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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Identification and Quantification of Carbon Phases in Conversion Fuel for the Transient Reactor Test Facility

As part of an overall effort to convert US research reactors to low-enriched uranium (LEU) fuel, a LEU conversion fuel is being designed for the Transient Reactor Test Facility (TREAT) at the Idaho National Laboratory. TREAT fuel compacts are comprised of UO{sub 2} fuel particles in a graphitic matrix material. In order to refine heat transfer modeling, as well as determine other physical and nuclear characteristics of the fuel, the amount and type of graphite and non-graphite phases within the fuel matrix must be known. In this study, we performed a series of complementary analyses, designed to allow detailed characterization of the graphite and phenolic resin based fuel matrix. Methods included Scanning Electron Microscopy, Raman spectroscopy, and X-ray Diffraction. Our results indicate that no single characterization technique will yield all of the desired information; however, through the use of statistical and empirical data analysis, such as curve fitting, partial least squares regression, volume extrapolation and spectra peak ratios, a degree of certainty for the quantity of each phase can potentially be obtained. Although no one type of analysis has provided a complete determination of the quantities and identities of all carbon phases present in the fuel matrix, additional information has been gathered and is being used to refine future analysis methods. From the three types of quantification performed, results have been within 1% (92.8%, 93.02% and 93.6% for BSE image analysis, XRD and Raman spectroscopy, respectively). More work remains to identify any trace crystalline carbonaceous phases that may be present, quantify any traces that are identified, and develop and use further complementary bulk phase quantification techniques. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Loads Response That is Due to Wake Steering on a Pair of Utility-Scale Wind Turbines

The goal of this report is to present the resulting turbine loads of a full-scale wind turbine wake steering field campaign. In this experiment, wake steering controls were applied to an upwind turbine (T2) and the downwind turbine (T3) was instrumented to measure mechanical loads. The test subjects (T2 & T3) were GE 1.5SLE CWE turbines located on a wind farm with a prevailing wind direction coming from the northwest. The data collection strategy involved toggling the wake steering controls of T2 between on and off. This resulted in two databases (baseline and wake steered) with similar turbulence intensities. Valid data was extracted and processed. The analysis involved scaling data to engineering units, applying coordinate transformations where applicable, calculating 10-minutes statistics, and calculating damage equivalent loads (DELs) to assess fatigue. Figures are shown as statistics and DELs binned by wind speed with supplemental scatter plots provided in the appendices. Results between the baseline and wake steered databases were compared. Overall, the binned statistics showed minimal differences in loading between the two cases. However, the DELs of the wake steered case were observed to be consistently smaller than the baseline for most of the turbine components. A potential for future work was recognized based on the findings of this report. Some areas of further study include an analysis for more granular loads sensitivity to different yaw offset angles and rotor wake overlap, an in-depth comparison of loads between the upwind and downwind turbines, and further fatigue analysis to quantify differences observed in this experiment.

17 WIND ENERGY↗
DOE OSTI2016

Measurement of Void Fraction Distribution in Steam-Water Two-Phase Flow in a 44 Bundle at 2 MPa

To contribute to the clarification of the Fukushima Daiichi Accident, JAEA is working on getting instantaneous void fraction distribution data in steam-water two-phase flow in rod bundle geometry under high pressure, high temperature condition, with using Wire Mesh Sensor (WMS) developed at JAEA for high pressure, high temperature condition (Up to 2.8 MPa, 230 deg. C), focusing on the low flow rate condition after the reactor scram. The data is expected to be used in the validation of the detailed two-phase flow codes TPFIT and ACE3D developed at JAEA. The time and space averaged void fraction data is also expected being used in the validation of the drift-flux models implemented in the two fluids codes, such as TRACE code. We are still in the middle of the data analyses and here we only report a part of the results. For the present bundle rod geometry, the void fraction shows easily gathered at the center area surrounded by 4 rods and the void fraction in the sub - channels between two adjacent rods are low. Under the same steam vapor flow rate condition, void faction increase with the decrease of the water flow rate. Under the same water flow rate, the void fraction increases with the increase of the steam vapor flow rate. In recent future, we will perform more experiments under further low water flow rate condition, including stagnation condition, to confirm this changing tendency. In the present experimental range, no effect of pressure on void fraction was observed. A 3-layer WMS, with a vertical distance between the adjacent layers of 1.3 mm, was used to measure the local instantaneous void fraction in this research. By using the correlation coefficient method, we can determine the time delay between the bubble detections from the upper and the lower layer sensors. Then the speed of the bubble, and its length, can be calculated. We are working on the establishment of the databank for the slug-churn bubbles information. (authors)

