Uncertainty Quantification in Damage Modeling of Heterogeneous Materials

Bogdanor, Michael; Mahadevan, Sankaran; Oskay, Caglar

doi:10.1615/intjmultcompeng.2013005821

Cited by 23 publications

(7 citation statements)

References 32 publications

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“…ML algorithms are receiving growing attention in different applications for engineering problems (Adeli, ; Bogdanor, Mahadevan, & Oskay, ; Bogdanor, Oskay, & Clay, ; Lin, Nie, & Ma, ; Nabian & Meidani, ; Rafiei & Adeli, ; Reich, ; Zhang & Oskay, ). The growing capabilities and possibilities to obtain big databases to use as training sets for ML models make it a convenient and effective tool for the solution of problems that are in general too expensive, or when the time required for the solution is not acceptable with respect to the need they are trying to address.…”

Section: Data‐based “System” Scale Modeling Of Bepmentioning

confidence: 99%

Multiscale modeling of backward erosion piping in flood protection system infrastructure

Fascetti

Oskay

2019

Computer aided Civil Eng

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This article presents a novel multiscale modeling approach to simulate the evolution of the backward erosion piping (BEP) process in flood protection systems (FPSs). A multiphase description of the BEP phenomenon is proposed for the numerical solution at the local scale and validated by means of full‐scale experimental results available in the literature. Results of the local scale simulations are used as the training set for a multilayer machine learning (ML) model to bridge the information between the local and system scales. Accuracy of the trained ML algorithms is demonstrated by comparing results obtained from detailed physics‐based numerical models. The novelty of the proposed methodology lies in its capability of real‐time predictions of the overall response at the system scale. A case study is presented where a portion of the Nashville Metro Levee System is analyzed over the span of a year, to assess the likelihood of BEP in the infrastructure. The capability of the model to accept water height data obtained from field measurements is exploited in the numerical simulations.

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Section: Data‐based “System” Scale Modeling Of Bepmentioning

confidence: 99%

Multiscale modeling of backward erosion piping in flood protection system infrastructure

Fascetti

Oskay

2019

Computer aided Civil Eng

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“…The present study is focused on demonstrating the deterministic predictive capability of the EHM model; however, Bayesian statistical methods have been employed within the EHM framework in previous investigations to predict laminate behavior under model parameter uncertainty. 49,50…”

Section: Calibration Blind Prediction and Recalibration Proceduresmentioning

confidence: 99%

Prediction of progressive fatigue damage and failure behavior of IM7/977-3 composites using the reduced-order multiple space-time homogenization approach

Bogdanor

Oskay

2016

Journal of Composite Materials

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This manuscript presents the blind prediction of fatigue life performance in three laminated carbon fiber reinforced polymer composite layups using a reduced-order space-time homogenization model. To bridge the spatial scales, the modeling approach relies on the Eigendeformation-based reduced order homogenization method. To bridge the time scales associated with a single load cycle and the overall life of the composite, a homogenization-based accelerated multiple-time-scale integrator with adaptive time stepping capability is employed. The proposed multiscale modeling approach was used to predict the evolution of composite stiffness and progressive damage accumulation as a function of loading cycles, as well as residual strength after fatigue in tension and compression, for three layups ([0,45,90,−45]2 s, [30,60,90,−60,−30]2 s, and [60,0,−60]3 s). Following blind prediction, the experimental data from the blind prediction specimens were employed to better understand the failure mechanisms and recalibrate the model. This study was performed as a part of the Air Force Research Laboratory's “Damage Tolerant Design Principles” Program.

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“…A considerable body of literature exists for both the forward stochastic analysis, concerned with propagation of uncertainty from the scale of microconstituents in a composite material to a structural level(–) and the inverse analysis, concerned with stochastic characterization of microstructural material properties typically based on the observations at a coarse scale. (–)…”

Section: Introductionmentioning

confidence: 99%

Computational certification under limited experiments

Fish

Yuan

Kumar³

2018

Numerical Meth Engineering

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Summary A computational certification framework under limited experimental data is developed. By this approach, a high‐fidelity model (HFM) is first calibrated to limited experimental data. Subsequently, the HFM is employed to train a low‐fidelity model (LFM). Finally, the calibrated LFM is utilized for component analysis. The rational for utilizing HFM in the initial stage stems from the fact that constitutive laws of individual microphases in HFM are rather simple so that the number of material parameters that needs to be identified is less than in the LFM. The added complexity of material models in LFM is necessary to compensate for simplified kinematical assumptions made in LFM and for smearing discrete defect structure. The first‐order computational homogenization model, which resolves microstructural details including the structure of defects, is selected as the HFM, whereas the reduced‐order homogenization is selected as the LFM. Certification framework illustration, verification, and validation are conducted for ceramic matrix composite material system comprised of the 8‐harness weave architecture. Blind validation is performed against experimental data to validate the proposed computational certification framework.

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Uncertainty Quantification in Damage Modeling of Heterogeneous Materials

Cited by 23 publications

References 32 publications

Multiscale modeling of backward erosion piping in flood protection system infrastructure

Multiscale modeling of backward erosion piping in flood protection system infrastructure

Prediction of progressive fatigue damage and failure behavior of IM7/977-3 composites using the reduced-order multiple space-time homogenization approach

Computational certification under limited experiments

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