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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations

Abstract Large‐scale discrete fracture network (DFN) simulators are standard fare for studies involving the sub‐surface transport of particles since direct observation of real world underground fracture networks is generally infeasible. While these simulators have successfully been used in several engineering applications, estimates of output quantities of interest (QoI) — such as breakthrough time of particles reaching the edge of the system — suffer from two distinct types of uncertainty. A run of a DFN simulator requires several parameters to be set that dictate the placement and size of fractures, the density of fractures, and the overall permeability of the system; uncertainty on the proper parameters will lead to uncertainty in the QoI, called epistemic uncertainty. Furthermore, since these input settings to DFN simulators control the stochastic processes which place fractures and govern flow, understanding how this randomness affects the QoI requires several runs of the simulator at distinct random seeds. The uncertainty in the QoI attributed to different realizations (i.e., different seeds) of the same random process (i.e., identical input parameters) leads to a second type of uncertainty, called aleatoric uncertainty. In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty. Beyond the specific takeaways on which input variables influence uncertainty in the QoI the most, a major contribution of this paper is the introduction of a statistically rigorous workflow for characterizing the uncertainty in DFN flow simulations that exhibit heteroskedasticity.

58 GEOSCIENCES↗
DOE OSTI2017

Reduced North American terrestrial primary productivity linked to anomalous Arctic warming

Warming temperatures in the Northern Hemisphere have enhanced terrestrial productivity. Despite the warming trend, North America has experienced more frequent and more intense cold weather events during winters and springs. These events have been linked to anomalous Arctic warming since 1990, and may affect terrestrial processes. Here we analyse many observation data sets and numerical model simulations to evaluate links between Arctic temperatures and primary productivity in North America. We find that positive springtime temperature anomalies in the Arctic have led to negative anomalies in gross primary productivity over most of North America during the last three decades, which amount to a net productivity decline of 0.31 PgC yr -1 across the continent. This decline is mainly explained by two factors: severe cold conditions in northern North America and lower precipitation in the South Central United States. In addition, United States crop-yield data reveal that during years experiencing anomalous warming in the Arctic, yields declined by approximately 1 to 4% on average, with individual states experiencing declines of up to 20%. We conclude that the strengthening of Arctic warming anomalies in the past decades has remotely reduced productivity over North America.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scalable 3D reconstruction for X-ray single particle imaging with online machine learning

X-ray free-electron lasers offer unique capabilities for measuring the structure and dynamics of biomolecules, helping us understand the basic building blocks of life. Notably, high-repetition-rate free-electron lasers enable single particle imaging, where individual, weakly scattering biomolecules are imaged under near-physiological conditions with the opportunity to access fleeting states that cannot be captured in cryogenic or crystallized conditions. Existing X-ray single particle reconstruction algorithms, which estimate the particle orientation for each image independently, are slow and memory-intensive when handling the massive datasets generated by emerging free-electron lasers. Here, we introduce X-RAI (X-Ray single particle imaging with Amortized Inference), an online reconstruction framework that estimates the structure of 3D macromolecules from large X-ray single particle datasets. X-RAI consists of a convolutional encoder, which amortizes pose estimation over large datasets, as well as a physics-based decoder, which employs an implicit neural representation to enable high-quality 3D reconstruction in an end-to-end, self-supervised manner. We demonstrate that X-RAI achieves state-of-the-art performance for small-scale datasets in simulation and challenging experimental settings and demonstrate its unprecedented ability to process large datasets containing millions of diffraction images in an online fashion. These abilities signify a paradigm shift in X-ray single particle imaging towards real-time reconstruction.

Computer science↗
DOE OSTI2022

Epidural anesthesia needle guidance by forward-view endoscopic optical coherence tomography and deep learning

Epidural anesthesia requires injection of anesthetic into the epidural space in the spine. Accurate placement of the epidural needle is a major challenge. To address this, we developed a forward-view endoscopic optical coherence tomography (OCT) system for real-time imaging of the tissue in front of the needle tip during the puncture. We tested this OCT system in porcine backbones and developed a set of deep learning models to automatically process the imaging data for needle localization. A series of binary classification models were developed to recognize the five layers of the backbone, including fat, interspinous ligament, ligamentum flavum, epidural space, and spinal cord. The classification models provided an average classification accuracy of 96.65%. During puncture, it is important to maintain a safe distance between the needle tip and the dura mater. Regression models were developed to estimate that distance based on the OCT imaging data. Based on the Inception architecture, our models achieved a mean absolute percentage error of 3.05% ± 0.55%. Overall, our results validated the technical feasibility of using this novel imaging strategy to automatically recognize different tissue structures and measure the distances ahead of the needle tip during the epidural needle placement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Two-dimensional forms of robust CO 2 reduction photocatalysts

