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Williams, Brian J.

Publications and source records attributed to Williams, Brian J..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-phenomenology Yield Characterization

This report serves as the first delivery of a four-year applied science effort to transform and advance the error bounds for the yield estimate of an explosion. Each year’s delivery will be in this form, culminating in the submission of this work for peer review to a scientific journal. Importantly, the yearly progress reports can then also be viewed as expanding drafts working towards a formal journal article submission. For the first tranche of funding, we collaborated with Air Force Technical Applications Center (AFTAC) scientists to identify unclassified real-world data that demonstrate and validate our advanced error propagation methods. Collaboration includes visits to AFTAC and telecons. For this development, we illustrate the fusion of seismic, acoustic, optical, and surface effect signatures from an explosion. The mathematics and code being adapted to this specific application (Williams et al., 2021) involves physics models of multiple sensor signatures. We have also identified related physics models and have integrated them into code. Current methods of underground explosion yield estimation for the Threshold Test Ban Treaty (TTBT) have served the US treaty monitoring mission well for decades. A research objective of the Defense Nuclear Nonproliferation Research and Development (DNN R&D) office of the National Nuclear Security Administration (NNSA) has always been to provide new technical capabilities for monitoring lower thresholds. The general model and error propagation code to be developed in this project is based on significant advances in error modeling and propagation needed to analyze data at lower detection thresholds. The second tranche of funding for this project began on May 1, 2022, and planned work for the second tranche includes: i) completing the integration of physical model code into the general error model framework; this code accommodates a wide range of linear/nonlinear source models, fixed/ random effects, and frequentist/Bayesian analyses (the purpose of which is not to dictate to users how to analyze data, but instead to allow users the maximum flexibility in their work); ii) illustrative application of code to identified data, and; iii) initial planning with AFTAC researchers on delivery of code to the Common Development Environment at AFTAC, and continued writing of the planned final journal article submission (year two of this progress report), with particular emphasis on descriptions of data identified for this effort.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Advanced Error Modeling for Prompt Yield Estimation [Slides]

Multi-Phenomenology Explosion Monitoring (MultiPEM) Toolbox provides a capability for post-detonation characterization focusing on yield estimation; Synthesize information from multiple sensor types; Leverage historical databases to inform statistical models; Allows for rapid or comprehensive analysis; Implemented in R code.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

MultiPEM Toolbox: User Manual [Rev. 2]

This document explains use of the Multi-Phenomenology Explosion Monitoring (Multi PEM) Toolbox, a collection of R scripts for estimating the unknown device parameters of a new event with uncertainty quantification. The methodology and application used for illustration in this user manual are fully documented in a Los Alamos National Laboratory technical report hereafter designated “WPA” for reference. Additional details on the application are found in a recent journal article. Two assessment types are available: rapid and complete. Rapid assessments are conducted in two stages, as described in Section 2. In the first stage, calibration data are used to estimate forward and error model parameters (WPA, §5.1) and (if relevant) errors-in-variables yield values for calibration sources (WPA, §3, Equation (3)). In the second stage, new event data are used to estimate the unknown new event device parameters (WPA, §5.2) with uncertainty quantification. Two options for treating the inferred first stage parameters in second stage Bayesian analysis are available: fixing them at their maximum likelihood estimate (default), or multiple imputation. Multiple imputation involves utilizing several posterior samples (imputations) of the first stage parameters as fixed values in the second stage posterior sampling of the new event device parameters. Second stage sampling is conducted across imputations in parallel to improve computational efficiency. This method produces improved uncertainty quantification of the new event device parameters compared with the default treatment of the first stage parameters, at the expense of additional computation. Complete assessments are conducted in a single stage, as described in Section 3. Calibration and (if relevant) new event data are used simultaneously to estimate all forward model, error model, and (if relevant) new event device parameters with uncertainty quantification on the latter. As the name suggests, rapid assessments generally run substantially faster than complete assessments (even with multiple imputation), because the results of first stage analysis can be stored and incorporated into estimating a relatively low-dimensional space of new event device parameters whenever relevant new event data becomes available. On the other hand, complete assessments must be run on the full set of model and device parameters with calibration and new event data every time the latter becomes available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Bayesian Optimal Sensor Augmentation Via Estimated Mutual Information

