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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

UQTk (V. 3.0) User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

UQTk User Manual (V.3.1.0)

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

UQTk Version 3.1.1 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.1 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

UQTk Version 3.0.4 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.4 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Penetrating power in radiography of high-Z materials as a function of bremsstrahlung spectrum endpoint energy

A modified radiographer’s equation is derived and fitted to simulation data so as to describe the increase in radiographic signal that comes with increasing bremsstrahlung spectrum endpoint energy. The dose contained in the incident spectrum follows an E28 power law, as often cited. However, the direct flux penetrating through a heavy, high-Z material, obeys a weaker scaling due to hardening of the radiation. The resultant expression depends on the thickness of the scene being radiographed, the composition of high-Z materials in the scene, and any filtering by other low-Z materials, such as Al or Be. The expression exhibits two asymptotic limits: a ‘thick’ limit at which radiation is extremely hardened and only a weak benefit to increasing endpoint energy is observed (also the most attenuating limit and therefore of dubious physical significance), and a ‘thin’ limit at which a minimum of high-Z material is present and the penetrating dose exhibits the 2.8 power scaling inherent in the incident spectrum. Behavior for intermediate thicknesses of W and Al are obtained by modeling incident and attenuated dose using the DOSECALC code and the Bayes’ Inference Engine (BIE). These data guide the optimization of MeV radiography over a wide range of energies where pair production plays a significant role in photon scattering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

UQTk Version 3.1.2 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.2 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Digital Signal Processing Using Deep Neural Networks

Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network, to accomplish several important communications network and digital signals processing (DSP) tasks. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer, and correct transmission channel effects, and directly demodulate baseband RF signals.

42 ENGINEERING↗
DOE OSTI2017

INTEGRAL Observations of GW170104

We used data from the International Gamma-Ray Astrophysics Laboratory ( INTEGRAL ) to set upper limits on the γ -ray and hard X-ray prompt emission associated with the gravitational-wave event GW170104, discovered by the Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo collaboration. The unique omnidirectional viewing capability of the instruments on board INTEGRAL allowed us to examine the full 90% confidence level localization region of the LIGO trigger. Depending on the particular spectral model assumed and the specific position within this region, the upper limits inferred from the INTEGRAL observations range from F {sub γ} = 1.9 × 10{sup −7} erg cm{sup −2} to F {sub γ} = 10{sup −6} erg cm{sup −2} (75 keV–2 MeV energy range). This translates into a ratio between the prompt energy released in γ -rays along the direction to the observer and the gravitational-wave energy of E {sub γ} / E {sub GW} < 2.6 × 10{sup −5}. Using the INTEGRAL results, we cannot confirm the γ -ray proposed counterpart to GW170104 by the Astro—Rivelatore Gamma a Immagini Leggero (AGILE) team with the mini-Calorimeter (MCAL) instrument. The reported flux of the AGILE/MCAL event, E2, is not compatible with the INTEGRAL upper limits within most of the 90% LIGO localization region. There is only a relatively limited portion of the sky where the sensitivity of the INTEGRAL instruments was not optimal and the lowest-allowed fluence estimated for E2 would still be compatible with the INTEGRAL results. This region was also observed independently by Fermi /Gamma-ray Burst Monitor and AstroSAT, from which, as far as we are aware, there are no reports of any significant detection of a prompt high-energy event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The Three-dimensional Collapse of a Rapidly Rotating 16 M ⊙ Star

I report on the three-dimensional (3D) hydrodynamic evolution of a rapidly rotating 16 M ⊙ star to iron core collapse. For the first time, I follow the 3D evolution of the angular momentum (AM) distribution in the iron core and convective shell burning regions for the final 10 minutes up to and including gravitational instability and core collapse. In 3D, convective regions show efficient AM transport that leads to an AM profile that differs in shape and magnitude from MESA within a few shell convective turnover timescales. For different progenitor models, such as those with tightly coupled Si/O convective shells, efficient AM transport in 3D simulations could lead to a significantly different AM distribution in the stellar interior affecting estimates of the natal neutron star or black hole spin. The results suggest that 3D AM transport in convective and rotating shell burning regions are critical components in models of massive stars and could qualitatively alter the explosion outcome and inferred compact remnant properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning

Although Transformer-based language representations achieve state-of-the-art accuracy on various natural language processing (NLP) tasks, the large model size has been challenging the resource constrained computing platforms. Weight pruning, as a popular and effective technique in reducing the number of weight parameters and accelerating the Transformer, has been investigated on GPUs. However, the Transformer acceleration using weight pruning on field-programmable gate array (FPGAs) remains unexplored. This paper investigates the column balanced block-wise pruning on Transformer and designs an FPGA acceleration engine to customize the balanced blockwise matrix multiplication. We implement the Transformer model with proper hardware scheduling, and the experiments show that the Transformer inference on FPGA achieves 10.35 ms latency with the batch size of 32, which is 10.96 × speed up comparing to CPU platform and 2.08 × speed up comparing to GPU platform.

