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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

A Goal Seeking Strategy for Constructing Systems from Alternative Components

This paper describes a methodology to efficiently construct feasible systems then modify feasible systems to meet successive goals by selecting from alternative components, a problem recognized to be n-p complete. The methodology provides a means to catalog and model alternative components. A presented system modeling Structure is robust enough to model a wide variety of systems and provides a means to compare and evaluate alternative systems. These models act as input to a methodology for selecting alternative components to construct feasible systems and modify feasible systems to meet design goals and objectives. The presented algorithm's ability to find a restricted solution, as defined by a unique set of requirements, is demonstrated against an exhaustive search of a sample of proposed shuttle modifications. The utility of the algorithm is demonstrated by comparing results from the algorithm with results from three NASA shuttle evolution studies using their value systems and assumptions.

Valentine, Mark E.↗
DOE OSTI2022

High-throughput detection of T-DNA insertion sites for multiple transgenes in complex genomes

Abstract Background Genetic engineering of crop plants has been successful in transferring traits into elite lines beyond what can be achieved with breeding techniques. Introduction of transgenes originating from other species has conferred resistance to biotic and abiotic stresses, increased efficiency, and modified developmental programs. The next challenge is now to combine multiple transgenes into elite varieties via gene stacking to combine traits. Generating stable homozygous lines with multiple transgenes requires selection of segregating generations which is time consuming and labor intensive, especially if the crop is polyploid. Insertion site effects and transgene copy number are important metrics for commercialization and trait efficiency. Results We have developed a simple method to identify the sites of transgene insertions using T-DNA-specific primers and high-throughput sequencing that enables identification of multiple insertion sites in the T 1 generation of any crop transformed via Agrobacterium . We present an example using the allohexaploid oil-seed plant Camelina sativa to determine insertion site location of two transgenes. Conclusion This new methodology enables the early selection of desirable transgene location and copy number to generate homozygous lines within two generations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Empirical radius formulas for canonical neutron stars from bidirectionally selecting features of equations of state in extended Bayesian analyses of observational data

Significant advancement in Bayesian inference of nuclear equation of state (EOS) from gravitational wave and x-ray observations of neutron stars (NSs) has been made by the nuclear astrophysics community especially since GW170817. By extending the traditional Bayesian analysis which normally ends at presenting the marginalized posterior probability distribution functions (PDFs) of individual EOS parameters and their correlations (or sometimes only the Pearson correlation coefficients which are only reliably useful when the variables are linearly correlated while they are actually often not), we search for a data-driven and robust empirical formula for the radius 𝑅 1.4 of canonical NSs in terms of the characteristic EOS parameters (features). We also identify the single most important but currently poorly known EOS parameter for determining the 𝑅 1.4 . Using three regression-model-building methodologies: bidirectional stepwise feature selection, least absolute shrinkage selection operator (LASSO) regression, and neural network regression on a large set of posterior EOSs and the corresponding 𝑅 1.4 values inferred from earlier comprehensive Bayesian analyses of NS observational data, we systematically and rigorously develop the most probable 𝑅 1.4 formulas with varying statistical accuracy and technical complexity. Here, the most important EOS parameters for determining 𝑅 1.4 are found consistently in each of the feature selection processes to be (in order of decreasing importance): curvature 𝐾 sym , slope 𝐿, skewness 𝐽 sym of nuclear symmetry energy, skewness 𝐽 0 , incompressibility 𝐾 0 of symmetric nuclear matter, and the magnitude 𝐸 sym ⁡(𝜌 0 ) of symmetry energy at the saturation density 𝜌 0 of nuclear matter.

Bayesian methods↗
NASA NTRS2019

Understanding Machine Learning in Earth Science: A Natural Language Processing Approach

Machine learning (ML) is being increasingly utilized in Earth science research. Benefits of ML include efficiency, reduction of human error, and ability to extract hidden patterns within data. However, the mutual lack of each other’s domain knowledge by ML and Earth science stands as a barrier to timely and effective implementation. Earth science, in particular, faces challenges in generating sample data, compared to those of traditional ML problems such as face recognition or stock predictions, where data is abundant and not lacking in ground truth, which is necessary for labeling. Earth science data are more varying in formats, such as HDF5 and image resolutions, and are not standardized across instruments, even within a given Earth science discipline. Previous studies have been done to outline the specific challenges that Earth science faces with ML, while others have focused on using existing publications to mine information efficiently. Other resources such as Scikit-Learn have developed decision trees for choosing appropriate machine learning algorithms, but application within Earth science subjects becomes much more complex. For the current study, we propose a methodology and tool that aids in implementation of ML in Earth science using natural language processing (NLP). Our work comprises three main parts: (1) analyzing existing publications related to ML and Earth science, using natural language processing: (2) extracting from the publications information on ML models subjects in Earth Science: and (3) visualizing the extracted relationships as a network graph. The resulting network graph should aid the Earth science communities in applying optimal ML algorithms and guiding data preparation through visualization of similar studies. The network graph and analysis of document similarity will be the basis of our next step, which is to develop a decision tree for selecting optimal machine learning methodologies for specified Earth science applications.

