摘要：
Non-probabilistic convex model utilizes a convex set to quantify the uncertainty domain of uncertain-but-bounded parameters, which is very effective for structural uncertainty analysis with limited or poor-quality experimental data. To overcome the complexity and diversity of the formulations of current convex models, in this paper, a unified framework for construction of the non-probabilistic convex models is proposed. By introducing the correlation analysis technique, the mathematical expression of a convex model can be conveniently formulated once the correlation matrix of the uncertain parameters is created. More importantly, from the theoretic analysis level, an evaluation criterion for convex modelling methods is proposed, which can be regarded as a test standard for validity verification of subsequent newly proposed convex modelling methods. And from the practical application level, two model assessment indexes are proposed, by which the adaptabilities of different convex models to a specific uncertain problem with given experimental samples can be estimated. Four numerical examples are investigated to demonstrate the effectiveness of the present study.
关键词：
Graphene platelets;Sandwich open-type shell;Multi-directional initially stresses;Three-dimensional elasticity theory;Linear and torsional gradient elastic foundation
摘要：
This paper presents the first attempt to investigate the mechanical behaviors of the three-dimensional theory of poroelasticity for functionally graded graphene platelets reinforced composite (FG-GPLRC) open-shell resting on a non-polynomial viscoelastic substrate involving friction force and under residual stresses. For this purpose, three parameters viscoelastic foundation is developed by taking into account the torsional interaction and horizontal friction force. The open-type shell comprises multilayers with uniformly dispersed graphene platelets (GPLs) in each fictitious layer of facing sheets. Still, its weight fraction changes layer-by-layer along the thickness direction. For solving the governing equations, the state-space based differential quadrature method is presented to determine the frequency response of the sandwich open-type shell. The influences of several parameters, such as various types of horizontal friction force, initial circumferential stress, linear and torsional gradient elastic foundation, and damping parameter, are investigated on the amplitude and frequency of the FG-GPLRC open-shell resting on a non-polynomial viscoelastic substrate. Results reveal that the poroelasticity theory has an overestimation of the frequency in comparison with 3D- elasticity. The fundamental and golden results of this paper are that by considering initial compressive stress, the system's stability increases, and the energy absorption of the structure improves.
关键词：
finite element method;laminated cylindrical nanoshell;sensitivity analysis;bi-directional thermal loading;FGDQM
摘要：
In this article, frequency characteristics, and sensitivity analysis of a size-dependent laminated composite cylindrical nanoshell under bi-directional thermal loading using Nonlocal Strain-stress Gradient Theory (NSGT) are presented. The governing equations of the laminated composite cylindrical nanoshell in thermal environment are developed using Hamilton's principle. The thermodynamic equations of the laminated cylindrical nanoshell are obtained using First-order Shear Deformation Theory (FSDT) and Fourier-expansion based Generalized Differential Quadrature element Method (FGDQM) is implemented to solve these equations and obtain natural frequency and critical temperature of the presented model. The novelty of the current study is to consider the effects of bi-directional temperature loading and sensitivity parameter on the critical temperature and frequency characteristics of the laminated composite nanostructure. Apart from semi-numerical solution, a finite element model was presented using the finite element package to simulate the response of the laminated cylindrical shell. The results created from finite element simulation illustrates a close agreement with the semi-numerical method results. Finally, the influences of temperature difference, ply angle, length scale and nonlocal parameters on the critical temperature, sensitivity, and frequency of the laminated composite nanostructure are investigated, in details.
关键词：
Most probable point (MPP);performance measure approach (PMA);reliability-based design optimization (RBDO);time-variant reliability;stochastic process
摘要：
Although a series of decoupled or single loop methods have been developed for reliability-based design optimization (RBDO) problems to improve the computational efficiency, it seems hard to extend these strategies to time-variant RBDO due to the complexity of the problems brought by the involvement of time. This paper proposes a new approach for time-variant reliability-based design optimization, expecting to provide an efficient tool for design of some complex structure under time-variant uncertainties. The main idea of the proposed method is the definition of the equivalent most probable point (EMPP). With the EMPP, the original time-variant RBDO problem can be transformed into an equivalent time-invariant RBDO problem formulated by performance measure approach (PMA). Hence, the existing PMA-based time-invariant RBDO methods can be applied to solve the equivalent problem. Therefore, those RBDO methods can be easily extended to time-variant RBDO problems, and hence the computational cost can be effectively reduced. Finally, two numerical examples and an engineering application are used to demonstrate the effectiveness of the proposed method.
