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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
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 OSTI2012

Controlled reversal of Co/Pt Dots for nanomagnetic logic applications

Domain reversal in perpendicular multilayer films is governed by an intrinsic distribution of anisotropy. However, control of the switching field distribution (SFD) of field-coupled, single domain Co/Pt dots is the key to building large integrated systems for nanomagnetic logic applications. In this work, partial Ga{sup +} focused ion beam (FIB) irradiation of single-domain Co/Pt dots is employed which locally reduces the anisotropy and renders the film-inherent SFD ineffective. Controlled reduction in the switching field compared to non-irradiated dots is achieved, depending on size and dose of irradiation. TEM images of an as-grown and irradiated Co/Pt stack show a change in morphology from distinct Co/Pt interfaces to intermixed and randomly oriented grains due to the Ga{sup +} ion impact. The presented method is highly suitable to control the switching behavior in field-coupled logic devices. Experimental results are used to demonstrate a nanomagnetic fanout operation.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Giant energy density in [001]-textured Pb(Mg1/3Nb2/3)O3-PbZrO3-PbTiO3 piezoelectric ceramics

Pb(Zr,Ti)O3 (PZT) based compositions have been challenging to texture or grow in a single crystal form due to the incongruent melting point of ZrO2. Here we demonstrate the method for achieving 90% textured PZT-based ceramics and further show that it can provide highest known energy density in piezoelectric materials through enhancement of piezoelectric charge and voltage coefficients (d and g). Our method provides more than 5 increase in the ratio d(textured)/d(random). A giant magnitude of d g coefficient with value of 59 000 10 15 m2 N 1 (comparable to that of the single crystal counterpart and 359% higher than that of the best commercial compositions) was obtained.

Yan, Yongke↗
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 OSTI2014

SU-E-T-501: Normal Tissue Toxicities of Pulsed Low Dose Rate Radiotherapy and Conventional Radiotherapy: An in Vivo Total Body Irradiation Study

Purpose: Pulsed low dose rate radiotherapy (PLDR) is a re-irradiation technique for therapy of recurrent cancers. We have previously shown a significant difference in the weight and survival time between the mice treated with conventional radiotherapy (CRT) and PLDR using total body irradiation (TBI). The purpose of this study was to investigate the in vivo effects of PLDR on normal mouse tissues.Materials and Methods: Twenty two male BALB/c nude mice, 4 months of age, were randomly assigned into a PLDR group (n=10), a CRT group (n=10), and a non-irradiated control group (n=2). The Siemens Artiste accelerator with 6 MV photon beams was used. The mice received a total of 18Gy in 3 fractions with a 20day interval. The CRT group received the 6Gy dose continuously at a dose rate of 300 MU/min. The PLDR group was irradiated with 0.2Gyx20 pulses with a 3min interval between the pulses. The mice were weighed thrice weekly and sacrificed 2 weeks after the last treatment. Brain, heart, lung, liver, spleen, gastrointestinal, urinary and reproductive organs, and sternal bone marrow were removed, formalin-fixed, paraffin-embedded and stained with H and E. Morphological changes were observed under a microscope. Results: Histopathological examination revealed atrophy in several irradiated organs. The degree of atrophy was mild to moderate in the PLDR group, but severe in the CRT group. The most pronounced morphological abnormalities were in the immune and hematopoietic systems, namely spleen and bone marrow. Brain hemorrhage was seen in the CRT group, but not in the PLDR group. Conclusions: Our results showed that PLDR induced less toxicity in the normal mouse tissues than conventional radiotherapy for the same dose and regimen. Considering that PLDR produces equivalent tumor control as conventional radiotherapy, it would be a good modality for treatment of recurrent cancers.

