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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 OSTI2020

High-Resolution Imaging of Cellulose Organization in Cell Walls by Field Emission Scanning Electron Microscopy

Field emission scanning electron microscopy (FESEM) is a powerful tool for analyzing surface structures of biological and nonbiological samples. However, when it is used to study fine structures of nanometer-sized microfibrils of epidermal cell walls, one often encounters tremendous challenges to acquire clear and undistorted images because of two major issues: (1) Preparation of samples suitable for high resolution imaging; due to the delicateness of some plant materials, such as onion epidermal cell walls, many things can happen during sample processing, which subsequently result in damaged samples or introduce artifacts. (2) Difficulties to acquire clear images of samples which are electron-beam sensitive and prone to charging artifacts at magnifications over 100,000×. In this chapter we described detailed procedures for sample preparation and conditions for high-resolution FESEM imaging of onion epidermal cell walls. Lastly, the methods can be readily adapted for other wall materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A parallel evolutionary multiple-try metropolis Markov chain Monte Carlo algorithm for sampling spatial partitions

We develop an Evolutionary Markov Chain Monte Carlo (EMCMC) algorithm for sampling spatial partitions that lie within a large, complex, and constrained spatial state space. Our algorithm combines the advantages of evolutionary algorithms (EAs) as optimization heuristics for state space traversal and the theoretical convergence properties of Markov Chain Monte Carlo algorithms for sampling from unknown distributions. Local optimality information that is identified via a directed search by our optimization heuristic is used to adaptively update a Markov chain in a promising direction within the framework of a Multiple-Try Metropolis Markov Chain model that incorporates a generalized Metropolis-Hastings ratio. We further expand the reach of our EMCMC algorithm by harnessing the computational power afforded by massively parallel computing architecture through the integration of a parallel EA framework that guides Markov chains running in parallel.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

3x3 array module of 8×8×32 mm 3 position-sensitive virtual frisch-grid CdZnTe detectors for imaging and spectroscopy of cosmic gamma-rays

Here, we evaluated the performance of the 3x3 array of 32-mm long position-sensitive virtual Frisch-grid (VFG) CdZnTe (CZT) detectors read out by the IDEAS GDS-100 data acquisition system. The array is a mechanically solid module designed for integration into a large effective area gamma-ray telescope proposed for imaging and spectroscopy of cosmic gamma-rays. The array employs the bar-shaped 8x8x32 mm 3 CZT crystals which recently became available from Redlen Technologies, Inc. and Kromek-eV Group PLC at affordable costs. The waveform sampling IDE3421 application-specific integrated circuit (ASIC), developed by the University of Michigan and IDEAS for CZT pixel detectors, was adapted to capture the signals from the array's VFG detectors. We showed good spectral performance of the Redlen detectors with an energy resolution of <1% FWHM at 662 keV and slightly poorer performance of the Kromek-eV detectors with a resolution of <2% measured at a reduced temperature of 13 °C. By testing the same detectors as single devices using hybrid charge-sensitive preamplifiers, we demonstrated improved energy resolutions at a normal temperature of ~25°C: <1% and 1.3-1.7%, respectively. However, the Kromek-eV detectors did not perform as well as the Redlen ones. We explained this by the space charge formation (polarization) decreasing the electric field and the electron drift times. The polarization was likely caused by the high injections of holes from the anode contacts. The absence of polarization in Redlen detectors suggested that the quality of the contacts might have played a role in this case.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗
DOE OSTI2017

Experimental development of low-frequency shear modulus and attenuation measurements in mated rock fractures: Shear mechanics due to asperity contact area changes with normal stress

