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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optimal 1D Ly α forest power spectrum estimation – II. KODIAQ, SQUAD, and XQ-100

ABSTRACT We measure the 1D Ly α power spectrum P1D from Keck Observatory Database of Ionized Absorption toward Quasars (KODIAQ), The Spectral Quasar Absorption Database (SQUAD), and XQ-100 quasars using the optimal quadratic estimator. We combine KODIAQ and SQUAD at the spectrum level, but perform a separate XQ-100 estimation to control its large resolution corrections in check. Our final analysis measures P1D at scales k < 0.1 s km−1 between redshifts $z$ = 2.0–4.6 using 538 quasars. This sample provides the largest number of high-resolution, high-S/N observations; and combined with the power of optimal estimator it provides exceptional precision at small scales. These small-scale modes (k ≳ 0.02 s km−1), unavailable in Sloan Digital Sky Survey and Dark Energy Spectroscopic Instrument analyses, are sensitive to the thermal state and reionization history of the intergalactic medium, as well as the nature of dark matter. As an example, a simple Fisher forecast analysis estimates that our results can improve small-scale cut-off sensitivity by more than a factor of 2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Fraction of broad absorption line quasars in different radio morphologies

ABSTRACT In this study, we investigated the orientation model of Broad Absorption Line (BAL) quasars using a sample of sources that are common in Sloan Digital Sky Survey (SDSS) Data Release (DR)-16 quasar catalogue and Very Large Array (VLA)-Faint Images of the Radio Sky at Twenty Centimeters (FIRST) survey. Using the radio cut-out images from the FIRST survey, we first designed a deep-learning model using convolutional neural networks (CNN) to classify the quasar radio morphologies into the core-only, young jet, single lobe, or triples. These radio morphologies are further sub-classified into core-dominated and lobe-dominated sources. The CNN models can classify the sources with a high precision of >98 ${{\ \rm per\ cent}}$ for all the morphological sub-classes. The average BAL fraction in the resolved core, core-dominated, and lobe-dominated quasars are consistent with the BAL fraction inferred from radio and infrared surveys. We also present the distribution of BAL quasars as a function of quasar orientation by using the radio core-dominance as an orientation indicator. A similar analysis is performed for HiBALs, LoBALs, and FeLoBALs. All the radio morphological sub-classes and BAL sub-classes show an increase in BAL fraction at high orientation angles of the jets with respect to the line of sight. Our analysis suggests that BAL quasars are more likely to be found in viewing angles close to the equatorial plane of the quasar. However, a pure orientation model is inadequate, and a combination of orientation and evolution is probably the best way to explain the complete BAL phenomena.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

AutoEnRichness: A hybrid empirical and analytical approach for estimating the richness of galaxy clusters

ABSTRACT We introduce AutoEnRichness, a hybrid approach that combines empirical and analytical strategies to determine the richness of galaxy clusters (in the redshift range of 0.1 ≤ z ≤ 0.35) using photometry data from the Sloan Digital Sky Survey Data Release 16, where cluster richness can be used as a proxy for cluster mass. In order to reliably estimate cluster richness, it is vital that the background subtraction is as accurate as possible when distinguishing cluster and field galaxies to mitigate severe contamination. AutoEnRichness is comprised of a multistage machine learning algorithm that performs background subtraction of interloping field galaxies along the cluster line of sight and a conventional luminosity distribution fitting approach that estimates cluster richness based only on the number of galaxies within a magnitude range and search area. In this proof-of-concept study, we obtain a balanced accuracy of 83.20 per cent when distinguishing between cluster and field galaxies as well as a median absolute percentage error of 33.50 per cent between our estimated cluster richnesses and known cluster richnesses within r200. In the future, we aim for AutoEnRichness to be applied on upcoming large-scale optical surveys, such as the Legacy Survey of Space and Time and Euclid, to estimate the richness of a large sample of galaxy groups and clusters from across the halo mass function. This would advance our overall understanding of galaxy evolution within overdense environments as well as enable cosmological parameters to be further constrained.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Uncertainty quantification of optical models in fission fragment deexcitation

