Adjoint-Based Sensitivities for Optimization of Satellite Electron/Proton Shields
Abstract not provided.
Publications
Adjoint-Based Sensitivities for Optimization of Satellite Electron/Proton Shields
Abstract not provided.
Adjoint-enabled Optimization and UQ for Radiation Shield Design
Abstract not provided.
Adjoint-enabled Robust Radiation Shield Design
Abstract not provided.
Adjoint-enabled Uncertainty Quantification for Satellite Shield Design
Abstract not provided.
Advanced Model Predictions of Mechanical Properties and Process Parameter Effects in High Throughput L-PBF Tension Specimens
Abstract not provided.
Advanced Uncertainty Quantification Methods for Circuit Simulation: Final Report LDRD 2016-0845
This report summarizes the methods and algorithms that were developed on the Sandia National Laboratory LDRD project entitled "Advanced Uncertainty Quantification Methods for Circuit Sim- ulation", which was project # 173331 and proposal # 2016-0845. As much of our work has been published in other reports and publications, this report gives an brief summary. Those who are in- terested in the technical details are encouraged to read the full published results and also contact the report authors for the status of follow-on projects.
Advanced uncertainty quantification methods for engineering applications
Abstract not provided.
Advances in Sensitivity Analysis for the Crystalline Reference Case
Abstract not provided.
Aggregate Presence and Impact of 3D Defect Populations In AM Stainless Steel
Abstract not provided.
Aleatory and epistemic uncertainty quantification for engineering applications
Abstract not provided.
Aleatory and Epistemic Uncertainty Quantification for Engineering Applications
Abstract not provided.
Algorithm development for Prognostics and Health Management (PHM)
This report summarizes the results of a three-year LDRD project on prognostics and health management. System failure over some future time interval (an alternative definition is the capability to predict the remaining useful life of a system). Prognostics are integrated with health monitoring (through inspections, sensors, etc.) to provide an overall PHM capability that optimizes maintenance actions and results in higher availability at a lower cost. Our goal in this research was to develop PHM tools that could be applied to a wide variety of equipment (repairable, non-repairable, manufacturing, weapons, battlefield equipment, etc.) and require minimal customization to move from one system to the next. Thus, our approach was to develop a toolkit of reusable software objects/components and architecture for their use. We have developed two software tools: an Evidence Engine and a Consequence Engine. The Evidence Engine integrates information from a variety of sources in order to take into account all the evidence that impacts a prognosis for system health. The Evidence Engine has the capability for feature extraction, trend detection, information fusion through Bayesian Belief Networks (BBN), and estimation of remaining useful life. The Consequence Engine involves algorithms to analyze the consequences of various maintenance actions. The Consequence Engine takes as input a maintenance and use schedule, spares information, and time-to-failure data on components, then generates maintenance and failure events, and evaluates performance measures such as equipment availability, mission capable rate, time to failure, and cost. This report summarizes the capabilities we have developed, describes the approach and architecture of the two engines, and provides examples of their use. 'Prognostics' refers to the capability to predict the probability of
An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics
Acta Materialia
Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this work, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization by exploiting high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.
An initial comparison of methods for representing and aggregating experimental uncertainties involving sparse data
Collection of Technical Papers - AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
This paper discusses the handling and treatment of uncertainties corresponding to relatively few data samples in experimental characterization of random quantities. The importance of this topic extends beyond experimental uncertainty to situations where the derived experimental information is used for model validation or calibration. With very sparse data it is not practical to have a goal of accurately estimating the underlying variability distribution (probability density function, PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a desired percentage of the actual PDF, say 95% included probability, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the random-variable range corresponding to the desired percentage of the actual PDF. The performance of a variety of uncertainty representation techniques is tested and characterized in this paper according to these two opposing objectives. An initial set of test problems and results is presented here from a larger study currently underway.
An Initial Comparison of Methods for Representing and Aggregating Experimental Uncertainties involving Sparse Data
Abstract not provided.
An Uncertainty Quantification Framework for Land Models
Abstract not provided.
Applicability of surrogate-based MCMC-Bayesian inversion of CLM at flux tower sites with various field conditions
Abstract not provided.
Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota
This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.
Application of finite element, global polynomial, and kriging response surfaces in Progressive Lattice Sampling designs
This paper examines the modeling accuracy of finite element interpolation, kriging, and polynomial regression used in conjunction with the Progressive Lattice Sampling (PLS) incremental design-of-experiments approach. PLS is a paradigm for sampling a deterministic hypercubic parameter space by placing and incrementally adding samples in a manner intended to maximally reduce lack of knowledge in the parameter space. When combined with suitable interpolation methods, PLS is a formulation for progressive construction of response surface approximations (RSA) in which the RSA are efficiently upgradable, and upon upgrading, offer convergence information essential in estimating error introduced by the use of RSA in the problem. The three interpolation methods tried here are examined for performance in replicating an analytic test function as measured by several different indicators. The process described here provides a framework for future studies using other interpolation schemes, test functions, and measures of approximation quality.
Application of Multifidelity Uncertainty Quantification Methods to a Subsurface Transport Model
Abstract not provided.
Approaches for representing and propagating uncertainty that will be useful for multi-scale modeling
Abstract not provided.
Arctic Climate Systems Analysis
This study began with a challenge from program area managers at Sandia National Laboratories to technical staff in the energy, climate, and infrastructure security areas: apply a systems-level perspective to existing science and technology program areas in order to determine technology gaps, identify new technical capabilities at Sandia that could be applied to these areas, and identify opportunities for innovation. The Arctic was selected as one of these areas for systems level analyses, and this report documents the results. In this study, an emphasis was placed on the arctic atmosphere since Sandia has been active in atmospheric research in the Arctic since 1997. This study begins with a discussion of the challenges and benefits of analyzing the Arctic as a system. It goes on to discuss current and future needs of the defense, scientific, energy, and intelligence communities for more comprehensive data products related to the Arctic; assess the current state of atmospheric measurement resources available for the Arctic; and explain how the capabilities at Sandia National Laboratories can be used to address the identified technological, data, and modeling needs of the defense, scientific, energy, and intelligence communities for Arctic support.
Automated Algorithms for Quantum-Level Accuracy in Atomistic Simulations: LDRD Final Report
This report summarizes the result of LDRD project 12-0395, titled "Automated Algorithms for Quantum-level Accuracy in Atomistic Simulations." During the course of this LDRD, we have developed an interatomic potential for solids and liquids called Spectral Neighbor Analysis Poten- tial (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The SNAP coef- ficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. Global optimization methods in the DAKOTA software package are used to seek out good choices of hyperparameters that define the overall structure of the SNAP potential. FitSnap.py, a Python-based software pack- age interfacing to both LAMMPS and DAKOTA is used to formulate the linear regression problem, solve it, and analyze the accuracy of the resultant SNAP potential. We describe a SNAP potential for tantalum that accurately reproduces a variety of solid and liquid properties. Most significantly, in contrast to existing tantalum potentials, SNAP correctly predicts the Peierls barrier for screw dislocation motion. We also present results from SNAP potentials generated for indium phosphide (InP) and silica (SiO 2 ). We describe efficient algorithms for calculating SNAP forces and energies in molecular dynamics simulations using massively parallel computers and advanced processor ar- chitectures. Finally, we briefly describe the MSM method for efficient calculation of electrostatic interactions on massively parallel computers.
Automated generation of quantum-accurate classical interatomic potentials for metals and semiconductors
Abstract not provided.
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