Born Qualified EAB Telecon
Abstract not provided.
Publications
Dakota applied to GRS mc4096 dataset
Abstract not provided.
Materials Data and Models Across Scales
Abstract not provided.
The NEAMS workbench and dakota: Providing an environment for uncertainty analyses
AISTech - Iron and Steel Technology Conference Proceedings
Abstract not provided.
Bayesian inversion of seismic and electromagnetic data for marine gas reservoir characterization using multi-chain Markov chain Monte Carlo sampling
Journal of Applied Geophysics
In this study we developed an efficient Bayesian inversion framework for interpreting marine seismic Amplitude Versus Angle and Controlled-Source Electromagnetic data for marine reservoir characterization. The framework uses a multi-chain Markov-chain Monte Carlo sampler, which is a hybrid of DiffeRential Evolution Adaptive Metropolis and Adaptive Metropolis samplers. The inversion framework is tested by estimating reservoir-fluid saturations and porosity based on marine seismic and Controlled-Source Electromagnetic data. The multi-chain Markov-chain Monte Carlo is scalable in terms of the number of chains, and is useful for computationally demanding Bayesian model calibration in scientific and engineering problems. As a demonstration, the approach is used to efficiently and accurately estimate the porosity and saturations in a representative layered synthetic reservoir. The results indicate that the seismic Amplitude Versus Angle and Controlled-Source Electromagnetic joint inversion provides better estimation of reservoir saturations than the seismic Amplitude Versus Angle only inversion, especially for the parameters in deep layers. The performance of the inversion approach for various levels of noise in observational data was evaluated — reasonable estimates can be obtained with noise levels up to 25%. Sampling efficiency due to the use of multiple chains was also checked and was found to have almost linear scalability.
Multi-chain Markov chain Monte Carlo methods for computationally expensive models
Abstract not provided.
Uncertainty Quantification and Data Science in Materials Applications
Abstract not provided.
Treatment of Nuclear Data Covariance Information in Sample Generation
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on developing a sampling capability that can handle the challenges of generating samples from nuclear cross-section data. The covariance information between energy groups tends to be very ill-conditioned and thus poses a problem using traditional methods for generated correlated samples. This report outlines a method that addresses the sample generation from cross-section matrices.
Dakota Overview
Abstract not provided.
Critical Defect Signatures and Impacts on Material Performance for Metal Laser Powder Bed Fusion
Abstract not provided.
Strength of Micro-Computed Tomography as an Indicator of Process History and Estimator of Mechanical Performance in AM Stainless Steel
Abstract not provided.
SAChES: Scalable Adaptive Chain-Ensemble Sampling
We present the development of a parallel Markov Chain Monte Carlo (MCMC) method called SAChES, Scalable Adaptive Chain-Ensemble Sampling. This capability is targed to Bayesian calibration of com- putationally expensive simulation models. SAChES involves a hybrid of two methods: Differential Evo- lution Monte Carlo followed by Adaptive Metropolis. Both methods involve parallel chains. Differential evolution allows one to explore high-dimensional parameter spaces using loosely coupled (i.e., largely asynchronous) chains. Loose coupling allows the use of large chain ensembles, with far more chains than the number of parameters to explore. This reduces per-chain sampling burden, enables high-dimensional inversions and the use of computationally expensive forward models. The large number of chains can also ameliorate the impact of silent-errors, which may affect only a few chains. The chain ensemble can also be sampled to provide an initial condition when an aberrant chain is re-spawned. Adaptive Metropolis takes the best points from the differential evolution and efficiently hones in on the poste- rior density. The multitude of chains in SAChES is leveraged to (1) enable efficient exploration of the parameter space; and (2) ensure robustness to silent errors which may be unavoidable in extreme-scale computational platforms of the future. This report outlines SAChES, describes four papers that are the result of the project, and discusses some additional results.
Extreme-Value Statistics Reveal Rare Failure-Critical Defects in Additive Manufacturing
Advanced Engineering Materials
Additive manufacturing enables the rapid, cost effective production of customized structural components. To fully capitalize on the agility of additive manufacturing, it is necessary to develop complementary high-throughput materials evaluation techniques. In this study, over 1000 nominally identical tensile tests are used to explore the effect of process variability on the mechanical property distributions of a precipitation hardened stainless steel produced by a laser powder bed fusion process, also known as direct metal laser sintering or selective laser melting. With this large dataset, rare defects are revealed that affect only ≈2% of the population, stemming from a single build lot of material. The rare defects cause a substantial loss in ductility and are associated with an interconnected network of porosity. The adoption of streamlined test methods will be paramount to diagnosing and mitigating such dangerous anomalies in future structural components.
Corroborating Tomographic Defect Metrics with Processing Parameters & Mechanical Response in Metal AM
Abstract not provided.
UQ Theories Principles and Tools: Dakota Topics
Abstract not provided.
Uncertainty Quantification for Additive Manufacturing Applications across scales
Abstract not provided.
Integration of Dakota into the NEAMS Workbench
This report summarizes a NEAMS (Nuclear Energy Advanced Modeling and Simulation) project focused on integrating Dakota into the NEAMS Workbench. The NEAMS Workbench, developed at Oak Ridge National Laboratory, is a new software framework that provides a graphical user interface, input file creation, parsing, validation, job execution, workflow management, and output processing for a variety of nuclear codes. Dakota is a tool developed at Sandia National Laboratories that provides a suite of uncertainty quantification and optimization algorithms. Providing Dakota within the NEAMS Workbench allows users of nuclear simulation codes to perform uncertainty and optimization studies on their nuclear codes from within a common, integrated environment. Details of the integration and parsing are provided, along with an example of Dakota running a sampling study on the fuels performance code, BISON, from within the NEAMS Workbench.
Model Discrepancy Calibration Across Experiments
Abstract not provided.
Relating Tomographic Defect Metrics to Mechanical Performance in Additively Manufactured Stainless Steel
Abstract not provided.
High Throughput Stochastic Tensile Performance of Additively Manufactured Stainless Steels
Abstract not provided.
Validation Metrics
Abstract not provided.
Gradient-Enhanced Polynomial Chaos Methods for Circuit Simulation
Abstract not provided.
Gradient-Enhanced Polynomial Chaos Methods for Circuit Simulation
Abstract not provided.
Uncertainty Quantification with thoughts on multiscale materials applications
Abstract not provided.
DAKOTA Update: Capabilities & Applications
Abstract not provided.
Results 126–150 of 335