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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy

Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

Abstract We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC–AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT–AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. We illustrate that a proficient construction of the latent space is key to achieving these results, enabling us to map these latent spaces using regression models. The proposed framework achieves a relative error of 2% on average and 12% in the worst-case scenario (i.e., the training data is small, but the parameter space is large.). We also show that our framework provides a speed-up of $$7 \times 10^{6}$$ 7 × 10 6 times, in the best case, and $$7 \times 10^{3}$$ 7 × 10 3 times on average compared to a finite element solver. Furthermore, this BT–AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Experimental investigation of current free double layers in helicon plasmas

The paper presents investigations of current free double layer (CFDL) that forms in helicon plasmas. In contrast to the other work reporting on the same subject, in the present investigations the double layer (DL) forms in a mirror-like magnetic field topology. The RF compensated Langmuir probe measurements show multiple DLs, which are in connection with, the abrupt fall of densities along with potential drop of about 24 V and 18 V. The DLs strengths (e ΔV{sub p})/(k T{sub e}) are about 9.5 and 6, and the corresponding widths are about 6 and 5 D lengths. The potential drop is nearly equal to the thermal anisotropies between the two plasma regions forming the DL, which is present in the plateau region of mirror, unlike the earlier studies on the DL formation in the region of strong gradients in the magnetic field. Also, it presents a qualitative discussion on the mechanism of DL formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Conditions for diffusion-limited and reaction-limited recombination in nanostructured solar cells

The performance of Dye-sensitized solar cells (DSC) and related devices made of nanostructured semiconductors relies on a good charge separation, which in turn is achieved by favoring charge transport against recombination. Although both processes occur at very different time scales, hence ensuring good charge separation, in certain cases the kinetics of transport and recombination can be connected, either in a direct or an indirect way. In this work, the connection between electron transport and recombination in nanostructured solar cells is studied both theoretically and by Monte Carlo simulation. Calculations using the Multiple-Trapping model and a realistic trap distribution for nanostructured TiO{sub 2} show that for attempt-to-jump frequencies higher than 10{sup 11}–10{sup 13} Hz, the system adopts a reaction limited (RL) regime, with a lifetime which is effectively independent from the speed of the electrons in the transport level. For frequencies lower than those, and depending on the concentration of recombination centers in the material, the system enters a diffusion-limited regime (DL), where the lifetime increases if the speed of free electrons decreases. In general, the conditions for RL or DL recombination depend critically on the time scale difference between recombination kinetics and free-electron transport. Hence, if the former is too rapid with respect to the latter, the system is in the DL regime and total thermalization of carriers is not possible. In the opposite situation, a RL regime arises. Numerical data available in the literature, and the behavior of the lifetime with respect to (1) density of recombination centers and (2) probability of recombination at a given center, suggest that a typical DSC in operation stays in the RL regime with complete thermalization, although a transition to the DL regime may occur for electrolytes or hole conductors where recombination is especially rapid or where there is a larger dispersion of energies of electron acceptors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Optical, electrical, thermal properties of cadmium chloride doped PVA – PVP blend

Films of polyvinylalcohol (PVA) – polyvinylpyrrolidone (PVP) blend doped with Cadmium Chloride (CdCl{sub 2}) in the doping range 1 wt% to 40 wt% were prepared by solution casting technique. These films were characterized using optical/UV-Vis- NIR spectroscopy, Differential Scanning Calorimetry (DSC) and DC electrical measurements. The UV-Visible spectra were quantitatively analyzed to yield the optical parameters. The UV-Visible Spectra show intermediate absorption bands (before the final absorption edge) due to formation of energy bands in the forbidden gap of PVA-PVP. There is a prominent absorption band at 2.9 eV, from 0.5 wt% up to 1.8 wt% doping level (DL) caused by the dopant (CdCl{sub 2}). The DC electrical studies showed an increase in activation energy from 2.8 eV at 0.5 wt% DL up to 3.5 eV at 4.4 wt% DL, reaching a low of 2.4 eV at 11.2 wt% DL. DSC scans show evidence of formation of chain fragments, at doping levels beyond 8 wt%.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Self-consistent theory of the Darrieus–Landau and Rayleigh–Taylor instabilities with self-generated magnetic fields

