodology for the construction, the enhancement and the handling of reduced-order models natively into the industrial ?nite element software Simcenter Samcef by proposing a weakly-invasive version of the LATIN-PGD method. By doing that, we combine the advantages of the Proper Generalized Decomposition (PGD) reduced-order modeling method with all facilities already included in the industrial software, implying de facto any kind of non-linearities materials, contacts, large transformations, etc. frequently encountered in industrial component models. Robustness being a fundamental component as well as performance, the proposed approach does not require any in-house external code that would entail redesigning entire non-linear parts to achieve a su?cient level of robustness. We present the results on di?erent industrial test-cases speci?c to the aeronautics sector which highlight interesting performance gains. Furthermore, we explain how fast and e?cient construction of such reduced basis or virtual charts can be interesting for the deployment of Digital Twins: coupled with a posteriori real in-service data, the rapid evaluation of these reduced-order models within an end-to-end industrial software chain enables predictive maintenance operations to be carried out more easily. References [1] Ladeveze, P. (1999). Nonlinear computational structural mechanics : new approaches and non-incremental methods of calculation. Springer Science Business Media. [2] Chinesta, F., Ladeveze, P. (2014). Separated Representations and PGD-Based Model Reduction. Fundamentals and Applications, International Centre for Mechanical Sciences, Courses and Lectures, 554. [3] Neron, D., Boucard, P. A., Relun, N. (2015). Time-space PGD for the rapid solution of 3D nonlinear parametrized problems in the many-query context. International Journal for Numerical Methods in Engineering, 103(4), 275-292. [4] Benner, P., Grivet-Talocia, S., Quarteroni, A., Rozza, G., Schilders, W., Silveira, L. M. (Eds.). (2020). Model order reduction: snapshot-based methods and algorithms. De Gruyter. [5] Barabinot, P., Scan?, R., Ladeveze, P., Neron, D., Cauville, B. (2021). Industrial Digital Twins based on the non-linear LATIN-PGD. Advanced Modeling and Simulation in Engineering Sciences, 8(1), 1-15.