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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Reduced Magnetization and Loss in Ag-Mg Sheathed Bi2212 Wires: Systematics With Sample Twist Pitch and Length

Suppression of magnetization and effective filament diameter (d eff ) with twisting was investigated for a series of recent Bi2212 strands manufactured by Oxford Superconducting Technologies. We measured magnetization as a function of field (out to 14 T), at 5.1 K, of twisted and nontwisted 37 × 18 double restack design strands. The samples were helical coils 5-6 mm in height and approximately 5 mm in diameter. The strand diameter was 0.8 mm. The magnetization of samples having twist pitches of 25.4, 12.7, and 6.35 mm were examined and compared to nontwisted samples of the same filament configuration. The critical state model was used to extract the 12-T d eff from magnetization data for comparison. Twisting the samples reduced d eff by a factor of 1.5-3. The d eff was shown to increase both with L and L p . Mathematical expressions, based upon the anisotropic continuum model, were fit to the data, and a parameter γ 2 , which quantifies the electrical connectivity perpendicular to the filament axis, was extracted. The bundle-to-bundle connectivity along the radial axis was found to be approximately 0.2%. The d eff was substantially reduced with L p . In addition, the importance of understanding sample length dependence for quantitative measurements is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multimaterial Aerosol Jet Printed Magnetic Nanocomposites for Microwave Circuits

Here this article demonstrates the process and characterization of additively manufactured polymer-matrix magnetic nanocomposites. These nanocomposites are composed of nickel-zinc ferrite (Ni 0.5 Zn 0.5 Fe 2 O 4 ) nanoparticles in a polyimide matrix. These materials are printed using multimaterial aerosol jet printing where aerosols of nickel-zinc ferrite and polyimide inks are mixed in place to fabricate structures with nonunity relative permeability. To characterize these materials, we use energy-dispersive X-ray spectroscopy to estimate the content of ferrite in the composite, fitted simulation models, and ring resonator measurements to estimate the permeability. In fully aerosol jet printed 1-mm 2 inductors, we demonstrate an increase in inductance density of 40% in a 4.5-turn microstrip spiral inductor using this printed composite. These fully printed inductors demonstrate an inductance density of 4.2-nH/mm 2 . EDS analysis shows films with 18 wt.% Ni 0.5 Zn 0.5 Fe 2 O 4 . Finally, we show microstrip transmission lines on printed composite compared to the same on polyimide subjected to a dc magnetic bias, displaying a magnetic resonance behavior.

42 ENGINEERING↗
DOE OSTI2021

An Automatic Processing Framework for In Situ Determination of Ecohydrological Root Water Content by Ground-Penetrating Radar

This report concerns Root water content (RWC) as a vital component in water flux in soil-plant-atmosphere continuum. Knowledge of RWC helps to better understand the root function and the soil-root interaction and improves water cycle modeling. However, due to the lack of appropriate methods, field monitoring of RWC is seriously constrained. In this study, we used ground-penetrating radar (GPR), a common geophysical technique, to characterize RWC of coarse roots noninvasively. An automatic GPR data processing framework was proposed to (1) identify hyperbolic root reflections and locate roots in GPR images and (2) extract waveform parameters from the reflected wave of identified roots. These waveform parameters were then used to establish an empirical model and a semiempirical model to determine RWC. We validated the developed models using GPR root data at three antenna center frequencies (500 MHz, 900 MHz, and 2 GHz) that were produced from simulation experiments (with RWC ranging from 70% to 150%) and field experiments in sandy soils (with RWC ranging from 66% to 144%). Our results show that both the empirical and the semiempirical models achieved a good performance in estimating RWC with similar accuracy, i.e., the prediction error [root-mean-square error (RMSE)] was less than 8% for the simulation data and 12% for the field data. For both models, the accuracy of RWC estimation was the highest when applied to 2-GHz data. This study renders a new opportunity to determine RWC under field conditions that enhances the application of GPR for root study and the understanding and modeling of ecohydrology in the rhizosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Flash Heating of a Hollow Rod to Determine Thermal Properties

In a nuclear reactor, prolonged exposure to high neutron flux levels can change material properties of reactor components. Due to the danger posed to personnel by highly radioactive components such as spent fuel, noncontact methods must be used to determine these properties. The objective of the current research is to test the viability of using a flash-heating method to determine the thermal diffusivity of a silicon carbide (SiC) tube, which could be used as cladding for a nuclear fuel rod. The flash-heating experiment was conducted at Oak Ridge National Laboratory and temperature results were analyzed as a two-Dimensional cross section which assumed axial symmetry. The model was fitted iteratively to the temperature measurements using nonlinear regression which required values for thermal diffusivity, Biot number and a term to determine the magnitude of the heat absorbed by the flash. The method could be expanded in the future to estimate the thermal diffusivity of spent nuclear fuel rods in an effort to increase the certainty of new reactor designs using precise thermal properties throughout the core life. A statistical analysis of the results of this work is provided as part of the analysis.

