Browse Publications Technical Papers 2024-28-0011

Automatic mode identification for TBIW / Powertrain 2024-28-0011

This paper addresses the critical task of global mode identification in the NVH domain, particularly focusing on the escalating complexity from subsystem to TBIW levels. Accurate identification of global modes for a full vehicle system demands substantial expertise and is integral to NVH post-processing. Our study introduces a novel tool/methodology developed by the IDIADA team for efficient Global/Local mode identification in subsystems or TBIW level models. Leveraging data extracted from .op2 files, including strain energy and displacement, the tool employs AI methodologies to generate easily interpretable graphs and pie charts. Compatible with major post processors like Hyper View/Meta post viewer, the Python-based tool operates efficiently via cloud technology, significantly reducing prediction time. The output not only predicts global mode numbers but also provides crucial insights into subsystem contributions, aiding in mode shape and continuity improvements. With robust data backing, our tool minimizes misinterpretation, offering a reliable solution for streamlined global mode identification in NVH applications.


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