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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

CORRLA-RS

The CORRLA-RS package provides a suite of statistical methods for sampling multidimensional distributions and to conduct sensitivity and correlation analysis of large scale data in the Rust programming language. The software provides a unique solution to multidimensional constrained sampling problems utilizing a combination of parallelized Markov Chain Monte Carlo methods and traditional rejection sampling. The sensitivity and correlation analysis methods are backed by a high performance randomized singular value decomposition implementation which enables datasets larger than the random access memory (RAM) size to be analyzed. Additionally, CORRLA-RS implements the active subspace identification method using a KD-Tree and the randomized singular value decomposition acting in concert.

Gurecky, William [Oak Ridge National Laboratory (O↗
DOE OSTI2014

A systematic way for the cost reduction of density fitting methods

We present a simple approach for the reduction of the size of auxiliary basis sets used in methods exploiting the density fitting (resolution of identity) approximation for electron repulsion integrals. Starting out of the singular value decomposition of three-center two-electron integrals, new auxiliary functions are constructed as linear combinations of the original fitting functions. The new functions, which we term natural auxiliary functions (NAFs), are analogous to the natural orbitals widely used for the cost reduction of correlation methods. The use of the NAF basis enables the systematic truncation of the fitting basis, and thereby potentially the reduction of the computational expenses of the methods, though the scaling with the system size is not altered. The performance of the new approach has been tested for several quantum chemical methods. It is demonstrated that the most pronounced gain in computational efficiency can be expected for iterative models which scale quadratically with the size of the fitting basis set, such as the direct random phase approximation. The approach also has the promise of accelerating local correlation methods, for which the processing of three-center Coulomb integrals is a bottleneck.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Mixed-State Entanglement from Local Randomized Measurements

In this work, we propose a method for detecting bipartite entanglement in a many-body mixed state based on estimating moments of the partially transposed density matrix. The estimates are obtained by performing local random measurements on the state, followed by postprocessing using the classical shadows framework. Our method can be applied to any quantum system with single-qubit control. We provide a detailed analysis of the required number of experimental runs, and demonstrate the protocol using existing experimental data [Brydges et al., Science 364, 260 (2019)].

1-dimensional spin chains↗
DOE OSTI2016

Sci-Thur AM: YIS – 05: Prediction of lung tumor motion using a generalized neural network optimized from the average prediction outcome of a group of patients

Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons for the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

SpotSDC: Revealing the Silent Data Corruption Propagation in High-Performance Computing Systems

We report the trend of rapid technology scaling is expected to make the hardware of high-performance computing (HPC) systems more susceptible to computational errors due to random bit flips. Some bit flips may cause a program to crash or have a minimal effect on the output, but others may lead to silent data corruption (SDC), i.e., undetected yet significant output errors. Classical fault injection analysis methods employ uniform sampling of random bit flips during program execution to derive a statistical resiliency profile. However, summarizing such fault injection result with sufficient detail is difficult, and understanding the behavior of the fault-corrupted program is still a challenge. In this article, we introduce SpotSDC, a visualization system to facilitate the analysis of a program's resilience to SDC. SpotSDC provides multiple perspectives at various levels of detail of the impact on the output relative to where in the source code the flipped bit occurs, which bit is flipped, and when during the execution it happens. SpotSDC also enables users to study the code protection and provide new insights to understand the behavior of a fault-injected program. Based on lessons learned, we demonstrate how what we found can improve the fault injection campaign method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Diagnosing heterogeneous dynamics in single-molecule/particle trajectories with multiscale wavelets

In this study, we describe a simple automated method to extract and quantify transient heterogeneous dynamical changes from large data sets generated in single-molecule/particle tracking experiments. Based on wavelet transform, the method transforms raw data to locally match dynamics of interest. This is accomplished using statistically adaptive universal thresholding, whose advantage is to avoid a single arbitrary threshold that might conceal individual variability across populations. How to implement this multiscale method is described, focusing on local confined diffusion separated by transient transport periods or hopping events, with three specific examples: in cell biology, biotechnology, and glassy colloid dynamics. The discussion is generalized within the framework of continuous time random walk. This computationally efficient method can run routinely on hundreds of millions of data points analyzed within an hour on a desktop personal computer.

