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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2019

Calibration of linear contact stiffnesses in discrete element models using a hybrid analytical-computational framework

Efficient selections of particle-scale contact parameters in discrete element modelling remain an open question. The purpose of this study is to provide a hybrid calibration framework to estimate linear contact stiffnesses (normal and tangential) for both two-dimensional and three-dimensional simulations. Analytical formulas linking macroscopic parameters (Young's modulus, Poisson's ratio) to mesoscopic particle parameters for granular systems are derived based on statistically isotropic packings under small-strain isotropic stress conditions. By taking the derived analytical solutions as initial approximations, the gradient descent algorithm automatically obtains a reliable numerical estimation. The introduced framework is validated with several numerical cases including randomly distributed monodisperse and polydisperse packings. The results show that this hybrid method practically reduces the time for artificial trials and errors to obtain reasonable stiffness parameters. The proposed framework can also be extended to other parameter calibration problems in DEM.

Calibration method↗
DOE OSTI2020

Detecting and tracking drift in quantum information processors

Abstract If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Vectorization techniques for probability distribution functions using VecCore

Probability distribution functions (PDFs) are very used in modeling random processes and physics simulations. Improving the performance of algorithms that generate many random numbers under complex PDFs is often a very challenging task when methods as direct functions are not available. In this work we present general strategies on how to vectorize some PDFs using VecCore library. We show the results for the Exponential, Gaussian, discrete Poisson and Gamma probability distributions.

Chaparro Amaro, Oscar R.↗
DOE OSTI2024

Simulations of classical three-body thermalization in one dimension

One-dimensional systems, such as nanowires or electrons moving along strong magnetic field lines, have peculiar thermalization physics. The binary collision of pointlike particles, typically the dominant process for reaching thermal equilibrium in higher-dimensional systems, cannot thermalize a 1D system. We study how dilute classical 1D gases thermalize through three-body collisions. We consider a system of identical classical point particles with pairwise repulsive inverse power-law potential V ij ∝ 1/|x i –x j | n or the pairwise Lennard-Jones potential. Using Monte Carlo methods, we compute a collision kernel and use it in the Boltzmann equation to evolve a perturbed thermal state with temperature T toward equilibrium. We explain the shape of the kernel and its dependence on the system parameters. Additionally, we implement molecular dynamics simulations of a many-body gas and show agreement with the Boltzmann evolution in the low-density limit. For the inverse power-law potential, the rate of thermalization is proportional to ρ 2 ⁢T$\frac{1}{2}$ – $\frac{1}{n}$, where ρ is the number density. Furthermore, the corresponding proportionality constant decreases with increasing n.

1-dimensional systems↗
DOE OSTI2018

Optical Properties of Multilayered Sol–Gel Zinc-Oxide Films

Study of structural, optical and photocatalytic properties of multilayered (1–8 layers) zinc oxide films deposited on glass substrates by sol-gel method showed, that after thermal treatment at 500°C they consist of random oriented hexagonal crystalline grains with size of 34–40 nm, forming larger particles with sizes of 100–150 nm, which do not depend on number of layers. With an increase in the number of layers, the intensity of exciton photoluminescence decreases by a factor of 10, the absorption of light in the visible and near IR ranges increases, and the efficiency of photocatalytic decomposition of the test organic dye rhodamine B increases by 10–12%. The observed changes are related to the increase in the total area of grain boundaries and to the change in the integral amount of oxygen vacancies and interstitial atoms as the number of layers increases, which makes it possible to control the properties of zinc oxide films for applications in optoelectronics, photovoltaics and photocatalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations

Abstract Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.

Siddiquee, Masudur R.↗
DOE OSTI2019

Impact of the SSO-ASTRO Margin Guideline on Rates of Re-excision After Lumpectomy for Breast Cancer: A Meta-analysis

Background: One in five patients undergoing initial lumpectomy for invasive breast cancer subsequently undergoes re-excision or mastectomy. A lack of clarity of when to re-excise based on lumpectomy margin width contributes to this high rate of reoperation. We sought to determine the impact of the Society of Surgical Oncology (SSO) and American Society of Radiation Oncologist (ASTRO) margin guideline on reoperation rates after lumpectomy. The guideline recommended omission of routine re-excision in specimens with “no ink on tumor”. Methods: A systematic literature review was performed. For eligible studies, a random-effects model was used for a meta-analysis of lumpectomy re-excision prevalence before and after publication of the SSO-ASTRO margin guideline. Study heterogeneity was measured by the Cochran’s Q test. Results: Five institutional, one population-based, and one national registry study met inclusion requirements. Sample size per study ranged from 237 to 26,102. There was significant interstudy heterogeneity (Q = 19.779; p = 0.003). Pooled re-excision prevalence was 22% (confidence interval [CI] 20–23) before and 14% (CI 12–15) after guideline publication. With the pre-guideline re-excision prevalence used as the reference value, the associated odds ratio for re-excision after the guideline was 0.65 (CI 0.54–0.78; p < 0.0001). Conclusions: The findings of a 35% reduction in the odds of re-excision after the guideline publication and a reduction in re-excision prevalence from 22 to 14% supports the notion that the SSO-ASTRO margin guideline was impactful. These findings are congruent with the projected reductions in re-excision at the time of guideline publication.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Surgical Resection for Metastatic Tumors in the Pancreas: A Single-Center Experience and Systematic Review

