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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Millimeter-wave Detections of Symbiotic Stars in SPT and ACT Data

We present the results of a joint targeted search of candidate symbiotic stars at millimeter wavelengths using the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). Candidates are selected from the New Online Database of Symbiotic Variables, restricting to objects that are within either the SPT-3G or ACT~DR6 footprint, covering most of the southern hemisphere and up to a declination of $+20^\circ$. Forced photometry on the 828 candidate symbiotic star locations in SPT and ACT data results in 31 unique objects detected with more than a $3σ$ significance using two frequency bands: 18 confirmed and 13 suspected symbiotic stars. We provide the SPT and ACT 95/98, 150, and 220~GHz light curves, along with optical and infrared light curves from 2016--2026, as well as spectral energy distributions, physical parameters from the literature, and brief summaries regarding the nature of each individual object. Using Herschel SPIRE data from 2013, we place upper limits on millimeter flux for CN Cha near the beginning of the optical rise in its 2012/2013 nova, which suggests a strong variability and lag at millimeter wavelengths and results in a rare observance of a Galactic millimeter slow transient. In addition, we provide coadded thumbnails and light curves for the remaining 797 candidate symbiotic stars that did not pass our detection thresholds. Millimeter-wave emission from symbiotic stars is primarily a combination of free-free emission of the ionization region and optically thick blackbody emission of the cooler dust components of the system. When combined with contemporaneous multi-wavelength observations, millimeter-wave observations can be used to test binary models of symbiotic stars and provide insight on the geometry and physical properties of these systems.

Tandoi, C. [Illinois U., Urbana, Astron. Dept.] (O↗
DOE OSTI2022

A New Residual Strain Mapping Program Using Energy Dispersive X-Ray Diffraction at the Advanced Photon Source

Here, the ability to non-destructively map the residual strain field inside an engineering component is important for predicting its fatigue life or developing processing methods to prevent failure or enhance performance. In this paper, we describe a new residual strain mapping program at the Advanced Photon Source, Argonne National Laboratory. The new program is based on energy dispersive x-ray diffraction (EDXRD). It is capable of non-destructively penetrating a several- cm thick polycrystalline sample fabricated from engineering alloys using high-energy x-rays and measuring the residual strain field with mm or better spatial resolution and approximately ±1 x 10 -4 strain resolution. A multi-element detector array is employed to measure multiple strain components simultaneously. The residual strain mapping setup is augmented with a high-energy tomography capability, allowing precise alignment of the material volume of interest for residual strain mapping and providing a complementary view of the structure to understand the measured strain field. These measurement capabilities are demonstrated using several strain mapping examples ranging from polycrystalline structural alloys to biological materials. We also provide some guidance for the future users of the program for a successful residual strain mapping experiment. We are expanding the capabilities of the new setup with various in situ capabilities including thermo-mechanical loading.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Tailorable Adhesives for Multi-material Joining, Facile Repair and Re-assembly

Joining of materials and components is inevitable as it allows versatility in assembly and repair along with reduction in time and cost of manufacturing. This work uses ‘active adhesives’ that inherit all the benefits of bonded joints, such as lightweight, eliminate holes and associated stress-concentrations, and overcomes the shortcomings of disassembly and repair. Thermoplastic adhesives modified by the incorporation of electrically conductive graphene nanoplatelets at a concentration above the percolation point provide a unique synergy of mechanical, thermal and electrical properties. While the choice of the thermoplastic is governed by the desired application the addition of the graphene nanoplatelets allows energy to be deposited primarily in the adhesive. The percolated network of graphene particles in the adhesive at less than 2% can quickly couple to high frequency radiation (microwave, or MW) via non-contact methods and increase the adhesive temperature to above the required processing temperatures. The adhesive melts and flows over the adherends and upon cooling forms a structural adhesive bond. Furthermore, the process can be used to disassemble the adhesive joint if repair or reworking is required. In this work, the concept of aforementioned active adhesives was studied using varying graphene contents in nylon-6 adhesive. Activation and re-assembly was studied on in-plane (lap-shear) joints. Structural behavior of re-assembled joints was found to be similar to thermally bonded joints. Overall, active adhesives such as the ones attempted in this work have a great potential in a wide range of applications wherein in-situ repair, re-assembly and recyclability are essential.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Advanced Materials and Manufacturing Technologies Nondestructive Examination Efforts at Idaho National Laboratory: Report of FY-24 Efforts

