Uncertainty analysis for prediction of elastic properties of unidirectional bamboo fiber-reinforced composites

Bisheh, Hossein

doi:10.1016/j.istruc.2023.05.032

Cited by 10 publications

(2 citation statements)

References 47 publications

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“…Rather, they investigated the effects of uncertainty sources on the different behaviors (especially dynamic behaviors) of these structures. A summary of these studies [15][16][17][18][19][20][21][22][23][24][25][26][27] is presented in Fig. 2.…”

Section: Figmentioning

confidence: 99%

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

Khayat,

Amoushahi

2024

Preprint

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Composite materials have a high level of uncertainty (intrinsic and non-intrinsic) due to the manufacturing process as well as the placement of different phases of their constituent materials. These uncertainties can be identified in both macro and micro scales. Identifying the behavior of structures made of composite materials without taking into account the uncertainties, whether due to identification or modeling, can lead to unrealistic results, especially in the dynamic behavior of structures. One of these cases is the identification of damage types in composite structures which is usually done by using dynamic responses. Damages in composite materials or structures usually occur during construction or operation. The correct modeling of uncertainty sources is one of the most important factors in identifying the geometry, location, and severity of damages accurately. The uncertainties related to the position and placement of carbon nanotubes (CNTs) can cause noticeable changes in the characteristics of composite materials reinforced with CNTs. For this reason, in the present study, we identified damages in CNT panels by considering all possible sources of uncertainty. A probabilistic multi-stage reliability-based method was proposed in this study to detect damage in these structures. In order to model the intrinsic and non-intrinsic sources of uncertainty, a modified point estimation method (MPEM) was used. In addition, an enhanced differential quadrature (DQ) method was used to model the CNT panels. In each step of the proposed algorithm, the probability of damage in each element of the panels was calculated by analyzing the possible damages. According to the results of the previous step, the elements with a low failure probability were gradually sifted in the next steps. The sieved elements in each step were considered as intact elements in the next step. This systematic filtering of design variables can simultaneously reduce the dimensions and speed up the optimization problem. Finally, the probability of damage was calculated based on the probability density function of various damage severities and positions. The developed approach was applied for damage detection on a laboratory-tested plate to illustrate the efficiency of the proposed method. The effects of using different damage positions and severity levels on the diagnosis results were discussed. The results demonstrated that the number of frequencies and modes of vibration required to identify the position and severity of damages accurately is different according to the damage scenarios and the percentage of uncertainty.

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Section: Figmentioning

confidence: 99%

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

Khayat,

Amoushahi

2024

Preprint

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“…Different analytical techniques, including the rule of mixture, Chamis, Halpin-Tsai, and Nielsen methods, were utilized to validate and compare the results obtained from FE analysis. Bisheh (2023) proposed virtual framework to anticipate the elastic properties of UD-NFC based on RVE micromechanical modeling formulation.…”

Section: Introductionmentioning

confidence: 99%

Effective Mechanical Properties Evaluation of Unidirectional and Bidirectional Composites Using Virtual Domain Approach at Microscale

Elmoghazy,

Abuelgasim,

Osman

et al. 2023

AAES

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In this investigation, a 3D micromechanical modeling methodology based on different types of unit cell configuration and representative volume element (RVE) is used to capture the micro-macro mechanical properties of unidirectional (UD) and bidirectional (BD) fiber-reinforced synthetic resin composites. In this regard, hexagonal unit cell and square unit cell were established in order to explore the influence of packing density on representative microstructure properties, considering no effects at the interphase zone between the fiber and the matrix. The virtual geometric generation of 2D-RVE fibres inclusion model process is driven by statistical Monte Carlo simulation algorithm, which is then incorporated into FE solver to develop 3D-BDC RVE laminate model within a virtual scheme. In addition, applying the linear constraint of displacement simulation ensures that the RVE behaves as expected under uniaxial and in-plane loading conditions, leading to more accurate results. This remote constraint helps capture the realistic behaviors of the UD and BD composites models and enables the evaluation of mechanical properties, deformation patterns, and stress distributions within the presented RVE.

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Generation of two‐level representative volume element model for uncertainty analysis of composite materials

Peng,

Jiang,

et al. 2024

Polymer Composites

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This work presents a new two‐level representative volume element model for generating random fiber distributions in continuous fiber composite materials. A representative volume element (RVE) model with random geometric boundaries is generated, and the adaptive connections between neighboring RVEs are realized to eliminate unreasonable connections that may occur at the boundaries of RVEs. Subsequently, a novel adaptive generation and construction algorithm for larger RVE (LRVE) is proposed to consider more micro‐scale geometric and material uncertainties. A computational framework is established to implement the adaptive construction of LRVE and determine the statistical uncertainties of continuous fiber composite materials. The uncertain statistical results of the proposed algorithm are compared with those of the random sequential expansion algorithm and the complete spatial random pattern, showing good agreements among them. Finally, the uncertainty of macro‐scale linear elastic anisotropic material parameters is evaluated by using the integrated software framework. The maximum relative error of the predicted macro‐scale elastic properties is only 8.70%, which outperforms other numerical analysis results in literatures. The proposed algorithm is helpful for further research on multi‐scale linear elastic uncertainty analysis and reliability design of continuous fiber composite materials.

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Uncertainty analysis for prediction of elastic properties of unidirectional bamboo fiber-reinforced composites

Cited by 10 publications

References 47 publications

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

A novel hybrid multi-stage probabilistic algorithm for the damage detection of carbon nanotube panels by considering the uncertainty of materials and mechanical properties

Effective Mechanical Properties Evaluation of Unidirectional and Bidirectional Composites Using Virtual Domain Approach at Microscale

Generation of two‐level representative volume element model for uncertainty analysis of composite materials

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