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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Structural Comparison of Human Anti-HIV-1 gp120 V3 Monoclonal Antibodies of the Same Gene Usage Induced by Vaccination and Chronic Infection

ABSTRACT Elucidating the structural basis of antibody (Ab) gene usage and affinity maturation of vaccine-induced Abs can inform the design of immunogens for inducing desired Ab responses in HIV vaccine development. Analyses of monoclonal Abs (MAbs) encoded by the same immunoglobulin genes at different stages of maturation can help to elucidate the maturation process. We have analyzed four human anti-V3 MAbs with the same VH1-3*01 and VL3-10*01 gene usage. Two MAbs, TA6 and TA7, were developed from a vaccinee in the HIV vaccine phase I trial DP6-001 with a polyvalent DNA prime/protein boost regimen, and two others, 311-11D and 1334, were developed from HIV-infected patients. The somatic hypermutation (SHM) rates in VH of vaccine-induced MAbs are lower than in chronic HIV infection-induced MAbs, while those in VL are comparable. Crystal structures of the antigen-binding fragments (Fabs) in complex with V3 peptides show that these MAbs bind the V3 epitope with a new cradle-binding mode and that the V3 β-hairpin lies along the antigen-binding groove, which consists of residues from both heavy and light chains. Residues conserved from the germ line sequences form specific binding pockets accommodating conserved structural elements of the V3 crown hairpin, predetermining the Ab gene selection, while somatically mutated residues create additional hydrogen bonds, electrostatic interactions, and van der Waals contacts, correlating with an increased binding affinity. Our data provide a unique example of germ line sequences determining the primordial antigen-binding sites and SHMs correlating with affinity maturation of Abs induced by vaccine and natural HIV infection. IMPORTANCE Understanding the structural basis of gene usage and affinity maturation for anti-HIV-1 antibodies may help vaccine design and development. Antibodies targeting the highly immunogenic third variable loop (V3) of HIV-1 gp120 provide a unique opportunity for detailed structural investigations. By comparing the sequences and structures of four anti-V3 MAbs at different stages of affinity maturation but of the same V gene usage, two induced by vaccination and another two by chronic infection, we provide a fine example of how germ line sequence determines the essential elements for epitope recognition and how affinity maturation improves the antibody's recognition of its epitope.

36 MATERIALS SCIENCE↗
DOE OSTI2015

The Effects of Hybrid Relative Maturity on Corn Stover for Ethanol Production and Biomass Composition

Full‐season corn ( Zea mays L.) hybrids take advantage of more of the growing season than shorter‐season hybrids often leading to greater grain and biomass yield. Many agronomic experiments aimed at corn stover production have been performed at forage harvest rather than later when stover is normally harvested for biofuel measurements. The objective of this research was to evaluate the influence of hybrid relative maturity (days RM) on stover ethanol production, ruminant digestibility, and biomass composition. Hybrids selected were high‐yielding commercial grain hybrids grown throughout Wisconsin and ranged from 85 to 115 d RM in 10 d RM increments during 2009, and in 5 d RM increments during 2010. Hybrids were harvested at physiological maturity or after a killing frost. Overall, stover and theoretical ethanol yields increased as RM increased at a linear rate of 0.211 Mg ha −1 RM −1 and 67.1 L ha −1 RM −1 . Stover nutritional and biomass composition improved as RM increased, but yield variability was greater than nutritional and biomass compositional variability. Increasing ethanol yields will likely occur by increasing stover yields rather than by altering stover composition. Therefore, until price premiums for stover composition are made available to farmers for ethanol production, the adoption of full‐season or longer maturing hybrids should be implemented for increased stover and ethanol yields.