McMasters, Robert L [Virginia Military Institute, ↗
DOE OSTI2011

Pressure Drop as a Function of Air Flow Rate for Roll-Punched Transpired Solar Collectors With Different Porosities

Transpired solar collectors are simple and cost-effective devices suited for preheating ventilation air for buildings. They work by drawing outside air through a south-facing perforated metal absorber. The absorber is heated by solar radiation, heat is transferred to the air as it is drawn through the perforations in the absorber, and the pre-heated air is delivered to the building. Oftenoverlooked in the design of transpired collector systems is the effect on collector efficiency of pressure drop across the absorber. The pressure drop is a function of the air flow rate through the perforations, the overall porosity of the absorber, and the properties of the air. The National Renewable Energy Laboratory (NREL) conducted a series of laboratory tests to characterize the pressuredrop as a function of the relevant parameters for six commercially-available absorber configurations; three porosities in aluminum and three in steel. Each absorber material is roll-punched to one of three depths to create absorbers with three different porosities. Pressure drop, air flow rate, air temperature, and air relative humidity data were collected during the tests. The data were fit toa model that can be used to predict pressure drop across the absorber as a function of air properties and flow rate. Use of these correlations is expected to aid designers in ensuring that transpired collector systems are designed for optimal thermal efficiency and cost effectiveness.

14 SOLAR ENERGY↗
DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗
DOE OSTI2012

Impact of flat panel-imager veiling glare on scatter-estimation accuracy and image quality of a commercial on-board cone-beam CT imaging system

Purpose: The purposes of this study is to measure the low frequency drop (LFD) of the modulation transfer function (MTF), associated with the long tails of the detector point spread function (PSF) of an on-board flat panel imager and study its impact on cone-beam CT (CBCT) image quality and scatter measurement accuracy. Methods: Two different experimental methods were used to characterize LFD and its associated PSF of a Varian OBI flat-panel detector system: the edge response function (ERF) method and the disk transfer function (DTF) method. PSF was estimated by fitting parametric models to these measurements for four values of the applied voltage (kVp). The resultant PSF was used to demonstrate the effect of LFD on image contrast and CT number accuracy in CBCT images reconstructed from synthetic datasets, as well as, accuracy of scatter measurements with the beam-stop method. Results: The MTFs derived from the measured ERF data revealed LFDs varying from 8% (at 60 kVp) to 10.5% (at 120 kVp), while the intensity of the long PSF tails was found to increase with increasing kVp. The veiling glare line spread functions derived from the ERF and DTF methods were in excellent agreement. Uncorrected veiling glare reduced contrast and the image intensity in CBCT reconstruction, near the phantom periphery (by 67 Hounsfield units in a 20 cm-in-diameter water phantom) and (to a smaller degree) near inhomogeneities. Use of the bow-tie filter mitigated these effects. Veiling glare also resulted in about 10%-15% overestimation of the scatter-to-primary ratio when measured with the beam-stop or beam-stop array method. Conclusions: The long tails of the detector PSF were found to have a modest dependence of beam spectrum, which is reflected on the MTF curve LFD. Our findings show that uncorrected veiling glare can affect quantitative accuracy and contrast in CBCT imaging, based on flat panel imager. In addition, it results in overestimation of the scatter-to-primary ratio, measured with the beam-stop methods.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

TH-E-BRF-04: Characterizing the Response of Texture-Based CT Image Features for Quantification of Radiation-Induced Normal Lung Damage

Purpose: Radiation induced lung damage (RILD) is an important dose-limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time-related response for texture-based lung CT image features that serve as potential quantitative measures of RILD. Methods: Pre- and post-RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre-RT and available follow-up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture-based features (including co-occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre-RT), planned dose and time post-RT. Results: Seventy-three follow-up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features. Conclusion: Characterizing the dose and time-related response of a large number of texture-based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-105: The Predicted Radiation Response of Non-Small Cell Lung Cancer for SRS, SBRT and HDR Brachytherapy

