Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation

Jekel, Charles F.; Romero, Vicente J.

doi:10.1115/vvs2019-5127

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Finally, if the model predictions are to be used to support estimation of small "tail" probabilities of response for robust/reliable design or safety/risk analysis, the sample results from the LHS sets in 2 would be processed in a different way. This is demonstrated in recent investigations in [26,[47][48][49] on 16 diversely shaped distributions and tail probability magnitudes from 10 À5 to 10 À1 . Reliably conservative and efficient estimates of small tail probabilities are obtained.…”

Section: Averaging Equally Legitimate Tis To Reduce Chances Of Extremmentioning

confidence: 65%

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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Section: Averaging Equally Legitimate Tis To Reduce Chances Of Extremmentioning

confidence: 65%

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

Self Cite

View full text Add to dashboard Cite

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Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model

Moon

Lee²,

Park³

et al. 2020

Struct Multidisc Optim

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Catastrophic risk: indication, quantitative assessment and management of rare extreme events using a non-expected utility framework

Geiger

2024

Ann Oper Res

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The paper develops a conceptual framework for the analysis and management of catastrophic risk. The framework serves to assess rare extreme events in systematic, quantitative and consistent ways. It dispenses with probabilistic extreme value theory, concentrating on descriptive statistics and simple probability distributions. Risk assessment is based on a recently developed axiomatic approach to non-expected utility preferences defined on the set of risky alternative courses of action available to an agent. The utility values of catastrophic risks are given an explicit algebraic representation, which shows them to be highly unstable (“elastic”) in the sense that they respond disproportionately to small perturbations of the decision outcomes and their probabilities. Various elasticity coefficients are defined for the outcome variables and utility preferences attached to them. They indicate whether a variable possibly takes on large negative values. The coefficients can also be defined as sample statistics and, thus, computed from observed data. The approach admits various applications to practical problems of disaster risk management. The applications include estimations of the effectiveness and cost-efficiency of risk management, the specification of limits of acceptance of catastrophic risk for regulatory purposes, and safety and security systems design and dimensioning.

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Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation

Cited by 4 publications

References 11 publications

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

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

Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model

Catastrophic risk: indication, quantitative assessment and management of rare extreme events using a non-expected utility framework

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