Photoelectrocatalysts that use sunlight to power the CO 2 reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO 2 reduction photoelectrocatalysts, we screen for candidate monolayers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO 2 reduction, determine exciton binding energies, and compute surface reactivity. We find visible light absorption for SiAs, ZnTe, and ZnSe monolayers, and that due to a lack of binding, CO selectivity is possible. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO 2 photoreduction candidates.

36 MATERIALS SCIENCE↗
DOE OSTI2017

An Advanced TALSPEAK Concept for Separating Minor Actinides. Part 2. Flowsheet Test with Actinide-spiked Simulant

A solvent extraction system has been developed for separating trivalent actinides from lanthanides. This “Advanced TALSPEAK” system uses 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester to extract the lanthanides into a n-dodecane-based solvent phase, while the actinides are retained in a citrate-buffered aqueous phase by complexation to N-(2-hydroxyethyl)ethylenediamine-N,N',N'-triacetic acid. Batch distribution measurements indicate that the separation of americium from the light lanthanides decreases as the pH decreases. For example, the separation factor between La and Am increases from 2.5 at pH 2.0 to 19.3 at pH 3.0. However, previous investigations indicated that the extraction rates for the heavier lanthanides decrease with increasing pH. So, a balance between these two competing effects is required. An aqueous phase in which the pH was set at 2.6 was chosen for further process development because this offered optimal separation, with a minimum separation factor of ~8.4, based on the separation between La and Am. Centrifugal contactor single-stage efficiencies were measured to characterize the performance of the system under flow conditions.

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

Nothing to see here: failed supernovae are faint or rare

ABSTRACT The absence of Type IIP core-collapse supernovae arising from progenitors above 17 solar masses suggests the existence of another evolutionary path by which massive stars end their lives. The direct collapse of a stellar core to a black hole without the production of a bright, explosive transient is expected to produce a long-lived, dim, red transient known as a failed supernova. Despite the detection of a number of candidates for disappearing massive stars in recent years, conclusive observational evidence for failed supernovae remains elusive. A custom-built pipeline designed for the detection of faint transients is used to re-analyse 10 yr of observations of 231 nearby galaxies from the PTF/ZTF surveys. This analysis recovers known supernovae, and yields a number of interesting transients. However, none of these are consistent with a failed supernova. Through Monte Carlo tests the recovery efficiency of our pipeline is quantified. By assuming failed supernovae occur as a Poissonian process with zero detections in the data set, 95 per cent upper limits to the rate of failed supernovae are calculated as a function of failed supernova absolute magnitude. We estimate failed supernovae to be less than 0.61, 0.33, 0.26, or 0.23 of the core-collapse SN rate for absolute magnitudes of −11, −12, −13, and −14, respectively. Finally, we show that if they exist, the Vera C. Rubin Observatory will find 1.7–3.7 failed SNe per year for an absolute bolometric luminosity of ∼6 × 1039 erg s−1 out to distances of 33–43 Mpc, depending on their assumed spectral energy distribution.

Byrne, R. A. (ORCID:0000000177661737)↗
DOE OSTI2025

Auriga Streams – I: disrupting satellites surrounding Milky Way-mass haloes at multiple resolutions

In a hierarchically formed Universe, galaxies accrete smaller systems that tidally disrupt as they evolve in the host’s potential. We present a complete catalogue of disrupting galaxies accreted onto Milky Way-mass haloes from the Auriga suite of cosmological magnetohydrodynamic zoom-in simulations. We classify accretion events as intact satellites, stellar streams, or phase-mixed systems based on automated criteria calibrated to a visually classified sample, and match accretions to their counterparts in haloes re-simulated at higher resolution. Most satellites at the present day have lost substantial amounts of stellar mass – 67 per cent have $f_\text{bound} < 0.97$ (our threshold of lost stellar mass to no longer be considered intact), while 53 per cent satisfy a more stringent $f_\text{bound} < 0.8$. Streams typically outnumber intact systems, contribute a smaller fraction of overall accreted stars, and are substantial contributors at intermediate distances from the host centre ($\sim$0.1 to $\sim 0.7R_\text{200m}$, or $\sim$35 to $\sim$250 kpc for the Milky Way). We also identify accretion events that disrupt to form streams around massive intact satellites instead of the main host. Streams are more likely than intact or phase-mixed systems to have experienced pre-processing, suggesting this mechanism is important for setting disruption rates around Milky Way-mass haloes. All of these results are preserved across different simulation resolutions, though we do find some hints that satellites disrupt more readily at lower resolution. The Auriga haloes suggest that disrupting satellites surrounding Milky Way-mass galaxies are the norm and that a wealth of tidal features waits to be uncovered in upcoming surveys.

