A novel adaptive-weight ensemble surrogate model base on distance and mixture error

Surrogate models are commonly used as a substitute for the computation-intensive simulations in design optimization. However, building a high-accuracy surrogate model with limited samples remains a challenging task. In this paper, a novel adaptive-weight ensemble surrogate modeling method is proposed to address this challenge. Instead of using a single error metric, the proposed method takes into account the position of the prediction sample, the mixture error metric and the learning characteristics of the component surrogate models. The effectiveness of proposed ensemble models are tested on five highly nonlinear benchmark functions and a finite element model for the analysis of the frequency response of an automotive exhaust pipe. Comparative results demonstrate the effectiveness and promising potential of proposed method in achieving higher accuracy.

A mode tracking method in modal metamodeling for structures with clustered eigenvalues

Decentralized vibration control of smart constrained layer damping plate

The traditional centralized control strategy to vibration suppression of large-scale thin plate structures may increase the design difficulty of the controller. In this article, a decentralized vibration active control method is proposed to suppress the vibration of the thin plate structure with smart constrained layer damping treatment. First, the dynamics model of the smart constrained layer damping plate is established based on the finite element method, and the characteristics of viscoelastic materials with temperature and frequency are described by Golla-Hughes-McTavish damping model. Subsequently, a decentralized subsystem control model is obtained from the balanced model reduction method and complex mode truncation method. The modal test proves that the theoretical model is accurate. Then, the particular emphasis is placed on the stability and vibration attenuation of a decentralized system, which is composed of multiple subsystems. The local state feedback stabilization, using interaction of local state feedback and output feedback, is introduced to achieve system stability. To solve the practical problem of local state feedback, a decentralized controller with an observer is developed by adopting the pole placement method. Finally, the numerical simulation and hardware-in-the-loop experiment under different excitation are performed to investigate the effectiveness of decentralized vibration active control. The results demonstrate that the decentralized controller can effectively suppress the vibration, especially under mixed periodic signal and Gauss white noise signal.

Uncertainty propagation of frequency response functions using a multi-output Gaussian Process model

Numerical modeling and model updating for smart laminated structures with viscoelastic damping

Active vibration control of thin-plate structures with partial SCLD treatment

Design Optimization of Vehicle Body NVH Performance Based on Dynamic Response Analysis

A Stochastic Multivariate Validation Method for Dynamic Systems

As computer models become more powerful and popular, the complexity of input and output data raises new computational challenges. One of the key difficulties for model validation is to evaluate the quality of a computer model with multivariate, highly correlated and non-normal data, the direct application of traditional validation approaches does not appear to be suitable. This paper proposes a stochastic method to validate the dynamic systems. Firstly, a dimension reduction utilizing kernel principal component analysis (KPCA) is used to improve the computational efficiency. A probability model is then established by non-parametric kernel density estimation (KDE) method, and differences between the test data and simulation results are finally extracted to further comparative validation. This new approach resolves some critical drawbacks of the previous methods and improves the processing ability to nonlinear problem to validation the dynamic model. The proposed method and process are successfully illustrated through a real-world vehicle dynamic system example. The results demonstrate that the method of incorporate with KPCA and KDE is an effective approach to solve the dynamic model validation problem.