Design of a PYTHON-Based Plug-In for Benchmarking Probabilistic Fracture Mechanics Computer Codes With Failure Event Data

Fong, Jeffrey T.; deWit, R.; Marcal, Pedro V.; Filliben, James J.; Heckert, N. Alan; Gosselin, Stephen R.

doi:10.1115/pvp2009-77974

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…It also represents the uncertainty error in each database which constitutes various uncertainties and errors due to data collection, damage analysis, and developing the model. Researchers from NIST have worked on solutions by developing tools for similar problems of UQ in aging bridges, pipelines by integrating the statistical design of experiments, AI, and developing intelligent codes [163]. As per figure 11, considering model M as a black box-AI model, and as the physics of the model are not fully understood and the features that define model M has unidentified uncertainties, it is necessary to introduce an important uncertainty source, uncertainty error for model M (UE-M) which is intrinsic to model M and represents all additional uncertainties.…”

Section: Uncertainty Quantification (Uq)mentioning

confidence: 99%

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian

Vadlamudi

Raihan

et al. 2021

Smart Mater. Struct.

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In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

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Section: Uncertainty Quantification (Uq)mentioning

confidence: 99%

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Elenchezhian

Vadlamudi

Raihan

et al. 2021

Smart Mater. Struct.

View full text Add to dashboard Cite

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“…In 2007, Simonen, Gosselin, Lydell, Rudland, and Wilkowski [68] identified ten uncertainty sources as to why two benchmarked PFM models, PRO-LOCA and PRAISE, predicted significantly higher failure probabilities of cracking than those derived from field data by a factor ranging from 30 to 10,000. In 2009, Fong, deWit, Marcal, Filliben, Heckert, and Gosselin [69] developed an uncertainty plug-in that allows a user to address those ten uncertainties and calculate a failure probability with uncertainty bounds. An example of this application of the uncertainty equation concept is given in Figure 9.…”

Section: (Case 2) Cumulative Average Leak Probability Estimationmentioning

confidence: 99%

A Risk-Uncertainty Formula Accounting for Uncertainties of Failure Probability and Consequence in a Nuclear Powerplant

Fong

Gosselin²,

Marcal³

et al. 2010

ASME 2010 Pressure Vessels and Piping Conference: Volume 6, Parts a and B

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This paper is a continuation of a recent ASME Conference paper entitled “Design of a Python-Based Plug-in for Benchmarking Probabilistic Fracture Mechanics Computer Codes with Failure Event Data” (PVP2009-77974). In that paper, which was co-authored by Fong, deWit, Marcal, Filliben, Heckert, and Gosselin, we designed a probability-uncertainty plug-in to automate the estimation of leakage probability with uncertainty bounds due to variability in a large number of factors. The estimation algorithm was based on a two-level full or fractional factorial design of experiments such that the total number of simulations will be small as compared to a Monte-Carlo method. This feature is attractive if the simulations were based on finite element analysis with a large number of nodes and elements. In this paper, we go one step further to derive a risk-uncertainty formula by computing separately the probability-uncertainty and the consequence-uncertainty of a given failure event, and then using the classical theory of error propagation to compute the risk-uncertainty within the domain of validity of that theory. The estimation of the consequence-uncertainty is accomplished by using a public-domain software package entitled “Cost-Effectiveness Tool for Capital Asset Protection, version 4.0, 2008” (http://www.bfrl.nist.gov/oae/ or NIST Report NISTIR-7524), and is more fully described in a companion paper entitled “An Economics-based Intelligence (EI) Tool for Pressure Vessels & Piping (PVP) Failure Consequence Estimation,” (PVP2010-25226, Session MF-23.4 of this conference). A numerical example of an application of the risk-uncertainty formula using a 16-year historical database of probability and consequence of main steam and hot reheat piping systems is presented. Implication of this risk-uncertainty estimation tool to the design of a risk-informed in-service inspection program is discussed.

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“…The statistical analysis performed in this work was based on the software Dataplot from NIST (National Institute of Standards and Technology) [22,23,24]. Dataplot is a multiplatform software for scientific visualization, statistical analysis and non-linear modeling.…”

Section: Tool For Statistical Analysis (Dataplot)mentioning

confidence: 99%

Composite Material Property Database Using Smooth Specimens to Generate Design Allowables With Uncertainty Estimation

Shah

Melo

Cimini

et al. 2010

ASME 2010 Pressure Vessels and Piping Conference: Volume 6, Parts a and B

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For brevity, the class of “composite materials” in this paper is intended to refer to one of its subclasses, namely, the fiber-reinforced composite materials. In developing composite material property databases, three categories of data are needed. Category 1 consists of all raw test data with detailed information on specimen preparation, test machine description, specimen size and number per test, test loading history including temperature and humidity, etc., test configuration such as strain gage type and location, grip description, etc. Category 2 is the design allowable derived from information contained in Category 1 without making further experimental tests. Category 3 is the same design allowable for applications such that new experiments prescribed by user to obtain more reliable properties for the purpose on hand. At present, most handbook-based composite material property databases contain incomplete information in Category 1 (raw data), where a user is given only the test average values of properties such as longitudinal, transverse, and shear moduli, major and out-of-plane Poisson’s ratios, longitudinal tensile and compressive, transverse tensile and compressive, and shear strengths, inter-laminar shear strength, ply thickness, hygrothermal expansion coefficients, specific gravity, fiber volume fraction, etc. The presentation in Category 1 ignores the inclusion of the entire test environment description necessary for a user to assess the uncertainty of the raw data. Furthermore, the design allowable listed in Category 2 is deterministically obtained from Category 1 and the user is given average design allowable without uncertainty estimation. In this paper, it is presented a case study where average design allowable failure envelopes of open hole specimens were obtained numerically for two different quasi-isotropic carbon fiber-epoxy laminates using the appropriate Category 1 data. Using the method of statistical design of experiments, it is then showed how the average design allowable can be supplemented with uncertainty estimates if the Category 1 database is complete. Application of this methodology to predicting reliability of composite structures is discussed.

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Design of a PYTHON-Based Plug-In for Benchmarking Probabilistic Fracture Mechanics Computer Codes With Failure Event Data

Cited by 3 publications

References 25 publications

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review

A Risk-Uncertainty Formula Accounting for Uncertainties of Failure Probability and Consequence in a Nuclear Powerplant

Composite Material Property Database Using Smooth Specimens to Generate Design Allowables With Uncertainty Estimation

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