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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accurate and rapid acoustic damage characterization in complex structures using sparse sensor networks and deep learning models

Damage diagnosis in critical components is essential for ensuring the safety and reliability of operations across industries, spanning manufacturing, aerospace, and energy. Traditional acoustic nondestructive testing methods primarily focus on detecting defects through the direct scattering of single-mode incident waves from the damage, which limit their applicability to simple structures and small inspection areas. Our earlier research demonstrated that machine learning algorithms combined with sparse sensor networks can identify critical defect signatures even from multiply scattered, multi-mode acoustic signals, indicating the potential for improved defect inspection in complex, real-world structures. In this work, we demonstrate the successful implementation of this approach in a fixed sensor configuration to rapidly and accurately detect simulated defects in a geometrically complex, real-world structure, a brake rotor hub. Three different types of defects were physically simulated on the surface of the hub, and the collected data were used to train an autoencoder-based deep learning model. Two models were tested, one using single measurements and the other using multiple measurements taking advantage of the spatial distribution of the sensor network. After training, the multi-measurement model achieved 100 % accuracy in identifying, classifying, and locating unseen, unique damages. This work illustrates the potential of the proposed method for a wide range of industrial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.

Ye, Fei↗
NASA NTRS1999

Tidal Energy Available for Deep Ocean Mixing: Bounds from Altimetry Data

Maintenance of the large-scale thermohaline circulation has long presented a problem to oceanographers. Observed mixing rates in the pelagic ocean are an order of magnitude too small to balance the rate at which dense bottom water is created at high latitudes. Recent observational and theoretical work suggests that much of this mixing may occur in hot spots near areas of rough topography (e.g., mid-ocean ridges and island arcs). Barotropic tidal currents provide a very plausible source of energy to maintain these mixing processes. Topex/Poseidon (T/P) satellite altimetry data have made precise mapping of open ocean tidal elevations possible for the first time. We can thus obtain empirical, spatially localized, estimates of barotropic tidal dissipation. These provide an upper bound on the amount of tidal energy that is dissipated in the deep ocean, and hence is available for deep mixing. We will present and compare maps of open ocean tidal energy flux divergence, and estimates of tidal energy flux into shallow seas, derived from T/P altimetry data using both formal data assimilation methods and empirical approaches. With the data assimilation methods we can place formal error bars on the fluxes. Our results show that 20-25% of tidal energy dissipation occurs outside of the shallow seas, the traditional sink for tidal energy. This suggests that up to 1 TW of energy may be available from the tides (lunar and solar) for mixing the deep ocean. The dissipation indeed appears to be concentrated over areas of rough topography.

Egbert, Gary D.↗
NASA NTRS1999

Tidal Energy Available for Deep Ocean Mixing: Bounds from Altimetry Data

Maintenance of the large-scale thermohaline circulation has long presented an interesting problem. Observed mixing rates in the pelagic ocean are an order of magnitude too small to balance the rate at which dense bottom water is created at high latitudes. Recent observational and theoretical work suggests that much of this mixing may occur in hot spots near areas of rough topography (e.g., mid-ocean ridges and island arcs). Barotropic tidal currents provide a very plausible source of energy to maintain these mixing processes. Topex/Poseidon satellite altimetry data have made precise mapping of open ocean tidal elevations possible for the first time. We can thus obtain empirical, spatially localized, estimates of barotropic tidal dissipation. These provide an upper bound on the amount of tidal energy that is dissipated in the deep ocean, and hence is available for deep mixing. We will present and compare maps of open ocean tidal energy flux divergence, and estimates of tidal energy flux into shallow seas, derived from T/P altimetry data using both formal data assimilation methods and empirical approaches. With the data assimilation methods we can place formal error bars on the fluxes. Our results show that 20-25% of tidal energy dissipation occurs outside of the shallow seas, the traditional sink for tidal energy. This suggests that up to 1 TW of energy may be available from the tides (lunar and solar) for mixing the deep ocean. The dissipation indeed appears to be concentrated over areas of rough topography.

Ray, Richard D.↗
NASA NTRS1999

Tidal Energy Available for Deep Ocean Mixing: Bounds From Altimetry Data

Maintenance of the large-scale thermohaline circulation has long presented a problem to oceanographers. Observed mixing rates in the pelagic ocean are an order of magnitude too small to balance the rate at which dense bottom water is created at high latitudes. Recent observational and theoretical work suggests that much of this mixing may occur in hot spots near areas of rough topography (e.g., mid-ocean ridges and island arcs). Barotropic tidal currents provide a very plausible source of energy to maintain these mixing processes. Topex/Poseidon satellite altimetry data have made precise mapping of open ocean tidal elevations possible for the first time. We can thus obtain empirical, spatially localized, estimates of barotropic tidal dissipation. These provide an upper bound on the amount of tidal energy that is dissipated in the deep ocean, and hence is available for deep mixing. We will present and compare maps of open ocean tidal energy flux divergence, and estimates of tidal energy flux into shallow seas, derived from T/P altimetry data using both formal data assimilation methods and empirical approaches. With the data assimilation methods we can place formal error bars on the fluxes. Our results show that 20-25% of tidal energy dissipation occurs outside of the shallow seas, the traditional sink for tidal energy. This suggests that up to 1 TW of energy may be available from the tides (lunar and solar) for mixing the deep ocean. The dissipation indeed appears to be concentrated over areas of rough topography.