36 MATERIALS SCIENCE↗
DOE OSTI2012

20 Gy Versus 44 Gy of Supplemental External Beam Radiotherapy With Palladium-103 for Patients With Greater Risk Disease: Results of a Prospective Randomized Trial

Purpose: The necessity of external beam radiotherapy (EBRT) as a supplement to prostate brachytherapy remains unknown. We report brachytherapy outcomes for patients with higher risk features randomized to substantially different supplemental EBRT regimens. Methods and Materials: Between December 1999 and June 2004, 247 patients were randomized to 20 Gy vs. 44 Gy EBRT followed by a palladium-103 boost (115 Gy vs. 90 Gy). The eligibility criteria included clinically organ-confined disease with Gleason score 7-10 and/or pretreatment prostate-specific antigen (PSA) level 10-20 ng/mL. The median follow-up period was 9.0 years. Biochemical progression-free survival (bPFS) was defined as a PSA level of {<=}0.40 ng/mL after nadir. The median day 0 prescribed dose covering 90% of the target volume was 125.7%; 80 men received androgen deprivation therapy (median, 4 months). Multiple parameters were evaluated for their effect on bPFS. Results: For the entire cohort, the cause-specific survival, bPFS, and overall survival rates were 97.7%, 93.2%, and 80.8% at 8 years and 96.9%, 93.2%, and 75.4% at 10 years, respectively. The bPFS rate was 93.1% and 93.4% for the 20-Gy and 44-Gy arms, respectively (p = .994). However, no statistically significant differences were found in cause-specific survival or overall survival were identified. When stratified by PSA level of {<=}10 ng/mL vs. >10 ng/mL, Gleason score, or androgen deprivation therapy, no statistically significant differences in bPFS were discerned between the two EBRT regimens. On multivariate analysis, bPFS was most closely related to the preimplant PSA and clinical stage. For patients with biochemically controlled disease, the median PSA level was <0.02 ng/mL. Conclusion: The results of the present trial strongly suggest that two markedly different supplemental EBRT regimens result in equivalent cause-specific survival, bPFS, and overall survival. It is probable that the lack of benefit for a higher supplemental EBRT dose is the result of the high-quality brachytherapy dose distributions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Modeling and optimizing of the random atomic spin gyroscope drift based on the atomic spin gyroscope

In order to improve the atom spin gyroscope's operational accuracy and compensate the random error caused by the nonlinear and weak-stability characteristic of the random atomic spin gyroscope (ASG) drift, the hybrid random drift error model based on autoregressive (AR) and genetic programming (GP) + genetic algorithm (GA) technique is established. The time series of random ASG drift is taken as the study object. The time series of random ASG drift is acquired by analyzing and preprocessing the measured data of ASG. The linear section model is established based on AR technique. After that, the nonlinear section model is built based on GP technique and GA is used to optimize the coefficients of the mathematic expression acquired by GP in order to obtain a more accurate model. The simulation result indicates that this hybrid model can effectively reflect the characteristics of the ASG's random drift. The square error of the ASG's random drift is reduced by 92.40%. Comparing with the AR technique and the GP + GA technique, the random drift is reduced by 9.34% and 5.06%, respectively. The hybrid modeling method can effectively compensate the ASG's random drift and improve the stability of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Do Epistemic Uncertainties Allow for Replacing Microstructural Experiments with Reconstruction Algorithms?

A novel problem in computational materials modeling is addressed: “Are the computational microstructure reconstruction techniques reliable enough to replace experiments?” Here, “reliable” computations are associated with producing “expected” reconstructions that are adequately close to the experimental data. The output of computational techniques can deviate from the experimental measurements because of the epistemic uncertainties in the algorithms. In this work, an analytical formulation for quantification of epistemic uncertainties in a microstructure reconstruction algorithm based on Markov random field is presented. The method is used to predict the large-scale spatial distribution of a microstructure given an experimental input measured over a small spatial domain. However, small variations are observed on the microstructural features of the synthesized samples due to the Markov random field algorithm. The proposed analytical technique aims to quantify these uncertainties and estimate their propagation to the macroscale material properties to provide a significant understanding on how reliable it is to replace the experiments with the Markov random field model to predict microstructural maps over large spatial domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

New Light on Disordered Ensembles: Ab Initio Structure Determination of One Particle from Scattering Fluctuations of One Particular from Scattering Fluctuatins of Many Copies

We report on the first experimental ab initio reconstruction of an image of a single particle from fluctuations in the scattering from an ensemble of copies, randomly oriented about an axis. The method is applicable to identical particles frozen in space or time (as by snapshot diffraction from an x-ray free electron laser). These fluctuations enhance information obtainable from an experiment such as conventional small angle x-ray scattering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials

As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Random center vortex lines in continuous 3D space-time

