Automatic Normal Mode Identification Methodology for TBIW/Powertrain

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 & 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 etc. 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].

• Record URL:
  • https://doi.org/10.4271/2024-28-0011
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Naphad, Aniruddha
  • Lama Borrajo, Ines
  • Patil Sr, Hitendra
  • Chandratre, Sudip
  • Rana, Upendra
• Conference:
  • International Automotive CAE Conference – Road to Virtual World
  • Location: Delhi , India
  • Date: 2024-10-23 to 2024-10-24
• Publication Date: 2024-10-17

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Cloud computing; Cooperation; Fuel tanks; Identification systems; Machine learning; Measurement of specific phenomena; Noise; Partnerships; Power trains; Vibration
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01935320
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2024-28-0011
• Files: TRIS, SAE
• Created Date: Oct 28 2024 4:33PM