We consider the problem of designing a sensor network to most efficiently locate the source of a seismic event. The left panel of Figure 1 shows 982 possible sensor locations in a region of interest. The objective is to infer the unknown location of an earthquake, indicated by the gold dot for illustration purposes. Assume that three sensors have already been placed for data collection. Travel times of the P-wave from the source to the three sensors are observed and used to infer the unknown source location via a Bayesian statistical model described subsequently. The right panel of Figure 1 shows samples from the resulting posterior distribution for source location along with a posterior estimate of this location.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Multiphenomenology explosion monitoring (MultiPEM): a general framework for data interpretation and yield estimation

SUMMARY An underground nuclear explosion (UNE) couples mechanical energy into crustal rock, which propagates as seismic and acoustic waves. These different physical phenomena transport, by different pathways, to standoff detectors at varying distances. The transport pathways attenuate the original signal but in different ways. Enabled by correct statistical weighting, signal attenuation models can be used to combine these disparate sensor data to estimate the yield of an UNE. Contemporaneous statistical models, used in yield estimation, can be improved with an advanced partition of error for these physical signal propagation models. We present an advanced multivariate approach to error modelling of multiphenomenology physical signatures. In addition to measurement error, our error model represents physical model biases as random with a physics-based covariance structure. To illustrate this proposed framework, we demonstrate the estimation of explosion yield using openly available seismic and acoustic data from chemical single-point explosions.

Williams, Brian J.↗
DOE OSTI2019

Quantification of Margins and Uncertainty for Multicomponent Systems

This report describes and illustrates several metrics for application to the analysis of a multicomponent system. Specifically, three metrics are calculated for each system component. The first metric is component failure probability (π). This will typically be expressed as the probability that a component performance variable (P) exceeds a specified or random threshold (T). The margin (M) for component performance is defined as M = P - T and will be the focus of analysis throughout this report. The second metric is referred to as margin to failure rate φ (MTF φ ). This is an estimate of the allowable “slack” in component performance that could be tolerated to modify its failure rate from π to a user-specified value φ. The third and final metric is margin sensitivity (MS). This quantity orders components based on the degree to which changes in their failure rates are affected by shifts in their margin distributions. We first motivate the metrics in an idealized setting, and then expand to the typical application scenarios where they must be estimated from experimental or simulated data. In this environment, we discuss methods for estimating the metrics with uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Gradient-Free Construction of Active Subspaces for Dimension Reduction

Recent developments in the field of reduced order modeling - and in particular, active subspace construction - have made it possible to efficiently approximate complex models by constructing low-order response surfaces based upon a small subspace of the original high dimensional parameter space. These methods rely upon the fact that the response tends to vary more prominently in a few dominant directions defined by linear combinations of the original inputs, allowing for a rotation of the coordinate axis and a consequent transformation of the parameters. In this talk, we discuss a gradient free active subspace algorithm that is feasible for high dimensional parameter spaces where finite-difference techniques are impractical. We illustrate an initialized gradient-free active subspace algorithm for a neutronics example implemented with SCALE6.1, for input dimensions up to 7700.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Gradient-Free Construction of Active Subspaces for Dimension Reduction in Complex Models with Applications to Neutronics

Recent developments in the field of reduced-order modeling---and, in particular, active subspace construction---have made it possible to efficiently approximate complex models by constructing low-order response surfaces based upon a small subspace of the original high-dimensional parameter space. These methods rely upon the fact that the response tends to vary more prominently in a few dominant directions defined by linear combinations of the original inputs, allowing for a rotation of the coordinate axis and a consequent transformation of the parameters. In this work, we discuss a gradient-free active subspace algorithm that is feasible for high-dimensional parameter spaces where finite-difference techniques are impractical. This analysis extends the gradient-free algorithm introduced in [A. Lewis, R. Smith, and B. Williams, Comput. Math. Appl., 72 (2016), pp. 1603--1615] in two significant ways: (i) we introduce an initialization algorithm to identify lower-dimensional subspaces of influential directions to seed the gradient-free algorithm for high-dimensional problems, and (ii) we analyze dimension selection criteria to verify the algorithms. Finally, we illustrate the initialized gradient-free active subspace algorithm for a neutronics example implemented with SCALE6.1 for input dimensions up to 7700.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Expansion and Age of the X-Ray Synchrotron-Dominated Supernova Remnant G330.2+1.0

