Simulated effects of sample size and grain neighborhood on the modeling of extreme value fatigue response

Stopka, Krzysztof S.; Yaghoobi, Mohammadreza; Allison, J.

doi:10.1016/j.actamat.2021.117524

Cited by 31 publications

(3 citation statements)

References 66 publications

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“…The statistical notion of fatigue life has been pointed repeatedly in the literature [79,80], where Gumbel [81,82], Weibull [83,84], and (log-)normal distributions [83] are often used to model the extreme value statistics. Utilizing CPFEM to investigate microstructure-sensitivity fatigue has been studied in the last decade or so and is still very much an active field of research, notably by David McDowell and collaborators [85,86,87,88,89,90,91,92].…”

Section: Discussionmentioning

confidence: 99%

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Tran¹,

Robbe²,

Lim³

2023

Preprint

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Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps, and constitutive models simultaneously is currently lacking. This paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

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Section: Discussionmentioning

confidence: 99%

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Tran¹,

Robbe²,

Lim³

2023

Preprint

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“…The study of extreme value theory in traffic safety started relatively late compared to its wide application in the fields of financial economics [39], environmental scientific [40], and structural engineering [41]. Songchitruksa et al [42] used extreme value theory to establish an extrapolation method for traffic conflict and collision relationships, making extreme value theory increasingly popular for traffic collision prediction and evaluation.…”

Section: Collision Prediction Based On Conflict Extremes Value Theorymentioning

confidence: 99%

A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways

Geng

Cao

et al. 2022

Sustainability

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Collision risk identification and prediction is an effective means to prevent truck accidents. However, most existing studies focus only on highways, not on two-lane rural highways. To predict truck collision probabilities and identify high-risk scenes on two-lane rural highways, this study first calculated time to collision and post-encroachment time using high-precision trajectory data and combined them with extreme value theory to predict the truck collision probability. Subsequently, a traffic feature parameter system was constructed with the driving behavior risk parameter. Furthermore, machine learning algorithms were used to identify critical feature parameters that affect truck collision risk. Eventually, extreme value theory based on time to collision and post-encroachment time incorporated a machine learning algorithm to identify high-risk truck driving scenes. The experiments showed that bivariate extreme value theory integrates the applicability of time to collision and post-encroachment time for different driving trajectories of trucks, resulting in significantly better prediction performances than univariate extreme value theory. Additionally, the horizontal curve radius has the most critical impact on truck collision; when a truck is driving on two-lane rural highways with a horizontal curve radius of 227 m or less, the frequency and probability of collision will be higher, and deceleration devices and central guardrail barriers can be installed to reduce risk. Second is the driving behavior risk: the driving behavior of truck drivers on two-lane rural highways has high-risk, and we recommend the installation of speed cameras on two-lane rural roads to control the driving speed of trucks and thus avoid dangerous driving behaviors. This study extends the evaluation method of truck collisions on two-lane rural highways from univariate to bivariate and provides a basis for the design of two-lane rural highways and the development of real-time dynamic warning systems and enforcement for trucks, which will help prevent and control truck collisions and alleviate safety problems on two-lane rural highways.

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“…Implementation of these constitutive models in finite element frameworks allows the study of deformation in microstructures and structures, with the broad objectives of establishing structure-property correlations and their effect on the mechanical properties and performance. Specifically, crystal plasticity models have been used for studying texture evolution during processing and in-service conditions [9,10,11,12], the effect of various microstructural attributes, such as grain orientations, inclusions and heterogeneities, on the local deformation behavior [13,14,15,16,17], orientation-dependent microscale and macroscale mechanical response [18,19,20] and lifing predictions [21,22]. Note that crystal plasticity models have also been implemented in self consistent modeling frameworks [23,24] and Fast Fourier Transform (FFT)-based frameworks [25,26].…”

Section: Introductionmentioning

confidence: 99%

$ρ$-CP: Open Source Dislocation Density Based Crystal Plasticity Framework for Simulating Thermo-Mechanical Deformation

Patra¹,

Chaudhary²,

Pai³

et al. 2023

Preprint

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This work presents an open source, dislocation density based crystal plasticity modeling framework, ρ-CP. A Kocks-type thermally activated flow is used for accounting for the temperature and strain rate effects on the crystallographic shearing rate. Slip system-level mobile and immobile dislocation densities, as well slip system-level backstress, are used as internal state variables for representing the substructure evolution during plastic deformation. A fully implicit numerical integration scheme is presented for the time integration of the finite deformation plasticity model. The framework is implemented and integrated with the open source finite element solver, Multiphysics Object-Oriented Simulation Environment (MOOSE). Example applications of the model are demonstrated for predicting the anisotropic mechanical response of single and polycrystalline hcp magnesium, strain rate effects and cyclic deformation of polycrystalline fcc OFHC copper, and temperature and strain rate effects on the deformation of polycrystalline bcc tantalum. Simulations of realistic Voronoitessellated microstructures as well as Electron Back Scatter Diffraction (EBSD) microstructures are demonstrated to highlight the model's ability to predict large deformation and misorientation development during plastic deformation.

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Simulated effects of sample size and grain neighborhood on the modeling of extreme value fatigue response

Cited by 31 publications

References 66 publications

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

A Conflict Measures-Based Extreme Value Theory Approach to Predicting Truck Collisions and Identifying High-Risk Scenes on Two-Lane Rural Highways

$ρ$-CP: Open Source Dislocation Density Based Crystal Plasticity Framework for Simulating Thermo-Mechanical Deformation

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