Zheng, Laura↗
DOE OSTI2011

Quasi-simultaneous Measurements of Ionic Currents by Vibrating Probe and pH Distribution by Ion-selective Microelectrode

This work reports a new methodology to measure quasi-simultaneously the local electric fields and the distribution of specific ions in a solution via selective microelectrodes. The field produced by the net electric current was detected using the scanning vibrating electrode technique (SVET) with quasi-simultaneous measurements of pH with an ion-selective microelectrode (pH-SME). The measurements were performed in a validation cell providing a 48 ?m diameter Pt wire cross section as a source of electric current. A time lag between acquiring each current density and pH data-point was 1.5 s due to the response time of pH-SME. The quasi-simultaneous SVET-pH measurements that correlate electrochemical oxidation-reduction processes with acid-base chemical equilibria are reported for the first time. No cross-talk between the vibrating microelectrode and the ion-selective microelectrode could be detected under given experimental conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Detecting multi‐scale riverine topographic variability and its influence on Chinook salmon habitat selection

Abstract Quantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1‐m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid‐wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley‐scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle‐pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small‐scale wavelet power, which is related to pool‐riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel‐unit identification, and provided geomorphic assessment without laborious field surveys.

Duffin, Jenna↗
DOE OSTI2024

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analytical Tools to Assess Polymer Biodegradation: A Critical Review and Recommendations

Many petroleum-derived plastic materials are highly recalcitrant and persistent in the environment, posing significant threats to human and ecological receptors due to their accumulation in ecosystems. In recent years, research efforts have focused on advancing biological methods for polymer degradation. Enzymatic depolymerization has emerged as particularly relevant for biobased plastic recycling, potentially scalable for industrial use. Biodegradation involves adsorption to the plastic solid surface, followed by an interfacial reaction, resulting in cleavage of bonds of polymer chains exposed on the surface. Here, widely varying substrate-specific kinetics are observed, with the polymer’s properties possessing a significant impact on the rate of this interfacial catalysis. Thus, there is a critical need for sensitive and accurate characterization of the material surface during and after interfacial depolymerization to fully understand the reaction mechanisms. Here, we provide a critical review of a range of techniques used in the analysis of material surfaces to characterize the chemical, topological, and morphological features relevant to the study of enzymatic biocatalysis, including microscopy techniques, spectroscopic techniques (e.g., X-ray diffraction analysis, Fourier transform infrared attenuated total reflectance spectroscopy, and mass spectrometry detection of analytes associated with degradation). Techniques for evaluation of surface energy and topology in their relevancy for sensitive detection of biological surface modifications are also discussed. In addition, this paper provides an overview of the strengths of these techniques and compares their performance in both sensitivity and throughput, including emerging techniques, which can be useful, particularly for the rapid analysis of the surface properties of polymeric materials in high-throughput screening of candidate biocatalysts. This research serves as a starting point in selecting and applying appropriate methodologies that provide direct evidence to the ongoing biotic degradation of polymeric materials.

Colachis, Matthew↗
DOE OSTI2017

The effect of angiotensin receptor neprilysin inhibitor, sacubitril/valsartan, on central nervous system amyloid-β concentrations and clearance in the cynomolgus monkey