Peng, Hongwu↗
DOE OSTI2023

Bayesian optimal experimental design for constitutive model calibration

Computational simulation is increasingly relied upon for high/consequence engineering decisions, which necessitates a high confidence in the calibration of and predictions from complex material models. However, the calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure—Interlaced Characterization and Calibration (ICC)—is introduced and demonstrated. Further, this framework leverages Bayesian optimal experimental design (BOED), which creates a line of communication between model calibration needs and data collection capabilities in order to optimize the information content gathered from the experiments for model calibration. Eventually, the ICC framework will be used in quasi real-time to actively control experiments of complex specimens for the calibration of a high-fidelity material model. This work presents the critical first piece of algorithm development and a demonstration in determining the optimal load path of a cruciform specimen with simulated data. Calibration results, obtained via Bayesian inference, from the integrated ICC approach are compared to calibrations performed by choosing the load path a priori based on human intuition, as is traditionally done. The calibration results are communicated through parameter uncertainties which are propagated to the model output space (i.e. stress–strain). In these exemplar problems, data generated within the ICC framework resulted in calibrated model parameters with reduced measures of uncertainty compared to the traditional approaches.

42 ENGINEERING↗
DOE OSTI2018

Two-temperature GRRMHD Simulations of M87

Here, we present axisymmetric two-temperature general relativistic radiation magnetohydrodynamic simulations of the inner region of the accretion flow onto the supermassive black hole M87. We address uncertainties from previous modeling efforts through inclusion of models for (1) self-consistent dissipative and Coulomb electron heating (2) radiation transport (3) frequency-dependent synchrotron emission, self-absorption, and Compton scattering. We adopt a distance D = 16.7 Mpc, an observer angle θ = 20°, and consider black hole masses M/M ⊙ = (3.3 x 10 9 , 6.2 x 10 9 ) and spins a star = (0.5, 0.9375) in a four-simulation suite. For each (M, a star ), we identify the accretion rate that recovers the 230 GHz flux from very long baseline interferometry measurements. We report on disk thermodynamics at these accretion rates (M · /M · Edd ~ 10 -5 ). The disk remains geometrically thick; cooling does not lead to a thin disk component. While electron heating is dominated by Coulomb rather than dissipation for r ≳ 10GM/c 2 , the accretion disk remains two-temperature. Radiative cooling of electrons is not negligible, especially for r ≲ 10GM/c 2 . The Compton y parameter is of order unity. We then compare derived and observed or inferred spectra, millimeter images, and jet powers. Simulations with M/M ⊙ = 3.3 × 10 9 are in conflict with observations. These simulations produce millimeter images that are too small, while the low-spin simulation also overproduces X-rays. For M/M ⊙ = 6.2 x 10 9 , both simulations agree with constraints on radio/IR/X-ray fluxes and millimeter image sizes. Simulation jet power is a factor 10 2 –10 3 below inferred values, a possible consequence of the modest net magnetic flux in our models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

OBSERVATIONS OF INFALLING AND ROTATIONAL MOTIONS ON A 1000 AU SCALE AROUND 17 CLASS 0 AND 0/I PROTOSTARS: HINTS OF DISK GROWTH AND MAGNETIC BRAKING?

We perform imaging and analyses of SMA 1.3 mm continuum, C{sup 18}O (2-1) and {sup 12}CO (2-1) line data of 17 Class 0 and 0/I protostars to study their gas kinematics on a 1000 AU scale. Continuum and C{sup 18}O (2-1) emission are detected toward all the sample sources and show central primary components with sizes of ∼600-1500 AU associated with protostars. The velocity gradients in C{sup 18}O (2-1) have wide ranges of orientations from parallel to perpendicular to the outflows, with magnitudes from ∼1 to ∼530 km s{sup –1} pc{sup –1}. We construct a simple kinematic model to reproduce the observed velocity gradients, estimate the infalling and rotational velocities, and infer the disk radii and the protostellar masses. The inferred disk radii range from <5 AU to >500 AU with estimated protostellar masses from <0.1 M {sub ☉} to >1 M {sub ☉}. Our results hint that both large and small disks are possibly present around Class 0 protostars, which could be a sign of disk growth at the Class 0 stage. In addition, the directions of the overall velocity gradients in 7 out of the 17 sources are close to perpendicular to their outflow axes (Δθ > 65°), which is a signature of significant rotational motions. From our model fitting, the specific angular momenta in these sources are estimated to be >2 × 10{sup –4} km s{sup –1} pc, suggesting that magnetic braking is unlikely efficient on a 1000 AU scale in these Class 0 and 0/I sources. In a sub-sample with observed magnetic field orientations, we find no source with large specific angular momenta together with closely aligned magnetic field and outflow axes. This possibly hints that the magnetic field, if originally aligned with the rotational axis, can play a role in removing angular momentum from infalling material at the Class 0 stage. We discuss our results in comparison with theoretical models of collapsing dense cores with and without magnetic fields in the context of disk formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A Pervasive Parallel Processing Framework for Data Visualization and Analysis at Extreme Scale

