关键词：
Minimum dynamic compliance;Plate/shell structures;Stiffness and mass transformation approach (SMTA);Topology optimization
摘要：
The aim of this article is to present a novel and viable topological design approach for minimizing dynamic compliance of stiffened plate/shell structures subjected to time-harmonic loading with prescribed excitation frequency. In this method, the generalized incremental frequency technique (GIF) is introduced to transform the optimization problem into several sub-problems by making the prescribed excitation frequency located within different sub-intervals constructed by adjacent resonance frequencies. Based on this, a set of local optimum designs are identified by associating with the smallest value of dynamic compliance in each sub-interval, and then the optimized solution is selected from among these candidate solutions. Furthermore, the GIF technique is integrated into a Lagrangian-based topology optimization framework, where the stiffening topologies are represented explicitly by a set of geometric primitives such as line segments. In order to get an optimal layout solution, a special interpolation scheme called stiffness and mass transformation approach (SMTA) is presented to separate the line segments from the underlying FEM grids, so that they can move freely within the design domain. To demonstrate the benefits this method affords for dynamic design problems, three numerical examples are validated in detail. In each of the cases the optimization enables a significant reduction in the dynamic compliance. The proposed method allows for more flexibility in topology optimization, which is applicable for large-scale practical dynamic design problems.
摘要：
A multi-objective reliability-based design optimization method is proposed based on probabilistic and ellipsoidal convex hybrid model. With reliability index as the constraint, a three-layer nested multi-objective optimization problem is involved. To reduce the computation costs, an efficient decoupling strategy is presented to transform the original problem into a two-layer nested optimization problem. Then the intergeneration projection genetic algorithm and the micro multi-objective genetic algorithm are employed as inner and outer layer optimization operator to solve the multi-objective optimization problem, respectively. Finally, the present method is applied to three numerical examples and the results demonstrate the effectiveness of the present method.
摘要：
Compared with the interval model, the ellipsoidal convex model can describe the correlation of the uncertain parameters through a multidimensional ellipsoid, and whereby excludes extreme combination of uncertain parameters and avoids over-conservative designs. In this paper, we attempt to propose an efficient multi-objective optimization method for uncertain structures based on ellipsoidal convex model. Firstly, each uncertain objective function is transformed into deterministic optimization problem by using nonlinear interval number programming (NINP) method and a possibility degree of interval number is applied to deal with the uncertain constraints. The penalty function method is suggested to transform the uncertain optimization problem into deterministic non-constrained optimization problem. Secondly, the approximation model based on radial basis function (RBF) is applied to improve computational efficiency. For ensuring the accuracy of the approximation models, a local-densifying approximation technique is suggested. Then, the micro multi-objective genetic algorithm (μMOGA) is used to optimize design parameters in the outer loop and the intergeneration projection genetic algorithm (IP-GA) is used to treat uncertain vector in the inner loop. Finally, two numerical examples and an engineering example are investigated to demonstrate the effectiveness of the present method.
通讯机构：
Changsha University of Science and Technology, Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha, China
摘要：
In many structural reliability analysis problems, probability approach is often used to quantify the uncertainty, while it needs a great amount of information to construct precise distributions of the uncertain parameters. However, in many practical engineering applications, distributions of some uncertain variables may not be precisely known due to lack of sufficient sample data. Hence, a complex hybrid reliability problem will be caused when the random and non-precise probability variables both exist in a same structure. In this paper, a new hybrid reliability analysis method is developed based on probability and probability box (p-box) models. Random distributions are used to deal with the uncertain parameters with sufficient information, while the probability box models are employed to deal with the non-precise probability variables. Due to the existence of the p-box parameters, a limit-state band will be resulted and the corresponding reliability index will belong to an interval instead of a fixed value. According to the interval analysis, the hybrid reliability model based on random and probability box variables is constructed and the complex nesting optimization problem will be involved in this hybrid reliability analysis. In order to obtain the minimal and maximal reliability index, the corresponding solution strategy is developed, in which the intergeneration projection genetic algorithm (IP-GA) with fine global convergence performance is employed as inner and outer optimization solver. Four numerical examples are investigated to demonstrate the effectiveness of the present method. (C) 2017 Elsevier Ltd. All rights reserved.
摘要：
In this paper, a new reliability analysis method for engineering structures is developed based on probability and probability box (p-box) models. Random variable distributions are used to deal with the uncertain parameters with sufficient information, while the p-box models are employed to deal with the uncertain-but-bounded variables. Due to the existence of the p-box parameters, a limit-state band will result and a complex nesting optimization problem will be involved in this reliability analysis. To reduce the computational burden, an efficient decoupling strategy is developed to solve the nesting optimization problem. Through interval analysis for the probability transformation process, the complex nesting optimization problem can be transformed to a single-layer optimization model. Then, the optimum solution and corresponding reliability index can be obtained by introducing a sequential quadratic programming (SQP) method. Four engineering numerical examples are investigated to demonstrate the effectiveness of the present method.
关键词：
Noise source identification;vibro-acoustic regression model;non-linear partial least squares;kernel function transform;industrial sewing machine
摘要：
This paper introduces an application of non-linear partial least squares for vibro-acoustic regression modeling and for an industrial sewing machine. In the vibro-acoustic regression model, the vibration accelerations of reference points are defined as explanatory variables, while the noise sound pressure of target points is defined as response variables, and the number of explanatory variables is determined initially by a correlation analysis in the time domain. To improve predictive accuracy while a non-linear relationship exists between the explanatory and response variables, the explanatory variables are preprocessed by kernel function transformation. The comparison of regressive noise sound pressure to experimental data indicates that the non-linear partial least squares regression model has high predictive accuracy. Furthermore, the contributions of vibration accelerations to noise sound pressure are analyzed, by which the structure optimizations are guided and practiced. The comparison of noise test results before and after optimization testifies to the effectiveness of the contribution analysis.
期刊：
汽车工程,2015年37(2):214-218 and 234 ISSN：1000-680X
作者机构：
[张志勇; 刘鑫] College of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China;[张志勇; 张义波; 谢小平] Hunan University, State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Changsha, China