Reservoir core measurements can help guide seismic monitoring of fluid-induced pressure variations in tight fractured reservoirs including those targeted for supercritical CO 2 injection. We present the first seismic-frequency ‘room-dry’ measurements of fracture specific shear stiffness, using artificially fractured standard granite samples with different degrees of mating, a well-mated tensile fracture from a dolomite reservoir core, as well as simple roughened polymethyl methacrylate (PMMA) surfaces. We have adapted a low-frequency (0.01 to 100 Hz) shear modulus and attenuation apparatus to explore the seismic signature of fractures and understand the mechanics of asperity contacts under a range of normal stress conditions. Our instrument is unique in its ability to measure at low normal stresses (0.5 – 20 MPa), simulating 'open' fractures in shallow or high fluid pressure reservoirs. The accuracy of our instrument is demonstrated by calibration and comparison to ultrasonic measurements and low-frequency direct shear measurements of intact samples from the literature. Pressure sensitive film was used to measure real contact area of the fracture surfaces. The fractured shear modulus for the majority of the samples shows an exponential dependence on real contact area. A simple numerical model, with one bonded circular asperity, predicts this behavior and matches the data for the simple PMMA surfaces. The rock surfaces reach their intact moduli at lower contact area than the model predicts, likely due to more complex geometry. Lastly, we apply our results to a Linear-Slip Interface Model to estimate reflection coefficients and calculate shear wave time delays due to the lower wave velocities through the fractured zone. We find that cross-well surveys could detect even well-mated hard rock fractures assuming the availability of high repeatability acquisition systems.

58 GEOSCIENCES↗
DOE OSTI2014

Task 6 - Subtask 1: PNNL Visit by JAEA Researchers to Evaluate the Feasibility of the FLESCOT Code for the Future JAEA Use for the Fukushima Surface Water Environmental Assessment

Four Japan Atomic Energy Agency (JAEA) researchers visited Pacific Northwest National Laboratory (PNNL) for seven working days and have evaluated the suitability and adaptability of FLESCOT to a JAEA’s supercomputer system to effectively simulate cesium behavior in dam reservoirs, river mouths, and coastal areas in Fukushima contaminated by the Fukushima Daiichi nuclear accident. PNNL showed the following to JAEA visitors during the seven-working day period: FLESCOT source code; User’s manual; FLESCOT description – Program structure – Algorism – Solver – Boundary condition handling – Data definition – Input and output methods – How to run. During the visit, JAEA had access to FLESCOT to run with an input data set to evaluate the capacity and feasibility of adapting it to a JAEA super computer with massive parallel processors. As a part of this evaluation, PNNL ran FLESCOT for sample cases of the contaminant migration simulation to further describe FLESCOT in action. JAEA and PNNL researchers also evaluated time spent for each subroutine of FLESCOT, and the JAEA researcher implemented some initial parallelization schemes to FLESCOT. Based on this code evaluation, JAEA and PNNL determined that FLESCOT is: applicable to Fukushima lakes/dam reservoirs, river mouth areas, and coastal water; and feasible to implement parallelization for the JAEA supercomputer. In addition, PNNL and JAEA researchers discussed molecular modeling approaches on cesium adsorption mechanisms to enhance the JAEA molecular modeling activities. PNNL and JAEA also discussed specific collaboration of molecular and computational modeling activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Effects of Subhalos on Interpreting Highly Magnified Sources Near Lensing Caustics

Large magnification factors near gravitational lensing caustics of galaxy-cluster lenses allow the study of individual stars or compact stellar associations at cosmological distances. We study how the presence of sub-galactic subhalos, an inevitable consequence of cold dark matter, can alter the property of caustics and hence change the interpretation of highly magnified sources that lie atop them. First, we consider a galaxy-cluster halo populated with subhalos sampled from a realistic subhalo mass function calibrated to N-body simulations. Then, we compare a semianalytical approximation and an adaptive ray-shooting method that we employ to quantify the property of the caustics. As a case study, we investigate Earendel, a z = 6.2 candidate of magnified single- or multiple-star system with a lone lensed image atop the critical curve in the Sunrise Arc. We find that the source size constraint (≲0.3 pc) previously derived from macrolens models should be relaxed by a factor of a few to 10 when subhalos are accounted for, therefore allowing the possibility of a compact star cluster. The subhalos could introduce an astrometric perturbation that is ≲0$^{"}_{.}$5, which does not contradict observation. These conclusions are largely robust to changes in the subhalo population. Subhalos therefore should be seriously accounted for when interpreting the astrophysical nature of similar highly magnified sources uncovered in recent high-z observations.