42 ENGINEERING↗
DOE OSTI2023

Impact of Flow Configurations on Response Time and Data Quality in Real-Time, In-Line Fourier Transform Infrared (FTIR) Monitoring of Viscous Flows

The real-time, in-line monitoring of continuous flow concentrations is widely conducted via infrared (IR) spectrometry by using a flow cell connected to a reactive flow stream. For protective purposes, the IR sensor tip is typically offset from the flow. This offset can cause the formation of a stagnant boundary layer above the sensor, especially when dealing with high-viscosity fluids. As a result, the IR signal response time is often controlled by the slow diffusional exchange of fluid in the boundary layer, as confirmed via 2D computational fluid dynamics (CFD) simulations. We evaluated several flow configuration modifications in a typical IR flow cell in order to identify the changes to the flow dynamics that enable improved response times with minimal changes to the cell configuration: the use of (i) vertical flow, where the standard horizontal flow over the sensor is redirected to contact vertically with the sensor, (ii) a static mixer to create radial flow momentum above the IR sensor, and (iii) horizontal or vertical nozzles to direct the flow toward the IR sensor. The vertical flow configuration did not show any significant improvement over the standard horizontal flow configuration. However, the static mixer, horizontal nozzle, and vertical nozzle configurations all resulted in markedly improved response times and signal quality, albeit at the expense of a higher pressure drop across the flow cell. These results point toward straightforward, user-accessible modifications of in-line IR flow cells that result in significant improvements in signal stability and acquisition times.

42 ENGINEERING↗
DOE OSTI2016

Development of Ultra Small Scale Mechanical Testing and Localized He Implantation for Nuclear Applications

Mechanical properties degradation of components deployed in nuclear applications has been a concern since the deployment of first generation nuclear power plants. Radiation damage along with environmental degradation, elevated temperatures, and long term deployment of nuclear structures pose a challenging environment for all materials in these applications. Traditionally, the mechanical properties are assessed by standardized macroscopic testing methods. While those are providing engineering relevant data these techniques do have limitations, especially on reactor irradiated materials, due to the large size of the samples. Limitations on the amount of radioactivity of large samples must be applied for research applications as they are conducted at universities. In addition it can be difficult to retrieve sufficiently large amounts of material from an operating reactor component. Therefore smaller sample sizes are desirable and reduce the amount of radioactive material which needs to be handled or material which needs to be retrieved. In addition, the number of tests which can be performed on materials irradiated in a test reactor program is limited due to limited space in specific dose and temperature positions. Downsizing of the mechanical test can overcome this issue and perform more tests on the same amount of irradiated materials, reducing the statistical uncertainties. In addition, smaller scale samples allow access to specific regions of interest in a material or component, be it the zone in front of a crack tip or the sampling of very small components like springs or welded areas. In addition ion beam irradiation has become an integral part of nuclear materials research to enhance the understanding of radiation damage in materials in a time and cost effective fashion. The drawback of ion beam irradiation is the small penetration depth of the ions into the material making localized microstructural characterization as well as small scale mechanical testing necessary. Another more fundamental motivation is the fact that since the beginning of electron microscopy, one has been concerned about imaging and quantifying defects in materials. Multiscale tools to image a large range of radiation induced defects have been established starting from atom probe tomography to transmission electron microscopy, up to scattering techniques. Mechanical testing, however, has always been performed on a large scale, and an attempt to match the same scale where a defect is observed with the materials' mechanical property response has not yet been realized. However, the question of separate effects testing on specific radiation induced defects remains and is an additional driving force for small scale mechanical testing. In this work we present an approach for small scale mechanical testing in irradiated materials and the variety of possible test methods developed today. We also present recent efforts of performing localized ion beam implantation to achieve a number of different doses in the same region of interest enhancing comparability and quantification of observed phenomena. It is the intention to give the reader an overview of the different microscale mechanical testing techniques possible today as well as an introduction of a novel ion beam irradiation method using a newly established capability. In this paper we can't discuss all aspects of all small scale mechanical testing of irradiated materials However, It does give an overview over the techniques enabled due to recent effort and enhanced instrument capability. While indentation or micro compression testing has been performed previously tensile testing and ion beam irradiation using the He ion beam microscope are novel approaches not attempted previously. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2021