60 APPLIED LIFE SCIENCES↗
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 OSTI2015

PPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction Prediction

Determining protein-protein interaction (PPI) in biological systems is of considerable importance, and prediction of PPI has become a popular research area. Although different classifiers have been developed for PPI prediction, no single classifier seems to be able to predict PPI with high confidence. We postulated that by combining individual classifiers the accuracy of PPI prediction could be improved. We developed a method called protein-protein interaction prediction classifiers merger (PPCM), and this method combines output from two PPI prediction tools, GO2PPI and Phyloprof, using Random Forests algorithm. The performance of PPCM was tested by area under the curve (AUC) using an assembled Gold Standard database that contains both positive and negative PPI pairs. Our AUC test showed that PPCM significantly improved the PPI prediction accuracy over the corresponding individual classifiers. We found that additional classifiers incorporated into PPCM could lead to further improvement in the PPI prediction accuracy. Furthermore, cross species PPCM could achieve competitive and even better prediction accuracy compared to the single species PPCM. This study established a robust pipeline for PPI prediction by integrating multiple classifiers using Random Forests algorithm. This pipeline will be useful for predicting PPI in nonmodel species.

59 BASIC BIOLOGICAL SCIENCES↗
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 ↗
DOE OSTI2024

Quaternized polyaromatics for use in electrochemical devices

Disclosed herein in various embodiments are aryl-ether free polyaromatic polymers based on random copolymer architecture with two, three, or more aromatic ring components and methods of preparing those polymers. The polymers of the present disclosure can be used as ion exchange membranes, e.g., as anion exchange membranes, and ionomer binders in alkaline electrochemical devices.

Bae, Chulsung↗
DOE OSTI2025

Power System Feature-Based Event Classification by Means of Multiple PMU Data

Abstract—Phasor Measurement Units (PMUs) provide time synchronized measurements across the power grid, enabling data driven event detection and classification for enhanced system monitoring and situational awareness. However, variations in event duration, spatial extent, and severity, along with coincident events, pose challenges for conventional classification models that require fixed-size inputs. This paper presents a feature-based framework that aggregates diverse attributes from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, and Multilayer Perceptron. A probabilistic post-processing scheme is further introduced to enable multi-label classification in the presence of overlapping events. Experiments using real-world PMU data demonstrate that the Random Forest model achieves 95% accuracy, while the proposed post-processing method yields an additional 3% improvement.

Nematirad, Reza↗
DOE OSTI2016

SU-F-J-65: Prediction of Patient Setup Errors and Errors in the Calibration Curve from Prompt Gamma Proton Range Measurements

Purpose: To understand the extent to which the prompt gamma camera measurements can be used to predict the residual proton range due to setup errors and errors in the calibration curve. Methods: We generated ten variations on a default calibration curve (CC) and ten corresponding range maps (RM). Starting with the default RM, we chose a square array of N beamlets, which were then rotated by a random angle θ and shifted by a random vector s. We added a 5% distal Gaussian noise to each beamlet in order to introduce discrepancies that exist between the ranges predicted from the prompt gamma measurements and those simulated with Monte Carlo algorithms. For each RM, s, θ, along with an offset u in the CC, were optimized using a simple Euclidian distance between the default ranges and the ranges produced by the given RM. Results: The application of our method lead to the maximal overrange of 2.0mm and underrange of 0.6mm on average. Compared to the situations where s, θ, and u were ignored, these values were larger: 2.1mm and 4.3mm. In order to quantify the need for setup error corrections, we also performed computations in which u was corrected for, but s and θ were not. This yielded: 3.2mm and 3.2mm. The average computation time for 170 beamlets was 65 seconds. Conclusion: These results emphasize the necessity to correct for setup errors and the errors in the calibration curve. The simplicity and speed of our method makes it a good candidate for being implemented as a tool for in-room adaptive therapy. This work also demonstrates that the Prompt gamma range measurements can indeed be useful in the effort to reduce range errors. Given these results, and barring further refinements, this approach is a promising step towards an adaptive proton radiotherapy.

60 APPLIED LIFE SCIENCES↗