Wilkens, Steve↗
DOE OSTI2024

Rapid Optimization of Total Variation with Applications in Imaging, Additive Manufacturing, and Qualification

Total Variation optimization penalizes the gradient of a control variable or state. While this work focuses on image processing in particular, it has also found applications in inverse problems and topology optimization. In image processing, the goal is to maintain faithfulness to the original image while denoising and/or deblurring. Additionally, bilevel optimization over the spatially varying regularization weights can illuminate interfaces such as damage regions and other anomalies. We will address two fundamental challenges with TV-optimization: (i) the typical slow convergence of existing TV-optimization methods, and (ii) the selection of spatially varying TV parameters to promote interface detection. Additionally, we will apply such techniques to image data collected in additive manufacturing. In said context, stochasticity in build events induces flaws in the manufactured piece, compromising the integrity of said part. There is a critical need for in-situ monitoring to spot anomalies once they form, and in this setting we apply our total variation and hyperparameter solvers. We will develop a customized algorithm based on for extreme-scale TV-optimization that achieves super-linear or quadratic-convergence, a critical property for real-time, image-by-image analysis. A worst-case outcome is a preprocessing step that enhances image quality in-situ, specifically for out-of-focus and noisy images.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Wind Plant Performance Prediction (WP3) Project

The methods for analysis of operational wind plant data are highly variable across the wind industry, leading to high uncertainties in the validation and bias-correction of preconstruction energy estimation methods. Lack of credibility in the preconstruction energy estimates leads to significant impacts on project financing and therefore the final levelized cost of energy for the plant. In this work, the variation in the evaluation of a wind plant's operational energy production as a result of variations in the processing methods applied to the operational data is examined. Preliminary results indicate that selection of the filters applied to the data and the filter parameters can have significant impacts in the final computed assessment metrics.

17 WIND ENERGY↗
DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

A Framework for Closed-Loop Optimization of an Automated Mechanical Serial-Sectioning System via Run-to-Run Control as Applied to a Robo-Met.3D

Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Here, using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

42 ENGINEERING↗
DOE OSTI2019

Tunable electron beam pulser for picoseconds stroboscopic microscopy in transmission electron microscopes

For two decades, time-resolved transmission electron microscopes (TEM) have relied on pulsed-laser photoemission to generate electron bunches to explore sub-microsecond to sub-picosecond dynamics. Despite the vast successes of photoemission time-resolved TEMs, laser-based systems are inherently complex, thus tend not to be turn-key. In this paper, we report on the successful retrofit of a commercial 200 keV TEM, without an external laser, capable of producing continuously tunable pulsed electron beams with repetition rates from 0.1 GHz up to 12 GHz and a tunable bunch length from tens of nanoseconds down to 10 ps. This innovation enables temporal access into previously inaccessible regimes: i.e., high repetition rate stroboscopic experiments. Combination of a pair of RF-driven traveling wave stripline elements, quadrupole magnets, and a variable beam aperture enables operation of the instrument in (1) continuous waveform (CW) mode as though the instrument was never modified (i.e. convention TEM operation mode, where the electrons from the emission cathode randomly arrive at the sample without resolvable time information), (2) stroboscopic (pump-probe) mode, and (3) pulsed beam mode for dose rate sensitive materials. In order to analyze the effect of a pulsed beam on image quality, we examined Au nanoparticles using bright field, high-resolution TEM imaging and selected area diffraction in both continuous and pulsed-beam mode. Furthermore, in comparison of conventional TEMs, the add-on beam pulser enables the observation of ultrafast dynamic behavior in materials that are reversible under synchronized excitation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Estimating Ocean Observation Impacts on Coupled Atmosphere‐Ocean Models Using Ensemble Forecast Sensitivity to Observation (EFSO)

Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble-based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density-based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF and investigated the impact of ocean observations under a weakly coupled DA framework. By removing the detrimental ocean observations detected by EFSO, the CFSv2 forecasts were significantly improved, showing the validation of impact estimation and the great potential of EFSO to be extended as a data selection criterion.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Device Fabrication using Crystalline CdTe and CdTe Ternary Alloys Grown by MBE

