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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 181 records · Page 10

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DOE OSTI2026

An ignition criterion for inertial fusion boosted by microturbulence

Turbulence on fine spatial scales enhances fusion reactivity, enabling ignition at lower temperature. A modified Lawson-like ignition criterion is derived for inertially confined plasmas harboring turbulent kinetic energy. For some turbulent energy spectra, hot spots ignite at lower energy density and smaller volume. While detrimental mixing effects typically accompany turbulence and obscure these advantages, targets might be engineered to drive flow in regions where it is beneficial. The optimal length scale for this driving is identified, typically lying in the micrometer range.

Alpha particles↗

DOE OSTI2023

Antimony nanobelt asymmetric membranes for sodium ion battery

In this study, composite asymmetric membranes containing antimony (Sb) nanobelts are prepared via a straightforward phase inversion method in combination with post-pyrolysis treatment. Sb nanobelt asymmetric membranes demonstrate improved cyclability and specific capacity as the alloy anode of sodium ion battery compared to Sb nanobelt thin films without asymmetric porous structure. The unique structure can effectively accommodate the large volume expansion of Sb-based alloy anodes, prohibit the loss of fractured active materials, and aid in the formation of stable artificial solid electrolyte interphases as evidenced by an outstanding capacity retention of ~98% in 130 cycles at 60 mA g -1 . A specific capacity of ~600 mAh g -1 is obtained at 15 mA g -1 (1/40C). When the current density is increased to 240 mA g -1 , ~80% capacity can be maintained (~480 mAh g -1 ). The relations among phase inversion conditions, structures, compositions, and resultant electrochemical properties are revealed through comprehensive characterization.

25 ENERGY STORAGE↗

DOE OSTI2022

On the origin of red spirals: does assembly bias play a role?

The formation of the red spirals is a puzzling issue in the standard picture of galaxy formation and evolution. Most studies attribute the colour of the red spirals to different environmental effects. We analyze a volume limited sample from the SDSS to study the roles of small-scale and large-scale environments on the colour of spiral galaxies. We compare the star formation rate, stellar age and stellar mass distributions of the red and blue spirals and find statistically significant differences between them at 99.9% confidence level. The red spirals inhabit significantly denser regions than the blue spirals, explaining some of the observed differences in their physical properties. However, the differences persist in all types of environments, indicating that the local density alone is not sufficient to explain the origin of the red spirals. Using an information theoretic framework, we find a small but non-zero mutual information between the colour of spiral galaxies and their large-scale environment that are statistically significant (99.9% confidence level) throughout the entire length scale probed. Such correlations between the colour and the large-scale environment of spiral galaxies may result from the assembly bias. Thus both the local environment and the assembly bias may play essential roles in forming the red spirals. The spiral galaxies may have different assembly history across all types of environments. We propose a picture where the differences in the assembly history may produce spiral galaxies with different cold gas content. Such a difference would make some spirals more susceptible to quenching. Finally, in all environments, the spirals with high cold gas content could delay the quenching and maintain a blue colour, whereas the spirals with low cold gas fractions would be easily quenched and become red.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Accurate predictions from small boxes: variance suppression via the Zel'dovich approximation

Simulations have become an indispensable tool for accurate modelling of observables measured in galaxy surveys, but can be expensive if very large dynamic range in scale is required. We describe how to combine Lagrangian perturbation theory models with N-body simulations to reduce the effects of finite computational volume in the prediction of ensemble average properties in the simulations within the context of control variates. In particular we use the fact that Zel'dovich displacements, computed during initial condition generation for any simulation, correlate strongly with the final density field. Since all the correlators of biased tracers can be computed with arbitrary precision for these displacements, pairing the Zel'dovich `simulation' with the N-body realization allows hundredfold reductions in sample variance for power spectrum or correlation function estimation. Additionally, Zel'dovich control variates can accurately extend matter or tracer field emulators to larger scales than previously possible, as well as improving measurements of statistics in simulations which are inherently limited to small volumes, such as hydrodynamical simulations of galaxy formation and reionization.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

PET Imaging of Leg Arteries for Determining the Input Function in PET/MRI Brain Studies Using a Compact, MRI-Compatible PET System

In this work, we used a compact, high-resolution, and magnetic resonance imaging (MRI)-compatible positron emission tomography (PET) camera (VersaPET) to assess the feasibility of measuring the image-derived input function (IDIF) from arteries in the leg with the ultimate goal of enabling fully quantitative PET brain imaging without blood sampling. We used this approach in five 18 F-FDG PET/MRI brain studies in which the input function was also acquired using the gold standard of serial arterial blood sampling. After accounting for partial volume, dispersion, and calibration effects, we compared the metabolic rates of glucose (MRglu) quantified from VersaPET IDIFs in 80 brain regions to those using the gold standard and achieved a bias and variability of <5% which is within the range of reported test-retest values for this type of study. We also achieved a strong linear relationship (R 2 >0.97) against the gold standard across regions. The results of this preliminary study are promising and support further studies to optimize methods, validate in a larger cohort, and extend to the modeling of other radiotracers.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Measurement of the transverse momentum distribution of Drell–Yan lepton pairs in proton–proton collisions at √s=13 TeV with the ATLAS detector