Caustic curve↗
DOE OSTI2017

ALMA Observations of Gas-rich Galaxies in z ∼ 1.6 Galaxy Clusters: Evidence for Higher Gas Fractions in High-density Environments

We present ALMA CO (2–1) detections in 11 gas-rich cluster galaxies at z ∼ 1.6, constituting the largest sample of molecular gas measurements in z > 1.5 clusters to date. The observations span three galaxy clusters, derived from the Spitzer Adaptation of the Red-sequence Cluster Survey. We augment the >5 σ detections of the CO (2–1) fluxes with multi-band photometry, yielding stellar masses and infrared-derived star formation rates, to place some of the first constraints on molecular gas properties in z ∼ 1.6 cluster environments. We measure sizable gas reservoirs of 0.5–2 × 10{sup 11} M {sub ☉} in these objects, with high gas fractions ( f {sub gas}) and long depletion timescales ( τ ), averaging 62% and 1.4 Gyr, respectively. We compare our cluster galaxies to the scaling relations of the coeval field, in the context of how gas fractions and depletion timescales vary with respect to the star-forming main sequence. We find that our cluster galaxies lie systematically off the field scaling relations at z = 1.6 toward enhanced gas fractions, at a level of ∼4 σ , but have consistent depletion timescales. Exploiting CO detections in lower-redshift clusters from the literature, we investigate the evolution of the gas fraction in cluster galaxies, finding it to mimic the strong rise with redshift in the field. We emphasize the utility of detecting abundant gas-rich galaxies in high-redshift clusters, deeming them as crucial laboratories for future statistical studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

SU-E-J-77: Dose Tracking On An MR-Linac for Online QA and Plan Adaptation in Abdominal Organs

Recent developments made MRI-guided radiotherapy feasible. Simultaneously performed imaging during dose delivery reveals the influence of changes in anatomy not yet known at the planning stage. When targeting highly motile abdominal organs, respiratory gating is commonly employed in MRI and investigated in external beam radiotherapy to mitigate malicious motion effects. The purpose of the presented work is to investigate anatomy-adaptive dose reconstruction in the treatment of abdominalorgans using concurrent (duplex) gating of an integrated MRlinac modality.Using navigators, 3D-MR images were sampled during exhale phase, requiring 3s per axial volume (360×260×100mm{sup 3}, waterselective T1w-FFE). Deformation vector fields (DVF) were calculated for all imaging dynamics with respect to initial anatomy, yielding an estimation of anatomy changes over the time of a fraction. A pseudo-CT was generated from the outline of a reference MR image, assuming a water-filled body. Consecutively, a treatment was planned on a fictional kidney lesion and optimized simulating a 6MV linac in a 1.5T magnetic field. After delivery, using the DVF, the pseudo-CT was deformed and dose accumulated for every individual gating interval yielding the true accumulated dose on the dynamic anatomy during beam-on.Dose-volume parameters on the PTV show only moderate changes when incorporating motion, i.e. ΔD{sub 99} (GTV)=0.3Gy with D{sub 99} (GTV)=20Gy constraints. However, local differences in the PTV region showed underdosages as high as 2.7Gy and overdosages up to 1.4Gy as compared to the optimized dose on static anatomy.A dose reconstruction toolchain was successfully implemented and proved its potential in the duplex gated treatment of abdominal organs by means of an MR-linac modality. While primary dose constraints were not violated on the fictional test data, large deviations could be found locally, which are left unaccounted for in conventional treatments. Dose-tracking of both target structures and organs at risk using 3D MRI during treatment enables truly adaptive hypofractionated radiotherapy. This work was funded by the SoRTS consortium, which includes the industry partners Elekta, Philips and Technolution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Characterisation of motor cortex organisation in patients with different presentations of persistent low back pain

