导航

Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (19): 229969.doi: 10.7527/S1000-6893.2024.29969

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles    

Efficient variational Bayesian model updating under observation uncertainty

Yanhe TAO1,2, Qintao GUO1,2(), Jin ZHOU1,2, Jiaqian MA1,2, Xiaofa LI3   

  1. 1.College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing  210016,China
    2.National Key Laboratory of Helicopter Dynamics,Nanjing  210016,China
    3.COMAC Shanghai Aircraft Design and Research Institute,Shanghai  201210,China
  • Received:2023-12-12 Revised:2023-12-26 Accepted:2024-01-17 Online:2024-02-06 Published:2024-02-02
  • Contact: Qintao GUO E-mail:guo_qintao@nuaa.edu.cn
  • Supported by:
    National Natural Science Foundation of China(U23B20105)

Abstract:

A model updating approach is proposed to address uncertainty in complex numerical models with stochastic responses. The approach involves using auto-regressive models for signal feature extraction, utilizing Mahalanobis distance as uncertainty quantification metric, and performing parameter posterior identification through variational Bayesian Monte Carlo. Initially, auto-regressive analysis is conducted on stationary stochastic signals to obtain model feature vectors for dimension reduction. The hybrid uncertainty of experimental observation data is then characterized utilizing the probability-box method. An approximate logarithmic likelihood is constructed based on the Mahalanobis distance between feature vectors of simulated data and experimental observation data. Finally, the variational Bayesian Monte Carlo method is used to solve the marginal likelihood, resulting in the identification of the posterior of parameters after very few iterations. Effectiveness of the proposed method for uncertainty updating in engineering structural models is validated through a numerical case of bolted connection structures and a civil aircraft wing model. The updated model exhibits high accuracy and retains good updating performance under certain levels of observation uncertainty.

Key words: model updating, uncertainty quantification, variational Bayesian, auto-regressive model, Mahalanobis distance

CLC Number: