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Automatic Normal Mode Identification Methodology for TBIW/Powertrain Applus+ IDIADA
- Format:
- Book
- Conference/Event
- Author/Creator:
- Naphad, Aniruddha, author.
- Conference Name:
- International Automotive CAE Conference Road to Virtual World (2024-10-23 : Delhi, India)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- Mode identification, particularly Modal Map Generation, is pivotal within the NVH (Noise, Vibration, and Harshness) domain for managing the performance of complex systems like TBIW/Powertrain. This study addresses the critical task of accurately identifying Global / Local behavior of a particular system as single entity (Complete TBIW, Power train) or all the systems attached to main structure (Sub Systems i.e Seat , Fuel Tank , Pump etc), which is crucial for effective NVH post-processing.Introducing a novel tool/methodology developed by the Applus IDIADA team, this paper presents an efficient approach to Global and Local mode identification across subsystems, TBIW, and Powertrain levels. Leveraging ".op2" file content, mainly Strain Energy Density[1] and Displacement [2], the tool integrates Machine Learning Techniques [3] to produce mode predictions along with detailed visual outputs such as graphs , pie chart , modal charts et cetera Implemented as a Python-based solution compatible with major Pre and Post processors, it operates seamlessly with cloud technology [4], thereby reducing prediction time significantly.Beyond predicting mode numbers, the tool also provides actionable insights into subsystem contributions, aiding in enhancing mode shape and continuity studies [5]. Validated with robust data analysis, it ensures reliability in streamlined methodology for Mode Identification for NVH applications[6]
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-28-0011
- Access Restriction:
- Restricted for use by site license
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