Multiple model reduction techniques have been proposed to tackle linear and non-linear problems. Intrusive model order reduction techniques exhibit high accuracy levels; however, they are rarely used as a standalone industrial tool because of the high level of expertise required in the construction and usage of these techniques. Moreover, the computation time benefit is compromised for highly non-linear problems. On the other hand, non-intrusive methods often struggle with accuracy in non-linear cases, typically requiring a large design of experiments and a large number of snapshots in order to achieve reliable performance. However, generating the stiffness matrix in a non-intrusive approach presents an optimal way to align accuracy with efficiency, combining the advantages of both intrusive and non-intrusive methods. This work introduces a minimally intrusive model order reduction technique that employs machine learning within a Proper Orthogonal Decomposition framework to achieve this alliance. By leveraging outputs from commercial full-order models, this method constructs a reduced-order model that operates effectively without requiring expert user intervention. The proposed technique is capable of approximating linear non-affine as well as non-linear terms. It is showcased for linear and non-linear structural mechanics problems.

International audienceMost often the dynamic analysis of rotor-to-stator rub is performed using 1D models. This leads to a small computational cost, but the reliability of the results is difficult to assess. This research work compares and analyzes the results of 1D and 3D rotor-stator contact problems, for different contact conditions more and less severe. The rotor vibrations are due to rotating imbalance at a given constant rotating speed. In this paper, it is shown that regarding the rotor orbits, the 3D and 1D models responses are very closed. However using a 3D model improves the simulation results. The 1D model actually suffers from limitations resulting from rigid-body displacement assumption of the rotor cross-section, which originates approximations in the rotor-to-stator contact modeling. Thus, the friction torque generated by the contact is overestimated in a 1D model. The 3D model, however, can present some local effects in the vicinity of the contact zone

The Sheet Molding Compound (SMC) process is essential in high-volume manufacturing of composite structures due to its scalability and efficiency. A primary challenge, however, lies in determining the initial charge shape that ensures complete mold filling without excessive overflow, typically resolved through labor-intensive trial and error. While simulations can anticipate the mold filling outcome, they often lack the capability to fine-tune the initial preform configuration, leading to inefficiencies in both time and material. This study presents an innovative, simulation-driven approach for accurately predicting initial charge shapes for two-dimensional (2D) mold designs. By employing Darcy’s Law and a fixed mesh grid framework, the methodology simulates a reverse material flow to trace the optimal preform shape. A complementary machine learning (ML) model was then developed to predict the preform shapes based on mold geometry, final thickness, and initial charge thickness. Serving as a digital twin of the SMC process, this ML model delivers results with comparable accuracy to simulations, significantly enhancing computational efficiency and avoiding common convergence issues in traditional simulations. This ML-driven digital twin approach also provides a robust proof of concept for addressing initial charge shapes in complex three-dimensional (3D) molds, where the computational demands of reverse flow simulations may present challenges. This combined simulation and ML framework equips manufacturers with a more precise and efficient tool for optimizing SMC processes, minimizing material waste, and reducing production time.