Development of a VRC01-class germline targeting immunogen derived from anti-idiotypic antibodies

An effective HIV-1 vaccine will likely need to elicit broadly neutralizing antibodies (bNAbs). Broad and potent VRC01-class bNAbs have been isolated from multiple infected individuals, suggesting that they could be reproducibly elicited by vaccination. Several HIV-1 envelope-derived germline-targeting immunogens have been designed to engage naive VRC01-class precursor B cells. However, they also present off-target epitopes that could hinder development of VRC01-class bNAbs. We characterize a panel of anti-idiotypic monoclonal antibodies (ai-mAbs) raised against inferred-germline (iGL) VRC01-class antibodies. By leveraging binding, structural, and B cell sorting data, we engineered a bispecific molecule derived from two aimAbs; one specific for VRC01-class heavy chains and one specific for VRC01-class light chains. The bispecific molecule preferentially activates iGL-VRC01 B cells in vitro and induces specific antibody responses in a murine adoptive transfer model with a diverse polyclonal B cell repertoire. This molecule represents an alternative non-envelope-derived germline-targeting immunogen that can selectively activate VRC01-class precursors in vivo.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Measurement of Spray Chamber Ignition Delay and Cetane Numbers for Aviation Turbine Fuels

Experiments using pure compounds, National Jet Fuels Combustion Program (NJFCP) test fuels, and commercial jet fuels were conducted to demonstrate the equivalence of the indicated cetane number (ICN) and derived cetane number (DCN) for jet fuels. The calibrated range for ICN was also extended to lower cetane number (CN) values (5 to 35) to allow CN quantification for jet fuel synthetic blending components (SBCs) with low CN. ICN and DCN were shown to be highly correlated for values above about 30. This study presents the most comprehensive comparison of these two methods published to date. Because of the importance of low-volume test methods for early-stage SBC production process development, we demonstrated that ICN and DCN can be accurately measured with 15 mL of fuel, well below 40 to 100 mL required by standard methods. ICN or DCN is important for jet fuels because fuels with lower CN are more prone to lean blowout (LBO), an undesirable operational failure in a jet engine. Comparing data on a fuel-to-air ratio (Φ) at LBO for the NJFCP fuels shows similar linear correlations for ICN and DCN. Ignition delay measurements at lower-pressure and higher-temperature conditions may be more directly relevant to LBO. At 675 °C, 0.5 MPa, and a global Φ of roughly 0.68, ignition delay time correlations to LBO were similar to those produced from DCN and ICN. A much weaker correlation was obtained with a global Φ value of 0.34.