Sacubitril/valsartan (LCZ696) is the first angiotensin receptor neprilysin inhibitor approved to reduce cardiovascular mortality and hospitalization in patients with heart failure with reduced ejection fraction. As neprilysin (NEP) is one of several enzymes known to degrade amyloid-β (Aβ), there is a theoretical risk of Aβ accumulation following long-term NEP inhibition. The primary objective of this study was to evaluate the potential effects of sacubitril/valsartan on central nervous system clearance of Aβ isoforms in cynomolgus monkeys using the sensitive Stable Isotope Labeling Kinetics (SILK™)-Aβ methodology. The in vitro selectivity of valsartan, sacubitril, and its active metabolite sacubitrilat was established; sacubitrilat did not inhibit other human Aβ-degrading metalloproteases. In a 2-week study, sacubitril/valsartan (50 mg/kg/day) or vehicle was orally administered to female cynomolgus monkeys in conjunction with SILK™-Aβ. Despite low cerebrospinal fluid (CSF) and brain penetration, CSF exposure to sacubitril was sufficient to inhibit NEP and resulted in an increase in the elimination half-life of Aβ1-42 (65.3%; p = 0.026), Aβ1-40 (35.2%; p = 0.04) and Aβtotal (29.8%; p = 0.04) acutely; this returned to normal as expected with repeated dosing for 15 days. CSF concentrations of newly generated Aβ (AUC{sub (0–24} {sub h)}) indicated elevations in the more aggregable form Aβ1-42 on day 1 (20.4%; p = 0.039) and day 15 (34.7%; p = 0.0003) and in shorter forms Aβ1-40 (23.4%; p = 0.009), Aβ1-38 (64.1%; p = 0.0001) and Aβtotal (50.45%; p = 0.00002) on day 15. However, there were no elevations in any Aβ isoforms in the brains of these monkeys on day 16. In a second study cynomolgus monkeys were administered sacubitril/valsartan (300 mg/kg) or vehicle control for 39 weeks; no microscopic brain changes or Aβ deposition, as assessed by immunohistochemical staining, were present. Further clinical studies are planned to address the relevance of these findings. - Highlights: • Sacubitril/valsartan reduced Aβ clearance on day 1, but not after 15 daily doses. • Sacubitril/valsartan increased CSF Aβ but did not result in increases in brain Aβ. • No Aβ brain pathology in 39 week monkey study with high dose sacubitril/valsartan.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Estimation of retired mobile phones generation in China: A comparative study on methodology

Highlights: • The sales data of mobile phones in China was revised by considering the amount of smuggled and counterfeit mobile phones. • The estimation of retired mobile phones in China was made by comparing some relevant methods. • The advanced result of estimation can help improve the policy-making. • The method suggested in this paper can be also used in other countries. • Some discussions on methodology are also conducted in order for the improvement. - Abstract: Due to the rapid development of economy and technology, China has the biggest production and possession of mobile phones around the world. In general, mobile phones have relatively short life time because the majority of users replace their mobile phones frequently. Retired mobile phones represent the most valuable electrical and electronic equipment (EEE) in the main waste stream because of such characteristics as large quantity, high reuse/recovery value and fast replacement frequency. Consequently, the huge amount of retired mobile phones in China calls for a sustainable management system. The generation estimation can provide fundamental information to construct the sustainable management system of retired mobile phones and other waste electrical and electronic equipment (WEEE). However, the reliable estimation result is difficult to get and verify. The priority aim of this paper is to provide proper estimation approach for the generation of retired mobile phones in China, by comparing some relevant methods. The results show that the sales and new method is in the highest priority in estimation of the retired mobile phones. The result of sales and new method shows that there are 47.92 million mobile phones retired in 2002, and it reached to 739.98 million in China in 2012. It presents an increasing tendency with some fluctuations clearly. Furthermore, some discussions on methodology, such as the selection of improper approach and error in the input data, are also conducted in order to improve generation estimation of retired mobile phones and other WEEE.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Towards computational design of zeolite catalysts for CO 2 reduction

Carbon dioxide, an energy waste by-product with significant environmental consequences can be utilized and converted into useful chemical products such as formic acid, formaldehyde, methanol or methane, but more energy and cost efficient catalytic processes are required. Here we develop the methodology for the intelligent selection of porous zeolites for dual-adsorption of hydrogen and carbon dioxide as templates for preparing the optimal catalytic environment for carbon dioxide reduction. Useful zeolite catalysts were computationally screened from over 300 thousand zeolite structures using a combination of molecular simulation and machine-learning techniques. Several of the top candidates were very promising energy-efficient templates for catalysis with the potential to perform at 50% above conventional reactors. As a result, it is also found that an optimal cavity size of around 6 Å is required to maximize the change in entropy–enthalpy upon adsorption with a maximum void space >30% to boost product formation per volume of material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

TARGET SELECTION FOR THE APACHE POINT OBSERVATORY GALACTIC EVOLUTION EXPERIMENT (APOGEE)

The Apache Point Observatory Galactic Evolution Experiment (APOGEE) is a high-resolution infrared spectroscopic survey spanning all Galactic environments (i.e., bulge, disk, and halo), with the principal goal of constraining dynamical and chemical evolution models of the Milky Way. APOGEE takes advantage of the reduced effects of extinction at infrared wavelengths to observe the inner Galaxy and bulge at an unprecedented level of detail. The survey's broad spatial and wavelength coverage enables users of APOGEE data to address numerous Galactic structure and stellar populations issues. In this paper we describe the APOGEE targeting scheme and document its various target classes to provide the necessary background and reference information to analyze samples of APOGEE data with awareness of the imposed selection criteria and resulting sample properties. APOGEE's primary sample consists of {approx}10{sup 5} red giant stars, selected to minimize observational biases in age and metallicity. We present the methodology and considerations that drive the selection of this sample and evaluate the accuracy, efficiency, and caveats of the selection and sampling algorithms. We also describe additional target classes that contribute to the APOGEE sample, including numerous ancillary science programs, and we outline the targeting data that will be included in the public data releases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Progress and prospects of thermo-mechanical energy storage – A critical review