Here, we take the first step towards incorporating compound nuclear observables at astrophysically relevant energies into the experimental evidence used to constrain optical models, by propagating the uncertainty in two global optical potentials, one phenomenological and one microscopic, to correlated fission observables using the Monte Carlo Hauser-Feshbach formalism. We compare to a wide range of historic and recent experimental fission measurements, and discuss in detail regions of disagreement. We find that the parametric optical model uncertainty in neutron-fragment correlated observables involving neutron energy is significant. On the other hand, we observe that other experimental features, particularly neutron-fragment correlations near the 132 Sn shell closure and the high energy component of neutron spectra, are unlikely to be explained by the optical potential, and will require further experimental and theoretical effort to explain.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process‐Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process-based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data-constrained representation of the sterile triploid Miscanthus (IL clone) within the process-based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam-derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO 2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field-measured biomass. Building on the calibrated operating state, parameter-response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post-calibration GPP R 2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R 2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

ecosys↗
DOE OSTI2015

SU-E-P-48: Evaluation of Intensity Modulated Radiotherapy (IMRT) with Three Different Commercial Planning Systems for the Treatment of Cervical Cancer

Purpose: To investigate the performances of three commercial treatment planning systems (TPS) for intensity modulated radiotherapy (IMRT) optimization regarding cervical cancer. Methods: For twenty cervical cancer patients, three IMRT plans were retrospectively re-planned: one with Pinnacle TPS,one with Oncentra TPS and on with Eclipse TPS. The total prescribed dose was 50.4 Gy delivered for PTV and 58.8 Gy for PTVnd by simultaneous integrated boost technique. The treatments were delivered using the Varian 23EX accelerator. All optimization schemes generated clinically acceptable plans. They were evaluated based on target coverage, homogeneity (HI) and conformity (CI). The organs at risk (OARs) were analyzed according to the percent volume under some doses and the maximum doses. The statistical method of the collected data of variance analysis was used to compare the difference among the quality of plans. Results: IMRT with Eclipse provided significant better HI, CI and all the parameters of PTV. However, the trend was not extension to the PTVnd, it was still significant better at mean dose, D50% and D98%, but plans with Oncentra showed significant better in the hight dosage volume, such as maximum dose and D2%. For the bladder wall, there were not notable difference among three groups, although Pinnacle and Oncentra systems provided a little lower dose sparing at V50Gy of bladder and rectal wall and V40Gy of bladder wall, respectively. V40Gy of rectal wall (p=0.037), small intestine (p=0.001 for V30Gy, p=0.010 for maximum dose) and V50Gy of right-femoral head (p=0.019) from Eclipse plans showed significant better than other groups. Conclusion: All SIB-IMRT plans were clinically acceptable which were generated by three commercial TPSs. The plans with Eclipse system showed advantages over the plans with Oncentra and Pinnacle system in the overwhelming majority of the dose coverage for targets and dose sparing of OARs in cervical cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Measurements of multiplicity fluctuations of identified hadrons in inelastic proton–proton interactions at the CERN Super Proton Synchrotron

Measurements of multiplicity fluctuations of identified hadrons produced in inelastic p+p interactions at 31, 40, 80, and 158 $\text {Ge}\text {V}/c$ beam momentum are presented. Three different measures of multiplicity fluctuations are used: the scaled variance $\omega $ and strongly intensive measures $\Sigma $ and $\Delta $. These fluctuation measures involve second and first moments of joint multiplicity distributions. Data analysis is preformed using the Identity method which corrects for incomplete particle identification. Strongly intensive quantities are calculated in order to allow for a direct comparison to corresponding results on nucleus–nucleus collisions. The results for different hadron types are shown as a function of collision energy. A comparison with predictions of string-resonance Monte-Carlo models: Epos, Smash and Venus, is also presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Evaluation of Graph Pattern Matching Workloads in Graph Analysis Systems

Graph analysis has emerged as a powerful method for data scientists to represent, integrate, query, and explore heterogeneous data sources. As a result, graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, the number of emerging graph analysis systems and the wide range of applications, coupled with a lack of apples-to-apples comparisons, make it difficult to understand the trade-offs between different systems and the graph operations for which they are designed. A fair comparison of these systems is a challenging task for the following reasons: multiple data models, non-standardized serialization formats, various query interfaces to users, and diverse environments they operate in. To address these key challenges, in this paper we present a new benchmark suite by extending the Lehigh University Benchmark (LUBM) to cover the most common capabilities of various graph analysis systems. We provide the design process of the benchmark, which generalizes the workflow for data scientists to conduct the desired graph analysis on different graph analysis systems. Equipped with this extended benchmark suite, we present performance comparison for nine subgraph pattern retrieval operations over six graph analysis systems, namely NetworkX, Neo4j, Jena, Titan, GraphX, and uRiKA. Through the proposed benchmark suite, this study reveals both quantitative and qualitative findings in (1) implications in loading data into each system; (2) challenges in describing graph patterns for each query interface; and (3) different sensitivity of each system to query selectivity. We envision that this study will pave the road for: (i) data scientists to select the suitable graph analysis systems, and (ii) data management system designers to advance graph analysis systems.