The Rayleigh–Taylor (RT) and Darrieus–Landau (DL) instabilities are studied in an inertial confinement fusion context within the framework of small critical-to-shell density ratio D R and weak acceleration regime, i.e., large Froude number Fr. The quasi-isobaric analysis in Sanz et al. is completed with the inclusion of non-isobaric and self-generated magnetic-field effects. The analysis is restricted to perturbation wavelengths k~1 larger than the conduction length scale at the ablation front, yet its validity ranges from wavelengths shorter and larger than the conduction layer width (distance between the ablation front and the critical surface). The use of a sharp boundary model leads to a single analytical expression of the dispersion relation encompassing both instabilities. The two new effects come into play by modifying the perturbed mass and momentum fluxes at the ablation front. The momentum flux (perturbed pressure at the spike) is the predominant stabilizing mechanism in the RT instability (overpressure) and the driving mechanism in the DL instability (underpressure). The non-isobaric effects notably modify the scaling laws in the DL limit, leading to an underpressure scaling as ~k –11/15 rather than ~k –2/5 obtained in the quasi-isobaric model. The magnetic fields are generated due to misalignment between pressure and density gradients (Biermann battery effect). They affect the hydrodynamics by bending the heat flux lines. Within the framework of this paper, they enhance ablation, resulting in a stabilizing effect that peaks for perturbation wavelengths comparable to the conduction layer width. Here, the combination of parameters D R Fr 2/3 defines the region of predominance of each instability in the dispersion relation. It is proven that the DL region falls outside of the parameter range in inertial confinement fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

SUPPRESSION OF ENERGETIC ELECTRON TRANSPORT IN FLARES BY DOUBLE LAYERS

During flares and coronal mass ejections, energetic electrons from coronal sources typically have very long lifetimes compared to the transit times across the systems, suggesting confinement in the source region. Particle-in-cell simulations are carried out to explore the mechanisms of energetic electron transport from the corona to the chromosphere and possible confinement. We set up an initial system of pre-accelerated hot electrons in contact with ambient cold electrons along the local magnetic field and let it evolve over time. Suppression of transport by a nonlinear, highly localized electrostatic electric field (in the form of a double layer) is observed after a short phase of free-streaming by hot electrons. The double layer (DL) emerges at the contact of the two electron populations. It is driven by an ion-electron streaming instability due to the drift of the back-streaming return current electrons interacting with the ions. The DL grows over time and supports a significant drop in temperature and hence reduces heat flux between the two regions that is sustained for the duration of the simulation. This study shows that transport suppression begins when the energetic electrons start to propagate away from a coronal acceleration site. It also implies confinement of energetic electrons with kinetic energies less than the electrostatic energy of the DL for the DL lifetime, which is much longer than the electron transit time through the source region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Dynamics of double layers, ion acceleration, and heat flux suppression during solar flares

Observations of flare-heated electrons in the corona typically suggest confinement of electrons. The confinement mechanism, however, remains unclear. The transport of coronal hot electrons into ambient plasma was recently investigated by particle-in-cell (PIC) simulations. Electron transport was significantly suppressed by the formation of a highly localized, nonlinear electrostatic potential in the form of a double layer (DL). In this work large-scale PIC simulations are performed to explore the dynamics of DLs in larger systems where, instead of a single DL, multiple DLs are generated. The primary DL accelerates return current electrons, resulting in high velocity electron beams that interact with ambient ions. This forms a Buneman unstable system that spawns more DLs. Trapping of heated return current electrons between multiple DLs strongly suppresses electron transport. DLs also accelerate ambient ions and produce strong ion flows over an extended region. This clarifies the mechanism by which hot electrons in the corona couple to and accelerate ions to form the solar wind. These new dynamics in larger systems reveal a more likely picture of DL development and their impact on the ambient plasma in the solar corona. They are applicable to the preparation for in situ coronal space missions like the Solar Probe Plus.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