Egbert, Gary D.↗
DOE OSTI2021

Deep Learning for Fish Identification from Sonar Data: CRADA 481 [Abstract only]

To help solve the challenges of hydropower energy production related to the potential for eel injury and mortality from passage through hydropower turbines, we will develop a deep learning method for identifying migrating eels from imaging sonar. This project continues with a prior project conducted by the Pacific Northwest National Laboratory (PNNL) and the Electric Power Research Institute (EPRI) in FY2018-2019. The proposed method employs Convolution Neural Network (CNN), a powerful deep learning method for image classification, to distinguish between images of eels and non-eel moving objects. We propose to collect more laboratory data and add more existing field data to train a powerful deep learning model. In addition to eels and sticks as classified in previous studies, we will add images containing several non-eel fish species and macrophyte mats to the training data. A multi-class classification model will be developed to distinguish these objects. Object detection algorithm will be explored and developed to locate and identify multiple objects in each sonar frame. Motion analysis will be performed to track the movement of objects in sonar video clips. We will also improve the data conversion algorithm so that it can read in both DIDSON and ARIS (both are imaging sonars developed by Sound Metrics Corp) data files and convert them to images with comparably high resolution, regardless of the varying detection ranges in different environments. The developed algorithms will be packaged as a software with a graphic user interface. The software will be evaluated by external collaborators in the field. The developed framework can be generalized for automatic monitoring of fish passage and migration using other imaging sonars like ARIS and will benefit the design and operation of ecologically friendly hydroelectric projects. The developed wavelet and CNN model configuration parameters can potentially be transferred to lamprey detection in similar riverine environments.

13 HYDRO ENERGY↗
DOE OSTI2021

Underwater plasma breakdown characteristics with respect to highly pressurized drilling applications

Deep earth drilling is a key technique to extract oil, gas, and geothermal heat from the earth. Many complex energy focusing methods have been explored as an alternative approach to reach these resources but most of them require high energy. However, by utilizing short time span liquid plasma discharges, energy focusing can be achieved within traditional drilling systems. These discharges induce a rapid expansion process and a resulting shockwave. It is believed that this focused energy will lower the required cutting force to progress through the rock. Lowering the required cutting force will allow for lower drill bit wear, quicker rate of penetration, and an overall cost savings of the project. Plasma breakdown characteristics at drilling relevant pressures, ranging from 1 to 350 atm, were studied. A resistance–capacitance circuit with an air gapped spark switch was utilized to generate pulsed plasma between the pressurized electrodes. It was found that the required breakdown voltage increases as the pressure increases. It was also found that a plasma channel formation and an associated breakdown may or may not occur between the electrodes at different pressures due to variation in required breakdown voltages. Breakdown time-lag in the dielectric medium (tap water, 780 μS cm –1 ) increased as the pressure was increased, which indicated a higher voltage drop at higher pressures (>100 atm). The plasma generated cavitation bubble with an associated shockwave occurred as pressures were increased. However, the bubble radius and the bubble duration decreased as the pressure was increased. The plasma generated shockwave speeds fall within the expected speed of sound in water. Lastly, preliminary rock cracking tests were performed on granite at high pressures (340 and 272 atm) and it was found that plasma is able to create cracks in the rock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Computational design of closely related proteins that adopt two well-defined but structurally divergent folds

The plasticity of naturally occurring protein structures, which can change shape considerably in response to changes in environmental conditions, is critical to biological function. While computational methods have been used for de novo design of proteins that fold to a single state with a deep free-energy minimum [P.-S. Huang, S. E. Boyken, D. Baker, Nature 537, 320–327 (2016)], and to reengineer natural proteins to alter their dynamics [J. A. Davey, A. M. Damry, N. K. Goto, R. A. Chica, Nat. Chem. Biol.13, 1280–1285 (2017)] or fold [P. A. Alexander, Y. He, Y. Chen, J. Orban, P. N. Bryan, Proc. Natl. Acad. Sci. U.S.A. 106, 21149–21154 (2009)], the de novo design of closely related sequences which adopt well-defined but structurally divergent structures remains an outstanding challenge. We designed closely related sequences (over 94% identity) that can adopt two very different homotrimeric helical bundle conformations—one short (~66 Å height) and the other long (~100 Å height)—reminiscent of the conformational transition of viral fusion proteins. Crystallographic and NMR spectroscopic characterization shows that both the short- and long-state sequences fold as designed. We sought to design bistable sequences for which both states are accessible, and obtained a single designed protein sequence that populates either the short state or the long state depending on the measurement conditions. The design of sequences which are poised to adopt two very different conformations sets the stage for creating large-scale conformational switches between structurally divergent forms.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1978

Study of electronic properties in proton- and electron-irradiated GaAlAs and GaAs solar cell materials

Diagnostical measurement techniques such as dark I-V, C-V, the thermally insulated capacitance, and the deep level transient spectroscopy methods were employed to study defect properties in the proton-irradiated n-GaAs materials. Defect energy levels, thermal emission rates, and capture cross sections of electrons as well as trap densities were deduced from these measurements and the results are presented. Correlations between the measured defect parameters and the dark I-V characteristics of the n-GaAs Schottky barrier diodes are also discussed. Defect energy levels (i.e., electron traps) determined are also compared with published data in order to identify their physical origins.

Li, S. S.↗
NASA NTRS1989

Deep-level transient spectroscopy of Al(x)Ga(1-x)As/GaAs using nondestructive acousto-electric voltage measurement

The amplitude and the transient time constant of the acoustoelectric voltage were measured as a function of temperature to determine the activation energy of deep levels in Al(x)Ga(1-x)As/GaAs grown by molecular-beam epitaxy. In comparison to other methods based on monitoring the capacitance transient, deep-level transient spectroscopy has several advantages. The technique is nondestructive and highly sensitive, and, because of the dependence of the polarity of the acoustoelectric voltage on the carrier type, it yields information about the charge of the transient carriers and the type of deep traps involved in the release or trapping of these carriers.

Tabib-Azar, Massood↗
DOE OSTI2026

Michel Electron Selection with SPINE for DUNE Far Detector Simulation

Michel electrons are a valuable input for particle detector calibration due to their consistent kinetic energy distribution. This report details the evaluation of a Michel electron identification method's application to simulated data from the DUNE (Deep Underground Neutrino Experiment) far detector. This method, which relies on the neural network-based particle classification software SPINE (Scalable Particle Imaging with Neural Embeddings), was developed and calibrated using simulated data for the SBND (Short-Baseline Neutrino Detector) experiment before being applied to simulated DUNE data from a 1x2x6 subset of far detector modules.

Wilson, Dante [Colorado State U.]↗
DOE OSTI2025

The NGC 3109 Satellite System: The First Systematic Resolved Search for Dwarf Galaxies Around an SMC-mass Host

We report the results of the deepest search to date for dwarf galaxies around NGC 3109, a barred spiral galaxy with a mass similar to that of the Small Magellanic Cloud (SMC), using a semiautomated search method. Using the Dark Energy Camera, we survey a region covering a projected distance of ∼70 kpc of NGC 3109 (D = 1.3 Mpc, R vir ∼ 90 kpc, M ∼ 10 8 M*) as part of the MADCASH and DELVE-DEEP programs. We introduce a newly developed semiresolved search method, used alongside a resolved search, to identify crowded dwarf galaxies around NGC 3109. Using both approaches, we successfully recover the known satellites Antlia and Antlia B. We identified a promising candidate, which was later confirmed to be a background dwarf through deep follow-up observations. Our detection limits are well defined, with the sample ∼80% complete down to M V ∼ −8.0, and include detections of dwarf galaxies as faint as M V ∼ −6.0. This is the first comprehensive study of a satellite system through resolved stars around an SMC mass host. Our results show that NGC 3109 has more bright (M V ∼ −9.0) satellites than the mean predictions from cold dark matter models, but well within the host-to-host scatter. A larger sample of LMC/SMC-mass hosts is needed to test whether or not the observations are consistent with current model expectations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗
DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
NASA NTRS1985

EL2 and related defects in GaAs - Challenges and pitfalls

The incorporation process of nonequilibrium vacancies in melt-grown GaAs is strongly complicated by deviations from stoichiometry and the presence of two sublattices. Many of the microdefects originating in these vacancies and their interactions introduce energy levels (shallow and deep) within the energy gap. The direct identification of the chemical or structural signature of these defects and its direct correlation to their electronic behavior is not generally possible. It is necessary, therefore, to rely on indirect methods and phenomenological models and deal with the associated pitfalls. EL2, a microdefect introducing a deep donor level, has been in the limelight in recent years because it is believed to be responsible for the semi-insulating behavior of undoped GaAs. Although much progress has been made towards understanding its origin and nature, some relevant questions remain unanswered. An attempt is made to assess the present status of understanding of EL2 in the light of most recent results.

Gatos, H. C.↗
NASA NTRS1985

EL2 and related defects in GaAs - Challenges and pitfalls

The incorporation process of nonequilibrium vacancies in melt-grown GaAs is strongly complicated by deviations from stoichiometry, and the presence of two sublattices. Many of the microdefects originating in these vacancies and their interactions introduce energy levels (shallow and deep) within the energy gap. The direct identification of the chemical or structural signature of these defects and its direct correlation to their electronic behavior is not generally possible. It is therefore necessary to rely on indirect methods and phenomenological models and be confronted with the associated pitfalls. EL2, a microdefect introducing a deep donor level, has been in the limelight in recent years because it is believed to be responsible for the semi-insulating behavior of undoped GaAs. Although much progress has been made towards understanding its origin and nature, some relevant questions remain unanswered. An attempt is made to assess the present status of understanding of EL2 in the light of the most recent results.

Gatos, H. C.↗
DOE OSTI2023

MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.

25 ENERGY STORAGE↗