Global sensitivity analysis for fiber reinforced composite fiber path based on D-MORPH-HDMR algorithm

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.

[1]  Weihua Zhang,et al.  Global sensitivity analysis using a Gaussian Radial Basis Function metamodel , 2016, Reliab. Eng. Syst. Saf..

[2]  Michael W. Hyer,et al.  Innovative design of composite structures: The use of curvilinear fiber format to improve buckling resistance of composite plates with central circular holes , 1990 .

[3]  Lorenzo Dozio,et al.  A variable-kinematic model for variable stiffness plates: Vibration and buckling analysis , 2016 .

[4]  Songqing Shan,et al.  Turning Black-Box Functions Into White Functions , 2011 .

[5]  Adriaan Beukers,et al.  Residual stresses in thermoplastic composites—A study of the literature—Part I: Formation of residual stresses , 2006 .

[6]  Zafer Gürdal,et al.  Optimization of a composite cylinder under bending by tailoring stiffness properties in circumferential direction , 2010 .

[7]  Guangyao Li,et al.  Advanced high strength steel springback optimization by projection-based heuristic global search algorithm , 2013 .

[8]  Herschel Rabitz,et al.  Global uncertainty assessments by high dimensional model representations (HDMR) , 2002 .

[9]  A. Sudjianto,et al.  An Efficient Algorithm for Constructing Optimal Design of Computer Experiments , 2005, DAC 2003.

[10]  Mohammad Rouhi,et al.  The effect of the percentage of steered plies on the bending-induced buckling performance of a variable stiffness composite cylinder , 2015 .

[11]  Z. Gürdal,et al.  Variable stiffness composite panels : Effects of stiffness variation on the in-plane and buckling response , 2008 .

[12]  Sung-Hoon Ahn,et al.  Measurement and Compensation of Spring-back of a Hybrid Composite Beam , 2007 .

[13]  H. Rabitz,et al.  General foundations of high‐dimensional model representations , 1999 .

[14]  A. Saltelli,et al.  Importance measures in global sensitivity analysis of nonlinear models , 1996 .

[15]  J. L. Oakeshott Warpage of carbon–epoxy composite channels , 2003 .

[16]  Wenjie Zuo,et al.  Sensitivity reanalysis of static displacement using Taylor series expansion and combined approximate method , 2015, Structural and Multidisciplinary Optimization.

[17]  Jun Li,et al.  Curing Deformation Analysis for the Composite T-shaped Integrated Structures , 2008 .

[18]  Sondipon Adhikari,et al.  Thermal uncertainty quantification in frequency responses of laminated composite plates , 2015 .

[19]  Suong V. Hoa,et al.  Effect of structural parameters on design of variable-stiffness composite cylinders made by fiber steering , 2014 .

[20]  Guangyao Li,et al.  High dimensional model representation (HDMR) coupled intelligent sampling strategy for nonlinear problems , 2012, Comput. Phys. Commun..

[21]  Paul M. Weaver,et al.  Buckling analysis and optimisation of variable angle tow composite plates , 2012 .

[22]  Souvik Chakraborty,et al.  Polynomial Correlated Function Expansion for Nonlinear Stochastic Dynamic Analysis , 2015 .

[23]  H. Rabitz,et al.  D-MORPH regression: application to modeling with unknown parameters more than observation data , 2010 .

[24]  Hu Wang,et al.  Adaptive MLS-HDMR metamodeling techniques for high dimensional problems , 2011, Expert Syst. Appl..

[25]  Guangyao Li,et al.  Variable stiffness composite material design by using support vector regression assisted efficient global optimization method , 2017 .

[26]  H. Rabitz,et al.  D-MORPH regression for modeling with fewer unknown parameters than observation data , 2012, Journal of Mathematical Chemistry.

[27]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[28]  Zhen Hu,et al.  Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis , 2016 .

[29]  H. Rabitz,et al.  Practical Approaches To Construct RS-HDMR Component Functions , 2002 .

[30]  Zafer Gürdal,et al.  Circumferential stiffness tailoring of general cross section cylinders for maximum buckling load with strength constraints , 2012 .

[31]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[32]  Fred Bacon,et al.  Global sensitivity analysis of the regional atmospheric chemical mechanism: an application of random sampling-high dimensional model representation to urban oxidation chemistry. , 2012, Environmental science & technology.

[33]  Zhenzhou Lu,et al.  An application of the Kriging method in global sensitivity analysis with parameter uncertainty , 2013 .

[34]  B. Tatting,et al.  Analysis and design of variable stiffness composite cylinders , 1998 .

[35]  Silvio Galanti,et al.  Low-Discrepancy Sequences , 1997 .

[36]  Souvik Chakraborty,et al.  Multivariate function approximations using the D-MORPH algorithm , 2015 .

[37]  Wesley J. Cantwell,et al.  Autoclave cure simulation of composite structures applying implicit and explicit FE techniques , 2013 .

[38]  N. V. Banichuk,et al.  Optimal orientation of orthotropic materials for plates designed against buckling , 1995 .

[39]  Zafer Gürdal,et al.  Design of variable–stiffness laminates using lamination parameters , 2006 .

[40]  Herschel Rabitz,et al.  Sparse and nonnegative sparse D-MORPH regression , 2015, Journal of Mathematical Chemistry.

[41]  Bruno Sudret,et al.  Computing derivative-based global sensitivity measures using polynomial chaos expansions , 2014, Reliab. Eng. Syst. Saf..

[42]  Samuel T. IJsselmuiden,et al.  Design of variable-stiffness composite panels for maximum buckling load , 2009 .

[43]  Yulfian Aminanda,et al.  Spring-back simulation of flat symmetrical laminates with angled plies manufactured through autoclave processing , 2016 .

[44]  Herschel Rabitz,et al.  Experimental Design of Formulations Utilizing High Dimensional Model Representation. , 2015, The journal of physical chemistry. A.

[45]  Anoush Poursartip,et al.  An experimental method for quantifying tool–part shear interaction during composites processing , 2003 .

[46]  Raphael T. Haftka,et al.  First- and Second-Order Sensitivity Analysis of Linear and Nonlinear Structures , 1986 .

[47]  Herschel Rabitz,et al.  Sixth International Conference on Sensitivity Analysis of Model Output Global Sensitivity Analysis for Systems with Independent and / or Correlated Inputs , 2013 .