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Reduced Order Modeling Research Challenge 2023: Nonlinear Dynamic Response Predictions for an Exhaust Cover Plate

  • Kyusic Park,
  • Matthew S. Allen,
  • Max de Bono,
  • Alessio Colombo,
  • Attilio Frangi,
  • Giorgio Gobat,
  • George Haller,
  • Tom Hill,
  • Shobhit Jain,
  • Boris Kramer,
  • Mingwu Li,
  • Loic Salles,
  • David A. Najera-Flores,
  • Simon Neild,
  • Ludovic Renson,
  • Alexander Saccani,
  • Harsh Sharma,
  • Yichang Shen,
  • Paolo Tiso,
  • Michael D. Todd,
  • Cyril Touzé,
  • Christopher Van Damme,
  • Alessandra Vizzaccaro,
  • Zhenwei Xu,
  • Ryan Elliot,
  • Ellad Tadmor

摘要

A variety of reduced order modeling (ROM) methods for geometrically nonlinear structures have been developed over recent decades, each of which takes a distinct approach, and may have different advantages and disadvantages for a given application. This research challenge is motivated by the need for a consistent, reliable, and ongoing process for ROM comparison. In this chapter, seven state-of-the-art ROM methods are evaluated and compared in terms of accuracy and efficiency in capturing the nonlinear characteristics of a benchmark structure: a curved, perforated plate that is part of the exhaust system of a large diesel engine. Preliminary results comparing the full-order and ROM simulations are discussed. The predictions obtained by the various methods are compared to provide an understanding of the performance differences between the ROM methods participating in the challenge. Where possible, comments are provided on insight gained into how geometric nonlinearity contributes to the nonlinear behavior of the benchmark system.