Abstract Persistence of low back pain is thought to be associated with different underlying pain mechanisms, including ongoing nociceptive input and central sensitisation. We hypothesised that primary motor cortex (M1) representations of back muscles (a measure of motor system adaptation) would differ between pain mechanisms, with more consistent observations in individuals presumed to have an ongoing contribution of nociceptive input consistently related to movement/posture. We tested 28 participants with low back pain sub‐grouped by the presumed underlying pain mechanisms: nociceptive pain, nociplastic pain and a mixed group with features consistent with both. Transcranial magnetic stimulation was used to study M1 organisation of back muscles. M1 maps of multifidus (deep and superficial) and longissimus erector spinae were recorded with fine‐wire electromyography and thoracic erector spinae with surface electromyography. The nociplastic pain group had greater variability in M1 map location (centre of gravity) than other groups ( p < .01), which may suggest less consistency, and perhaps relevance, of motor cortex adaptation for that group. The mixed group had greater overlap of M1 representations between deep/superficial muscles than nociceptive pain (deep multifidus/longissimus: p = .001, deep multifidus/thoracic erector spinae: p = .008) and nociplastic pain (deep multifidus/longissimus: p = .02, deep multifidus/thoracic erector spinae: p = .02) groups. This study provides preliminary evidence of differences in M1 organisation in subgroups of low back pain classified by likely underlying pain mechanisms. Despite the sample size, differences in cortical re‐organisation between subgroups were detected. Differences in M1 organisation in subgroups of low back pain supports tailoring of treatment based on pain mechanism and motor adaptation.

Elgueta‐Cancino, Edith↗
DOE OSTI2019

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

Simulations of biological macromolecules are important in understanding the physical basis of complex processes such as protein folding. However, even with increasing computational capacity and specialized architectures, the ability to simulate protein folding at atomistic scales still remains challenging. This stems from the dual aspects of high dimensionality of protein conformational landscapes, and the inability of atomistic molecular dynamics (MD) simulations to sufficiently sample these landscapes to observe folding events. Machine learning/deep learning (ML/DL) techniques, when combined with atomistic MD simulations offer the opportunity to potentially overcome these limitations by: (1) effectively reducing the dimensionality of MD simulations to automatically build latent representations that correspond to biophysically relevant reaction coordinates (RCs), and (2) driving MD simulations to automatically sample potentially novel conformational states based on these RCs. We examine how coupling DL approaches with MD simulations can lead to effective approaches to fold small proteins on supercomputers. In particular, we study the computational costs and effectiveness of scaling DL-coupled MD workflows implemented using RADICAL-Cybertools in folding two prototypical systems, namely Fs-peptide and the fast-folding variant of the villin head piece protein. We demonstrate that a DL-coupled MD workflow is able to effectively learn latent representations and drive adaptive simulations. Compared to traditional MD-based approaches, our approach achieves an effective performance gain in sampling the folded states by at least 2.3x. Together, our study provides quantitative basis to understand how coupling DL approaches to MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.

Lee, Hyungro↗
DOE OSTI2020

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they were sampled at sub-Nyquist rates. In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube. Perhaps most surprisingly, chemical signal amplification generally seems to increase as the level of sampling decreases. In some examples, the chemical signal is significantly stronger in a data cube reconstructed from 10% CS sampling than it is in the raw, 100% sampled data cube. We explore this phenomenon in two real-world datasets including the Physical Sciences Inc. Fabry-Perot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of these datasets contains the release of a chemical simulant, such as glacial acetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we use the adaptive coherence estimator (ACE) to detect a target signal in the hyperspectral data cube. We end the paper by suggesting some theoretical justifications for why chemical signals would be amplified in CS sampled and reconstructed hyperspectral data cubes and discuss some practical implications.

Hyperspectral imagery, Compressive Sensing, single↗
DOE OSTI2025

Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil–environment relationships

