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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Comprehensive High Throughput One‐Pot Analysis of Glycoproteins through Tool for Rapid Analysis of Glycopeptide by Permethylation (TRAP) Method.

Glycoproteins plays key roles in various cellular functions such as protein folding, protein targeting, receptor function, cell–cell recognition, protein stability, signaling, and other cellular functions. Currently a comprehensive and universal tool for the automated analysis of glycopeptides through their tandem mass spectrometry and data processing is a tedious process. We recently developed a method, Tool for Rapid Analysis of glycopeptide by Permethylation (TRAP), that simplifies glycoprotein analysis by integrating glycan sequencing and glycopeptide analysis in a single experiment. We employed this approach for the detailed analysis of glycosidic linkages, composition, glycan sequencing, glycosylation site mapping and sequencing of peptide moiety of glycopeptides of glycoproteins from both prokaryotes and eukaryotes. Here we are presenting an easy to use, robust, suitable for large as well as small sample sets and also cost‐effective glycoprotein analysis tool through TRAP approach. A permethylation protocol which can be automated and suitable for reliable permethylation of glycopeptides from the protease digest of glycoproteins was developed. Initial studies on glycopeptides from a model glycoprotein bovine RNAse B indicates that improved sensitivity and throughput of permethylation reactions can be achieved in conducting the reaction in microscale. This approach would enable convenient, rapid, and reliable high‐throughput screening of protein glycosylation and can be employed as a cost‐effective kit for glycosylated biomarker discoveries. In order to improvise the comprehensive glycosylation analysis and throughput, we have also developed an LC‐MS/MS platform for the analysis of glycopeptides processed through TRAP. We have conducted the analysis of standard glycoproteins such as bovine RNase B, human transferrin, and bovine fetuin and our preliminary results indicate that TRAP hyphened with LC‐MS/MS enables characterization of isomers of each glycoform at each glycosylation sites of complex glycoprotein mixtures. Support or Funding Information This research was supported by the National Institutes of Health (NIH) funded research, Grant R21GM122633; resource for biomedical glycomics, Grant P41GM103490; instrument grant, Grant S10OD018530; and U.S. Department of Energy grant, Grant DE‐SC0015662, to the Complex Carbohydrate Research Center. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Shajahan, Asif↗
DOE OSTI2023

Dark photon superradiance: Electrodynamics and multimessenger signals

We study the electrodynamics of a kinetically mixed dark photon cloud that forms through superradiance around a spinning black hole, and design strategies to search for the resulting multimessenger signals. A dark photon superradiance cloud sources a rotating dark electromagnetic field which, through kinetic mixing, induces a rotating visible electromagnetic field. Standard model charged particles entering this field initiate a transient phase of particle production that populates a plasma inside the cloud and leads to a system which shares qualitative features with a pulsar magnetosphere. We study the electrodynamics of the dark photon cloud with resistive magnetohydrodynamics methods applicable to highly magnetized plasma, adapting techniques from simulations of pulsar magnetospheres. We identify turbulent magnetic field reconnection as the main source of dissipation and electromagnetic emission, and compute the peak luminosity from clouds around solar-mass black holes to be as large as 10 43 erg/s for observationally allowed dark photon parameter space. The emission is expected to have a significant x-ray component and to potentially be periodic, with period set by the dark photon mass. The luminosity is comparable to the brightest x-ray sources in the Universe, allowing for searches at distances of up to hundreds of Mpc with existing telescopes. Finally, we discuss observational strategies, including targeted electromagnetic follow-ups of solar-mass black hole mergers and targeted continuous gravitational wave searches of anomalous pulsars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗
DOE OSTI2015

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Magmatic water content controls the pre-eruptive depth of arc magmas

We report that vanguard efforts in forecasting volcanic eruptions are turning to physics-based models, which require quantitative estimates of magma conditions during pre-eruptive storage. Below active arc volcanoes, observed magma storage depths vary widely (~0 to 20 kilometers) and are commonly assumed to represent levels of neutral buoyancy. Here we show that geophysically observed magma depths (6 ± 3 kilometers) are greater than depths of neutral buoyancy, ruling out this commonly assumed control. Observed depths are instead consistent with predicted depths of water degassing. Intrinsically wetter magmas degas water and crystallize deeper than dry magmas, resulting in viscosity increases that lead to deeper stalling of ascending magma. The water–depth relationship provides a critical constraint for forecasting models by connecting depth of eruption initiation to its volatile fuel.

