Reliability-based design optimization of composite stiffened panels in post-buckling regime

López, Carlos; Bacarreza, Omar; Baldomir, Aitor; Hernández, Santiago; Aliabadi, M.H.

doi:10.1007/s00158-016-1568-1

Cited by 28 publications

(12 citation statements)

References 59 publications

(62 reference statements)

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“…Genetic algorithms are used to perform the EI maximization since they are effective with non-linear and non-convex function. Additionally, they are well suited to work with mixed variable optimization problems [36].…”

Section: The Efficient Global Optimization Algorithmmentioning

confidence: 99%

Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading

Valladares¹,

Jones²,

Tovar³

2018

SAE Technical Paper Series

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Working under his direction in the Engineering Design Research Laboratory has helped me to grow professionally and personally. I have received invaluable lessons from him that I will remember for the rest of my life. I also want to thank the members of my committee, Dr. Sohel Anwar and Dr. Alan Jones for their invaluable suggestions to improve this research project. I gratefully acknowledge the financial support that I received form the Fulbright Commission of Ecuador and the Department of Mechanical Engineering. Without their support, I could have never had this life-changing experience.

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Section: The Efficient Global Optimization Algorithmmentioning

confidence: 99%

Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading

Valladares¹,

Jones²,

Tovar³

2018

SAE Technical Paper Series

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“…Modern optimisation techniques, also known as probabilistic optimisation (PO), on the other hand, account for uncertainties (Salas and Venkataraman 2008) that could affect the design objectives, such as uncertainties associated with mechanical properties or the geometry. Two well-stablished PO techniques are robust-design optimisation (RDO) and reliability-based design optimisation (RBDO) (Chen and Qiu 2018;das Neves Carneiro and Antonio 2018;Fang et al 2018;Hu and Duan 2018;Kaveh et al 2018;López et al 2017;Montoya et al 2015;Sohouli et al 2018;Strömberg 2017). RDO focuses on minimising the sensitivity of the objective function to random changes in the uncertain variables in the system, while RBDO aims at achieving a certain confidence in reliability of the product under a prescribed probabilistic constraint.…”

Section: Introductionmentioning

confidence: 99%

“…Other studies have been performed on stiffened panel deterministic optimisation using surrogate modelling, for instance, by Bisagni and Lanzi (2002), Lanzi and Giavotto (2006), Irisarri et al (2011) and Marín et al (2012). López et al (2017) conducted deterministic and reliability-based design optimisations of composite stiffened panels in post-buckling regime; they conducted a decoupled RBDO which separates the reliability analysis from the deterministic optimisation. Further RBDO studies of stiffened panel have been conducted by, for instance, Qu and Haftka (2003), who conducted RBDO and computed the reliability constraints employing Monte Carlo sampling and a design response surface, and Díaz et al (2016), who performed a comparison of stochastic expansions and moment-based methods for the reliability analysis while using genetic and gradient-based techniques for deterministic optimisation.…”

Section: Introductionmentioning

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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“…However, gradient-based methods can only find local optima and cannot directly include discrete design variables (such as a discrete set of ply thicknesses or angles). Thus, some approaches have used non-gradient-based optimisation methods, such as particle swarm optimisation (PSO) (Lopez et al , 2011; Chen et al , 2008) and Genetic Algorithms (Dey et al , 2015; Díaz et al , 2016; López et al , 2017) to address these issues, which also adds significant computational cost. Surrogate models can again help to reduce computational cost (especially if MCS, multi-scale analysis and large numerical models are also used).…”

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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Reliability-based design optimization of composite stiffened panels in post-buckling regime

Cited by 28 publications

References 59 publications

Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading

Surrogate-Based Global Optimization of Composite Material Parts under Dynamic Loading

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

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

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