State of the art of composite structures in non-deterministic framework: A review

Tomar, Sanjay Singh; Zafar, Sunny; Talha, Mohammad; Gao, Wei; Hui, David

doi:10.1016/j.tws.2018.09.016

Cited by 54 publications

(15 citation statements)

References 238 publications

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“…Composite materials are being widely used in many industries for the improved stiffness-to-weight ratio compared with alloys. However, the multi-phase structure and the manufacturing process of composites allows many material and geometrical uncertainties to occur [1][2][3][4][5][6][7][8]. As a result, composites are often designed deterministically using high factors of safety to avoid failure, which is considered conservative and restricts the use of the composites [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Such an approach requires a reliable and accurate analysis method that can analyse the effect of various uncertainties on design performance. Monte Carlo simulation (MCS) is one method to find the solution of stochastic problems for its simple and direct implementation [8,15]. It generates uncertainties in the form of random properties based on their statistical properties.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale surrogate-based framework for reliability analysis of unidirectional FRP composites

Omairey

Dunning

Sriramula

2019

Composites Part B: Engineering

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In this paper, a Finite Element-based surrogate model is developed to efficiently estimate stiffness properties of unidirectional composite laminas, while accounting for geometric and material property uncertainties at micro, meso and laminate scale, all within a probabilistic framework. In the multi-scale build-up nature of composites, uncertainties occur in material properties and geometric characteristics. These uncertainties present a challenge in estimating composite material properties. The currently available property estimation/homogenisation tools are mainly divided into two categories: analytical methods constrained by configuration assumptions, and numerical homogenisation using Finite Element Analysis (FEA). The latter is more flexible and accurate, but computationally expensive. Hence, this paper develops a surrogate model based on a limited number of experimental FEA data points. Additionally, a transition phase is developed between micro and laminate scales that enables modelling of spatially varying uncertainties. As a result, this framework significantly decreases analysis duration compared with FEA techniques, and, because it is derived from FEA data points, can accurately represent a wide range of uncertainties.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiscale surrogate-based framework for reliability analysis of unidirectional FRP composites

Omairey

Dunning

Sriramula

2019

Composites Part B: Engineering

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“…As stated Sriramula and Chryssanthopoulos [21], these uncertainities mght be considered at a constitutent, ply or component level. The estimation of this probability of failure could be carried out by means of approximation methods such as the First (FORM) and Second Order Reliability (SORM) methods or even by means of simulation strategies such as the Monte Carlo Sampling (MCS) method [41]. MCS highlights for its simple and direct implementation [38,39].…”

Section: Introductionmentioning

confidence: 99%

Digital image correlation and reliability-based methods for the design and repair of pressure pipes through composite solutions

García-Martín

López-Rebollo

Sánchez-Aparicio

et al. 2020

Construction and Building Materials

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This work aims to develop an approach for the reliability-based analysis for the design and repair of pressurized pipes by means of composite solutions. To this end, the approach uses a simulation method to estimate the failure probability of the solution based on the Monte Carlo approach and a Polynomial Chaos Expansion surrogate metamodeling strategy. This combination allows us to reduce the computational time required for evaluating the system´s probability of failure as well as extracting the Sobol' indices during the sensitivity analysis stage. The uncertainties related with the composite solution were obtained by means of the Digital Image Correlation approach, allowing us to extract the Probabilistic Distribution Functions (PDF) of its main mechanical parameters. This methodology is validated through the design and repair of a pressurized pipe using a carbon fiber solution and roll wrapping technology. The results show the strong potential of the proposed methodology for the safety evaluation of pressurized composite pipes.

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“…Consequently, a wider range of uncertainties is expected to feed into the structural system, undermining deterministic optimisation solutions that assume design variables are always at their mean value. These uncertain parameters are a result of complex engineering processes involved in the fabrication of composites (Tomar et al , 2018), and their inherent and irreducible nature classifies them as aleatory uncertainties that can be defined in the form of a probabilistic distribution (Agarwal et al , 2004). If uncertainties are not accounted for, then the performance of the optimised composite component will be non-optimal, potentially leading to poor reliability and failure (Sriramula and Chryssanthopoulos, 2009).…”

Section: Introductionmentioning

confidence: 99%

Multi-scale reliability-based design optimisation framework for fibre-reinforced composite laminates

Omairey

Dunning

Sriramula

2020

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Purpose The purpose of this study is to enable performing reliability-based design optimisation (RBDO) for a composite component while accounting for several multi-scale uncertainties using a large representative volume element (LRVE). This is achieved using an efficient finite element analysis (FEA)-based multi-scale reliability framework and sequential optimisation strategy. Design/methodology/approach An efficient FEA-based multi-scale reliability framework used in this study is extended and combined with a proposed sequential optimisation strategy to produce an efficient, flexible and accurate RBDO framework for fibre-reinforced composite laminate components. The proposed RBDO strategy is demonstrated by finding the optimum design solution for a composite component under the effect of multi-scale uncertainties while meeting a specific stiffness reliability requirement. Performing this using the double-loop approach is computationally expensive because of the number of uncertainties and function evaluations required to assess the reliability. Thus, a sequential optimisation concept is proposed, which starts by finding a deterministic optimum solution, then assesses the reliability and shifts the constraint limit to a safer region. This is repeated until the desired level of reliability is reached. This is followed by a final probabilistic optimisation to reduce the mass further and meet the desired level of stiffness reliability. In addition, the proposed framework uses several surrogate models to replace expensive FE function evaluations during optimisation and reliability analysis. The numerical example is also used to investigate the effect of using different sizes of LRVEs, compared with a single RVE. In future work, other problem-dependent surrogates such as Kriging will be used to allow predicting lower probability of failures with high accuracy. Findings The integration of the developed multi-scale reliability framework with the sequential RBDO optimisation strategy is proven computationally feasible, and it is shown that the use of LRVEs leads to less conservative designs compared with the use of single RVE, i.e. up to 3.5% weight reduction in the case of the 1 × 1 RVE optimised component. This is because the LRVE provides a representation of the spatial variability of uncertainties in a composite material while capturing a wider range of uncertainties at each iteration. Originality/value Fibre-reinforced composite laminate components designed using reliability and optimisation have been investigated before. Still, they have not previously been combined in a comprehensive multi-scale RBDO. Therefore, this study combines the probabilistic framework with an optimisation strategy to perform multi-scale RBDO and demonstrates its feasibility and efficiency for an fibre reinforced polymer component design.

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State of the art of composite structures in non-deterministic framework: A review

Cited by 54 publications

References 238 publications

Multiscale surrogate-based framework for reliability analysis of unidirectional FRP composites

Multiscale surrogate-based framework for reliability analysis of unidirectional FRP composites

Digital image correlation and reliability-based methods for the design and repair of pressure pipes through composite solutions

Multi-scale reliability-based design optimisation framework for fibre-reinforced composite laminates

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