Purpose: The data on the α/β ratio of non-small cell lung cancer (NSCLC) is scarce in the literature. We have previously proposed a generalized LQ (gLQ) model to address the high dose dilemma of the LQ model. In this study, we applied the gLQ model to both the patients and in vitro cell irradiation data treated with a large range of doses, and investigated the α/β ratio in NSCLC. Methods: 150 patients with T1T2 and non-T1T2 stages were treated with stereotactic body radiotherapy (SBRT). In vitro datasets of 14 NSCLC cell lines from the National Cancer Institute published in Eur J Cancer Clin Oncol. 25(3):527–534 (1989) and 7 NSCLC cell lines published in Cancer Res 57:4285–300 (1997) were included. The gLQ model was used to fit datasets. The least χ2 method was adopted to determine the goodness of fit. Errors of the model parameters were determined by propagating minimal χ2. The α/β ratios from both the patients and these in vitro NSCLC cell lines were obtained. Results: The average of α/β ratios for T1T2 and non-T1T2 NSCLC was 1.45 Gy. The same type of cell lines irradiated with different modalities but almost the same dose rate yielded approximately the same α/β ratio. The average of α/β ratios for NSCLC cell lines in this study was 5.45 Gy. Conclusion: The difference in the α/β ratios between the patients and in vitro cell data is expected and the lower α/β ratio for patients suggests the higher radiosensitivity, which could be associated with higher tumor perfusion or other tumor microenvironmental effects. The α/β ratios derived from the gLQ model can be used in high dose regions or high fraction sizes and are useful to extend our clinical experience accumulated from conversional low-dose fractionation to high dose irradiation schedules.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-DE-201-07: Measurement of Real-Time Dose for Tandem and Ovoid Brachytherapy Procedures Using a High Precision Optical Fiber Radiation Detector

Purpose: Development of a novel on-line dosimetry tool is needed to move toward patient-specific quality assurance measurements for Ir-192 HDR brachytherapy to verify accurate dose delivery to the intended location. This work describes the development and use of a nano-crystalline yttrium oxide inorganic scintillator based optical-fiber detector capable of acquiring real-time high-precision dose measurements during tandem and ovoid (T&O) gynecological (GYN) applicator Ir-192 HDR brachytherapy procedures. Methods: An optical-fiber detector was calibrated by acquiring light output measurements in liquid water at 3, 5, 7, and 9cm radial source-detector-distances from an Ir-192 HDR source. A regression model was fit to the data to describe the relative light output per unit dose (TG-43 derived) as a function of source-detector-distance. Next, the optical-fiber detector was attached to a vaginal balloon fixed to a Varian Fletcher-Suit-Delclos-style applicator (to mimic clinical setup), and localized by acquiring high-resolution computed tomography (CT) images. To compare the physical point dose to the TPS calculated values (TG-43 and Acuros-BV), a phantom measurement was performed, by submerging the T&O applicator in a liquid water bath and delivering a treatment template representative of a clinical T&O procedure. The fiber detector collected scintillation signal as a function of time, and the calibration data was applied to calculate both real-time dose rate, and cumulative dose. Results: Fiber cumulative dose values were 100.0cGy, 94.3cGy, and 348.9cGy from the tandem, left ovoid, and right ovoid dwells, respectively (total of 443.2cGy). A plot of real time dose rate during the treatment was also acquired. The TPS values at the fiber location were 458.4cGy using TG-43, and 437.6cGy using Acuros-BV calculated as Dm,m (per TG-186). Conclusion: The fiber measured dose value agreement was 3% vs TG-43 and −1% vs Acuros-BV. This fiber detector opens up new possibilities for performing patient-specific quality assurance for Ir-192 HDR GYN procedures. Funding from Coulter Foundation, Duke Bio-medical Engineering. Company is being created around the detector technology. Duke holds patents on the technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Rheological Properties of Enzymatically Hydrolyzed Corn Stover Pretreated via Deacetylation and Mechanical Refining

Lignocellulosic biomass is a feedstock for fuels and chemicals that does not compete with food resources and has less contaminants than refuse-derived biomass feedstocks. To convert lignocellulosics to biofuels or value-added products, multiple processing steps are typically necessary. One method of producing biofuels from lignocellulosic biomass utilizes a deacetylation and mechanical refining pretreatment and an enzymatic hydrolysis reaction to produce fermentable sugars from cellulose and hemicellulose. The rheological properties of biomass, such as yield stress and plastic viscosity, change during enzymatic hydrolysis and alter the energy requirements for pumping and mixing, an important consideration for the design of processing equipment. The dynamic changes in rheological properties that occur in a corn stover feedstock undergoing enzymatic hydrolysis are characterized in this work, and the influence on pressure losses in piping systems is estimated. Two rheometer geometries were fabricated with stereolithography 3D printing to reduce wall slip and sample ejection. The slurries have complex rheological behaviors that include shear-thinning behavior. Shear stress ramps were performed on samples at 20 and 50 degrees C using the custom geometries, and the Herschel-Bulkley model was fit to the data. The dynamic nature of the rheological properties is correlated with changes in the average fiber length at various extents of reaction, and the influence of solids concentration on the observed rheology and piping pressure losses is discussed.

