Joint Center for Intelligent New Energy Vehicle

Shanghai, China

Joint Center for Intelligent New Energy Vehicle

Shanghai, China
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Ye F.,Hunan University | Ye F.,Joint Center for Intelligent New Energy Vehicle | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Structural and Multidisciplinary Optimization | Year: 2017

In this work, a surrogate assisted optimization method is utilized to optimize buckling loads of variable stiffness composites made by fiber steering. To improve the efficiency of optimization procedure, an expected improvement criterion is employed. Moreover, considering uncertainties of the fiber placement, a robust surrogate, least square support vector regression (LSSVR) considering empirical and structural risks is integrated with the expected improvement (EI) criterion and applied to two applications. The first case is the fiber path design of a variable stiffness plate under the compression load. The second one is the fiber path design of a variable stiffness cylinder under the bending load. According to results of the optimization, the buckling load of the variable stiffness plate has 52.63% improvement than the constant stiffness plate and 24.3% improvement than the quasi-isotropic plate. The buckling load of the variable stiffness cylinder has 40.22% improvement than the constant stiffness cylinder and 31.25% improvement than the quasi-isotropic cylinder. Furthermore, to verify the robustness of optimal design variables for the variable stiffness cylinder, the perturbed optimum design is presented and demonstrates that the results are reliable. © 2017 Springer-Verlag Berlin Heidelberg


Feng H.,Hunan University | Feng H.,Joint Center for Intelligent New Energy Vehicle | Cui X.,Hunan University | Cui X.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Journal of Computational Physics | Year: 2017

A stable nodal integration method (SNIM) is presented to solve static and quasi-static electromagnetic problems in this paper. The analysis domain is firstly discretized into a set of triangular or tetrahedral elements, and linear interpolation is adopted within each element. A weakened weak formulation based on the nodes is further considered, framing the so-called node-based smoothing domains. Equivalent smoothing domains are then acquired as circular or spherical regions, where the gradient of shape function is expanded as the first order Taylor form. Subsequently, four or six temporary integration points on the region are picked to obtain items of the stiffness matrix and the external load vector. By simplifying the equations, the stiffness matrix can be received in quite concise form with one point integration and stabilization terms, which are calculated on original node-based smoothing domains. The implementation of SNIM on electromagnetic problems is thus realized. The proposed formulation is validated against both analytical solutions and traditional methods, and its effectiveness and potentialities can be well represented and clarified by numerical examples. © 2017 Elsevier Inc.


Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | Chen L.,Hunan University | Chen L.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Structural and Multidisciplinary Optimization | Year: 2017

This study presents a quantitative sensitivity analysis for the assessment of fiber reinforced composites (FRCs). Global sensitivity analysis (GSA) approach is based on the variance based method incorporating Random Sampling-High Dimensional Model Representation (RS-HDMR) expansion in which component functions are determined by diffeomorphic modulation under observable response preserving homotopy (D-MORPH) regression. The advantage of the D-MORPH regression lies in its capability to solve linear algebraic equations with a limited number of sample points. The main purpose is to investigate the influence of fiber path, regarded as the design variable, on the formability and structural performance of FRCs. Wherein, spring-back and load-carrying capacity are two meaningful problems to be addressed. Two typical FRCs are included that an L-shaped part with straight fiber path using autoclave manufacturing process and a variable stiffness composite cylindrical shell under pure bending. The work not only focuses on the ranking of design variables but also hopes to find out their interactions represented by the second order global sensitivity indexes. After being tested by three typical numerical functions, the GSA algorithm highlights that spring-back of FRC using autoclave manufacturing process is most sensitive to fiber orientation angles on plies close to the tool. And buckling performance of the VS cylinder is dominated by fiber orientation angles at compression/tension regions. © 2017 Springer-Verlag Berlin Heidelberg


Cheng Z.,Hunan University | Cheng Z.,Joint Center for Intelligent New Energy Vehicle | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle
Computers and Structures | Year: 2017

This study presents a meshless-based local reanalysis (MLR) method. The purpose of this study is to extend reanalysis methods to the Kriging interpolation meshless method due to its high efficiency. In this study, two reanalysis methods: combined approximations CA) and indirect factorization updating (IFU) methods are utilized. Considering the computational cost of meshless methods, the reanalysis method improves the efficiency of the full meshless method significantly. Compared with finite element method (FEM) based reanalysis methods, the main superiority of meshless-based reanalysis method is to break the limitation of mesh connection. The meshless-based reanalysis is much easier to obtain the stiffness matrix Km even for solving the mesh distortion problems. However, compared with the FEM−based reanalysis method, the critical challenge is to use much more nodes in the influence domain due to high order interpolation. Therefore, a local reanalysis method which only needs to calculate the local stiffness matrix in the influence domain is suggested to improve the efficiency further. Several typical numerical examples are tested and the performance of the suggested method is verified. © 2017 Elsevier Ltd


Xu Z.,Hunan University of Science and Technology | Junjia C.,Hunan University | Junjia C.,Joint Center for Intelligent New Energy Vehicle | Guangyao L.,Hunan University | Guangyao L.,Joint Center for Intelligent New Energy Vehicle
Materials Letters | Year: 2017

The Al-Cu alloy bars were subjected to electromagnetic upsetting. The microstructural evolution of the adiabatic shear band (ASB) was characterized and revealed. The results showed that multiple slip systems were activated at high strain rates and contribute to the formation of ASB. Dynamic nucleation particles in the boundary of slip bands were formed under the continuous dynamic recrystallization mechanism. In addition, equiaxed dynamic recrystallization grains were produced by a rotating dynamic recrystallization mechanism. © 2017 Elsevier B.V.