We report new Chandra observations of one of the few Galactic supernova remnants whose X-ray spectrum is dominated by nonthermal synchrotron radiation, G330.2+1.0. We find that between 2006 and 2017, some parts of the shell have expanded by about 1%, giving a free-expansion (undecelerated) age of about 1000 yr, and implying shock velocities there of 9000 km/s for a distance of 5 kpc. Somewhat slower expansion is seen elsewhere around the remnant periphery, in particular in compact knots. Because some deceleration must have taken place, we infer that G330.2+1.0 is less than about 1000 yr old. Thus, G330.2+1.0 is one of only four Galactic core-collapse remnants of the last millennium. The large size, low brightness, and young age require a very low ambient density, suggesting expansion in a stellar-wind bubble. We suggest that in the east, where some thermal emission is seen and expansion velocities are much slower, the shock has reached the edge of the cavity. The high shock velocities can easily accelerate relativistic electrons to X-ray-emitting energies. A few small regions show highly significant brightness changes by 10%–20%, both brightening and fading, a phenomenon previously observed in only two supernova remnants, indicating strong and/or turbulent magnetic fields.

Borkowski, Kazimierz J.↗
NASA NTRS2018

The Expansion of the Young Supernova Remnant 0509-68.7 (N103B)

We present a second epoch of Chandra observations of the Type Ia Large Magellanic Cloud supernova remnant(SNR) 0509-68.7 (N103B) obtained in 2017. When combined with the earlier observations from 1999, we have a17.4 year baseline with which we can search for evidence of the remnant's expansion. Although the lack of strongpoint source detections makes absolute image alignment at the necessary accuracy impossible, we can measure thechange in the diameter and the area of the remnant, and find that it has expanded by an average velocity of 4170(2860, 5450) km s1. This supports the picture of this being a young remnant; this expansion velocity correspondsto an undecelerated age of 850 years, making the real age somewhat younger, consistent with results from lightecho studies. Previous infrared observations have revealed high densities in the western half of the remnant, likelyfrom circumstellar material, so it is probable that the real expansion velocity is lower on that side of the remnantand higher on the eastern side. A similar scenario is seen in Kepler's SNR. N103B joins the rare class ofMagellanic Cloud SNRs with measured proper motions.

Williams, Brian J.↗
DOE OSTI2018

Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling

Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process) to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper, the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

A Deep X-Ray View of the Synchrotron-Dominated Supernova Remnant G330.2+1.0

We present moderately deep (125 ks) XMM-Newton observations of supernova remnant G330.2+1.0. This remnant is one of only a few known that fall into the "synchrotron-dominated" category, with the emission almost entirely dominated by a nonthermal continuum. Previous X-ray observations could only characterize the spectra of a few regions. Here, we examine the spectra from 14 regions surrounding the entire rim, finding that the spectral properties of the nonthermal emission do not vary significantly in any systematic way from one part of the forward shock to another, unlike several other remnants of this class. We confirm earlier findings that the power-law index, Γ, ranges from about 2.1-2.5, while the absorbing column density is generally between (2.0-2.6) × 10(exp 22) cm(exp -2). Fits with the srcut model find values of the roll-off frequency in the range of 10(exp 17.1)-10(exp 17.5) Hz, implying energies of accelerated electrons of approximately 100 TeV. These values imply a high shock velocity of approximately 4600 km s(exp -1), favoring a young age of the remnant. Diffuse emission from the interior is nonthermal in origin as well, and fits to these regions yield similar values to those along the rim, also implying a young age. Thermal emission is present in the east, and the spectrum is consistent with approximately 650 km s(exp -1) shock wave encountering interstellar or circumstellar material with a density of approximately 1 cm(exp -3).

Williams, Brian J.↗
DOE OSTI2017

CASL Dakota Capabilities Summary

The Dakota software project serves the mission of Sandia National Laboratories and supports a worldwide user community by delivering state-of-the-art research and robust, usable software for optimization and uncertainty quantification. These capabilities enable advanced exploration and riskinformed prediction with a wide range of computational science and engineering models. Dakota is the verification and validation (V&V) / uncertainty quantification (UQ) software delivery vehicle for CASL, allowing analysts across focus areas to apply these capabilities to myriad nuclear engineering analyses.

97 MATHEMATICS AND COMPUTING↗