We fabricated epitaxial CdTe:In/CdTe:As homojunction and CdZnTe/CdTe and CdMgTe/CdTe heterojunction devices grown on bulk CdTe substrates in order to study the fundamental device physics of CdTe solar cells. Selection of emitter-layer alloys was based on passivation studies using double heterostructures as well as band alignment. Initial results show significant device integration challenges, including low dopant activation, high resistivity substrates and the development of low-resistance contacts. To date, the highest open-circuit voltage is 715 mV in a CdZnTe/CdTe heterojunction following anneal, while the highest fill factor of 52% was attained in an annealed CdTe homojunction. In general, all currentvoltage measurements show high series resistance, capacitancevoltages measurements show variable doping, and quantum efficiency measurements show low collection. Ongoing work includes overcoming the high resistance in these devices and addressing other possible device limitations such as non-optimum junction depth, interface recombination, and reduced bulk lifetime due to structural defects.

14 SOLAR ENERGY↗
DOE OSTI2023

Is Knowledge about Running Applications Helping Improve Runtime Prediction of HPC Jobs?

High-performance computing systems rely upon scheduling algorithms to achieve high utilization. These schedulers rely upon user estimates of job resource requirements, such as runtime, to determine optimal scheduling of incoming jobs. These user estimates, however, are prone to error. To mitigate this error, significant research has been directed at providing better estimates of job runtime, usually employing machine learning techniques. These techniques are dependent upon the input features selected. Among the possible features is the primary application used by the job. In a survey of more than 20 papers directed at improving runtime prediction, only four included primary application as an input feature. We focus this investigation specifically on the value of adding primary application as an input feature, and find that it does improve model performance, especially for jobs with longer runtimes, though this improvement varies based on the application used. We recommend further research to determine the cause of this variability as well as an optimal strategy for employing a mixture of models both including and not including primary application as a feature.

MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A long-term study of aerosol–cloud interactions and their radiative effect at the Southern Great Plains using ground-based measurements

Empirical estimates of the microphysical response of cloud droplet size distribution to aerosol perturbations are commonly used to constrain aerosol–cloud interactions in climate models. Instead of empirical microphysical estimates, here macroscopic variables are analyzed to address the influence of aerosol particles and meteorological descriptors on instantaneous cloud albedo and the radiative effect of shallow liquid water clouds. Long-term ground-based measurements from the Atmospheric Radiation Measurement (ARM) program over the Southern Great Plains are used. A broad statistical analysis was performed on 14 years of coincident measurements of low clouds, aerosol, and meteorological properties. Here two cases representing conflicting results regarding the relationship between the aerosol and the cloud radiative effect were selected and studied in greater detail. Microphysical estimates are shown to be very uncertain and to depend strongly on the methodology, retrieval technique and averaging scale. For this continental site, the results indicate that the influence of the aerosol on the shallow cloud radiative effect and albedo is weak and that macroscopic cloud properties and dynamics play a much larger role in determining the instantaneous cloud radiative effect compared to microphysical effects. On a daily basis, aerosol shows no correlation with cloud radiative properties (correlation = -0.01 ± 0.03), whereas the liquid water path shows a clear signal (correlation = 0.56 ± 0.02).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Metadynamics investigation of lanthanide solvation free energy landscapes and insights into separations energetics