RECONNECTION-DRIVEN DOUBLE LAYERS IN THE STRATIFIED PLASMA OF THE SOLAR TRANSITION REGION: SUPPLY OF HOT PLASMA INTO THE CORONA

A novel mechanism for the supply of hot plasma into the corona from the chromosphere is suggested here; the mechanism involves collisionless magnetic reconnection (CMR) in the transition region (TR) followed by double layer (DL) formation in the enhanced expansion of the chromospheric cold plasma mixed with CMR-heated hot electrons. It is well known that (i) the CMR produces energetic electrons and (ii) DLs naturally form in expanding dense plasmas containing a minor population of hot electrons. We apply these plasma physics facts to the dynamics of stratified plasma in the TR. In the TR where densities fall below ∼10{sup 16} m{sup −3}, all collisional mean-free paths, electron–ion, ion–neutral, and electron–neutral, become long enough to render plasma collisionless at kinetic scale lengths, making CMR and DL formation possible. The DLs accelerate the chromospheric cold ions to energies comparable to the energy of the hot electrons. When the upflowing energized ions neutralized by the escaping hot electrons thermalize, the resulting hot tenuous plasma supplies an energy flux ∼3 × 10{sup 5} erg cm{sup −2} s{sup −1} = 3 × 10{sup 2} J m{sup −2} s{sup −1} into the corona. The CMR–DL mechanism introduces sudden transitions in the TR as microstructures in both density and energy. The global transition in the TR could be a fractal structure containing such microscopic features. If not impossible, it is difficult to measure such microstructures, but it seems that the coronal heating begins in the nearly collisionless TR by CMR and DL formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.

Environmental sciences↗
DOE OSTI2023

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Abstract Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Materials Science↗
DOE OSTI2023

How the birth and death of shear layers determine confinement evolution: from the L → H transition to the density limit

Electric field profile structure—especially its shear—is a natural order parameter for the edge plasma, and characterizes confinement regimes ranging from the H-mode (Wagner et al. 1982 Phys. Rev. Lett.49, 1408–1412 (doi:10.1103/PhysRevLett.49.1408)) to the density limit (DL) (Greenwald et al. 1988 Nucl. Fusion28, 2199–2207 (doi:10.1088/0029-5515/28/12/009)). The theoretical developments and lessons learned during 40 years of H-mode studies (Connor & Wilson 1999 Plasma Phys. Control. Fusion42, R1–R74 (doi:10.1088/0741-3335/42/1/201); Wagner 2007 Plasma Phys. Control. Fusion49, B1–B33 (doi:10.1088/0741-3335/49/12b/s01)) are applied to the shear layer collapse paradigm (Hong et al. 2017 Nucl. Fusion58, 016041 (doi:10.1088/1741-4326/aa9626)) for the onset of DL phenomena. Results from recent experiments on edge shear layers and DL phenomenology are summarized and discussed in the light of L → H transition physics. The theory of shear layer collapse is then developed. In this work, we demonstrate that shear layer physics captures both the well known current (Greenwald) scaling of the DL (Greenwald 2002 Plasma Phys. Control. Fusion44, R27–R53 (doi:10.1088/0741-3335/44/8/201); Greenwald et al. 2014 Phys. Plasmas21, 110501 (doi:10.1063/1.4901920)), as well as the emerging power scaling (Zanca, Sattin, JET Contributors 2019 Nucl. Fusion59, 126011 (doi:10.1088/1741-4326/ab3b31)). The derivation of the power scaling theory exploits an existing model, originally developed for the L → H transition (Diamond, Liang, Carreras, Terry 1994 Phys. Rev. Lett.72, 2565–2568 (doi:10.1103/PhysRevLett.72.2565); Kim & Diamond 2003 Phys. Rev. Lett.90, 185006 (doi:10.1103/PhysRevLett.90.185006)). We describe the enhanced particle transport events that occur following shear layer collapse. Open problems and future directions are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗
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 OSTI2018

DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks

Hyperparameters employed by deep learning (DL) methods play a substantial role in the performance and reliability of these methods in practice. Unfortunately, finding performance-optimizing hyperparameter settings is a notoriously difficult task. Hyperparameter search methods typically have limited production-strength implementations or do not target scalability within a highly parallel machine, portability across different machines, experimental comparison between different methods, and tighter integration with workflow systems. In this paper, we present DeepHyper, a Python package that provides a common interface for the implementation and study of scalable hyperparameter search methods. It adopts the Balsam workflow system to hide the complexities of running large numbers of hyperparameter configurations in parallel on high-performance computing (HPC) systems. We implement and study asynchronous model-based search methods that consist of sampling a small number of input hyperparameter configurations and progressively fitting surrogate models over the input-output space until exhausting a user-defined budget of evaluations. We evaluate the efficacy of these methods relative to approaches such as random search, genetic algorithms, Bayesian optimization, and hyperband on DL benchmarks on CPU- and GPU-based HPC systems.

Balaprakash, Prasanna↗
DOE OSTI2019

Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing

Deep learning (DL) has been used for many natural language processing (NLP) tasks due to its superior performance as compared to traditional machine learning approaches. In DL models for NLP, words are represented using word embeddings, which capture both semantic and syntactic information in text. However, 90-95% of the DL trainable parameters are associated with the word embeddings, resulting in a large storage or memory footprint. Therefore, reducing the number of word embedding parameters is critical, especially with the increase of vocabulary size. In this work, we propose a novel approximate word embeddings approach for convolutional neural networks (CNNs) used for text classification tasks. The proposed approach significantly reduces the number of model trainable parameters without noticeably sacrificing in computing performance accuracy. Compared to other techniques, our proposed word embeddings technique does not require modifications to the DL model architecture. We evaluate the performance of the the proposed word embeddings on three classification tasks using two datasets, composed of Yelp and Amazon reviews. The results show that the proposed method can reduce the number of word embeddings parameters by 98% and 99% for the Yelp and Amazon datasets respectively, with no drop in computing accuracy.

Alawad, Mohammed↗
DOE OSTI2025

Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction

X-ray computed tomography (CT) is an important noninvasive medical imaging modality for studying the structural details of internal organs. Image reconstruction in CT is an inverse problem of recovering an object's internal structure from the absorption profile of X-ray beams (sinogram) measured using a detector. The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. Motivated by the success of deep learning (DL), researchers have begun to leverage training data and enhanced computing capabilities in recent years to produce high-fidelity reconstructed images. Nonetheless, much of the academic research in DL algorithms for CT has focused primarily on the two-dimensional setting (with simplified forward operators and noise model) for proofs-of-concept, and a comprehensive benchmarking of various classical and data-driven CT reconstruction approaches has not beenundertaken. The key objective of our CT reconstruction grand challenge was to promote methodological advancements for both classical and DL-based approaches for clinical CT with a reasonably accurately simulated 3D CT forward operator and noise model. We have utilized the publicly available LIDC-IDRI dataset and simulated sinograms and FDK images corresponding to two dose levels (clinical- and low-dose, constituting two tracks of the challenge) starting from the normal-dose images as the ground truth. In this paper, we summarize the motivation, context, and results of our challenge, and highlight the future research directions in DL for clinical CT.

X-ray tomography↗
DOE OSTI2023

Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control

This article proposes a deep learning (DL)-based control algorithm—DL velocity-based model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Herein, simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.

97 MATHEMATICS AND COMPUTING↗