Bridging Model Order reDuction with mAchine Learning in Vibro-Acoustics

ended

Within the design and operational phase of mechanical structures it is difficult to reconcile high acoustic performance and a low ecological footprint. To mitigate noise impact, noise reduction strategies often lead to increased product mass and volume, exacerbating material consumption, thereby contradicting with the sustainability objectives of the EU. The concept of Digital Twins (DTs) can offer a great solution to this dilemma, optimizing simultaneously for both these opposing goals. Being a digital counterpart of a noise-emitting asset, DTs can predict its behavior under different scenarios and, by exploiting real-time data, they can make well-advised decisions for its design and maintenance. While existing DTs are either static when relying on physics-based models or lack physical interpretability with data-driven models, BiMODAL VA seeks to revolutionize this field, by bridging the best of both worlds. Namely, by smartly combining physics-based Reduced Order Models (ROMs) with machine learning based models, the primary objective of BiMODAL VA is to propose novel hybrid vibro-acoustic DTs, that will be simultaneously faster, more accurate, dynamically evolving and interpretable. Towards this objective the project is split into three science subobjectives that include i) the development of a data-driven correction methodology for vibro-acoustic ROMs based on the concept of closure modeling, ii) creating hybrid models for iterative designs, where physical prototypes data are not available for all iterations, and iii) developing dynamic ROMs to accommodate a potential change in physics (e.g. system degradation). The Experienced Researcher within BiMODAL VA will achieve these objectives by exploiting not only the strong transatlantic network on DT technologies, consisting of two top academic institutions (KUL, UCSD) but also the remarkable training package that will equip him with excellent technical and transferable skills relevant both to industry and academia.