Accurate modelling and mapping soil organic carbon are crucial for supporting soil health restoration and climate change mitigation at both regional and global scales. However, regional soil predictions often suffer from data scarcity and high prediction uncertainty. Utilizing a pre-trained global-to-regional soil carbon predictive model can be a potential solution to address this challenge. Despite its promise, how to construct and apply the global-scale model to enhance regional-scale soil carbon mapping remains largely unexplored. Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. Based on large amount of environmental covariate data and 106,167 soil samples across the globe, we verify our hypothesis of the effectiveness of this 'global-to-regional' modelling strategy. The pre-trained model can be then transferred and fine-tuned to bridge the regional- and global-scale soil–environment relationships. We applied and validated this modelling strategy in four regional-scale study areas, three in the Northern Hemisphere and one in the Southern Hemisphere, each with distinct environmental background. Compared to traditional modelling approaches as a baseline, four case studies all demonstrated significant improvement in prediction accuracy across diverse environments and varying data availabilities. The average percentage improvement across all regions is 10.93% (absolute values decreased by 1.20 g kg−1 averagely) in MAE and 29.04% (absolute values increased by 0.10 averagely) in CCC. The applicability and future horizons of using GSoilCPM were further discussed. We further reveal that regions with fewer soil samples or lower baseline accuracy benefit more from the pre-trained global model. Our findings highlight the advantages of leveraging the generalized knowledge from global models to enhance specifically localized soil modelling, positioning a potential paradigm shift in digital soil mapping, and far-reaching implications for soil monitoring and land management.

Deep learning↗
DOE OSTI2014

Quantum partition functions of composite particles in a hydrogen-helium plasma via path integral Monte Carlo

We compute two- and three-body cluster functions that describe contributions of composite entities, like hydrogen atoms, ions H{sup −}, H{sub 2}{sup +}, and helium atoms, and also charge-charge and atom-charge interactions, to the equation of state of a hydrogen-helium mixture at low density. A cluster function has the structure of a truncated virial coefficient and behaves, at low temperatures, like a usual partition function for the composite entity. Our path integral Monte Carlo calculations use importance sampling to sample efficiently the cluster partition functions even at low temperatures where bound state contributions dominate. We also employ a new and efficient adaptive discretization scheme that allows one not only to eliminate Coulomb divergencies in discretized path integrals, but also to direct the computational effort where particles are close and thus strongly interacting. The numerical results for the two-body function agree with the analytically known quantum second virial coefficient. The three-body cluster functions are compared at low temperatures with familiar partition functions for composite entities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating

Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNNs), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum RNNs (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum–classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A new method for in situ structural investigations of nano-sized amorphous and crystalline materials using mixed-flow reactors

Structural investigations of amorphous and nanocrystalline phases forming in solution are historically challenging. Few methods are capable of in situ atomic structural analysis and rigorous control of the system. A mixed-flow reactor (MFR) is used for total X-ray scattering experiments to examine the short- and long-range structure of phases in situ with pair distribution function (PDF) analysis. The adaptable experimental setup enables data collection for a range of different system chemistries, initial supersaturations and residence times. The age of the sample during analysis is controlled by adjusting the flow rate. Faster rates allow for younger samples to be examined, but if flow is too fast not enough data are acquired to average out excess signal noise. Slower flow rates form older samples, but at very slow speeds particles settle and block flow, clogging the system. Proper background collection and subtraction is critical for data optimization. Overall, this MFR method is an ideal scheme for analyzing the in situ structures of phases that form during crystal growth in solution. As a proof of concept, high-resolution total X-ray scattering data of amorphous and crystalline calcium phosphates and amorphous calcium carbonate were collected for PDF analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Characterization of geometrical factors for quantitative angle-resolved photoelectron spectroscopy

For conventional angle-resolved x-ray photoelectron spectroscopy (ARXPS), the area under the core-level peaks depends mainly on the in-depth distribution of chemical species at the top surface of a specimen. But the x-ray photoelectron spectroscopy (XPS) intensity is also affected by tool-related geometrical factors such as the shape of the x-ray beam, the spectrometer analysis volume, and the manipulator rotation axis. Data analysis is therefore typically based on normalization with respect to the signal from the substrate. Here, we present an original method to perform quantitative ARXPS without normalization, involving evaluation of these geometrical factors. The method is illustrated for a multiprobe XPS system using a methodology based on a specific software (XPSGeometry{sup Registered-Sign }), but is a general process that can be adapted to all types of XPS equipment, even those not specifically designed for ARXPS. In that case, this method enables bringing the sample as close as possible to the manipulator axis of rotation in order to perform automatic acquisitions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