09 BIOMASS FUELS↗
DOE OSTI2023

Thermal adaptation of soil microbial growth traits in response to chronic warming

ABSTRACT Adaptation of soil microbes due to warming from climate change has been observed, but it remains unknown what microbial growth traits are adaptive to warming. We studied bacterial isolates from the Harvard Forest Long-Term Ecological Research site, where field soils have been experimentally heated to 5°C above ambient temperature with unheated controls for 30 years. We hypothesized that Alphaproteobacteria from warmed plots have (i) less temperature-sensitive growth rates; (ii) higher optimum growth temperatures; and (iii) higher maximum growth temperatures compared to isolates from control plots. We made high-throughput measurements of bacterial growth in liquid cultures over time and across temperatures from 22°C to 37°C in 2–3°C increments. We estimated growth rates by fitting Gompertz models to the growth data. Temperature sensitivity of growth rate, optimum growth temperature, and maximum growth temperature were estimated by the Ratkowsky 1983 model and a modified Macromolecular Rate Theory (MMRT) model. To determine evidence of adaptation, we ran phylogenetic generalized least squares tests on isolates from warmed and control soils. Our results showed evidence of adaptation of higher optimum growth temperature of bacterial isolates from heated soils. However, we observed no evidence of adaptation of temperature sensitivity of growth and maximum growth temperature. Our project begins to capture the shape of the temperature response curves, but illustrates that the relationship between growth and temperature is complex and cannot be limited to a single point in the biokinetic range. IMPORTANCE Soils are the largest terrestrial carbon sink and the foundation of our food, fiber, and fuel systems. Healthy soils are carbon sinks, storing more carbon than they release. This reduces the amount of carbon dioxide released into the atmosphere and buffers against climate change. Soil microbes drive biogeochemical cycling and contribute to soil health through organic matter breakdown, plant growth promotion, and nutrient distribution. In this study, we determined how soil microbial growth traits respond to long-term soil warming. We found that bacterial isolates from warmed plots showed evidence of adaptation of optimum growth temperature. This suggests that increased microbial biomass and growth in a warming world could result in greater carbon storage. As temperatures increase, greater microbial activity may help reduce the soil carbon feedback loop. Our results provide insight on how atmospheric carbon cycling and soil health may respond in a warming world.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

New method of automated statistical analysis of polymer-stabilized metal nanoparticles in electron microscopy images

The efficiency of the synthesis and application of metal/nonmetal particles several nanometers in size, prepared in solutions and stabilized with polymers, can be increased by applying a reliable statistical analysis and determining particle-size distributions for different systems. A new method for processing electron microscopy images of nanoparticles ≤10 nm in size and the program Analyzer of Nanoparticles designed for rapid recognition and measurements are presented. The program combines two approaches: threshold image processing for particle recognition and fitting the model of the particles with neighborhood background to the real experimental images.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

The same key to different doors—temperature puzzles

The notion of temperature in many body elementary particle processes is in a common use for decades. Thermal models have become simple and universal effective tools to describe particle production—not only in high energy heavy ion collisions but also in high energy elementary particle collisions. We perform a critical analysis of the temperature concepts in such processes. Although the temperature concept is a very useful tool, nevertheless it should be used with the care, taking into account that usually it is just model dependent fitted parameter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

The Network Completion Problem: Inferring Missing Nodes and Edges in Networks

Network structures, such as social networks, web graphs and networks from systems biology, play important roles in many areas of science and our everyday lives. In order to study the networks one needs to first collect reliable large scale network data. While the social and information networks have become ubiquitous, the challenge of collecting complete network data still persists. Many times the collected network data is incomplete with nodes and edges missing. Commonly, only a part of the network can be observed and we would like to infer the unobserved part of the network. We address this issue by studying the Network Completion Problem: Given a network with missing nodes and edges, can we complete the missing part? We cast the problem in the Expectation Maximization (EM) framework where we use the observed part of the network to fit a model of network structure, and then we estimate the missing part of the network using the model, re-estimate the parameters and so on. We combine the EM with the Kronecker graphs model and design a scalable Metropolized Gibbs sampling approach that allows for the estimation of the model parameters as well as the inference about missing nodes and edges of the network. Experiments on synthetic and several real-world networks show that our approach can effectively recover the network even when about half of the nodes in the network are missing. Our algorithm outperforms not only classical link-prediction approaches but also the state of the art Stochastic block modeling approach. Furthermore, our algorithm easily scales to networks with tens of thousands of nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Generalized Canonical Polyadic Tensor Decomposition

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback--Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. Furthermore, we demonstrate the flexibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

97 MATHEMATICS AND COMPUTING↗