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Meeting 2016 TMS Annual Meeting & Exhibition
Symposium Computational Methods for Uncertainty Quantification, Model Validation, and Stochastic Predictions
Presentation Title Understanding the Effect of Experimental Uncertainty on the Multistage Fatigue Model
Author(s) Justin Hughes, William Williams, Mark F. Horstemeyer
On-Site Speaker (Planned) Justin Hughes
Abstract Scope Understanding how the accuracy and precision of materials modeling is affected by calibration to uncertain data is necessary for the production of robust and reliable designs. By utilizing computational resources, monte carlo brute force methods can be employed to help understand how the variability of experiments affects modeling results. A monte carlo random sampling method is applied to the physically-based, microstructure sensitive MultiStage Fatigue (MSF) model and output distributions are quantified. Model sensitivities and uncertainties are investigated for experimentally derived parameters.
Proceedings Inclusion? Planned: A print-only volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accuracy of Kinetics in Coarse-Grained Molecular Dynamics
Advancements in Methods for Materials Discovery and Validation
Assessing the Accuracy of DFT Formation Energies
Atomistic Study of Carbon Nanotubes: Effect of Cut-off Distance
Bayesian Calibration of a Physical Model for Plastic Flow Behavior of TRIP Steels
Citrination: Open Infrastructure for Ingesting, Storing, and Mining Materials Data
Computational Simulation and Physical Validation of Welded Aluminum Structures
Data Analysis in Mesoscale Model of Ductile Damage
Database Optimization for Empirical Interatomic Potentials
Density-Functional Theory Energy Density Method: Extracting Information and Identifying Finite-size Errors
Density Functional Theory and Prediction of Energy Storage Materials Properties
Development of the ReaxFF Force Field for Complex Materials and Interfaces
Effect of K-point Convergence on Derived Properties for Pure Crystals
Elasticity Size Effects in ZnO Nanowires and Subjective Definitions of Cross-sectional Area: An Overlooked Source of Uncertainty
Evaluation of Phase-Field Models Through Stochastic Quantification of Microstructure and Data Analytics
Exploring the Effects of Micro-texture on Engineering-scale Performance
Functional Uncertainty Quantification for Multi-fidelity and Multi-scale Simulations
Grain Deformation in a Cast Ni Superalloy: Comparing Experimental and Modelling Results
How Important are the Smallest Grains on Grain Aggregate Mechanics?
Materials and Data Development for Airframes
Microstructure-Uncertainty Propagation in Sheet Metal Forming FE-Simulations
Multiscale Modeling of with Quantified Uncertainties and Cloud Computing: Towards Computational Materials Design
Probabilistic Homogenization of Crystal Plasticity Modeling for Ti Alloys
Quality Control: Has Your DFT Code Been Δ-approved?
Quantifying Model-Form Uncertainty in Molecular Dynamics Simulation
Searching Transition States under Model-Form Uncertainty in Density Functional Theory Simulation
Uncertainty Propagation in a Computational Fatigue Model of an Airframe Structure
Uncertainty Quantification Algorithms for Large-scale Systems
Uncertainty Quantification and Propagation for Validation of a Microstructure Sensitive Model for Prediction of Fatigue Crack Initiation
Understanding the Effect of Experimental Uncertainty on the Multistage Fatigue Model
Using Correlations between Materials Properties in Potential Development Procedure for Metals

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