galaxies: haloes↗
DOE OSTI2019

rascalc : a jackknife approach to estimating single- and multitracer galaxy covariance matrices

ABSTRACT To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large-scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs ∼104 times faster than previous codes, computing finely binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, rascalc, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input data set (without a prior 2PCF model). Deviations between large-scale model covariances from a mock survey and those from a large suite of mocks are found to be indistinguishable from noise. In addition, the choice of input mock is shown to be irrelevant for desired noise levels below ∼105 mocks. Coupled with its generalization to multitracer data sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Logical error rates for the surface code under a mixed coherent and stochastic circuit-level noise model inspired by trapped ions

With fault-tolerant quantum computing (FTQC) on the horizon, it is critical to understand sources of logical errors in plausible hardware implementations of quantum error-correcting codes. Detailed error modeling of computational instructions on particular FTQC architectures will enable the better prediction of error propagation in FT-encoded quantum circuits while revealing where greater attention is needed in hardware design. In this work, we consider logical error rates for the surface code implemented on a hypothetical grid-based trapped-ion quantum charge-coupled device architecture. Specifically, we construct logical channels for the idling surface code and examine its diamond error under a mixed coherent and stochastic circuit-level noise model inspired by trapped ions. We include the coherent dephasing noise that is known to accumulate during physical qubit idling and transport in these systems, determining idling and transport durations using the time-resolved output of an open-source trapped-ion surface code compiler. To estimate expectation values of logical Pauli observables following hardware circuits containing non-Clifford sources of noise, we utilize a Monte Carlo technique to sample from an underlying quasiprobability distribution of Clifford circuits that we independently simulate in a phase-sensitive fashion. We verify error suppression up to code distance 𝑑 = 11 at coherent dephasing rates near and below those of current-generation trapped-ion quantum computers and find that logical error rates align with those of analogous fully stochastic simulations in this regime. Exploring higher dephasing rates at 𝑑 = 3−5, we find evidence for growing coherent rotations about all three logical Pauli axes, increased diagonal logical error process matrix elements relative to those of stochastic simulations, and a reduced dephasing rate threshold. Overall, our work paves a way toward realistic hardware emulation of small fault-tolerant quantum processes, e.g., members of an FTQC instruction set.

Quantum benchmarking↗
DOE OSTI2025

Search for heavy neutral resonances decaying to tau lepton pairs in proton-proton collisions at $\sqrt{s}$=13 TeV

A search for heavy neutral gauge bosons (𝑍′) decaying into a pair of tau leptons is performed in proton-proton collisions at $\sqrt{s}$ =13 TeV at the CERN LHC. The data were collected with the CMS detector and correspond to an integrated luminosity of 138 fb −1 . The observations are found to be in agreement with the expectation from standard model processes. Limits at 95% confidence level are set on the product of the 𝑍′ production cross section and its branching fraction to tau lepton pairs for a range of 𝑍′ boson masses. For a narrow resonance in the sequential standard model scenario, a 𝑍′ boson with a mass below 3.5 TeV is excluded. This is the most stringent limit to date from this type of search.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Guiding Self-Assembly of Active Colloids by Temporal Modulation of Activity

Self-organization phenomena in ensembles of self-propelled particles open pathways to the synthesis of new dynamic states not accessible by traditional equilibrium processes. The challenge is to develop a set of principles that facilitate the control and manipulation of emergent active states. In this study, we report that dielectric rolling colloids energized by a pulsating electric field self-organize into alternating square lattices with a lattice constant controlled by the parameters of the field. We combine experiments and simulations to examine spatiotemporal properties of the emergent collective patterns and investigate the underlying dynamics of the self-organization.We reveal the resistance of the dynamic lattices to compression and expansion stresses leading to a hysteretic behavior of the lattice constant. The general mechanism of pattern synthesis and control in active ensembles via temporal modulation of activity can be applied to other active colloidal systems.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis

Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments.