This report details FY-24 nondestructive examination (NDE) efforts at Idaho National Laboratory (INL) in support of the Advanced Materials and Manufacturing Technologies (AMMT) program. While the goal of this endeavor is to develop a multi-modal, multi-length scale workflow for nondestructive characterization of advanced manufactured (AM) nuclear reactor components, substantial development remains until this is a reality. In support of this effort X-ray computed tomography (XCT), X-ray diffraction (XRD), neutron computed tomography (nCT), neutron diffraction, lock-in thermography (LIT), multi-point lock-in thermography (MLIT), and positron annihilation spectroscopy (PAS) were all used on AM specimens to examine defects such as voids, porosity, and residual stress. In addition to summarizing the results of these NDE applications, recommendations for integrating these into a more comprehensive undertaking to promote NDE of engineering-scale components are also included.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Flame Edge Dynamics and Interaction in a Multi-Nozzle Can Combustor with Fuel Staging

The characterization and mitigation of thermoacoustic combustion instabilities in gas turbine engines is necessary to reduce pollutant emissions, premature wear, and component failure associated with unstable flames. Fuel staging, a technique in which the fuel flow to a multi-nozzle combustor is unevenly distributed between the nozzles, has been shown to mitigate the intensity of self-excited combustion instabilities in multiple nozzle combustors. In our previous work, we hypothesized that staging suppresses instability through a phase-cancellation effect in which the heat release rate from the staged nozzle oscillates out of phase with that of the other nozzles, leading to destructive interference that suppresses the instability. This previous theory, however, was based on chemiluminescence imaging, which is a line-of-sight integrated technique. In this work, we use high-speed laser-induced fluorescence to further investigate instability suppression in two staging configurations: center-nozzle and outer-nozzle staging. An edge-tracking algorithm is used to compute local flame edge displacement as a function of time, allowing instability-driven edge oscillation phase coherence and other instantaneous flame dynamics to be spectrally and spatially resolved. Analysis of flame edge oscillations shows the presence of convecting coherent fluctuations of the flame edge caused by periodic vortex shedding. When the system is unstable, these two flame edges oscillate together as a result of high-intensity longitudinal-mode acoustic oscillations in the combustor that drive periodic vortex shedding at each of the nozzle exits. In the stable cases, however, the phase between the oscillations of the center and outer flame edges is greater than 90 degrees (~114 degrees), suggesting that the phase-cancellation hypothesis may be valid. This analysis allows a better understanding of the instantaneous flame dynamics behind flame edge oscillation phase offset and fuel staging-based instability suppression.

Doleiden, Daniel↗
DOE OSTI2021

Biocompatible Electrostatic Layered Systems for Viral Elimination in the Nose/Throat

An anti-viral coating for the nose & throat is needed to quickly address the spread of COVID-19 infections and to aid future pandemics. Current nasal delivery systems are typically a 1-spray homogenous solution, which is convenient but may not be as efficient or durable as a multi-spray solution that contains a binding layer to anchor the anti-viral components. Many of the current nasal solutions being investigated to aid in pandemic response have solution-based mechanisms of action and are quickly cleared from the nose/mouth limiting their efficacy lifetime. A multi-spray approach utilizing strong intermolecular forces between polymeric materials and anti-viral agents to provide a robust biocompatible coating is expected to have increased physical and chemical properties to combat viral infection. This work evaluated which tailored biocompatible materials are needed to generate a layered system to combat viral infection. This layered system takes advantage of electrostatic interactions to bind anti-viral components, shown in Figure 1. Proof of concept success was demonstrated through cell toxicity studies and anti-viral assays on both the individual layer components and the complete layered system. Project work began with chemical modifications of Xantham Gum to hydrolyzing xanthan gum to give a negatively charged polymer with varying degrees of ionic character and investigate bonding efficiencies. However, this was abandoned in favor of comparing COTS available materials listed in Table 1 (Results section). Concentration and adhesion studies were performed on layers created using the listed polymeric materials and anti-viral agents. Finally, biocompatibility of materials, layers, and coating system were confirmed through cytotoxicity studies and the efficacy of the anti-viral properties tested with phi6 bacteriophage as a SARS-CoV2 surrogate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrated Water-Power System Resilience Analysis in a Southeastern Idaho Irrigation District: Minidoka Case Study