Background: Metastatic lesion to the pancreas accounts for approximately 2% of pancreatic neoplasms. There is no prospective, randomized or case-controlled study evaluating the role of pancreatic metastasectomy. Methods: The PubMed, EMBASE, and Cochrane Library electronic databases were searched for studies published between January 1, 2001 and December 31, 2017. Studies with five or more patients who received pancreatic metastasectomy and data from our institution (29 patients) were included. The Kaplan–Meier method was used for survival analysis. Results: A total of 414 patients from 20 institutions who underwent pancreatic resections were included. Of the reported 31 kinds of primary neoplasms, renal-cell carcinoma (RCC) comprised the most (54.3%). At the time of diagnosis, although 40.3% patients were asymptomatic, abdominal pain (34.8%) and jaundice (20.6%) were relatively common. As for surgical type, pancreatoduodenectomy, total pancreatectomy, distal pancreatectomy, and enucleation took up 37.9%, 11.4%, 43.5%, and 7.2% respectively. The mortality and morbidity rates were 1.4% and 48.3% respectively. Patients with symptoms at the time of diagnosis had significantly shorter survival compared with asymptomatic patients (p = 0.017). Those with RCC as primary tumor had significantly longer survival compared with non-RCC patients (p < 0.001). Positive margin also predicts worse prognosis (p = 0.035). Conclusions: Pancreatic metastasectomy is safe and associated with acceptable short- and intermediate-term results. In the conditions of RCC as the primary tumor, being asymptomatic, or negative resection margin, a better prognosis after resection can be achieved.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Questionable Benchmarks [Slides]

An investigation and initial review of ‘questionable benchmarks’ was performed at LANL. Multiple methods were used, including machine learning techniques, to identify 'questionable benchmarks' and/or benchmarks with low uncertainties. Those benchmarks were then reviewed for obvious errors. This is not a recommendation to ICSBEP, but can be used as a starting point for a more comprehensive review. A journal paper is being written on this work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Artificial Intelligence

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The LSST AGN Data Challenge: Selection Methods

Abstract Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1974

On the robustness of a Bayes estimate

This paper examines the robustness of a Bayes estimator with respect to the assigned prior distribution. A Bayesian analysis for a stochastic scale parameter of a Weibull failure model is summarized in which the natural conjugate is assigned as the prior distribution of the random parameter. The sensitivity analysis is carried out by the Monte Carlo method in which, although an inverted gamma is the assigned prior, realizations are generated using distribution functions of varying shape. For several distributional forms and even for some fixed values of the parameter, simulated mean squared errors of Bayes and minimum variance unbiased estimators are determined and compared. Results indicate that the Bayes estimator remains squared-error superior and appears to be largely robust to the form of the assigned prior distribution.

Canavos, G. C.↗
NASA NTRS1979

Effect of mid- and high-spatial frequencies on optical performance

In many of today's telescopes the effects of surface errors on image quality and scattered light are very important. The influence of optical fabrication surface errors on the performance of an optical system is discussed. The methods developed by Hopkins (1957) for aberration tolerancing and Barakat (1972) for random wavefront errors are extended to the examination of mid- and high-spatial frequency surface errors. The discussion covers a review of the basic concepts of image quality, an examination of manufacturing errors as a function of image quality performance, a demonstration of mirror scattering effects in relation to surface errors, and some comments on the nature of the correlation functions. Illustrative examples are included.

Noll, R. J.↗
NASA NTRS1983

The design of optimum remote-sensing instruments

Remote-sensing instruments allow values for certain properties of a target to be retrieved from measurements of radiation emitted, reflected or transmitted by the target. The retrieval accuracy is affected by random variations in the many target properties which affect the measurements. A method is described, by which statistical properties of the target and theoretical models of its electromagnetic behavior can be used to choose values for the instrument parameters which maximize the retrieval accuracy. The technique is applicable to a wide range of remote-sensing instruments.

Peckham, G. E.↗