We present a model of center vortices, represented by closed random lines in continuous 2+1-dimensional space-time. These random lines are modeled as being piece-wise linear and an ensemble is generated by Monte Carlo methods. The physical space in which the vortex lines are defined is a cuboid with periodic boundary conditions. Besides moving, growing and shrinking of the vortex configuration, also reconnections are allowed. Our ensemble therefore contains not a fixed, but a variable number of closed vortex lines. This is expected to be important for realizing the deconfining phase transition. Using the model, we study both vortex percolation and the potential V(R) between quark and anti-quark as a function of distance R at different vortex densities, vortex segment lengths, reconnection conditions and at different temperatures. We have found three deconfinement phase transitions, as a function of density, as a function of vortex segment length, and as a function of temperature. The model reproduces the qualitative features of confinement physics seen in SU(2) Yang-Mills theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Preliminary Study on TRISO Fuel Cross Section Generation

Cross section self-shielding methodologies for TRISO fuel were assessed to provide accurate multigroup cross sections for a high-fidelity reactor physics code so that the code is able to accurately model and simulate advanced reactors with TRISO fuel. Initially, the two existing methodologies (the SCALE method and the Sanchez-Pomraning method) were studied and implemented to MC2-3 for detailed performance tests. Additionally, a new spatial self-shielding method, named the iterative local spatial self-shielding (ILSS) method, for particulate fuels was developed based on the disadvantage factor and implemented to MC2-3 as well. The new method approximately accounts for the effect of randomly distributed particles on the particle shadowing effect using a homogenized compact region surrounding a particle of interest at the center. The self-shielded cross sections of the particle at the center are determined iteratively since the cross sections of the homogenized compact region are calculated using them. For the energy range above 100 keV where the fuel-to-moderator ratio is more important than the random distribution of particles, a single particle unit-cell model is used by preserving the average amount of moderator per fuel particle in the system. The three self-shielding methods implemented in MC2-3 were tested using numerical benchmark problems made based on fuel compact problems of a prismatic-type very high temperature reactor. Test results indicated that the ILSS method produced slightly better results than the SCALE and Sanchez-Pomraning methods, compared to the Serpent-2 Monte Carlo results obtained with 25 independent random particle configurations. The SCALE and Sanchez-Pomraning methods tend to underestimate the heterogeneity effect by 150 and 100 pcm, respectively, while the new ILSS method overestimates the heterogeneity effect by 70 pcm. In future, the new self-shielding method will be extended to perform pebble calculations and compare results with those from the SCALE and Sanchez-Pomraning methods. Furthermore, the new method will be optimized for practical applications to on-the-fly resonance treatment for lattice or whole-core calculations for advanced reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Formation of Vertically Oriented Channels during Calcination of Surfactant-Templated Titania-Doped Mesoporous Silica Thin Films

Here, the mesoporous structure development during calcination of titania-doped mesoporous silica thin films is characterized by in situ grazing-incidence small angle x-ray scattering (GISAXS). Varying amounts of titania (≤ 6 mol%) are incorporated in cetyltrimethylammonium bromide (CTAB) templated film by two methods in this study: dispersion of titania in the silica matrix, and fixing titania at the pore surface by complexation of the titania precursor with a sugar-based co-surfactant, dodecyl maltoside (C 12 G 2 ). Before calcination, films templated with only CTAB display a mesoporous structure with a mixture of in-plane 2D hexagonal and randomly oriented porous structure, while C 12 G 2 -complexed film possessed randomly oriented mesostructure only. Both methods yield a final structure of hexagonal close-packed pores orthogonally oriented to the substrate after calcination at 500 °C. This is attributed to weak Si-O-Ti bonds which allow sintering to occur at 500 °C, combined with unidirectional thermal contraction of the film in the vertical direction. Anisotropic stress and annealing of the films allows the randomly oriented pores to merge vertically and form channels with pore diameter of 2.3 ± 0.3 nm. Titania doping greater than 1% is required for this transformation in films with no C 12 G 2 surfactant used while the transformation was observed for films with C 12 G 2 complexation even at low (0.005%) titania doping, perhaps accentuated by orientational disorder introduced by the sugar surfactant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Acupuncture-Like Transcutaneous Electrical Nerve Stimulation Versus Pilocarpine in Treating Radiation-Induced Xerostomia: Results of RTOG 0537 Phase 3 Study