This study investigates the joint water–power system resilience of an irrigation district in southeastern Idaho. Irrigation districts face difficulties in the delivery of water to farmers under drought conditions, during equipment failures, or unplanned infrastructure disruptions. The resilience of interconnected water and power systems can be better analyzed and understood through an integrated approach, using a model that connects the dependencies between the two halves of the system. Using a multi-agent system model capturing both water and power system components, as well as their linkages, we capture the interdependencies of these systems and highlight opportunities for improvement when faced with disruptions. Through simulation scenarios, we examine the system resilience using system performance, quantified as the percentage of met demand of the power and water system, when subjected to drought water year, an unforeseen water demand increase, power outage and dam failure. Scenario results indicate that the effects of low flow years are mostly felt in the power system; unexpected increases in water demand marginally impact irrigation system performance; dams and pumps present vulnerabilities of the system, causing substantial unmet demand during disruptions. Noting the interdependencies between the water–power system halves while leveraging an integrated simulation allows for an insightful analysis of the system impacts during disruptions.

13 HYDRO ENERGY↗
DOE OSTI2023

Identification of inulin-responsive bacteria in the gut microbiota via multi-modal activity-based sorting

Prebiotics are defined as non-digestible dietary components that promote the growth of beneficial gut microorganisms. In many cases, however, this capability is not systematically evaluated. Here, we develop a methodology for determining prebiotic-responsive bacteria using the popular dietary supplement inulin. We first identify microbes with a capacity to bind inulin using mesoporous silica nanoparticles functionalized with inulin. 16S rRNA gene amplicon sequencing of sorted cells revealed that the ability to bind inulin was widespread in the microbiota. We further evaluate which taxa are metabolically stimulated by inulin and find that diverse taxa from the phyla Firmicutes and Actinobacteria respond to inulin, and several isolates of these taxa can degrade inulin. Incubation with another prebiotic, xylooligosaccharides (XOS), in contrast, shows a more robust bifidogenic effect. Interestingly, the Coriobacteriia Eggerthella lenta and Gordonibacter urolithinfaciens are indirectly stimulated by the inulin degradation process, expanding our knowledge of inulin-responsive bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metabolite, Protein, and Lipid Extraction (MPLEx): a method that simultaneously inactivates Middle East respiratory syndrome coronavirus and allows analysis of multiple host cell components following infection

Mass spectrometry (MS)-based, integrated proteomics, metabolomics, and lipidomics (collectively, multi-omics) studies provide a very detailed snap shot of virus-induced changes to the host following infection and can lead to the identification of novel prophylactic and therapeutic targets for preventing or lessening disease severity. Multi-omics studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of Biosafety Level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of studies with Middle East respiratory syndrome coronavirus (MERS-CoV) are challenging as the requirements of Biosafety Level 3 containment limit the numbers of samples that can be safely managed. To address these issues, the multi-omics sample preparation technique MPLEx (metabolite, protein, and lipid extraction) was developed to partition a single sample into three distinct parts (metabolites, proteins and lipids) for multi-omics analysis, while simultaneously inactivating MERS-CoV by solubilizing and disrupting the viral envelope and denaturing viral proteins. Here we describe the MPLEx protocol, highlight the step of inactivation, and describe the details of downstream processing, instrumental analysis of the three separate analytes and their subsequent informatics pipelines.

Nicora, Carrie D.↗
DOE OSTI2021

Adaptive resource allocation for surrogate modeling of systems comprised of multiple disciplines with varying fidelity

We present an adaptive algorithm for constructing surrogate models for integrated systems composed of a set of coupled components. With this goal we introduce ‘coupling’ variables with a priori unknown distributions that allow approximations of each component to be built independently. Once built, the surrogates of the components are combined and used to predict system-level quantities of interest (QoI) at a fraction of the cost of interrogating the full system model. We use a greedy experimental design procedure, based upon a modification of Multi-Index Stochastic Collocation (MISC), to minimize the error of the combined surrogate. This is achieved by refining each component surrogate in accordance with its relative contribution to error in the approximation of the system-level QoI. Our adaptation of MISC is a multi-fidelity procedure that can leverage ensembles of models of varying cost and accuracy, for one or more components, to produce estimates of system-level QoI. Several numerical examples demonstrate the efficacy of the proposed approach on systems involving feed-forward and feedback coupling. For a fixed computational budget, the proposed algorithm is able to produce approximations that are orders of magnitude more accurate than approximations that treat the integrated system as a black-box.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Understanding Multi-Ion Transport Mechanisms in Bipolar Membranes