09 BIOMASS FUELS↗
DOE OSTI2022

Progress toward picosecond on-chip magnetic memory

Here, we offer a perspective on the prospects of ultrafast spintronics and opto-magnetism as a pathway to high-performance, energy-efficient, and non-volatile embedded memory in digital integrated circuit applications. Conventional spintronic devices, such as spin-transfer-torque magnetic-resistive random-access memory (STT-MRAM) and spin–orbit torque MRAM, are promising due to their non-volatility, energy-efficiency, and high endurance. STT-MRAMs are now entering into the commercial market; however, they are limited in write speed to the nanosecond timescale. Improvement in the write speed of spintronic devices can significantly increase their usefulness as viable alternatives to the existing CMOS-based devices. In this article, we discuss recent studies that advance the field of ultrafast spintronics and opto-magnetism. An optimized ferromagnet–ferrimagnet exchange-coupled magnetic stack, which can serve as the free layer of a magnetic tunnel junction (MTJ), can be optically switched in as fast as ~3 ps. Integration of ultrafast magnetic switching of a similar stack into an MTJ device has enabled electrical readout of the switched state using a relatively larger tunneling magnetoresistance ratio. Purely electronic ultrafast spin–orbit torque induced switching of a ferromagnet has been demonstrated using ~6 ps long charge current pulses. We conclude our Perspective by discussing some of the challenges that remain to be addressed to accelerate ultrafast spintronics technologies toward practical implementation in high-performance digital information processing systems.

42 ENGINEERING↗
DOE OSTI2022

On reading Youden: Learning about the practice of statistics and applied statistical research from a master applied statistician

From reading William John “Jack” Youden’s books and articles, Youden (1900-1971), an analytical chemist, becomes an applied statistician by the time he joins the National Bureau of Standards (NBS) in 1948. Here, this article traces his transition from chemist to applied statistician and what his body of work mostly at NBS (1948-1965) demonstrates about his practice of statistics and the role that applied statistical research plays in it. There is much we can learn from a master applied statistician.

42 ENGINEERING↗
DOE OSTI2023

Group-equivariant autoencoder for identifying spontaneously broken symmetries

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Lastly, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

42 ENGINEERING↗
DOE OSTI2016

PATHA: Performance Analysis Tool for HPC Applications

Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. In order to address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data. Furthermore, it utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). This study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Performance and Emission Investigations of Jatropha and Karanja Biodiesels in a Single-Cylinder Compression-Ignition Engine Using Endoscopic Imaging

Biofuels produced from non-edible sources that are cultivated on marginal lands represent a viable source of renewable and carbon-neutral energy. In this context, biodiesel obtained from Jatropha and Karanja oil seeds have received significant interest, especially in South Asian subcontinent. Both of these fuels are produced from non-edible plant seeds with high oil content, which can be grown on marginal lands. In this research, we have investigated the performance and emission characteristics of Jatropha and Karanja methyl esters (biodiesel) and their blends with diesel. Another objective is to examine the effect of long-term storage on biodiesel’s oxidative stability. The biodiesels were produced at Indian Institute of Technology Kanpur, (IIT Kanpur), India, and the engine experiments were performed in a single cylinder, 4-stroke, compression ignition engine at Argonne National Laboratory (ANL), Chicago. An endoscope was used to visualize in-cylinder combustion events and examine the soot distribution. The effects of fuel and start of injection (SOI) on engine performance and emissions were investigated. Results indicated that ignition delay was shorter with biodiesel. Consequently the cylinder pressure and premixed heat release were higher for diesel compared to biodiesel. Engine performance data for biodiesel (J100, K100) and biodiesel blends (J30, K30) showed an increase in break thermal efficiency (BTE) (10.9%, 7.6% for biodiesel and blend, respectively), BSFC (13.1% and 5.6%), and NOx emission (9.8% and 12.9%), and a reduction in BSHC (8.64% and 12.9%), and BSCO (15.56% and 4.0%). The soot analysis from optical images qualitatively showed that biodiesel and blends produced less soot compared to diesel. The temperature profiles obtained from optical imaging further supported higher NOx in biodiesels and their blends compared to diesel. Additionally, the data indicated that retarding the injection timing leads to higher BSFC, but lower flame temperatures and NOx levels along with higher soot formation for all test fuels. The physicochemical properties such as fatty acid profile, cetane number, and oxygen content in biodiesels support the observed combustion and emission characteristics of the fuels tested in this study. Finally, the effect of long-term storage is found to increase the glycerol content, acid value and cetane number of the two biodiesels, indicating some oxidation of unsaturated fatty acids in the fuels.

09 BIOMASS FUELS↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