58 GEOSCIENCES↗
DOE OSTI2017

Stationary quasi-breathers in monatomic FCC metals

The characteristics of quasi-breathers in monatomic fcc metals are investigated by the molecular dynamics method. The standard deviations of the frequencies of atoms constituting a breather from the fundamental frequency of oscillations are calculated. It is shown that the quasi-breather lifetime in the models under investigation depends on the initial excitation conditions as well as on the metal type.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Mechanisms of B Meson Suppression in Ultrarelativistic Heavy Ion Collisions

Different mechanisms of B meson suppression in lead ion collisions at an energy of the Large Hadron Collider √(s{sub NN}) = 5.02 TeV have been comparatively analyzed. It has been shown that the experimentally observed weak momentum dependence of the B meson suppression factor can be reproduced by the HYDJET++ model including the nuclear shadowing of initial parton distributions and radiative and collisional energy losses of heavy quarks in hot matter. A significant difference in the effects of the initial and final states on light and heavy hadron suppression has been revealed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Effects of reformed fuel on dual-fuel combustion particulate morphology

Advancements in catalytic reforming have demonstrated the ability to generate syngas (a mixture of CO and hydrogen) from a single hydrocarbon stream. This syngas mixture can then be used to replace diesel fuel and enable dual-fuel combustion strategies. The role of port-fuel injected syngas, composed of equal parts hydrogen and carbon monoxide by volume, was explored experimentally for soot reduction benefits under diesel pilot ignition and reactivity controlled compression ignition strategies. Particle size distribution measurements were made with a scanning mobility particle sizer and condensation particle counter for different levels of syngas substitution. To explain the experimental findings, computational fluid dynamics simulations utilizing a detailed stochastic soot model were used to validate and initialize additional simulations that isolate mixing and chemistry effects. Based on these simulations, the influence of adding syngas on soot particle size and quantity is discussed.

42 ENGINEERING↗
DOE OSTI2024

Efficient general method for numerically modeling laser pulse propagation, overlap, and lifetime effects in amplifiers

An efficient numerical time-dependent general method is developed to address incoherent pulse overlap and lifetime effects in laser amplifiers. The alternating propagation-population laser energetics method (APPLE) has been validated against a semi-discrete coupled rate equation numerical method (SDRE) and analytic formalisms in bounding cases. APPLE is based on decoupled rates applied to a time-dependent framework where both space-time-dependent populations and pulse energetics are consistently updated in each time step. A significant advantage of APPLE lies in its conceptual simplicity, ease of implementation, and relatively small computational cost. SDRE tracks the populations through coupled rates and uses the method of lines to discretize the hyperbolic partial differential transport equations allowing for use of ordinary differential equation solvers. With reasonably sized mesh, we report both energetic and power pulse shape relative differences on the order of one percent between the models over a large range of initial conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Forecasting the 2013–2014 influenza season using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza-like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013-2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method produced 50% and 95% credible intervals for the 2013-2014 ILI observations that contained the actual observations for most weeks in the forecast. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has passed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dataset for scientific paper "Simulated plant‑mediated oxygen input has strong impacts on fine‑scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands", a modeling study based on field observation at the tidal salt marshes of the Parker River Estuary, Massachusetts, United States

This dataset is the raw and processed data for the paper "Simulated plant ‑ mediated oxygen input has strong impacts on fine ‑ scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands". This study investigated how plant-mediated oxygen input affects subsurface biogeochemical reactions of organic carbon degradation and the resulting methane emissions of coastal wetlands by model simulation. We used the subsurface geochemical simulator PFLOTRAN for the modeling, which produced the simulated changes in porewater chemical substances and methane emissions over 10 days under different scenarios of plant-mediated oxygen input.Specifically, this dataset contains: 1) the input files for PFLOTRAN of all simulation runs conducted in this study. Those files are with an extension of ".in", containing information of the biogeochemical reaction network (stoichiometry, reaction rate, Monod constants, etc), fluid flow rate and oxygen concentration in the fluid which together simulated the plant-mediated oxygen input, the configuration of artificial reactions that simulated the methane fluxes, etc. The PFLOTRAN input files are text files, which can be opened by NotePad, but running these input files will require proper installation of PFLOTRAN (instruction: https://documentation.pflotran.org/user_guide/how_to/installation/installation.html). 2) the raw and processed model output from PFLOTRAN of all simulation runs, and 3) the python scripts used to process the raw model output, including random allocation of root cells, converting raw data into organized formats, calculating the methane fluxes based on the model output, data visualization, etc. The raw and processed model output from PFLOTRAN are in .spydata format, which can be viewed with Python. and 3) the python scripts for data processing and analysis are programming scripts, which can be opened with Python.This modeling work, in particular the model parameterization of root density and initial conditions of porewater concentrations of biogeochemical substances, was based on field measurements at the salt marsh of the Upper Parker River Estuary, Massachusetts, United States.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Measurement of the Muon Atmospheric Production Depth with the Water Cherenkov Detectors of the Pierre Auger Observatory

