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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Quantitative Structure–Property Relationship (QSPR) Analysis of ZrO 2 -Containing Soda-Lime Borosilicate Glasses

Quantitative structure–property relationship (QSPR) analysis is a promising approach to correlate structural features with properties of glass materials that lack long-range order and usually have complex structures. By using carefully chosen descriptors based on structural models generated from molecular dynamics (MD) simulations, correlations with properties and insights on glass behaviors can be obtained. Zirconia can significantly alter glass properties including chemical durability, even in a small amount, and hence plays an important role in vitrification of nuclear waste where long-term chemical durability is desired. In this study, borosilicate glasses with the composition of xZrO 2 –(61 – x)SiO 2 –17B 2 O 3 –18Na 2 O–4CaO with x = 0, 1, 2, 4, 6, and 8 were simulated using classical MD simulations with the recently developed composition-dependent potentials. Short-range (e.g., bond distance and coordination numbers) and medium-range (e.g., Qn distribution, network connectivity, and ring-size distribution) structural features altered by ZrO 2 were obtained and analyzed. The use of a descriptor (Fnet descriptor) that combines short-range structural characteristics, from MD simulations, and the cation-oxygen single bond strength was found to provide excellent linear correlations with the density and initial dissolution rate of these glasses. Furthermore, the results show that by combining MD simulations and QSPR analysis the composition and structural effect on the properties of complex multicomponent glasses can be elucidated, thus suggesting that this is a promising approach for future glass research and new composition design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Classical versus quantum models in machine learning: insights from a finance application

Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from real-world applications is one of the key milestones towards a quantum ready era. Here, we take a first step towards addressing this challenge by performing a comparison of the widely used classical ML models known as restricted Boltzmann machines (RBMs), against a recently proposed quantum model, now known as quantum circuit Born machines (QCBMs). Both models address the same hard tasks in unsupervised generative modeling, with QCBMs exploiting the probabilistic nature of quantum mechanics and a candidate for near-term quantum computers, as experimentally demonstrated in three different quantum hardware architectures to date. To address the question of the performance of the quantum model on real-world classical data sets, we construct scenarios from a probabilistic version out of the well-known portfolio optimization problem in finance, by using time-series pricing data from asset subsets of the S&P500 stock market index. It is remarkable to find that, under the same number of resources in terms of parameters for both classical and quantum models, the quantum models seem to have superior performance on typical instances when compared with the canonical training of the RBMs. Our simulations are grounded on a hardware efficient realization of the QCBMs on ion-trap quantum computers, by using their native gate sets, and therefore readily implementable in near-term quantum devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Score-based diffusion models for generating liquid argon time projection chamber images

For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2013

Generator Dynamic Model Validation and Parameter Calibration Using Phasor Measurements at the Point of Connection

Disturbance data recorded by phasor measurement units (PMU) offers opportunities to improve the integrity of dynamic models. However, manually tuning parameters through play-back events demands significant efforts and engineering experiences. In this paper, a calibration method using the extended Kalman filter (EKF) technique is proposed. The formulation of EKF with parameter calibration is discussed. Case studies are presented to demonstrate its validity. The proposed calibration method is cost-effective, complementary to traditional equipment testing for improving dynamic model quality.

Huang, Zhenyu↗
DOE OSTI2014

Modeling laser generation in cholesteric liquid crystals using kinetic equations

The paper presents the results of calculating the threshold pumping power necessary for laser generation in cholesteric liquid crystals doped with fluorescent dyes. Calculations have been performed with allowance for (i) specific spectral properties of the chiral cholesteric medium as a photonic structure and (ii) the spatially distributed character of feedback in the active medium. The dependences of the threshold pumping power on the sample thickness, dye concentration, and leading front widths of long pumping pulses have been studied.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Modeling particulate generation during high energy explosive events

Laser produced plasmas are used to simulate high energy events and are useful for a variety of applications e.g. in industrial sectors, such as semi-conductor manufacturing, as well as medical, environmental, and energy sectors. Particulate generation in laser produced plasmas is related to key features of the plasma: temperature and pressure evolution during plume expansion, plasma chemistry etc. Several uncertainties persist in relating initial high energy events (~microseconds) to particulate formation that happens as the plasma cools and evolves (~milliseconds).

42 ENGINEERING↗