Multiscale reliability analysis of a composite stiffened panel

Akula, Venkata M. K.

doi:10.1016/j.compstruct.2014.06.001

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…The RDO conducted in this section assumes that the mechanical properties have a random normal distribution with 5% coefficient of variation (Akula 2014;Yang et al 2013) and the mean values as given in Table 1. A direct Monte Carlo sampling approach is employed to conduct a robustness analysis within the optimisation loop.…”

Section: Rdo With Uncertainties In Mechanical Propertiesmentioning

confidence: 99%

Probabilistic optimisation of mono-stringer composite stiffened panels in post-buckling regime

Farokhi

Bacarreza

Aliabadi

2020

Struct Multidisc Optim

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In this paper, a multi-objective probabilistic design optimisation approach is presented for reliability and robustness analysis of composite structures and demonstrated on a mono-omega-stringer stiffened panel. The proposed approach utilises a global surrogate model of the composite structure while accounting for uncertainties in material properties as well as geometry. Unlike the multi-level optimisation approach which freezes some parameters at each level, the proposed approach allows for all parameters to change at the same time and hence ensures global optimum solutions in the given parameter design space (for both probabilistic and deterministic optimisations) within a certain degree of accuracy. The proposed approach is used in this study to conduct extensive multi-objective probabilistic and deterministic optimisations (without considering safety factors) on a mono-stringer stiffened panel. In particular, a global surrogate model is developed utilising the computational power of a highperformance computing facility. The inputs of the surrogate model are the omega-stringer geometry and the mechanical properties of the composite material, while the outputs are the fundamental linear buckling load (LBL) and the nonlinear postbuckling strength (NPS). LBL and NPS are obtained via detailed parametric finite element models of the mono-stringer stiffened panel; in the nonlinear model, the interface between the skin and the omega-stringer is modelled via cohesive elements to allow for debonding in the post-buckled regime. Extensive multi-objective optimisations are conducted on the surrogate model using deterministic and probabilistic approaches to examine the omega-stringer geometric parameters mostly affecting the system robustness and reliability. The differences between deterministic and probabilistic designs are highlighted as well.

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Section: Rdo With Uncertainties In Mechanical Propertiesmentioning

confidence: 99%

Probabilistic optimisation of mono-stringer composite stiffened panels in post-buckling regime

Farokhi

Bacarreza

Aliabadi

2020

Struct Multidisc Optim

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“…Two different options are pursued connecting stochastic analysis with finite element analysis (FEA) in order to perform SFEA on engineering models. The first option entails the use of dedicated commercial FEA software with stochastic analysis and scripting capability [36,37,38]. Stochastic analysis and scripting capabilities are hereby integrated into the software, making it convenient for a user to perform SFEA since the software applies uncertainty to input variables.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Finite Element Analysis Framework for Modelling Mechanical Properties of Particulate Modified Polymer Composites

Moghaddam

2019

Materials

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Polymers have become indispensable in many engineering applications because of their attractive properties, including low volumetric mass density and excellent resistance to corrosion. However, polymers typically lack in mechanical, thermal, and electrical properties that may be required for certain engineering applications. Therefore, researchers have been seeking to improve properties by modifying polymers with particulate fillers. In the research presented herein, a numerical modeling framework was employed that is capable of predicting the properties of binary or higher order composites with randomly distributed fillers in a polymer matrix. Specifically, mechanical properties, i.e., elastic modulus, Poisson’s ratio, and thermal expansion coefficient, were herein explored for the case of size-distributed spherical filler particles. The modeling framework, employing stochastic finite element analysis, reduces efforts for assessing material properties compared to experimental work, while increasing the level of accuracy compared to other available approaches, such as analytical methods. Results from the modeling framework are presented and contrasted with findings from experimental works available in the technical literature. Numerical predictions agree well with the non-linear trends observed in the experiments, i.e., elastic modulus predictions are within the experimental data scatter, while numerical data deviate from experimental Poisson’s ratio data for filler volume fractions greater than 0.15. The latter may be the result of morphology changes in specimens at higher filler volume fractions that do not comply with modelling assumptions.

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“…Chen and Soares 19 used an improved first-order reliability method (FORM) to evaluate the reliability of composite laminates and stiffened plates under axial compression. Akula 20 used the Monte Carlo Simulation to analyze the reliability of stiffened composite plates.…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of ultimate compressive strength for stiffened composite panels

Zhao

Liu

Yang

2016

Journal of Reinforced Plastics and Composites

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The prediction and reliability evaluation of ultimate compressive strength of stiffened composite panels are both essential for safe structural design. In this paper, the progressive failure analysis method is utilized to predict ultimate compressive strength of stiffened composite panels by using ABAQUS. Since a large number of ultimate compressive strength must be analyzed in the process of reliability evaluation by using standard reliability methods, the relationship between the buckling load and the ultimate compressive strength is used to establish a new limit state function to reduce the computational efforts. Only one time the ultimate compressive strength is analyzed in the process of reliability evaluation by using the proposed method. The results of numerical examples show that (a) the relative errors between the ultimate compressive strength obtained by using the proposed method and the experimental data are 2.53% and 1.68% for samples A and B, respectively; (b) the reliability results obtained by using the proposed method are very close to the ones obtained by using the classical method, and the ratios of the proposed method to the classical method are only 1.18% and 1.66% for samples A and B in terms of the total computational time.

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Multiscale reliability analysis of a composite stiffened panel

Cited by 21 publications

References 14 publications

Probabilistic optimisation of mono-stringer composite stiffened panels in post-buckling regime

Probabilistic optimisation of mono-stringer composite stiffened panels in post-buckling regime

Stochastic Finite Element Analysis Framework for Modelling Mechanical Properties of Particulate Modified Polymer Composites

Reliability analysis of ultimate compressive strength for stiffened composite panels

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