Lanthanide ion solvation chemistry in nonaqueous phases is key to understanding and developing effective separation processes for these critical materials. Due to the complexity and inherent disorder of the solution phase, a comprehensive picture of the solvated metal ion is often difficult to generate solely from conventional spectroscopic approaches and electronic structure calculations, particularly in the extractant phase. In this work, we use classical molecular dynamics (MD) simulation with an advanced sampling technique, metadynamics, supplemented by experimental spectroscopy and speciation analysis, to measure lanthanide solvation free energy landscapes. We define coordination-based collective variables to probe the entire range of solvation configurations in the organic phase of lanthanum (La), europium (Eu), and lutetium (Lu) nitrate salts bound with a commonly used extractant, N,N′-dimethyl, N,N′-dioctylhexylethoxymalonamide (DMDOHEMA). The known lanthanide extraction trend of La ≈ Eu > Lu is readily explained by the measured free energy surfaces, which show consistent DMDOHEMA coordination from La to Eu, followed by loss of DMDOHEMA coordination from Eu to Lu. These simulations suggest how ligand crowding at the metal center can control selectivity, in this case resulting in the opposite extraction trend as observed with other conventional extractants, where the enthalpic contribution from increasing lanthanide charge density across the series dominates the extraction energetics. We also find that the presence of inner-sphere water, verified by time-resolved fluorescence, diversifies the accessible solvation structures. As a result, understanding solvation requires consideration of an entire thermodynamic ensemble, rather than the single dominant lowest-energy structure, as is often considered out of necessity in interpretation of spectroscopic data or in electronic structure-based ligand design approaches. In general, we demonstrate how metadynamics uniquely enables investigation of complex, multidimensional solvation energetic landscapes, and how it can explain selectivity trends where extraction is controlled by more complex mechanisms than simple charge density-based selectivity.

Wang, Xiaoyu↗
DOE OSTI2025

eDNAjoint: An R package for interpreting paired or semi‐paired environmental DNA and traditional survey data in a Bayesian framework

Abstract Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites. To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods. We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables. eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Keller, Abigail G. [Department of Environment Scie↗
DOE OSTI2020

CONTROLLED END GAS AUTO IGNITION WITH EXHAUST GAS RECIRCULATION ON A STOICHIOMETRIC, SPARK IGNITED, NATURAL GAS ENGINE

The goal of this study is to address fundamental limitations to achieving diesel-like efficiencies in heavy duty on-highway natural gas (NG) engines. Engine knock and misfire are barriers to pathways leading to higher efficiency engines. This study explores enabling technologies for development of high efficiency stoichiometric, spark ignited, natural gas engines. These include design strategies for fast and stable combustion and higher dilution tolerance. Additionally, advanced control methodologies are implemented to maintain stable operation between knock and misfire limits. To implement controlled end- gas autoignition (C-EGAI) strategies a Combustion Intensity Metric (CIM) is used for ignition control with the use of a Woodward large engine control module (LECM). Tests were conducted using a single cylinder, variable compression ratio, cooperative fuel research (CFR) engine with baseline conditions of 900 RPM, engine load of 800 kPa indicated mean effective pressure (IMEP), and stoichiometric air/fuel ratio. Exhaust gas recirculation (EGR) tests were performed using a custom EGR system that simulates a high pressure EGR loop and can provide a range of EGR rates from 0 to 40%. The experimental measurements included the variance of EGR rate, compression ratio, engine speed, IMEP, and CIM. These five variables were optimized through a Modified Box- Benken design Surface Response Method (RSM), with brake efficiency as the merit function. A positive linear correlation between CIM and f-EGAI was identified. Consequently, CIM was used as the feedback control parameter for C-EGAI. As such, implementation of C-EGAI effectively allowed for the utilization of high EGR rates and CRs, controlling combustion between a narrower gap between knock and lean limits. The change from fixed to parametric ignition timing with CIM targeted select values of f-EGAI with an average coefficient of variance (COV) of peak pressure of 5.4. The RSM efficiency optimization concluded with operational conditions of 1080 RPM, 1150 kPa IMEP, 10.55:1 compression ratio, and 17.8% EGR rate with a brake efficiency of 21.3%. At this optimized point of peak performance, a f-EGAI for C-EGAI was observed at 34.1% heat release due to auto ignition, a knock onset crank angle value of 10.3° aTDC and ignition timing of - 24.7° aTDC. This work has demonstrated that combustion at a fixed f-EGAI can be maintained through advanced ignition control of CIM without experiencing heavy knocking events.