Cui X.Y.,Hunan University | Cui X.Y.,Joint Center for Intelligent New Energy Vehicle | Wang G.,Hunan University | Wang G.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Applied Mathematical Modelling | Year: 2016

This paper proposed a nodal integration model for elasto-static, free vibration, forced vibration and geometric nonlinear analyses of axisymmetric thin shells using two-node truncated conical elements. The formulation is based on the Kirchhoff-Love theory, in which only the two translational displacements are treated as the independent field variables. A gradient smoothing technique (GST) is employed to relax the continuity requirement for trial function, so that linear shape functions can be used to interpolate both the tangent and normal displacement fields to the meridian. Based on each node, the integration domains are further formed, where the membrane strains and curvature changes are computed using a strain smoothing operation incorporated with a tensor transformation manipulation. The smoothed Galerkin weakform is then used to establish the discretized system equations. In order to accurately track the deformation path in geometric nonlinear analysis, the Newton-Raphson iteration in conjunction with the arc-length technique are employed here to solve the equilibrium equation. Numerical examples demonstrate that the present method can achieves higher accuracy and lower computing cost compared with the conventional finite element model. © 2015.


Li E.,Central South University | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle
Engineering Computations (Swansea, Wales) | Year: 2016

Purpose - For global optimization, an important issue is a trade-off between exploration and exploitation within limited number of evaluations. Efficient global optimization (EGO) is an important algorithm considering such condition termed as expected improvement (EI). One of major bottlenecks of EGO is to keep the diversity of samples. Recently, Multi-Surrogate EGO uses more samples generated by multiple surrogates to improve the efficiency. However, the total number of samples is commonly large. The purpose of this paper is to suggest a bi-direction multi-surrogate global optimization to overcome this bottleneck. Design/methodology/approach - As the name implies, two different ways are used. The first way is to EI criterion to find better samples similar to EGO. The second way is to use the second term of EI to find accurate regions. Sequentially, the samples in these regions should be evaluated by multiple surrogates instead of exact function evaluations. To enhance the accuracy of these samples, Bayesian inference is employed to predicted the performance of each surrogate in each iteration and obtain the corresponding weight coefficients. The predicted response value of a cheap sample is evaluated by the weighted multiple surrogates combination. Therefore, both accuracy and efficiency can be guaranteed based on such frame. Findings - According to the test functions, it empirically shows that the proposed algorithm is a potentially feasible method for complicated underlying problems. Originality/value - A bi-direction sampling strategy is suggested. The first way is to use EI criterion to generate samples similar to the EGO. In this way, new samples should be evaluated by real functions or simulations called expensive samples. Another way is to search accurate region according to the second term of EI. To guarantee the reliability of samples, a sample selection scenario based on Bayesian theorem is suggested to select the cheap samples. The authors hope this strategy help them to construct more accurate model without increasing computational cost. © Emerald Group Publishing Limited.


Li E.,Central South University | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | Ye F.,Hunan University | Ye F.,Joint Center for Intelligent New Energy Vehicle
Applied Soft Computing Journal | Year: 2016

Curse of dimensionality is a key issue in engineering optimization. When the dimension increases, distribution of samples becomes sparse due to expanded design space. To obtain accurate and reliable results, the amount of samples often grows exponentially with the dimensions. To improve the efficiency of the surrogate with limited samples, a Two-level Multi-surrogate Assisted Optimization (TMAO) is suggested. The framework of the TMAO is to decompose a complicated problem into separable and non-separable components. In the first-level, High Dimensional Model Representation (HDMR) is utilized to determine the correlations among input variables. Then, a high dimensional problem can be decomposed into separable and non-separable components. Thus, the dimension of the original problem might be reduced significantly. Moreover, considering noises and outliers, Support Vector Regression (SVR)-HDMR is utilized to obtain more reliable surrogate. Expected Improvement (EI) criterion is suggested to generate new samples to save computational cost. In the second-level, to handle the non-separable component, a multi-surrogate assisted sampling strategy is suggested. Compared with other methods, the distinctive characteristic of the suggested sampling strategy is to use different surrogates to search potential uncertain regions. Considering the diversity of surrogates, more feature samples might be generated close to the local optimum. Even though it is still difficult to find a global solution, it could help us to find a feasible solution in practice. To verify the performance of the suggested method, several high dimensional mathematical functions are tested by the suggested method. The results demonstrate that all test functions can be successfully solved. © 2016 Elsevier B.V. All rights reserved.


Lei F.,Hunan University | Du B.,Hunan University | Liu X.,Changsha University | Xie X.,Joint Center for Intelligent New Energy Vehicle | Chai T.,Hunan University
Energy | Year: 2016

In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. © 2016 Elsevier Ltd


Li E.,Central South University | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle
Advances in Engineering Software | Year: 2016

Differential Evolution (DE) is one of the most powerful stochastic real parameter optimizers. An alternative adaptive DE algorithm called Expected Improvement (EI)-High Dimensional Model Representation (HDMR)-DE is suggested. The EI criterion and the Kriging-HDMR are used to adjust scale factor F and crossover constant Cr, respectively. Considering the expensive computational cost of evaluation, the Kriging is integrated to evaluate the objective function when an accuracy criterion is met. To compare the performance, the suggested method, it has been compared with four popular adaptive DE algorithms over 25 standard numerical benchmarks derived from the IEEE Congress on Evolutionary Computation 2005 competition. To verify the feasibility of the suggested algorithm, a real-world application, time-dependent variable Blank Hold Force (BHF) optimization problem is also carried out by the EI-HDMR-DE. The results show that the EI-HDMR-DE improves the performance of adaptive DE and has potential capability to solve some complicated real-world applications. © 2016 Elsevier Ltd. All rights reserved.

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