Probabilistic optimization of engine mount to enhance vibration characteristics using first-order reliability-based target cascading

Youngjun Kim, Jongsoo Lee

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Uncertainties cause tremendous failures, especially in large-scale system design, because they are accumulated from each of the subsystems. Analytical target cascading is a multidisciplinary design optimization method that enables the achievement of a concurrent and consistent design for large-scale systems. To address the uncertainties in analytical target cascading efficiently, we propose reliability-based target cascading combined with first-order reliability assessment algorithms, such as mean-value first-order second moment, performance measure analysis, and reliability index analysis. The effectiveness of the implemented algorithms was first demonstrated via a mathematical programming problem and then a practical engineering problem, involving automotive engine mount optimization, for minimizing both the difference between torque roll axis and elastic roll axis and the vibration transmissibility under mode purity requirements. The optimized design solutions are compared among three reliability assessment algorithms of reliability-based target cascading, and the uncertainty propagation with Gaussian distributions was quantified and verified. The probabilistic design results indicate that the first-order reliability-based target cascading methods successfully identify more reliable and conservative optimized solutions than analytical target cascading.

Original languageEnglish
Pages (from-to)759-773
Number of pages15
JournalJVC/Journal of Vibration and Control
Issue number7-8
Publication statusPublished - 2021 Apr

Bibliographical note

Publisher Copyright:
© The Author(s) 2020.

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Automotive Engineering
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering


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