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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Real-world CMB lensing quadratic estimator power spectrum response

I describe a method to estimate response matrices of Cosmic Microwave Background (CMB) lensing power spectra estimators to the true sky power under realistic conditions. Applicable to all lensing reconstruction pipelines based on quadratic estimators (QE), it uses a small number of Gaussian CMB Monte-Carlos and specially designed QE's in order to obtain sufficiently accurate matrices with little computational effort. This method may be used to improve the modelling of CMB lensing band-powers by incorporating at least some of the non-idealities encountered in CMB lensing reconstruction. These non-idealities always include masking, and often inhomogeneous filtering, either in the harmonic domain or pixel space. I obtain these matrices for Planck latest lensing reconstructions, and then show that the residual couplings induced by masking explain very well the residual multiplicative bias seen on the Planck simulations, removing the need for an empirical correction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Spatial and temporal evaluations of the liquid argon purity in ProtoDUNE-SP

Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift electron lifetime was measured to be above 30 ms using both systems. A particular focus will be placed on the measured purity of argon as a function of position in the detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Chromatic effects on the PSF and shear measurement for the Roman Space Telescope High-Latitude Wide Area Survey

Weak gravitational lensing (WL) is a key cosmological probe that requires precise measurement of galaxy images to infer shape distortions, or shear, and constrain cosmology. Accurate estimation of the point spread function (PSF) is crucial for shear measurement, but the wavelength dependence of the PSF introduces chromatic biases that can systematically impact shear inference. We focus on biases arising from spectral energy distribution (SED) differences between stars, used for PSF modelling, and galaxies, used for shear measurement. We investigate these effects in Roman’s four design reference mission WL bands (Y106, J129, H158, F184) and wide filter (W146). Using Roman-like image simulations, we quantify the induced shear biases and compare them to requirements on those biases. Multiplicative biases over all galaxies hover around ~0.2 per cent in the WL bands and 2 per cent in the wide filter, exceeding the mission requirement of |m| < 0.032 per cent and relaxed requirement of |m| < 0.1 per cent⁠. In individual redshift bins, biases can reach 0.4–0.9 per cent for the WL bands and 3–6 per cent for the wide filter. Additive biases remain acceptable in the WL bands but exceed systematic limits in the wide filter. We develop and test PSF-level corrections, showing that a first-order correction reduces biases within survey requirements for the WL bands; however, higher-order terms are necessary for the wide filter. Our results highlight the necessity of chromatic corrections for precision WL with Roman and provide a framework for mitigating these biases. Finally, we compare analytical colour-based corrections to self-organizing maps and find that both methods effectively reduce biases.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Smart Pixel Sensors for the HL-LHC

Large-scale particle physics experiments produce tens of terabytes of data every second. Innovative methods to manage the data rate at the HL-LHC, which expects to operate at 10x the luminosity of what the LHC was initially designed for, are needed. AI-on the chip provides a way to intelligently filter out low momentum clusters in the pixel detector. This will open up an opportunity to use the pixel detector for the first time in the CMS Level-1 trigger, and lead to increased sensitivity to new physics measurements and searches. We have taped out our first chip, which incorporates a $p_T$ filtering algorithm on an ASIC chip. Our initial $p_T$ filtering algorithm considers clusters that are tracked by CMS. We will report on ongoing studies seeking to enhance the performance of our filter by utilizing unsupervised learning on untracked clusters, thus increasing background rejection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Apparatus and methods of detecting transient discharge modes and/or closed loop control of pulsed systems and method employing same

A system and method for differentiating between different modes of pulsed electrical discharges via of an amplitude to time (ATC) conversion circuit is described. A bipolar ATC circuit is used to add together the positive and negative portions of an attenuated and filtered signal derived either from the voltage or current of a pulse. Alternatively, a unipolar ATC circuit may be employed. The resulting processed signal is compared against a reference voltage to generate an output signal that is active for the amount of time that the processed signal exceeds the reference voltage. Discharge mode is determined based on three factors: did a pulse occur, if a pulse occurred when did the pulse start relative to the original pulse event, and what is the duty cycle of the pulse. Subsequent pulse generated may be controlled accordingly.