The evolution of the computing world from teraflop to petaflop has been relatively effortless, with several of the existing programming models scaling effectively to the petascale. The migration to exascale, however, poses considerable challenges. All industry trends infer that the exascale machine will be built using processors containing hundreds to thousands of cores per chip. It can be inferred that efficient concurrency on exascale machines requires a massive amount of concurrent threads, each performing many operations on a localized piece of data. Currently, visualization libraries and applications are based off what is known as the visualization pipeline. In the pipeline model, algorithms are encapsulated as filters with inputs and outputs. These filters are connected by setting the output of one component to the input of another. Parallelism in the visualization pipeline is achieved by replicating the pipeline for each processing thread. This works well for today’s distributed memory parallel computers but cannot be sustained when operating on processors with thousands of cores. Our project investigates a new visualization framework designed to exhibit the pervasive parallelism necessary for extreme scale machines. Our framework achieves this by defining algorithms in terms of worklets, which are localized stateless operations. Worklets are atomic operations that execute when invoked unlike filters, which execute when a pipeline request occurs. The worklet design allows execution on a massive amount of lightweight threads with minimal overhead. Only with such fine-grained parallelism can we hope to fill the billions of threads we expect will be necessary for efficient computation on an exascale machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

H β and H γ Absorption-line Profile Inconsistencies in Laboratory Experiments Performed at White Dwarf Photosphere Conditions

The spectroscopic method relies on hydrogen Balmer absorption lines to infer white dwarf (WD) masses. These masses depend on the choice of atmosphere model, hydrogen atomic line shape calculation, and which Balmer series members are included in the spectral fit. In addition to those variables, spectroscopic masses disagree with those derived using other methods. Here we present laboratory experiments aimed at investigating the main component of the spectroscopic method: hydrogen line shape calculations. These experiments use X-rays from Sandia National Laboratories’ Z-machine to create a uniform ~15 cm 3 hydrogen plasma and a ~4 eV backlighter that enables recording high-quality absorption spectra. The large plasma, volumetric X-ray heating that fosters plasma uniformity, and the ability to collect absorption spectra at WD photosphere conditions are improvements over past laboratory experiments. Analysis of the experimental absorption spectra reveals that electron density (n e ) values derived from the Hγ line are ~34% ± 7.3% lower than from Hβ. Two potential systematic errors that may contribute to this difference were investigated. A detailed evaluation of self-emission and plasma gradients shows that these phenomena are unlikely to produce any measurable Hβ–Hγ n e difference. WD masses inferred with the spectroscopic method are proportional to the photosphere density. Hence, the measured Hβ–Hγ n e difference is qualitatively consistent with the trend that WD masses inferred from their Hβ line are higher than that resulting from the analysis of Hβ and Hγ. In conclusion, this evidence may suggest that current hydrogen line shape calculations are not sufficiently accurate to capture the intricacies of the Balmer series.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

A Deep Generative Deconvolutional Image Model

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm; the algorithm is implemented on graphical processor units (GPU) to enable large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.

convolutional neural network↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

IMPLICATIONS OF RAPID CORE ROTATION IN RED GIANTS FOR INTERNAL ANGULAR MOMENTUM TRANSPORT IN STARS

Core rotation rates have been measured for red giant stars using asteroseismology. These data, along with helioseismic measurements and open cluster spin-down studies, provide powerful clues about the nature and timescale for internal angular momentum transport in stars. We focus on two cases: the metal-poor red giant KIC 7341231 ({sup O}tto{sup )} and intermediate-mass core helium burning stars. For both, we examine limiting case studies for angular momentum coupling between cores and envelopes under the assumption of rigid rotation on the main sequence. We discuss the expected pattern of core rotation as a function of mass and radius. In the case of Otto, strong post-main-sequence coupling is ruled out and the measured core rotation rate is in the range of 23-33 times the surface value expected from standard spin-down models. The minimum coupling timescale (0.17-0.45 Gyr) is significantly longer than that inferred for young open cluster stars. This implies ineffective internal angular momentum transport in early first ascent giants. By contrast, the core rotation rates of evolved secondary clump stars are found to be consistent with strong coupling given their rapid main-sequence rotation. An extrapolation to the white dwarf regime predicts rotation periods between 330 and 0.0052 days, depending on mass and decoupling time. We identify two key ingredients that explain these features: the presence of a convective core and inefficient angular momentum transport in the presence of larger mean molecular weight gradients. Observational tests that can disentangle these effects are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