This paper describes precision measurements of the transverse momentum $p_\mathrm {T}^{\ell \ell }$ ($\ell =e,\mu $) and of the angular variable $\phi ^{*}_{\eta }$ distributions of Drell–Yan lepton pairs in a mass range of 66–116 GeV. The analysis uses data from 36.1 fb$^{-1}$ of proton–proton collisions at a centre-of-mass energy of $\sqrt{s}=13\,$TeV collected by the ATLAS experiment at the LHC in 2015 and 2016. Measurements in electron-pair and muon-pair final states are performed in the same fiducial volumes, corrected for detector effects, and combined. Compared to previous measurements in proton–proton collisions at $\sqrt{s}=7$ and $8\,$TeV, these new measurements probe perturbative QCD at a higher centre-of-mass energy with a different composition of initial states. They reach a precision of 0.2$\%$ for the normalized spectra at low values of $p_\mathrm {T}^{\ell \ell }$. The data are compared with different QCD predictions, where it is found that predictions based on resummation approaches can describe the full spectrum within uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

DLIO: A DATA-CENTRIC BENCHMARK FOR DEEP LEARNING APPLICATIONS

SF-22-136 Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. DLIO, is a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times. The storage vendor can also use DLIO as a guide for designing and optimize the storage and filesystem targeting at deep learning application.

ZHENG, HUIHUO↗

DOE OSTI2025

Sulfate Conversion of Reillex HPQ Anion Exchange Resin for Disposal (Interim Report)

This report describes preliminary data to validate the Savannah River Plutonium Processing Facility’s (SRPPF) flowsheet for conversion of used Reillex HPQ anion exchange resin from the nitrate form to the sulfate form. The nitrate form is an oxidizer and therefore does not meet acceptance criteria for disposal at the Waste Isolation Pilot Plant (WIPP). The purpose of this study is to develop data to support acceptance for this disposition pathway. Due to the challenges characterizing the nitrate concentration on solid resin, the data developed to date are based upon indirect analysis of the ion exchange column effluent by ion chromatography. These challenges are discussed and two methods for quantification of nitrate directly on the resin are recommended for further development: TGA-MS and permanganate digestion followed by IC. The resin used for this work was provided in the chloride form; this is the form in which resin is supplied by the manufacturer. However, it had to be converted to the nitrate form, which is the form that will be used in SRPPF’s ion exchange process, prior to use in the sulfate conversion experiments. The chloride-form resin was characterized. A lab-scale procedure for the conversion of Reillex HPQ resin from the chloride to nitrate form was validated. The nitrate-form resin was assessed for particle size and chloride concentration to ensure it met SRPPF’s facility specifications. The baseline sulfate conversion flowsheet was tested. However, nitrate was still detectable in the effluent after approximately 10 bed volumes of 1 M sodium sulfate had been passed through the resin bed. Additional experiments were performed to assess the effect of increasing the feed volume, reducing the flowrate, the use of 2 M sulfuric acid instead of sodium sulfate, and the use of irradiated resin. The sulfuric acid test was the only one which provided a nondetectable nitrate concentration (<0.002 M) in the effluent. Detectable nitrate in the column effluent suggests that nitrate is still present on the resin itself.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

A Sensitive Electrochemical Cholinesterase-Inhibiting Biosensor for Organophosphorus Pesticides Based on Ti 3 C 2 TX MXene Quantum Dots

Organophosphorus pesticides (OPs) pose significant environmental and health risks due to their widespread use and toxicity, primarily by inhibiting acetylcholinesterase. Traditional detection methods are often slow and costly, highlighting the urgent need for advanced, sensitive, and accessible technologies. This study developed a highly sensitive electrochemical cholinesterase-inhibiting biosensor for OP pesticides, utilizing Ti 3 C 2 T x MXene Quantum Dots (MQDs), which was synthesized via a hydrothermal method. The biosensor’s performance was characterized using electrochemical impedance spectroscopy, differential pulse voltammetry (DPV), and cyclic voltammetry. DPV proved to be the optimal technique, exhibiting an ultralow detection limit of 1 × 10 −17 M and a wide linear range (10 −14 –10 −8 M) for chlorpyrifos (a model OP) with an estimated inhibition constant of 62 nM. The biosensor demonstrated high selectivity for OPs (chlorpyrifos, acephate, glyphosate) over a non-target pyrethroid (permethrin), confirmed by distinct electrochemical signatures and compared to in vitro cholinergic activity assays in bean beetle homogenates. The enhanced performance is attributed to the high surface-to-volume ratio, quantum confinement effects, and superior conductivity of the MQDs, as well as the robust enzyme immobilization facilitated by glutaraldehyde cross-linking and a chitosan matrix. This work presents a promising platform for rapid, sensitive, and selective detection of OP pesticides, with potential applications in environmental monitoring and public health protection.