Purpose and Objectives: This report presents the analysis of the RTOG 0537 multicenter randomized study that compared acupuncture-like transcutaneous stimulation (ALTENS) with pilocarpine (PC) for relieving radiation-induced xerostomia. Methods and Materials: Eligible patients were randomized to twice-weekly 20-minute ALTENS sessions for 24 sessions during 12 weeks or PC (5 mg 3 times daily for 12 weeks). The primary endpoint was the change in the University of Michigan Xerostomia-Related Quality of Life Scale (XeQOLS) scores from baseline to 9 months from randomization (MFR). Secondary endpoints included basal and citric acid primed whole salivary production (WSP), ratios of positive responders (defined as patients with ≥20% reduction in overall radiation-induced xerostomia symptom burden), and the presence of adverse events based on the Common Terminology Criteria for Adverse Events version 3. An intention-to-treat analysis was conducted. Results: One hundred forty-eight patients were randomized. Only 96 patients completed the required XeQOLS and were evaluable at 9 MFR (representing merely 68.6% statistical power). Seventy-six patients were evaluable at 15 MFR. The median change in the overall XeQOLS in ALTENS and PC groups at 9 and 15 MFR were −0.53 and −0.27 (P=.45) and −0.6 and −0.47 (P=.21). The corresponding percentages of positive responders were 81% and 72% (P=.34) and 83% and 63% (P=.04). Changes in WSP were not significantly different between the groups. Grade 3 or less adverse events, mostly consisting of grade 1, developed in 20.8% of patients in the ALTENS group and in 61.6% of the PC group. Conclusions: The observed effect size was smaller than hypothesized, and statistical power was limited because only 96 of the recruited 148 patients were evaluable. The primary endpoint—the change in radiation-induced xerostomia symptom burden at 9 MFR—was not significantly different between the ALTENS and PC groups. There was significantly less toxicity in patients receiving ALTENS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Divergence of Solutions to Perturbation-Based Advection-Dispersion Moment Equations

Moment equation methods are popular and powerful tools for modeling transport processes in randomly heterogeneous porous media, but the application of these methods to advection-dispersion equations often leads to erroneous oscillations. Perturbative methods, required to close systems of moment equations, become inaccurate for large perturbations; however, little quantitative theory exists for determining when this occurs for advection-dispersion equations. We consider three different methods (asymptotic approximation, Eulerian truncation, and iterative solution) for closing and solving advection-dispersion moment equations describing transport in stratified porous media with random permeability. We obtain approximate analytical expressions for time above which the asymptotic approximation to the mean diverges, in particular quantifying the impact that dispersion has on delaying---but not eliminating---divergence. We demonstrate that Eulerian truncation and iterative solution methods do not eliminate divergent behavior either. Our divergence criteria provide \emph{a priori} estimates that signal a warning to the practitioner of stochastic advection-dispersion equations to carefully consider whether to apply perturbative approaches.

58 GEOSCIENCES↗
DOE OSTI2019

Improved Stress Tolerance of Saccharomyces cerevisiae by CRISPR-Cas-Mediated Genome Evolution

In bioprocesses, a microorganism with high tolerance to various stresses would be advantageous for efficient bio-based chemical production. Yeast Saccharomyces cerevisiae has long been used in the food industry because of its safety and convenience, and genetically engineered S. cerevisiae strains have been constructed and used for the production of various bio-based chemicals. In this study, we developed a novel genome shuffling method for S. cerevisiae using CRISPR-Cas. By using this, the thermotolerant mutant strain T8-292, which can grow well at 39 °C, was successfully created. The strain also showed higher cell viability in low pH and high ethanol concentration. In addition, the differences in genome structure between mutant and parent strains were suggested by random amplified polymorphic DNA PCR method. Our genome shuffling method could be a promising strategy for improvement of various stress tolerance in S. cerevisiae.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical (QC) predictions to coarse-grained (CG) resolutions using deep neural networks (DNNs). While DNNs can learn complex representations that prove challenging for kernel-based methods, they are susceptible to overfitting and the overconfidence of uncertainty estimations. Here, we develop ECG within a GPU-accelerated Deep Kernel Learning (DKL) framework to enable CG QC predictions using range-separated hybrid density functional theory (DFT), obtaining a 107 speedup relative to naive all-atom QC. By treating the predicted electronic properties as random Gaussian Processes, DKL incorporates CG mapping degeneracy by learning the distribution of electronic energies as a function of CG configuration. DKL-ECG accurately reproduces molecular orbital energies from range-separated DFT while facilitating efficient training via active learning using the uncertainties provided by DKL. Further, we show that while active learning algorithms enable efficient sampling of a more diverse configurational space relative to random sampling, all explored query methods exhibit comparable performance for the examined system. We attribute this result to the significant overlap of the feature space and output property distributions across multiple temperatures.

97 MATHEMATICS AND COMPUTING↗