SOURCE-LINKED INTELLIGENCE

Bridging Model Order reDuction with mAchine Learning in Vibro-Acoustics

CORDIS · observation · Publication date unknown

Bridging Model Order reDuction with mAchine Learning in Vibro-Acoustics 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 revo

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

424129.44

unit

EUR