Bipolar membranes (BPMs) have the potential to become critical components in electrochemical devices for a variety of electrolysis and electrosynthesis applications. Because they can operate under large pH gradients, BPMs enable favorable environments for electrocatalysis at the individual electrodes. Critical to the implementation of BPMs in these devices is understanding the kinetics of water dissociation that occurs within the BPM as well as the co- and counter-ion crossover through the BPM, which both present significant obstacles to developing efficient and stable BPM-electrolyzers. In this study, a continuum model of multi-ion transport in a BPM is developed and fit to experimental data. Specifically, concentration profiles are determined for all ionic species, and the importance of a water-dissociation catalyst is demonstrated. The model describes internal concentration polarization and co- and counter-ion crossover in BPMs, determining the mode of transport for ions within the BPM and revealing the significance of salt-ion crossover when operated with pH gradients relevant to electrolysis and electrosynthesis. Lastly, a sensitivity analysis reveals that the performance and lifetime of BPMs can be improved substantially by using of thinner dissociation catalysts, managing water transport, modulating the thickness of the individual layers in the BPM to control salt-ion crossover, and increasing the ion-exchange capacity of the ion-exchange layers in order to amplify the water-dissociation kinetics at the interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analysis of the Spontaneous Emission Limited Linewidth of an Integrated III–V/SiN Laser

Abstract This article describes a calculation of the spontaneous emission limited linewidth of a semiconductor laser consisting of hybrid or heterogeneously integrated, silicon and III–V intracavity components. Central to the approach are a) description of the multi‐element laser cavity in terms of composite laser/free‐space eigenmodes, b) use of multimode laser theory to treat mode competition and multiwave mixing, and c) incorporation of quantum‐optical contributions to account for spontaneous emission effects. Application of the model is illustrated for the case of linewidth narrowing in an InAs quantum‐dot laser coupled to a high‐ SiN cavity.

42 ENGINEERING↗
DOE OSTI2021

A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis; Méthodologie de modélisation dynamique hiérarchique de type boîte grise pour les systèmes de refroidissement à détente directe afin de soutenir l'analyse de stabilité de la commande

In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

42 ENGINEERING↗
DOE OSTI2024

Incident beamline design for a modern cold triple axis spectrometer at the High Flux Isotope Reactor

A modern cold triple axis spectrometer is being planned for the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. Here, we describe the design of an incident beamline that will put a flux of ~ 10 8 $\frac {n}{cm^{2}s}$ on a sample with an area of 2 cm X 2 cm. It takes current physical constraints at HFIR into account and it can accommodate both single and multiplexed analyzer-detector secondary spectrometers and large superconducting magnets. The proposed incident beamline includes a multi-channel guide with horizontal focusing, a neutron velocity selector, components to facilitate an incident beam polarization option, and a double-focusing pyrolytic graphite monochromator. Here, this work describes the process of optimizing the guide system and monochromator and summarizes the expected performance of the incident beamline for non-polarized operation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Scalable Proximity-Based Methods for Large-Scale Analysis of Atom Probe Data

Powered by recent advances in data acquisition technologies, today's state-of-the-art atom probe microscopes yield data sets with sizes ranging from a few million atoms to billions of atoms. Analysis of these atomic data sets within rea-sonable turnaround times is a pressing data analysis challenge for material scientists currently equipped with software systems that do not scale to these massive data sets. Here, we present the shared memory component of a larger ongoing effort to develop a multi-feature data analysis framework capable of analyzing atom probe data of all sizes and scales from desktop multicore machines to large-scale high-performance computing platforms with hybrid (shared and distributed memory) architectures. Our focus here is on a broad class of popular atom probe data analysis methods that rely on core time-consuming k-NN queries. We present a scalable, heuristic algorithm for k-NN queries using three-dimensional range trees. To demonstrate its efficacy, the k-NN algorithm is integrated with two use cases of atom probe data analysis methods and the resulting analysis times are shown to speedup by over 20X on a 32-core Cray XC40 node using workloads up to 8 million atoms, which is already beyond the at-scale capabilities of existing atom probe software. Using this k-NN algorithm, we also introduce a novel parameter estimation method for a class of cluster finding methods, called friends-of-friends (FoF) methods, to completely bypass their expensive pre-processing steps. In each case, we validate the results on a variety of control data sets.

Lu, Hao↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