Reliability, weight, and cost tradeoffs in the design of composite laminates for cryogenic environments

Qu, Xueyong; Venkataraman, Satchi; Haftka, Raphael T.; Johnson, Theodore F.

doi:10.2514/6.2001-1327

Cited by 12 publications

(12 citation statements)

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“…Qu et al [27] did a study of reliability, weight, and cost tradeoffs in composite laminates for cryogenic applications in which the composites would be subject to significant mechanical loading. Their paper indicated that to reduce the residual stresses that cause micro-cracking, laminates with small changes in angle between plies could be used.…”

Section: Micro-cracks / CMmentioning

confidence: 99%

Damage trends in cryogenically cycled carbon/polymer composites

Bechel

Kim

2004

Composites Science and Technology

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Section: Micro-cracks / CMmentioning

confidence: 99%

Damage trends in cryogenically cycled carbon/polymer composites

Bechel

Kim

2004

Composites Science and Technology

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“…Qu et al (2003) investigated different response surface options for performing reliability-based optimization of composite laminates. Two sets of response surface approximations were used.…”

Section: Approximations In Reliability-based Designmentioning

confidence: 99%

“…Response surface approximation can filter the noise in the response functions obtained from either physical experiments or numerical solutions. Since the approximation is a simple polynomial function, it is inexpensive to use for procedures such as global optimization (Liu et al 2000), discrete variable optimization (Harrison et al 1995), and non-deterministic design (Qu et al 2003) which require a large number of function evaluations. Since each response surface can be generated independently, it is possible for experts to develop their own models and combine them for multidisciplinary design optimization.…”

Section: Approximations In Reliability-based Designmentioning

confidence: 99%

“…The first set, termed the analysis response surface (ARS), are fitted to the response functions as a function of both the random and design variables. Qu et al (2003) used the ARS to approximate the maximum stress in an angle ply laminate calculated from a non-linear thermomechanical analysis. The approximation provided a simple polynomial function which could predict ply level strains as a function of the ply angles, ply thicknesses, and material properties.…”

Section: Approximations In Reliability-based Designmentioning

confidence: 99%

“…Researchers have investigated the use of response surface methodology to develop efficient and less expensive surrogate models for probability calculations (e.g. Qu and Haftka 2003).…”

Section: Introductionmentioning

confidence: 99%

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Reliability optimization using probabilistic sufficiency factor and correction response surface

Venkataraman

2006

Engineering Optimization

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Reliability-based design optimization for low failure probability often requires millions of function analyses. Response surface approximation of the response functions (analysis response surface(ARS)) is often used to reduce the cost of failure probability calculations. Failure probabilities obtained from numerical sampling schemes are noisy and unsuitable for gradient-based optimization. To overcome this, response surfaces have been fitted to the failure probability of the designs (design response surface (DRS)) as a function of the design variables and used in optimization. Two shortcomings of the approach are that (i) the ARS fitting is extremely expensive for a large number of variables, especially for the high accuracy required to obtain very accurate reliability estimates, and (ii) DRS introduces fitting errors which affect the tails of the distributions which are significant for low failure probabilities. An approach to obtaining high-accuracy reliability estimates using the probabilistic sufficiency factor and correction response surface is investigated in this article. The method is demonstrated using a thin-walled box beam structure designed for minimum weight with failure probability constraints. The design is subjected to buckling, strength, and displacement constraints. Two methods of correcting low-fidelity analyses are compared for accuracy and efficiency. It is shown that correction to the response function is more accurate than the correction fitted to the probabilistic sufficiency factor.

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The Analysis of System Reliability

Pan

Cui

2020

Ocean Engineering &Amp; Oceanography

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The ability of an engineering system or products completing the prescribed functions under the prescribed condition and within the prescribed time is called reliability. In the design stage, prescribed functions of an engineering system usually can be expressed as the function of input variable x, which usually can be called the state function g(X). The value of the state function can judge whether functions of an engineering system can meet the design requirements.The most common judging logic is two-valued logic. When the state function g (X) exceeds a certain status (a certain allowed value), an engineering system can't meet a function requirement prescribed as the design. The particular state is called the limit state of the function. If an engineering system can conform to functional requirements specified as the design, then the system can be judged as "reliable", conversely it can be judged as "invalid" or "unreliable". Usually, the reliability of an engineering system is divided as the zero point of a state function.Reliable: Invalid:Then the judging function of two-valued logic (also called the indicative function) can be written as:

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Reliability, weight, and cost tradeoffs in the design of composite laminates for cryogenic environments

Cited by 12 publications

References 1 publication

Damage trends in cryogenically cycled carbon/polymer composites

Damage trends in cryogenically cycled carbon/polymer composites

Reliability optimization using probabilistic sufficiency factor and correction response surface

The Analysis of System Reliability

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