Hong, Seokyong↗
DOE OSTI2023

Noble-Lab/crema

Confidence Estimation for Mass Spectrometry Proteomics. Crema is an open-source Python tool that estimates the false discovery rate (FDR) of a set of peptide-spectrum matches that result from proteomics data analysis. Crema implements standard FDR estimation methods that are widely used within the field. This package is meant to be simple, easy to use, stand-along, and easy to maintain.

See, Donavan↗
DOE OSTI2020

PARAFAC_T1

This is a method of performing trilinear analysis on large data sets using a modification of the PARAFAC-ALS algorithm. It iteratively decomposes the data matrix into a core matrix and three loading matrices based on the Tucker1 model. The algorithm is particularly useful for data sets that are too large to upload into a computer?s main memory. While the performance advantage in utilizing our algorithm is dependent on the number of data elements and dimensions of the data array, we have seen a significant performance improvement over operating PARAFAC-ALS on the full data set. SAND2020-12649 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗
DOE OSTI2020

Examining Perceptions of Spanish Language Health Information Among Hispanics Living in the United States: A Qualitative Study Assessing Videos, Brochures, and Websites

Background Nationally, Hispanics experience health disparities manifested as a higher prevalence of chronic diseases in comparison to non-Hispanic Whites. Factors that influence health disparities in this population include inadequate dissemination of culturally and linguistically appropriate health information. Method Our qualitative analysis is based on data obtained from three focus groups with Spanish-speaking Hispanic adults conducted at a clinic in Nebraska. Participants were asked about their perceptions of health information in the form of videos, brochures, and websites. We followed the consolidated criteria for reporting qualitative research and used the theoretical framework of content analysis. Results For videos, attributes associated with higher levels of trust included a friendly health professional, the logo or name of a health care institute, and a reference specifying “se habla Español.” For brochures, attributes associated with trust included use of visual aids, use of numerous colors and big font sizes, the year of printing, and a health care agency logo. For websites, characteristics associated with trust were inclusion of plain language, use of pictures and videos, and date of last update. In all focus groups, participants perceived the use of mixed English/Spanish language in health information from pamphlets and websites as unprofessional. The use of unknown governmental logos in health promotional videos and websites was perceived as untrustworthy information. Conclusions Spanish-speaking Hispanics prefer health information supported by a health care agency or delivered by a health care professional. Health communication strategies should avoid the use of mixed English/Spanish language in sensitive information especially when combined with governmental logos.

De Alba Rosales, Armando↗
DOE OSTI2021

Evaluating proxies for the drivers of natural gas productivity using machine-learning models

We report the extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.

58 GEOSCIENCES↗
DOE OSTI2026

Data for Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process- Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process- based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data- constrained representation of the sterile triploid Miscanthus (IL clone) within the process- based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam- derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field- measured biomass. Building on the calibrated operating state, parameter- response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post- calibration GPP R2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

Carbon↗
DOE OSTI2013

A methodology to quantify the release of spent nuclear fuel from dry casks during security-related scenarios

Assessing the risk to the public and the environment from a release of radioactive material produced by accidental or purposeful forces/environments is an important aspect of the regulatory process in many facets of the nuclear industry. In particular, the transport and storage of radioactive materials is of particular concern to the public, especially with regard to potential sabotage acts that might be undertaken by terror groups to cause injuries, panic, and/or economic consequences to a nation. For many such postulated attacks, no breach in the robust cask or storage module containment is expected to occur. However, there exists evidence that some hypothetical attack modes can penetrate and cause a release of radioactive material. This report is intended as an unclassified overview of the methodology for release estimation as well as a guide to useful resource data from unclassified sources and relevant analysis methods for the estimation process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Using Field-Metered Data to Quantify Annual Energy Use of Portable Air Conditioners