关键词：
Angle descent mean value;Minimum performance target point;Time-dependent reliability based design optimization;Time-dependent sequential optimization and reliability assessment;Time-dependent single loop approach
摘要：
Aiming at accurately and efficiently solve the Time-dependent Reliability Based Design Optimization (T-RBDO), two advanced solution strategies respectively called Time-dependent Sequential Optimization and Reliability Assessment (T-SORA) and Time-dependent Single Loop Approach (T-SLA) are established. Before constructing the T-SORA and T-SLA, a two-stage optimization is firstly established, in which the first stage makes the minimum instantaneous reliability indexes of probabilistic constraints satisfy reliability index targets and the second stage performs the time-dependent reliability indexes to match the reliability index targets. Two equivalent time-independent RBDOs are obtained based on the established two-stage optimization, and the key point is estimating the Minimum Performance Target Point (MPTP) in each stage. Two single optimization processes are constructed in T-SORA to efficiently estimate MPTPs in its two stages respectively. Two sequential single procedures are established in T-SLA to efficiently solve MPTPs and design parameter solutions in its two stages respectively, in which a new MPTP searching technique named Angle Descent Mean Value (ADMV) is proposed to approximate MPTP in each iteration. Furthermore, time-dependent reliability analysis is only required in the second stage, which will further improve the computational efficiency. Several numerical and engineering examples are introduced to show the effectiveness of the proposed T-SORA and T-SLA for solving T-RBDO. (C) 2020 Elsevier B.V. All rights reserved.
通讯机构：
[Li, Hangyang] H;Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410082, Hunan, Peoples R China.
关键词：
hybrid electric vehicle;continuously variable transmission;driving pattern recognition;adaptive-ECMS;learning vector quantization
摘要：
The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.
摘要：
Parametric correlation exists widely in engineering problems. This paper presents an approach of evidence-theory-based design optimization (EBDO) with parametric correlations, which provides an effective computational tool for the structural reliability design involving epistemic uncertainties. According to the existing samples, the most fitting copula function is selected to formulate the joint basic probability assignment (BPA) of the correlated variables. The joint BPA is applied in the constraint reliability analysis, and an approximate technology is given to enhance the efficiency. A decoupling strategy is proposed for transforming the nested optimization of EBDO into a sequential iterative process of deterministic optimization and reliability analysis. The effectiveness of the proposed approach is demonstrated through two numerical examples and an engineering application.
关键词：
Uncertainty propagation;Multimodal probability density function;Convergence mechanism;Sparse grid numerical integration;Maximum entropy method
摘要：
In practical engineering applications, random variables may follow multimodal distributions with multiple modes in the probability density functions, such as the structural fatigue stress of a steel bridge carrying both highway and railway traffic and the vibratory load of a blade subject to stochastic dynamic excitations, etc. Traditional uncertainty propagation methods are mainly used to treat random variables with only unimodal probability density functions, which, therefore, tend to result in large computational errors when multimodal probability density functions are involved. In this paper, an uncertainty propagation method is developed for problems in which multimodal probability density functions are involved. Firstly, the multimodal probability density functions of input random variables are established using the Gaussian mixture model. Secondly, the uncertainties of the input random variables are propagated to the response function through an integration of the sparse grid numerical method and maximum entropy method. Finally, the convergence mechanism is developed to improve the uncertainty propagation accuracy step by step. Two numerical examples and one engineering application are studied to demonstrate the effectiveness of the proposed method.
关键词：
Time-dependent reliability-based design optimization;Probabilistic and interval uncertainties;Sequential single-loop optimization;Minimum performance target point searching;Worst case scenario
摘要：
Time-dependent reliability-based design optimization with both probabilistic and interval uncertainties is a cost-consuming problem in engineering practice which generally needs huge computational burden. In order to deal with this issue, a sequential single-loop optimization strategy is established in this work. The established sequential single-loop optimization strategy converts the original triple-loop optimization into a sequence of deterministic optimization, the estimations of time instant and interval value that corresponding to the worst case scenario, and the minimum performance target point searching. Two key points in the sequential single-loop optimization strategy guarantee the high efficiency of the proposed strategy. One is that no iterative searching step is needed to find the minimum performance target point at each iteration in the proposed sequential single-loop optimization strategy. The other is that only the correction step needs the reliability analysis to correct the design parameter solutions. In the example section, four minimum performance target point searching techniques are combined with the sequential single-loop optimization strategy to solve the corresponding optimization problems so to illustrate the effectiveness of the established strategy. (C) 2019 Elsevier Inc. All rights reserved.