Sullivan, Brendan T.↗
DOE OSTI2018

Modular Spiking Neural Circuits for Mapping Long Short-Term Memory on a Neurosynaptic Processor

Due to the distributed and asynchronous nature of neural computation through low-energy spikes, brain-inspired hardware systems offer high energy efficiency and massive parallelism. One such platform is the IBM TrueNorth neurosynaptic system. Recently, TrueNorth compatible representation learning algorithms have emerged, achieving close to the state-of-the-art performance in various data sets. However, its application in temporal sequence processing models, such as recurrent neural networks (RNNs), is still only at the proof of concept level. There is an inherent difficulty in capturing temporal dynamics of an RNN using spiking neurons, which is only exasperated by the hardware constraints in connectivity and synaptic weight resolution. This paper presents a design flow that overcomes these difficulties and maps a special case of recurrent networks called long short-term memory (LSTM) onto a spike-based platform. The framework utilizes various approximation techniques, such as activation discretization, weight quantization, and scaling and rounding, spiking neural circuits that implement the complex gating mechanisms, and a store-and-release technique to enable neuron synchronization and faithful storage. While the presented techniques can be applied to map LSTM to any spiking neural network (SNN) simulator/emulator, here we choose the TrueNorth chip as the target platform by adhering to its hardware constraints. Three LSTM applications, parity check, extended Reber grammar, and question classification, are evaluated. The tradeoffs among accuracy, performance, and energy tradeoffs achieved on TrueNorth are demonstrated. Finally, this is compared with the performance on an SNN platform without hardware constraints, which represents the upper bound of the achievable accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Multistep inversion workflow for 3D long-offset damped elastic waves in the Fourier domain

We present a new workflow for imaging damped three-dimensional elastic wavefields in the Fourier domain. The workflow employs a multiscale imaging approach, in which offset lengths are laddered, where frequency content and damping of the data are changed cyclically. Thus, the inversion process is launched using short-offset and low-frequency data to recover the long spatial wavelength of the image at a shallow depth. Increasing frequency and offset length leads to the recovery of the fine-scale features of the model at greater depths. For the fixed offset, we employ (in the imaging process) a few discrete frequencies with a set of Laplace damping parameters. The forward problem is solved with a finite-difference frequency-domain method based on a massively parallel iterative solver. The inversion code is based upon the solution of a least squares optimisation problem and is solved using a nonlinear gradient method. It is fully parallelised for distributed memory computational platforms. Our full-waveform inversion workflow is applied to the 3D Marmousi-2 and SEG/EAGE Salt models with long-offset data. The maximum inverted frequencies are 6 Hz for the Marmousi model and 2 Hz for the SEG/EAGE Salt model. The detailed structures are imaged successfully up to the depth approximately equal to one-third of the maximum offset length at a resolution consistent with the inverted frequencies.

3D full waveform inversion↗
DOE OSTI2018

Low-Background Method of Isotope Markers for Measuring the Efficiency of Intercalation of Graphite by Potassium Atoms

The result of low-background measurements of the gamma activity of graphite-potassium intercalated sample is presented. A germanium gamma spectrometer, used for measurements, was located in the low-background chamber of Baksan Neutrino Observatory. For 384 hours of exposure, 768 decays of K-40 isotope nuclei were registered. This activity corresponds to 85 μg/cm{sup 2} potassium atoms embedded in to graphite lattice. A computer simulation of the intercalation process and the gamma-ray spectrum set is also presented. The accuracy of the potassium concentration determination can be brought to 10{sup –}11–10{sup –}12 g/g for mixture enriched with K-40 isotope.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Pareto Optimization of Analog Circuits Using Reinforcement Learning

Analog circuit optimization and design presents a unique set of challenges in the IC design process. Many applications require the designer to optimize for multiple competing objectives, which poses a crucial challenge. Motivated by these practical aspects, we propose a novel method to tackle multi-objective optimization for analog circuit design in continuous action spaces. In particular, we propose to (i) extrapolate current techniques in Multi-Objective Reinforcement Learning to continuous state and action spaces and (ii) provide for a dynamically tunable trained model to query user defined preferences in multi-objective optimization in the analog circuit design context.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Simple Low-process Radiation Simulation For Unity Game Engine

This is a set of Unity components that use the Unity game engine's transform and collision systems to provide a reasonably representative approximation of any number of unique radiation sources and the collective dose they deliver to a user.

Dunker, Jack [Idaho National Laboratory (INL), Ida↗