Ultra-high-energy cosmic rays (UHECR) are particles of uncertain origin and composition, with energies above 1 EeV (10 18 eV or 0.16 J). The measured flux of UHECR is a steeply decreasing function of energy. The largest and most sensitive apparatus built to date to record and study cosmic ray Extensive Air Showers (EAS) is the Pierre Auger Observatory. The Pierre Auger Observatory has produced the largest and finest amount of data ever collected for UHECR. A broad physics program is being carried out covering all relevant topics of the field. Among them, one of the most interesting is the problem related to the estimation of the mass composition of cosmic rays in this energy range. Currently the best measurements of mass are those obtained by studying the longitudinal development of the electromagnetic part of the EAS with the Fluorescence Detector. However, the collected statistics is small, specially at energies above several tens of EeV. Although less precise, the volume of data gathered with the Surface Detector is nearly a factor ten larger than the fluorescence data. So new ways to study composition with data collected at the ground are under investigation. The subject of this thesis follows one of those new lines of research. Using preferentially the time information associated with the muons that reach the ground, we try to build observables related to the composition of the primaries that initiated the EAS. A simple phenomenological model relates the arrival times with the depths in the atmosphere where muons are produced. The experimental confirmation that the distributions of muon production depths (MPD) correlate with the mass of the primary particle has opened the way to a variety of studies, of which this thesis is a continuation, with the aim of enlarging and improving its range of applicability. We revisit the phenomenological model which is at the root of the analysis and discuss a new way to improve some aspects of the model. We carry out a thorough revision of the original analysis with the aim of understanding the different contributions to the total bias and resolution when building MPDs on an event-by-event basis. We focus on an alternative way to build MPDs by considering average MPDs for ensembles of air-showers, with the aim of enlarging the range of applicability of this kind of analysis. Finally, we analyze how different improvements in the Surface Detector electronics and its internal configuration affect the resolution of the MPD. We conclude by summarizing the main results and discussing potential ways to improve MPD-based mass composition studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Pythia Jet Finding Study with Trento Backgrounds

We present results applying the Pythia SlowJet Finder to Pythia generated QCD and QED hard processes in the presence of simulated heavy ion backgrounds. The hard process events are generated with Pythia version 8.219 for √s NN =200 GeV proton-proton collisions and the backgrounds are generated by the Reduced Thickness Event-by-event Nuclear Topology model T R ENTo for Au-Au collisions with a nucleon-nucleon cross-section of 4.23 fm 2 . The T R ENTo model is used to calculate the initial entropy and ellipticity from which the total charged particle multiplicity and elliptic ow are determined. We report results in the form of event displays, total p T distributions, and fragmentation distributions for SlowJet applied to Pythia events with and without the simulated heavy ion backgrounds.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Development and Implications of a Predictive Cost Methodology for Modular Pumped Storage Hydropower (m-PSH) Projects in the United States

The slow pace of Pumped Storage Hydropower development in the US over the past twenty years has led to widespread interest in the feasibility and viability of alternative PSH designs, development schemes, and technologies. Since 2011, Oak Ridge National Lab has been exploring the economic viability of modular Pumped Storage Hydropower (m-PSH) development through targeted case studies, revenue simulations, and analysis of innovative configurations and designs. This paper outlines the development and supporting analysis of a scalable, comprehensive cost modeling tool designed to simulate the initial capital costs for a variety of potential m-PSH projects and deployment scenarios. The tool is used to explore and determine innovative research strategies that can improve the economic viability of m-PSH in US markets.