As many regions of the United States experience rising temperatures, consumers have come to rely increasingly on cooling appliances (including portable air conditioners) to provide a comfortable indoor temperature. Home occupants sometimes use a portable air conditioner (PAC) to maintain a desired indoor temperature in a single room or enclosed space. Although PACs in residential use are few compared to centrally installed and room air conditioning (AC) units, the past few years have witnessed an increase of PACs use throughout the United States. There is, however, little information and few research projects focused on the energy consumption and performance of PACs, particularly studies that collect information from field applications of PACs. The operation and energy consumption of PACs may differ among geographic locations and households, because of variations in cooling load, frequency, duration of use, and other user-selected settings. In addition, the performance of building envelope (thermal mass and air leakage) as well as inter-zonal mixing within the building would substantially influence the ability to control and maintain desirable indoor thermal conditions. Lawrence Berkeley National Laboratory (LBNL) conducted an initial field-metering study aimed at increasing the knowledge and data related to PAC operation and energy consumption in the United States. LBNL performed its field-metering study from mid-April to late October 2014. The study, which monitored 19 sites in the Northeastern United States (4 in upstate New York and 15 near Philadelphia), collected real-time data on PAC energy consumption along with information regarding housing characteristics, consumer behavior, and environmental conditions that were expected to affect PAC performance. Given the limited number of test sites, this study was not intended to be statistically representative of PAC users in the United States but rather to understand the system response to the cooling demand and to some extent, the operating hours of the studied units. Specifically, the primary objectives of the field-metering study were to (1) expand knowledge of the installation, energy consumption profiles, consumer patterns of use, and environmental parameters related to PAC use; (2) develop distributions of hours of PAC operation for three operating modes: standby, 1 fan-only, and cooling; and (3) describe how individual consumers’ selection of PAC capacity, the area of the space to be cooled, the temperature set point, and environmental conditions affect energy use. Beginning to understand the energy consumption of PACs operating in American homes and commercial settings will help develop a more accurate energy use profile that characterizes relevant variables. This report on LBNL’s field-metering study of PAC energy use describes: a general definition of a PAC and how it operates (section 2); current practices and sources of data for estimating PAC energy use (section 3); the process LBNL used to select field-metering sites, along with characteristics of the sites and the PACs studied (section 4); data collection methods and instrumentation (section 5); analysis methods (section 6); results and discussion (section 7); and conclusions (section 8).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Messaging for Impact: Behavioral Science-Based Communication Strategies to Advance Energy Efficiency

Currently, five legal authorities require U.S. federal agencies to purchase energy-efficient products. However, compliance with these requirements has consistently been low (~55%). Recent surveys indicate that energy efficiency requirements are not communicated properly within federal agencies and energy-efficiency is not a top priority. In the constantly evolving energy industry, with many solutions for a clean energy future, effective communication methods are vital for widespread adoption of energy efficiency. We have been conducting experiments within the U.S. federal sector that test the effectiveness of messaging strategies developed by using behavioral science principles like loss aversion, framing, and herd behavior. This will identify effective communications methods that encourage energy efficiency adoption. In this study, we measure the effectiveness of different communication strategies by monitoring the web traffic of hyperlinks placed within our digital communications and outreach materials. To determine effective strategies that influence federal buyer behavior, the web traffic from subjects that received the behavioral science-based messaging (treatment group) is compared to that from subjects that received regular messaging (control group) for each of the strategies. This paper provides an overview of the behavioral science principles that we applied, data collection methods, and results of our analysis. Since the lack of effective communication of energy-efficiency policies is also a common problem in the private sector, the messaging strategies developed in this study can be applicable to other large private sector institutions

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Comprehensive Chemical Fingerprinting by Multidimensional GC and Supervised Machine Learning

This project leverages advances in machine learning based data analysis techniques and untargeted omic analytical methods to progress nuclear nonproliferation technologies beyond current capabilities. The developed approaches can be used to identify and detect complex chemical fingerprints of facilities of interest. These techniques have been developed for fields such as metabolomics and genomics but have not been applied to nuclear nonproliferation applications. Adaptation of these techniques for volatile organic compound analysis has far reaching application within the scientific community including environmental chemistry, atmospheric physics, and climate sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Milestone 7714 National Opacity Program: High-Fidelity Iron Data on NIF

Establish the level of reproducibility of NIF iron opacity shots, quantify to-date improvements in data quality through, e.g. background reduction, improved signal, and improved analysis methods, and formalize the approach that will be taken in the future to compare NIF data, Z data, and theory.

74 ATOMIC AND MOLECULAR PHYSICS↗