Fitzpatrick, Joseph F.↗
DOE OSTI2022

Real-space density kernel method for Kohn–Sham density functional theory calculations at high temperature

Kohn–Sham density functional theory calculations using conventional diagonalization based methods become increasingly expensive as temperature increases due to the need to compute increasing numbers of partially occupied states. In this work, we present a density matrix based method for Kohn–Sham calculations at high temperatures that eliminates the need for diagonalization entirely, thus reducing the cost of such calculations significantly. Specifically, we develop real-space expressions for the electron density, electronic free energy, Hellmann–Feynman forces, and Hellmann–Feynman stress tensor in terms of an orthonormal auxiliary orbital basis and its density kernel transform, the density kernel being the matrix representation of the density operator in the auxiliary basis. Using Chebyshev filtering to generate the auxiliary basis, we next develop an approach akin to Clenshaw–Curtis spectral quadrature to calculate the individual columns of the density kernel based on the Fermi operator expansion in Chebyshev polynomials and employ a similar approach to evaluate band structure and entropic energy components. We implement the proposed formulation in the SPARC electronic structure code, using which we show systematic convergence of the aforementioned quantities to exact diagonalization results, and obtain significant speedups relative to conventional diagonalization based methods. Finally, we employ the new method to compute the self-diffusion coefficient and viscosity of aluminum at 116 045 K from Kohn–Sham quantum molecular dynamics, where we find agreement with previous more approximate orbital-free density functional methods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

Emad-Ud-Din, Muhammad (ORCID:0000000279515538)↗
DOE OSTI2024

Birefringent Color Filter by Layered Metal‐Organic Chalcogenides: In‐Plane Anisotropy and Odd/Even Effect

Anisotropic 2D materials are gaining interest recently as building blocks for angular‐dependent optical/electrical devices. However, the fundamental understanding of their structure‐property‐relationship is limited, which hinders further modulation of their unique characteristics via structure tailoring. Here the in‐plane structural anisotropy and the tunable optical/electrical properties of a series of radiation‐sensitive (X‐ray, e‐beam) metal‐organic chalcogenide (MOC) single crystals are comprehensively revealed with ligands of variable length/parity. Their monoclinic crystallography is determined at atomic resolution by a simple method that couples X‐ray/electron diffraction with first‐principles calculations. The in‐plane inorganic backbone of the MOCs exhibits a strong lattice anisotropy with odd/even alternations, which originates from that of the out‐of‐plane organic motifs via organic/inorganic accommodation. Such structural anisotropy is implied mechanically by the preferred orientation of crystal cleavage. It triggers a maximum ≈8 × distinction of in‐plane electrical conductivity of the semiconducting MOCs, plus a distinct birefringence (maximum Δn ≈ 0.03) with a dispersive orientation of dielectric axes, which rotate up to 25.7° from UV to visible‐light regime, inspiring an emerging pathway for color filtering via single crystal rotation. Such in‐plane optical characteristics also exhibit odd/even alternation and can be flexibly tuned by the designable out‐of‐plane ligands.

birefringence↗
DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Impact of gel concentration on filter fluxes in microfiltration of Hanford tank wastes and simulants

Abstract Treatment processes have been proposed that will utilize crossflow filtration to concentrate sludge waste streams at the Department of Energy's Hanford Site. Challenges associated with solid–liquid separation of the waste streams drive a necessary evaluation of available Hanford high level waste (HLW) filtration data. Limiting flux conditions during crossflow filtration are elucidated with the formation of a cake layer on the membrane surface. A mass transfer coefficient between the gel and bulk concentrations plays a critical role in determining filter flux. A correlation between the gel concentration and mass transfer coefficient is made to assist in determining filter performance of select HLW streams. As a process alternative to crossflow filtration, gravity settling of waste streams may be deployed as a solid–liquid separation technique. However, this results in a contrasting performance with the centrifuged solids concentration. A method was developed to estimate expected filtration and settling performance based on physical characterization data for Hanford tank waste samples. By assessing the estimated processing performance of HLW, technical support can be provided during flowsheet planning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Pickaxe: a Python library for the prediction of novel metabolic reactions

Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Planetary Boundary Layer Height (PBLH) over SGP from 1998 to 2023