13 HYDRO ENERGY↗
DOE OSTI2016

Rapid Crossing of a Resonance During Tune Modulation by Synchrotron Oscillations

It had become a standard practice to constrain the particle’s tune footprint while designing the ring lattice so that the particle tunes fit between harmful resonances that limit ring dynamic aperture (DA). This approach, known as “tune confinement”, puts tight limits on the magnitude of the tune shifts with amplitude and with momentum. The latter requires labor-intensive optimization of the off-energy dynamic aperture and control over the corresponding tune footprint for the large energy deviations and is a prerequisite to achieving reasonable lifetime if it is dominated by the Touschek scattering. As nonlinearities of the modern ring lattices are much enhanced as compared with the previous generation of synchrotron light sources, it is becoming more and more difficult to keep the off-momentum tune footprint confined. While one could reach reasonable on-energy DA suitable for injection into the machine, the off-energy DA optimization is much more complex and needs to satisfy more constraints so to reach large energy acceptance and secure decent lifetime at the high ring current. In certain cases when the lattice solution with the confined off-energy tune footprint cannot be found, one may ask whether crossing of a resonance stopband from off-momentum particle will lead to loss of this particle. Intuitively, if the particle crosses the stopband quickly, one may expect that the betatron oscillation amplitude will not increase substantially thereby keeping the particle within the machine acceptance. At the same time the tight tolerances of the modern lattice elements as designed and produced afford much narrower resonance stopbands being compared with the machines built decades ago. Under these circumstances the question above may be asked as what are the conditions for operating a ring with a large chromatic tune footprint, which crosses resonances, while not observing particle losses. In this paper we try to answer the questions above by studying the model of a particle with large initial momentum deviation (as, for example, driven by the Touschek scattering event), which crosses stopband of a half-integer resonance while executing synchrotron oscillations in the highly chromatic ring lattice.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Validation of Pin Span Superposition to Reconstruct Gridded Bundle Flows

This milestone shows a demonstration of the surface map model to map the surface temperature for single-phase computational fluid dynamics simulation data from STAR-CCM+ to the subchannel code CTF using a two-step process that captures global and local rod surface temperature behavior based on simulation boundary conditions and position. This model can be used to improve results from CTF by transforming surface temperatures obtained from CTF to model data that resembles data from STAR-CCM+. A summary of the current two-step model process and results of the initial investigations with a single subchannel and a 5 x 5 set of fuel rods are shown and discussed within, with suggestions for further improvements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Mapping the Concentration Distribution of Contaminant Plumes to the Computational Grid of the Plateau to River Model (Ver. 8.3)

This environmental calculation file (ECF) outlines the process conducted for defining the initial condition groundwater plumes for select contaminants of interest (COIs) to the Plateau River model version 8.3 (P2Rv8.3) domain (CP-57037, Model Package Report: Plateau to River Groundwater Model Version 8.3). Various data sources were combined into a single set of files defining plume conditions at time 1/1/2018. The resulting files are intended for use as inputs for transport modeling to support forecasts of the Central Plateau contaminant plume fate and transport.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

First-principles Modeling and Design of Solid-State Interfaces for the Protection and Use of Lithium Metal Anodes

Li-ion batteries are one of the most advanced energy storage technologies in use today. Li-ion batteries are used in a multitude of applications ranging from consumer electronics, medical devices, sensors and grid storage. However, improving the capacity and energy density delivered by current Li-ion technology requires advanced materials research into novel chemical systems. In this project we have focused particularly in the use of solid-state electrolytes with lithium metal electrodes. Research into all solid-state batteries (ASSB) with Li metal electrodes has significantly expanded in recent years, however most studies reported experimental findings, which left substantial room for theoretical and modeling work as a tool to understand and determine design principles allowing reliable and safe use of ASSBs with Li metal. Among the remaining obstacles preventing reliable use of ASSBs with a Li metal electrode, the stability of the interface between the solid electrolyte and Li metal, and the propagation/dendrite formation of Li metal and resulting mechanical degradation of the electrolyte are key phenomenon that are yet to be fully understood. In the current project we have addressed these two coupled phenomena using first principles calculations and mesoscale continuum modeling. We have obtained chemical and electrochemical stability windows for several solid electrolyte materials. Additionally, from mathematical and numerical modeling of Li protrusion and dendrite initiation during plating and stripping we have determined design criteria in terms of chemical, electrochemical, and mechanical properties and operating conditions for which stable deposition can occur. We also considered the effects of mixed electronic-ionic conduction in solid electrolytes, which has more recently been suggested as another important mechanism involved in ASSB failure. Throughout our work we have successfully addressed important questions necessary for the use of ASSB’s. We have determined guiding principles for materials properties and operating conditions necessary to operate ASSB’s. And have proposed novel solid electrolyte materials with predicted chemical stability an ionic conductivity. Although this represents significant progress in our understanding, open questions remain in order to fully develop reliable and safely operate ASSBs with Li metal. Future work, building on this project will require further experimental, theoretical and simulation efforts to address remaining questions.

25 ENERGY STORAGE↗