摘要：
Use of multidisciplinary analysis in reliability-based design optimization (RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization (RBMDO). To enhance the efficiency and convergence of the overall solution process, a decoupling algorithm for RBMDO is proposed herein. Firstly, to decouple the multidisciplinary analysis using the individual disciplinary feasible (IDF) approach, the RBMDO is converted into a conventional form of RBDO. Secondly, the incremental shifting vector (ISV) strategy is adopted to decouple the nested optimization of RBDO into a sequential iteration process composed of design optimization and reliability analysis, thereby improving the efficiency significantly. Finally, the proposed RBMDO method is applied to the design of two actual electronic products: an aerial camera and a car pad. For these two applications, two RBMDO models are created, each containing several finite element models (FEMs) and relatively strong coupling between the involved disciplines. The computational results demonstrate the effectiveness of the proposed method.
关键词：
Robustness;grid-connected inverter;H-infinity controller;adaptive backstepping control;L-2-gain robust control
摘要：
To improve the robustness and stability of the photovoltaic grid-connected inverter system, a nonlinear backstepping-based H
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controller is proposed. A generic dynamical model of grid-connected inverters is built with the consideration of uncertain parameters and external disturbances that cannot be accurately measured. According to this, the backstepping H
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controller is designed by combining techniques of adaptive backstepping control and L2-gain robust control. The Lyapunov function is used to design the backstepping controller, and the dissipative inequality is recursively designed. The storage functions of the DC capacitor voltage and grid current are constructed, respectively, and the nonlinear H
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controller and the parameter update law are obtained. Experimental results show that the proposed controller has the advantage of strong robustness to parameter variations and external disturbances. The proposed controller can also accurately track the references to meet the requirements of high-performance control of grid-connected inverters.
摘要：
A novel LS-SVM control method is proposed for general unknown nonlinear systems. A linear kernel LS-SVM model is firstly developed for input/output (I/O) approximation. The LS-SVM control law is then derived directly from this developed model without any approximation and assumption. It further proves that the control error is fully equal to the LS-SVM modeling error. This means that a desirable control performance can be achieved because the LS-SVM has been proven to have an outstanding modeling ability in the previous studies. Case studies finally demonstrate the effectiveness of the proposed LS-SVM control approach.
摘要：
Featured Application This paper develops an evidence-theory-based robustness optimization (EBRO) method, which aims to provide a potential computational tool for engineering problems with epistemic uncertainty. This method is especially suitable for robust designing of micro-electromechanical systems (MEMS). On one hand, unlike traditional engineering structural problems, the design of MEMS usually involves micro structure, novel materials, and extreme operating conditions, where multi-source uncertainties inevitably exist. Evidence theory is well suited to deal with such uncertainties. On the other hand, high performance and insensitivity to uncertainties are the fundamental requirements for MEMS design. The robust optimization can improve performance by minimizing the effects of uncertainties without eliminating these causes. Abstract The conventional engineering robustness optimization approach considering uncertainties is generally based on a probabilistic model. However, a probabilistic model faces obstacles when handling problems with epistemic uncertainty. This paper presents an evidence-theory-based robustness optimization (EBRO) model and a corresponding algorithm, which provide a potential computational tool for engineering problems with multi-source uncertainty. An EBRO model with the twin objectives of performance and robustness is formulated by introducing the performance threshold. After providing multiple target belief measures (Bel), the original model is transformed into a series of sub-problems, which are solved by the proposed iterative strategy driving the robustness analysis and the deterministic optimization alternately. The proposed method is applied to three problems of micro-electromechanical systems (MEMS), including a micro-force sensor, an image sensor, and a capacitive accelerometer. In the applications, finite element simulation models and surrogate models are both given. Numerical results show that the proposed method has good engineering practicality due to comprehensive performance in terms of efficiency, accuracy, and convergence.