PBLH is a critical parameter influencing weather phenomena, air quality, and various meteorological processes. However, accurately retrieving PBLH has been a challenging task due to limitations such as coarse temporal resolution and measurement drift in traditional radiosonde observations. To address these limitations, we have devised a lidar-based methodology that capitalizes on a newly developed algorithm for PBLH retrieval. This algorithm demonstrates enhanced capabilities in capturing diurnal fluctuations in PBLH compared to existing lidar-based methods (Su et al. 2020). In addition, we have refined this algorithm specifically for PBLH retrieval under cloudy conditions through a novel scheme (Su et al. 2022). To ensure data reliability, a quality-control process has been implemented to filter out questionable data points. Accompanying the data set is a quality-control flag for ease of reference. It should be noted that we have assimilated all available radiosonde observations to provide a more robust estimate of PBLH, making the data set valuable for a variety of related studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Meter-scale High-temperature Superconducting RF Waveguides with Integrated Filters and Attenuators

Here, we report the performance of coplanar, multifilament waveguides manufactured from crystalline films of a well-known HTS compound, YBCO, transferred to a low-loss dielectric substrate, such as E-Kapton. The coplanar two-line waveguide delivers low cross-talk, below 20 dB level, and low attenuation, below 4 dB/m at 6 GHz, 77 K. Controlled-impedance 50 Ω waveguides would require a method for reliable patterning narrow, less than 100-micron wide, YBCO traces over a meter length. We report a lithography-etching approach that minimizes the difference in the etching rate of YBCO and the metallization layers of Ag and Cu. An array of metalized vias isolates the traces. We describe the via metallization process that is not damaging to YBCO and compatible with dense, 2 mm period, arrays of vias.

36 MATERIALS SCIENCE↗
DOE OSTI2024

System and method for multi-channel pyrometer allowing non-contact temperature measurements down to 800 K on the microsecond scale

The present disclosure relates to a system for sensing temperature changes on a microsecond scale. The system uses a multi-channel pyrometer that works in the NIR spectrum to receive thermal radiation. Each channel includes an interference filter tuned to pass thermal radiation within a specified wavelength range, and a detector. Each detector detects thermal radiation focused on it. Each channel further includes an interference filter which reflects thermal radiation which does not pass through it to a subsequent downstream interference filter of a subsequent channel. Each subsequent interference filter is oriented to reflect the thermal radiation not passing through it to a next downstream one of the subsequent interference filters. A subsystem is included for receiving the output from the detectors and determining sensed temperature data therefrom, allowing measurement of temperatures down to 800 K.

Montgomery, Jeffrey↗
DOE OSTI2025

AC electrical transport detection of magnetic properties of small, granular, and heterogeneous samples

Experimental constraints and sample limitations can preclude ideal measurements of electrical transport properties of materials. In such situations, AC electrical transport methods are often employed due to a significant increase in signal-to-noise ratio they can provide. However, dynamic effects that are not often accounted for may be present that may modify the signals in these measurements. In particular, dynamic filtering effects are prominent in small, granular, and heterogeneous materials. We demonstrate that a lock-in amplifier based circuit can distinguish between these DC transport and AC filtering effects. Here, we further demonstrate that this filtering can reveal distinct signatures of magnetic transitions while providing a measure of sample quality.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

CAPG: comprehensive allopolyploid genotyper

Genotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls. We introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole-genome resequencing data. We demonstrate CAPG in allotetraploids, where it performs better than Genome Analysis Toolkit’s HaplotypeCaller applied to reads aligned to the combined subgenomic references.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Barrier-Certificated Reinforcement Learning Approach for Enhancing Power System Transient Stability

Increasing integration of renewable resources brings more flexibility and poses new challenges to modern power systems, leading to highly nonlinear and complex dynamics. Here, this paper aims to provide a general solution framework to traditional control problems, such as frequency control and voltage control, which attempt to maintain the stability of either synchronous generators-governed or inverter-governed systems when subjected to a disturbance and simultaneously guarantee operational constraints, providing a complete complement to existing works on control design. Building on reinforcement learning (RL) and control barrier functions, the framework includes two subsystems, i.e., a model-free controller and a barrier-certification system, which discover RL-based control actions and sequentially filter them using a barrier certificate to satisfy operational constraints. Calculating a barrier function is generally challenging for a complex power system. This is addressed by representing the barrier function using neural networks (NNs) and data-based approaches. An adaptive method is introduced to certify the neural barrier function that perseveres barrier conditions, which is more compatible with online implementation. The proposed framework synthesizes a stabilizing controller that satisfies predefined safety regions. The effectiveness of the proposed framework is demonstrated via several comparative case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.

47 OTHER INSTRUMENTATION↗