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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
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 ↗
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↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2017

Analysis of Factors Affecting Successful Clinical Trial Enrollment in the Context of Three Prospective, Randomized, Controlled Trials

Purpose: Challenges can arise when attempting to maximize patient enrollment in clinical trials. There have been limited studies focusing on the barriers to enrollment and the efficacy of alternative study design to improve accrual. We analyzed barriers to clinical trial enrollment, particularly the influence of timing, in context of three prospective, randomized oncology trials where one arm was considered more aggressive than the other. Methods and Materials: From June 2011 to March 2015, patients who were enrolled on 3 prospective institutional protocols (an oligometastatic non-small cell lung cancer [NSCLC] trial and 2 proton vs intensity modulated radiation therapy trials in NSCLC and esophageal cancer) were screened for protocol eligibility. Eligible candidates were approached about trial participation, and patient characteristics (age, sex, T/N categorization) were recorded along with details surrounding trial presentation (appointment number). Fisher's exact test, Student's t tests, and multivariate analysis were performed to assess differences between enrolled and refusal patients. Results: A total of 309 eligible patients were approached about trial enrollment. The enrollment success rate during this time span was 52% (n=160 patients). Enrolled patients were more likely to be presented trial information at an earlier appointment (oligometastatic protocol: 5 vs 3 appointments [P<.001]; NSCLC protocol: 4 vs 3 appointments [P=.0018]; esophageal protocol: 3 vs 2 appointments [P=.0086]). No other factors or patient characteristics significantly affected enrollment success rate. Conclusion: Improvement in enrollment rates for randomized control trials is possible, even in difficult accrual settings. Earlier presentation of trial information to patients is the most influential factor for success and may help overcome accrual barriers without compromising trial design.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

The influence of random packed column parameters on the liquid holdup and interfacial area

Abstract Carbon dioxide capture via solvent absorption in packed columns has emerged as a potential technology to mitigate coal‐fired power plant CO 2 emissions. Parameters, including packing types, solvent properties, and operating conditions, could potentially affect the packed column CO 2 capture efficiency. To understand the importance of those parameters and help packed column optimization, a design of experiments (DoEs) method was proposed to generate input parameter matrix. Combined with multiphase computational fluid dynamics (CFD), the random packed column parameter influence on the liquid holdup and interfacial area can be efficiently investigated. Surrogate‐based sensitivity analysis shows that the solvent flow rate and contact angle are key factors dictating liquid holdup and interfacial area. On the other hand, solvent viscosity has a marginal impact on the interfacial area. The sensitivity scores were calculated for each input parameter to guide the selection of dimensionless numbers for the liquid holdup and interfacial area correlation development.

42 ENGINEERING↗
DOE OSTI2016

Outcome According to Elective Pelvic Radiation Therapy in Patients With High-Risk Localized Prostate Cancer: A Secondary Analysis of the GETUG 12 Phase 3 Randomized Trial

Purpose: The role of pelvic elective nodal irradiation (ENI) in the management of prostate cancer is controversial. This study analyzed the role of pelvic radiation therapy (RT) on the outcome in high-risk localized prostate cancer patients included in the Groupe d'Etude des Tumeurs Uro-Genitales (GETUG) 12 trial. Methods and Materials: Patients with a nonpretreated high-risk localized prostate cancer and a staging lymphadenectomy were randomly assigned to receive either goserelin every 3 months for 3 years and 4 cycles of docetaxel plus estramustine or goserelin alone. Local therapy was administered 3 months after the start of systemic treatment. Performance of pelvic ENI was left to the treating physician. Only patients treated with primary RT were included in this analysis. The primary endpoint was biochemical progression-free survival (bPFS). Results: A total of 413 patients treated from 2002 to 2006 were included, of whom 358 were treated using primary RT. A total of 208 patients received pelvic RT and 150 prostate-only RT. Prostate-specific antigen (PSA) concentration, Gleason score, or T stage did not differ according to performance of pelvic RT; pN+ patients more frequently received pelvic RT than pN0 patients (P<.0001). Median follow-up was 8.8 years. In multivariate analysis, bPFS was negatively impacted by pN stage (hazard ratio [HR]: 2.52 [95% confidence interval [CI]: 1.78-3.54], P<.0001), Gleason score 8 or higher (HR: 1.41 [95% CI: 1.03-1.93], P=.033) and PSA higher than 20 ng/mL (HR: 1.41 [95% CI: 1.02-1.96], P=.038), and positively impacted by the use of chemotherapy (HR: 0.66 [95% CI: 0.48-0.9], P=.009). There was no association between bPFS and use of pelvic ENI in multivariate analysis (HR: 1.10 [95% CI: 0.78-1.55], P=.60), even when analysis was restricted to pN0 patients (HR: 0.88 [95% CI: 0.59-1.31], P=.53). Pelvic ENI was not associated with increased acute or late patient reported toxicity. Conclusions: This unplanned analysis of a randomized trial failed to demonstrate a benefit of pelvic ENI on bPFS in high-risk localized prostate cancer patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Nano-metrology: The art of measuring X-ray mirrors with slope errors <100 nrad

We present a comprehensive investigation of the systematic and random errors of the nano-metrology instruments used to characterize synchrotron X-ray optics at Diamond Light Source. With experimental skill and careful analysis, we show that these instruments used in combination are capable of measuring state-of-the-art X-ray mirrors. Examples are provided of how Diamond metrology data have helped to achieve slope errors of <100 nrad for optical systems installed on synchrotron beamlines, including: iterative correction of substrates using ion beam figuring and optimal clamping of monochromator grating blanks in their holders. Simulations demonstrate how random noise from the Diamond-NOM’s autocollimator adds into the overall measured value of the mirror’s slope error, and thus predict how many averaged scans are required to accurately characterize different grades of mirror.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