摘要：
This paper develops an uncertainty propagation analysis method to analyze transmit/receive (T/R) modules with uncertain parameters, such as variability and tolerances in the physical parameters and geometry produced in the manufacturing processes. The method is a combination of the variance decomposition-based sensitivity analysis and the moment-based arbitrary polynomial chaos (MBaPC). First, the electromagnetic simulation model of a practical T/R module is created. Secondly, based on the model, the sensitivity analysis is carried out to determine the sensitive parameters to the amplitude difference and the phase difference between the input and output electromagnetic signal. Thirdly, their four order statistical moments are calculated using the MBaPC. At last, according to the maximum entropy principle, the statistical moments are used to fit the probability distribution functions of the amplitude difference and the phase difference of the T/R module. The results computed by MBaPC have been validated accurate and efficient compared with Monte Carlo simulation approach.
作者机构：
[Hu, Te-Te; Lu, Xin-Jiang; Zhang, Y; Zhang, Yi; Fan, Bin] Cent S Univ, State Key Lab High Performance Complex Mfg, Sch Mech & Elect Engn, Changsha 410083, Hunan, Peoples R China.;[Fan, Bin] Hunan City Univ, Coll Mech & Elect Engn, Yiyang 413002, Peoples R China.
通讯机构：
[Lu, XJ; Zhang, Y] C;Cent S Univ, State Key Lab High Performance Complex Mfg, Sch Mech & Elect Engn, Changsha 410083, Hunan, Peoples R China.
关键词：
Robust LS-SVM;robust modeling;cluster;noise;nonlinearly distributed data
摘要：
Real datasets are often distributed nonlinearly. Although many least squares support vector machine (LS-SVM) methods have successfully modeled this kind of data using a divide-and-conquer strategy, they are often ineffective when nonlinear data are subject to noise due to a lack of robustness within each sub-model. In this paper, a robust clustered LS-SVM is proposed to model this type of data. First, the clustering method is used to divide the sample data into several sub-datasets. A local robust LS-SVM model is then developed to capture the local dynamics of the corresponding sub-dataset and to be robust to noise. Subsequently, a global regularization is constructed to intelligently coordinate all local models. These new features ensure that the global model is smooth and continuous and has a good generalization and robustness. Through the use of both artificial and real cases, the effectiveness of the proposed robust clustered LS-SVM is demonstrated.
期刊：
Biomass Conversion and Biorefinery,2021年 ISSN：2190-6815
通讯作者：
Jermsittiparsert, Kittisak
作者机构：
[Dai, Zuocai] Hunan City Univ, Coll Mech & Elect Engn, Yiyang 413002, Hunan, Peoples R China.;[Dai, Zuocai] Key Lab Energy Monitoring & Edge Comp Smart City, Yiyang 413002, Hunan, Peoples R China.;[Chen, Zhengxian] Columbia Univ, Dept Mech Engn, New York, NY 10027 USA.;[Selmi, Abdellatif] Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Civil Engn, Al Kharj 11942, Saudi Arabia.;[Selmi, Abdellatif] Ecole Natl Ingenieurs Tunis ENIT, Civil Engn Lab, BP 37, Tunis 1002, Tunisia.
通讯机构：
[Jermsittiparsert, Kittisak] D;[Jermsittiparsert, Kittisak] H;Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam.;Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam.;Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China.
关键词：
Biomass;Higher heating value;ELM;Prediction
摘要：
Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are important for energy applications. In this paper, an extreme learning machine (ELM) is used to estimate efficiency. ELM was implemented for single-layer feed-forward neural network (SLFN) architectures. Because biomass modeling could be a very challenging task for conventional mathematical, it is suitable to apply machine learning models which could overcome nonlinearities of the process. The main attempt in this study was to develop a machine learning model for prediction of the higher heating values of biomass based on proximate analysis. According the prediction accuracy (coefficient of determination and root mean square error) of the higher heating value of the biomass, the inputs' influence was determined on the higher heating value. According to the obtained results, fixed carbon has less moderate coefficient, ash has less correlation coefficient, and volatile matter has the most correlation coefficient. Therefore, the volatile matter percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the ash has the smallest relevance on the higher heating value of the biomass based on machine learning approach.
