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Muyu Zhang

Bio: Muyu Zhang is an academic researcher from Jiangsu University. The author has contributed to research in topics: Digital image correlation & Crack tip opening displacement. The author has an hindex of 1, co-authored 2 publications receiving 10 citations.

Papers
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Journal ArticleDOI
Jian Zhang1, Xinxin Yue1, Jiajia Qiu1, Muyu Zhang1, X. M. Wang2 
TL;DR: A novel ensemble of surrogates is proposed to take advantage of both global and local measures, and a unified strategy is conceived over the entire design space with proper trade-off between these two measures.
Abstract: Surrogate models are widely used in engineering design and optimization to substitute computationally expensive simulations for efficient approximation of system behaviours. However, since actual s...

23 citations

Journal ArticleDOI
Wenjie Qian1, Zhang Huiying1, Jianguo Zhu1, Li Jian1, Jian Zhang1, Muyu Zhang1 
TL;DR: In this article, the authors presented a comprehensive study on determining the interface adhesive strength of the polymer coating/substrate system to improve its durability by using the crack tip opening displacement (CTOD) method.

5 citations


Cited by
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Journal ArticleDOI
Xinxin Yue1, Jian Zhang1, Weijie Gong1, Min Luo2, Libin Duan1 
TL;DR: The results show that the proposed PCE-HDMR has much superior accuracy and robustness in terms of both global and local error metrics while requiring fewer number of samples, and its superiority becomes more significant for polynomial-like functions, higher-dimensional problems, and relatively larger PCE degrees.
Abstract: Metamodel-based high-dimensional model representation (HDMR) has recently been developed as a promising tool for approximating high-dimensional and computationally expensive problems in engineering design and optimization. However, current stand-alone Cut-HDMRs usually come across the problem of prediction uncertainty while combining an ensemble of metamodels with Cut-HDMR results in an implicit and inefficient process in response approximation. To this end, a novel stand-alone Cut-HDMR is proposed in this article by taking advantage of the explicit polynomial chaos expansion (PCE) and hierarchical Cut-HDMR (named PCE-HDMR). An intelligent dividing rectangles (DIRECT) sampling method is adopted to adaptively refine the model. The novelty of the PCE-HDMR is that the proposed multi-hierarchical algorithm structure by integrating PCE with Cut-HDMR can efficiently and robustly provide simple and explicit approximations for a wide class of high-dimensional problems. An analytical function is first used to illustrate the modeling principles and procedures of the algorithm, and a comprehensive comparison between the proposed PCE-HDMR and other well-established Cut-HDMRs is then made on fourteen representative mathematical functions and five engineering examples with a wide scope of dimensionalities. The results show that the proposed PCE-HDMR has much superior accuracy and robustness in terms of both global and local error metrics while requiring fewer number of samples, and its superiority becomes more significant for polynomial-like functions, higher-dimensional problems, and relatively larger PCE degrees.

16 citations

Journal ArticleDOI
Jian Zhang1, Xinxin Yue1, Jiajia Qiu, Lijun Zhuo1, Jianguo Zhu1 
TL;DR: A novel methodology for developing sparse PCE is proposed by making use of the efficiency of greedy coordinate descent in sparsity exploitation and the capability of Bregman iteration in accuracy enhancement, which shows that the proposed method is superior to the benchmark methods in terms of accuracy while maintaining a better balance among accuracy, complexity and computational efficiency.

14 citations

Journal ArticleDOI
13 Jun 2022-Symmetry
TL;DR: This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenarios, and theories and advances on probabilistic, non-probabilistic and hybrid ones are discussed.
Abstract: Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems. This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenarios. To this end, the mathematical models for uncertainty quantification are firstly reviewed, and theories and advances on probabilistic, non-probabilistic and hybrid ones are discussed. Subsequently, numerical methods for uncertainty propagation are broadly reviewed under different computational strategies. Thirdly, several popular single surrogate models and novel hybrid techniques are reviewed, together with some general criteria for accuracy evaluation. In addition, sample generation techniques to improve the accuracy of surrogate models are discussed for both static sampling and its adaptive version. Finally, closing remarks are provided and future prospects are suggested.

12 citations

Journal ArticleDOI
TL;DR: In this paper , a prediction-oriented active sparse polynomial chaos expansion (PAS-PCE) is proposed for reliability analysis, which makes use of the Bregman-iterative greedy coordinate descent in effectively solving the least absolute shrinkage and selection operator based regression for sparse PCE approximation with a small set of initial samples.

11 citations

Journal ArticleDOI
TL;DR: This paper is among the first to use the XFEM in studying the robust topology optimization under uncertainty and there is no need for any post-processing techniques, so the effectiveness of this method is justified by the clear and smooth boundaries obtained.
Abstract: This research presents a novel algorithm for robust topology optimization of continuous structures under material and loading uncertainties by combining an evolutionary structural optimization (ESO) method with an extended finite element method (XFEM). Conventional topology optimization approaches (e.g. ESO) often require additional post-processing to generate a manufacturable topology with smooth boundaries. By adopting the XFEM for boundary representation in the finite element (FE) framework, the proposed method eliminates this time-consuming post-processing stage and produces more accurate evaluation of the elements along the design boundary for ESO-based topology optimization methods. A truncated Gaussian random field (without negative values) using a memory-less translation process is utilized for the random uncertainty analysis of the material property and load angle distribution. The superiority of the proposed method over Monte Carlo, solid isotropic material with penalization (SIMP) and polynomial chaos expansion (PCE) using classical finite element method (FEM) is demonstrated via two practical examples with compliances in material uncertainty and loading uncertainty improved by approximately 11% and 10%, respectively. The novelty of the present method lies in the following two aspects: (1) this paper is among the first to use the XFEM in studying the robust topology optimization under uncertainty; (2) due to the adopted XFEM for boundary elements in the FE framework, there is no need for any post-processing techniques. The effectiveness of this method is justified by the clear and smooth boundaries obtained.

9 citations