The share of electricity generated by intermittent renewable energy sources is increasing (now at 26% of global electricity generation) and the requirements of affordable, reliable and secure energy supply designate grid-scale storage as an imperative component of most energy transition pathways. The most widely deployed bulk energy storage solution is pumped-hydro energy storage (PHES), however, this technology is geographically constrained. Alternatively, flow batteries are location independent and have higher energy densities than PHES, but remain associated with high costs and low lifetimes, which highlights the importance of developing and utilizing additional larger-scale, longer-duration and long-lifetime energy storage alternatives. Here, we review a class of promising bulk energy storage technologies based on thermo-mechanical principles, which includes: compressed-air energy storage (CAES), liquid-air energy storage (LAES) and pumped-thermal electricity storage (PTES). The thermodynamic principles upon which these thermo-mechanical energy storage (TMES) technologies are based are discussed and a synopsis of recent progress in their development is presented, assessing their ability to provide reliable and cost-effective solutions. The current performance and future prospects of TMES systems are examined within a unified framework and a thermoeconomic analysis is conducted to explore their competitiveness relative to each other as well as when compared to PHES and flow battery systems. This includes carefully selected thermodynamic and economic methodologies for estimating the component costs of each configuration in order to provide a detailed and fair comparison at various system sizes. The analysis reveals that the technical and economic characteristics of TMES systems are such that, especially at higher discharge power ratings and longer discharge durations, they can offer promising performance (round-trip efficiencies higher than 60%) along with high lifetimes (> 30 years), low specific costs (often below 100 $/kWh), low ecological footprints and unique sector-coupling features compared to other storage options. TMES systems have significant potential for further progress and the thermoeconomic comparisons in this paper can be used as a benchmark for their future evolution.

25 ENERGY STORAGE↗
DOE OSTI2024

Geographical Insights into Suicide Mortality Through Spatial Machine Learning

Suicide mortality is a leading cause of death in the United States, with an upward trend that emphasizes its significance as a public health issue. Previous research has employed global models like ordinary least squares (OLS) regression and local models such as geographically weighted regression (GWR). While local models are useful for analyzing spatial variations in suicide mortality, they share limitations with traditional global models, particularly about their inability to handle multi-collinearity and non-linear relationships. Machine learning approaches, like random forests (RF), can address some of these limitations but often fail to account for spatial variability. This gap highlights the need for spatial ML models specifically designed to tackle suicide mortality. This research seeks to fill this void by using a geographically weighted random forest model (GWRF) to examine the associations between county-level suicide mortality in the U.S. from 2010 to 2020 and various social and environmental determinants of health. A key aspect of our methodology is disciplined feature selection, which reduces the pool of explanatory variables by about 90%. This refinement enhances the explanatory power of both global (R2 improved from 0.59 to 0.67) and local (R2 improved from 0.64 to 0.67) RF models while reducing their run times. An analysis of the importance scores for these selected features reveals that the drivers of suicide mortality vary by context. Thus, to effectively address regional disparities and inform targeted public health interventions, a holistic approach that incorporates multiple county-level characteristics is essential.

Lebakula, Viswadeep [ORNL] (ORCID:0000000152935914↗
DOE OSTI2014

Selecting Essential Information for Biosurveillance—A Multi-Criteria Decision Analysis

The National Strategy for Biosurveillancedefines biosurveillance as ‘‘the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.’’ However, the strategy does not specify how ‘‘essential information’’ is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being ‘‘essential’’. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of ‘‘essential information’’ for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2011

Electricity Supply Infrastructure Improvements: Final Technical Status Report, December 2010

This report is about a work effort where the overall objectives were to establish a methodology and approach for selected transmission and distribution (T&D) grid modernization; monitor the results; and report on the findings, recommendations, and lessons learned. The work reported addressed T&D problems and solutions, related reliability issues, equipment and operation upgrades, and respectivefield testing.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

Methodology for Safety Conscious Work Environment Self-Assessment at Los Alamos National Laboratory

As part of a DOE complex-wide requirement, Los Alamos National Laboratory must conduct a self-assessment of safety conscious work environment (SCWE) by the end of fiscal year 2013 (FY2013). This report describes the methodology used to randomly select employees and managers for focus groups and individual interviews as part of this assessment. One of the first tasks is to identify the metrics that are used to measure the LANL safety conscious work environment. DOE’s guidance document 450.4-1C provides a listing of attributes that are present in institutions with excellent SCWE. Questions used in the self-assessment surveys and interviews are designed to elicit employee feedback with respect to these attributes.

99 GENERAL AND MISCELLANEOUS↗