Chemistry↗

DOE OSTI2022

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

58 GEOSCIENCES↗

DOE OSTI2023

ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Cloud Droplet Probe

The Cloud Droplet Probe (CDP) is designed to measure cloud droplet size distribution from 2 µm to 50 µm. The CDP and an appropriate data system can also calculate various other parameters including particle concentrations, effective diameter (ED), Median Volume Diameter (MVD), and Liquid Water Content (LWC).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

ARM Aerial Facility (AAF) - Unmanned Aircraft Systems, Cloud Droplet Probe with QC and lat/lon/alt

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Fully integrated multi-phase buck converter with coupled air core inductors

A multi-phase buck switching converter having grouped pairs of phases, each phase using two magnetically coupled air-core inductors. For each group, a first driver circuit controlling switching of a first power transistor switching circuit coupled to a first air-core inductor output for driving an output load at the first phase. A second driver circuit controlling switching of a second power transistor switching circuit coupled to a second air-core inductor output for driving said output load at the second phase. The first and second phases are spaced 180° apart. The coupled air-core inductors per group of such orientation, separation distance and mutual inductance polarity relative to each other such that magnetic coupling between the two or more inductors at each phase results in a net increase in effective inductance per unit volume. Each air-core inductor is a metal slab of defined length, height and thickness formed using back-end-of-line semiconductor manufacturing process.

Zhang, Xin↗

DOE OSTI2021

Fully integrated multi-phase buck converter with coupled air core inductors

Zhang, Xin↗

DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Technology Maturation of Hot Isostatic Pressing for Nuclear Waste Treatment - 20259

ANSTO is constructing a nuclear waste treatment facility for the immobilization of intermediate-level liquid waste (ILLW) from its nuclear medicine production [1]. This facility shall deploy ANSTO Synroc process technology that converts a liquid waste stream into a durable solid wasteform. Hot Isostatic pressing (HIP) shall be employed to consolidate the dry powdered waste effectively reducing the waste volume and immobilizing the waste components. This paper presents the technology maturation for HIP technology for utilisation in a nuclear waste treatment facility. The HIP has been designed to operate in a remote shielded enclosure, fully integrated with the front-end powder production. Attention has been given in the HIP system design to ensure that it can operate and be maintained to achieve the required production output of the facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Solidification dynamics in metal additive manufacturing: analysis of model assumptions

Solidification characteristics in the meltpool drive the process-microstructure relationship which helps dictate the material properties of as-built parts in additive manufacturing; therefore, being able to accurately and quickly simulate the size, shape, and solidification characteristics in the melt pool is of great interest to the field. In this study, we investigate various important physical phenomena (dynamic material properties, fluid-flow, radiation and vaporization) which can either be included or neglected in a continuum finite volume model (FVM) and their effect on the solidification conditions. Additionally, since the simplest form of such a model (conduction only) has an analytic solution which is much faster, its viability is also considered. Since the inclusion of some of these physical phenomena will inherently change the net energy input as well as the amount of energy needed to achieve melting of a control volume, each set of included phenomena had an effective absorption efficiency which was calibrated to closely match the dimensions of the melt pool to that of the ground truth data. The ground truth data for this study was defined to be the output of the FVM which included all the physical phenomena (OF). This study then goes on to compare the effects on solidification conditions each of these calibrated models has. It was found that most of the change in solidification conditions comes from the inclusion of latent heat. A posterior correlation factor (PCF) is then introduced to enable an analytic model to predict similar solidification conditions to OF model.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A numerical study of the spectral radiative properties of packed bed with mixed bauxite and silica spheres

Bauxite and silica particles have gained increasing attention for applications in the field of concentrated solar power. In this work, a Monte Carlo ray-tracing simulation is performed to predict the radiative properties (absorptance, reflectance, and transmittance) of packed beds with mixed bauxite and silica spherical particles at wavelengths of 0.5, 2.6, 9.0, and 9.35 µm. These wavelengths are representative for the visible, near-infrared, and the mid-infrared regions that are important for solar and thermal radiation, respectively. A repeating unit-column approach is used to mathematically represent the particle bed. The effects of particle mixing ratio, volume fraction, and wavelength on the predicted radiative properties are examined. The obtained radiative properties are inputted to an inverse method to retrieve the effective absorption and scattering coefficients as well as the scattering albedo, which may be later used in a continuous-scale radiative heat transfer analysis. Furthermore, the independent scattering model is used to obtain the absorption coefficient and scattering albedo based on the absorption and scattering cross sections predicted by a Monte Carlo algorithm for a single particle. It is shown that the independent scattering model underpredicts the scattering coefficient for opaque particles but overpredicts the scattering coefficient for semitransparent particles for sufficiently high particle volume fractions. Here, the radiative properties calculated from the independent scattering model are compared to the full Monte Carlo simulation of the particle bed to examine the influence of particle mixing on dependent scattering.

42 ENGINEERING↗