Comparison of Methods for Calculating B-Basis Crack Growth Life Using Limited Tests

Bhachu, Kanwardeep S.; Haftka, Raphael T.; Kim, N. H.

doi:10.2514/1.j054094

Cited by 11 publications

(5 citation statements)

References 27 publications

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“…Within aircraft structural sizing, a designer uses two broad kinds of allowables. A designer uses basis values for tensile and fatigue strengths [19] and typical values for many other quantities, e.g., moduli and physical properties (Ref. [14] 1.4.1.1 basis).…”

Section: Motivating Questionmentioning

confidence: 99%

When Are Allowables Conservative?

Rosario

Fenrich²,

Iaccarino

2021

AIAA Journal

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Aircraft designers are legally required to treat manufacturing variability so as to minimize the probability of structural failure, pursuant to Code of Federal Regulations (CFR) 25.613. The usual design strategy employed for compliance with the regulations is the use of allowable values. However, allowables are not guaranteed to satisfy the regulations as written. In this work, we thoroughly investigate the use of allowables via a formal reliability analysis using quantile evaluation. We find that allowables are provably conservative for simple settings but admit anticonservative structural analysis under nonmonotone structural response, strongly nonnormal distributions, correlated material properties, and high-dimensional material property spaces such as those arising in laminate composites. This analysis develops intuition for when allowables are a conservative approximation of a true reliability analysis. We introduce an inexpensive procedure to detect analysis pathologies and illustrate its use on a set of aerospace-relevant structural analysis problems.

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Section: Motivating Questionmentioning

confidence: 99%

When Are Allowables Conservative?

Rosario

Fenrich²,

Iaccarino

2021

AIAA Journal

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“…Investigations in [26,31,32] have concluded 95/90 TIs to be preferable to many other UQ methods tried or critically assessed for estimating, from very sparse sample data, conservative but not overly conservative bounds on the central 95% of response. The other methods tried or critically evaluated include bootstrapping [33], optimized four-parameter Johnson-family distribution fit to the response samples [34], nonparametric kernel density estimation specifically designed for sparse data [35], nonparametric cubic spline PDF fit to the data based on maximum likelihood [36], and Bayesian sparse-data approaches [37].…”

Section: Uncertainty Processing and Interpretation Of Failure Pressurmentioning

confidence: 99%

Propagating Stress-Strain Curve Variability in Multi-Material Problems: Temperature-Dependent Material Tests to Plasticity Models to Structural Failure Predictions

Romero¹,

Black²,

Orient³

et al. 2020

Engineering Failure Analysis

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This chapter presents a practical methodology for characterizing and propagating the effects of temperature-dependent material strength and failure-criteria variability to structural model predictions. The application involves a cylindrical canister ("can") heated and pressurized to failure. Temperature dependence and material sample-to-sample stochastic variability are inferred from very limited experimental data of a few replicate uniaxial tension tests at each of seven temperatures spanning the 800°C temperature excursion experienced by the can, for each of several stainless steel alloys that make up the can. The load-displacement curves from the material tests are used to determine effective temperature-dependent stress-strain relationships in ductile-metal plasticity models used in can-level model predictions. Particularly challenging aspects of the problem are the appropriate inference, representation, and propagation of temperature dependence and material stochastic variability from just a few experimental data curves at a few temperatures (as sparse discrete realizations or samples from a random field of temperature-dependent stress-strain behavior), for multiple such materials involved in the problem. Currently unique methods are demonstrated that are relatively simple and effective. 1 references [1-7] describes aspects of the associated activities, including experiments, modeling and simulation, code and calculation verification, and advanced model validation and uncertainty quantification (UQ) methods.The modeling and verification, validation, and uncertainty quantification (VVUQ) activities were performed under a multiyear "abnormal thermalmechanical breach" (T-M breach) task [1] of a Predictive Capability Assessment Project (PCAP) in the Verification & Validation (V&V) subelement of the U.S. Dept. of Energy Advanced Simulation and Computing (ASC) program. The goal of the PCAP T-M breach task was to assess the error and quantify the uncertainty in modeling the thermal-chemical-mechanical response and weld-related breach failure of sealed canisters ("cans") weakened by high temperatures and pressurized by heat-induced pyrolysis of foam. The planned outcome of the PCAP T-M breach task was to measure improvements in prediction accuracy over time as the models and computer platforms became more capable.The Sandia Weapon System Engineering and Assessment Technology Campaign (WSEAT) program supported the project by conducting material characterization tests and validation experiments [2] (see Figure 1). This partnership provided an opportunity to develop a fully integrated process from design of experiments through model validation assessment, with uncertainty reduced as much as possible and propagated through the process.Breach failures were expected to occur, and in the tests, they did occur, at the circumferential perimeter (laser) weld that joins the top lid to the can sidewalls. This is because the weld thickness is significantly less than the can lid and sidewalls (see Figure 2), and the tests/cans of...

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“…Bigerelle et al 18,19 quantified the uncertainty in Paris law material constant using the bootstrap. Bhachu et al 16,20 compared several common approaches for fatigue crack growth problems. Romero et al 21,22 tested the performance of the TI method, kernel density method, Johnson method, and non-parametric method.…”

Section: Introductionmentioning

confidence: 99%

“…Bhachu et al. 16,20 compared several common approaches for fatigue crack growth problems. Romero et al.…”

Section: Introductionmentioning

confidence: 99%

Reduced allowable strength of composite laminate for unknown distribution due to limited tests

Zhang

Kim

Palliyaguru

et al. 2020

Journal of Composite Materials

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In design under uncertainty, random distributions are often determined by expensive sampling tests. A key question is whether to invest in more samples or live with a reduced performance by fewer samples due to large uncertainty. The question is particularly difficult to answer when the type of distribution is unknown. This paper investigates the tradeoff between performance and conservativeness in estimating B-basis allowables, using experiments on composite plates with holes. Two approaches that do not require a distribution type are examined: (1) bootstrap confidence intervals and (2) Hanson-Koopmans non-parametric method. Based on the study, it is found that the Hanson-Koopmans method was more conservative than the bootstrap method because the latter penalized allowables for small-size samples. For a small number of samples (less than 29), conservative estimations are preferred over accuracy to account for the large uncertainty. Based on this observation, the bootstrap-assisted Hanson-Koopmans method is proposed to enhance the conservativeness. For the tested cases, the performance penalty using the bootstrap-assisted Hanson-Koopmans method for a small number of samples is found to be substantial.

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Comparison of Methods for Calculating B-Basis Crack Growth Life Using Limited Tests

Cited by 11 publications

References 27 publications

When Are Allowables Conservative?

When Are Allowables Conservative?

Propagating Stress-Strain Curve Variability in Multi-Material Problems: Temperature-Dependent Material Tests to Plasticity Models to Structural Failure Predictions

Reduced allowable strength of composite laminate for unknown distribution due to limited tests

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