Cooperative Fuel Research Engine, Auto Ignition, E↗
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 OSTI2018

Exploring the feasibility of lossy compression for PDE simulations

Checkpoint restart plays an important role in high-performance computing (HPC) applications, allowing simulation runtime to extend beyond a single job allocation and facilitating recovery from hardware failure. Yet, as machines grow in size and in complexity, traditional approaches to checkpoint restart are becoming prohibitive. Current methods store a subset of the application’s state and exploit the memory hierarchy in the machine. However, as the energy cost of data movement continues to dominate, further reductions in checkpoint size are needed. Lossy compression, which can significantly reduce checkpoint sizes, offers a potential to reduce computational cost in checkpoint restart. This article investigates the use of numerical properties of partial differential equation (PDE) simulations, such as bounds on the truncation error, to evaluate the feasibility of using lossy compression in checkpointing PDE simulations. Restart from a checkpoint with lossy compression is considered for a fail-stop error in two time-dependent HPC application codes: PlasComCM and Nek5000. Results show that error in application variables due to a restart from a lossy compressed checkpoint can be masked by the numerical error in the discretization, leading to increased efficiency in checkpoint restart without influencing overall accuracy in the simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Kinetic modeling of particle size distribution of soot in a premixed burner-stabilized stagnation ethylene flame

A detailed model of soot formation is proposed, which consists of a gas-phase kinetic model for the pyrolysis and oxidation of selected hydrocarbon fuels and a kinetic mechanism of soot nucleation and mass/size growth through coagulation and surface reactions. The gas-phase model (Ranzi et al., 2012) was expanded to include the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to four-to-five ring PAHs, with a modular and hierarchical approach. The discrete sectional approach was employed to solve the size evolution of the particle size distribution function (PSDF). Analogy and similarity rules were employed to describe heterogeneous reaction kinetics of soot surface reactions. A variable collision efficiency was assumed for the coalescence of small soot particles. Larger particles were assumed to undergo aggregation. The predicted PSDFs are found to be in reasonably good agreement with the experimental data for nascent soot measured in an atmospheric-pressure premixed ethylene–oxygen–argon flame in the burner-stabilized stagnation flame configuration. Sensitivity analyses of the PSDF, number density, and volume fraction were carried out with respect to the rate parameters of addition reactions of acetylene, PAHs, resonantly stabilized radical reactions, and coalescence and aggregation. The findings demonstrate that the reaction of PAHs and acetylene with soot surfaces and the kinetics of coalescence and aggregation exhibit dominant effects on the detailed and global soot properties for the flame studied, in agreement with conclusions of a large range of previous modeling studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The frequency of gaseous debris discs around white dwarfs

ABSTRACT A total of 1–3 per cent of white dwarfs are orbited by planetary dusty debris detectable as infrared emission in excess above the white dwarf flux. In a rare subset of these systems, a gaseous disc component is also detected via emission lines of the Ca ii 8600 Å triplet, broadened by the Keplerian velocity of the disc. We present the first statistical study of the fraction of debris discs containing detectable amounts of gas in emission at white dwarfs within a magnitude and signal-to-noise ratio limited sample. We select 7705 single white dwarfs spectroscopically observed by the Sloan Digital Sky Survey (SDSS) and Gaia with magnitudes g ≤ 19. We identify five gaseous disc hosts, all of which have been previously discovered. We calculate the occurrence rate of a white dwarf hosting a debris disc detectable via Ca ii emission lines as $0.067\, \pm \, ^{0.042}_{0.025}$ per cent. This corresponds to an occurrence rate for a dusty debris disc to have an observable gaseous component in emission as 4 ± $_{2}^{4}$ per cent. Given that variability is a common feature of the emission profiles of gaseous debris discs, and the recent detection of a planetesimal orbiting within the disc of SDSS J122859.93+104032.9, we propose that gaseous components are tracers for the presence of planetesimals embedded in the discs and outline a qualitative model. We also present spectroscopy of the Ca ii triplet 8600 Å region for 20 white dwarfs hosting dusty debris discs in an attempt to identify gaseous emission. We do not detect any gaseous components in these 20 systems, consistent with the occurrence rate that we calculated.

Manser, Christopher J.↗