Probabilistic evaluation of fuselage-type composite structures

Shiao, Michael; Chamis, Christos C.

doi:10.1016/s0266-8920(98)00027-7

Cited by 42 publications

(21 citation statements)

References 2 publications

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“…The only input parameter that is added to the model is the constant value, a, in Equation (10), which varies for different autoclave conditions. Equation (10) The modification to the heat transfer behavior in the model that improved the agreement with experimental data was in the current case based on established physical principles, Equation (10). However, there may be parameters that have an appreciable effect on the thermal behavior that are not included in the model, for example, varying contact resistance between the tool and the part.…”

Section: Model Calibration With Gradient-based Algorithmsmentioning

confidence: 98%

“…Li et al [7] also carries out reliability analysis in the context of composites processing, but with a response surface approach and only a few random variables. Shiaoa and Chamis [10], Arafath et al [11], and Chamis [12] adopt the same approach for probabilistic analysis of other composite structures.…”

Section: Introductionmentioning

confidence: 99%

“…Convective heat transfer coefficient on the (a) part's top and, (b) tool's bottom surface using sensitivity analysis and Equation(10).…”

mentioning

confidence: 99%

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Application of Response Sensitivity in Composite Processing

Bebamzadeh

Haukaas

Vaziri

et al. 2010

Journal of Composite Materials

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This article demonstrates the use of response sensitivities in numerical simulation of composite processing via four different application examples: real-time result validation, model calibration, reliability analysis, and optimization. The analyses are carried out with integrated simulation software with new response sensitivity capabilities. Notably, the response sensitivities are computed by the direct differentiation method. This is an efficient and accurate alternative to the finite difference approaches. A brief review of the derivation and implementation of sensitivity equations is provided. The primary objective of this article is to demonstrate and promote a suite of techniques to incorporate uncertainties into the simulation of the composite manufacturing process, facilitated by efficient sensitivity computations.

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Section: Model Calibration With Gradient-based Algorithmsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Application of Response Sensitivity in Composite Processing

Bebamzadeh

Haukaas

Vaziri

et al. 2010

Journal of Composite Materials

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show abstract

“…These studies are usually built on some assumed probabilistic models for the uncertainties in the material properties. Thus, the studies carried out in [2][3][4] make assumptions about the constituent level (micro-scale) uncertainties in composites and their effects in the mechanical properties exhibited at the meso-scales. Similar studies where assumptions are made at the meso-scales (ply level) and macro-scale (coupon/component level) have been reported in [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Experimental characterisation of random field models for CFRP composite panels

Sasikumar

Suresh

Vijayaghosh

et al. 2015

Composite Structures

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“…The propagation of uncertainty from random constituents up through the effective composite properties to compute the reliability of composite structures using perturbation based stochastic finite elements has been investigated in Refs. [5,33]. More recently, a Bayesian statistical approach was proposed to perform calibration of constituent material properties at the scale of the characteristic volume of a unidirectional ply based on ply-level experimental data [4].…”

Section: Introductionmentioning

confidence: 99%

Multiscale modeling of failure in composites under model parameter uncertainty

2015

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This manuscript presents a multiscale stochastic failure modeling approach for fiber reinforced composites. A homogenization based reduced-order multiscale computational model is employed to predict the progressive damage accumulation and failure in the composite. Uncertainty in the composite response is modeled at the scale of the microstructure by considering the constituent material (i.e., matrix and fiber) parameters governing the evolution of damage as random variables. Through the use of the multiscale model, randomness at the constituent scale is propagated to the scale of the composite laminate. The probability distributions of the underlying material parameters are calibrated from unidirectional composite experiments using a Bayesian statistical approach. The calibrated multiscale model is exercised to predict the ultimate tensile strength of quasi-isotropic openhole composite specimens at various loading rates. The effect of random spatial distribution of constituent material properties on the composite response is investigated.

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Probabilistic evaluation of fuselage-type composite structures

Cited by 42 publications

References 2 publications

Application of Response Sensitivity in Composite Processing

Application of Response Sensitivity in Composite Processing

Experimental characterisation of random field models for CFRP composite panels

Multiscale modeling of failure in composites under model parameter uncertainty

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