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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Microfluidic, One-Batch Synthesis of Pd Nanocrystals on N-Doped Carbon in Surfactant-Free Deep Eutectic Solvents for Formic Acid Electrochemical Oxidation

One of the grand challenges that impedes practical applications of nanomaterials is the lack of robust manufacturing methods that are scalable, cheap, and environmentally friendly. In this work, we address this challenge by developing a microfluidic approach that produces surfactant-free Pd nanocrystals (NCs) uniformly loaded on N-doped porous carbon in a one-batch process. The deep eutectic solvent (DES) prepared from choline chloride and ethylene glycol was employed as a novel synthesis solvent, and its extended hydrogen networks and abundant ionic species effectively stabilize Pd facets and confine nanocrystal sizes without using surfactants. The microreactors provide faster heat exchange and more uniform mass transport, which in combination with DES produced Pd NCs with better-defined shape and predominately exposed Pd (100) facet. Furthermore, we describe that the N-doped functional groups in porous carbon direct dense and uniform heterogeneous growth of Pd NCs in a one-batch process, thereby eliminating a separate catalyst deposition step that is often involved in conventional synthesis. The Pd NCs in the one-batch-produced Pd/C catalysts exhibited a size distribution of similar to ~13 ± 3.5 nm and a high ESCA of 46.0 m 2 /g and delivered 362 mA/mg for formic acid electrochemical oxidation with improved stability, demonstrating the unique potentials of microfluidic reactors and DES for the controllable and scalable synthesis of electrocatalyst materials for practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Local Resistance Measurement for Degradation of c-Si Heterojunction with Intrinsic Thin Layer (HIT) Solar Modules

Silicon heterojunction with intrinsic thin layer (or HIT) modules typically degrade at a rate of less than 1% annually in solar fields with dominant degradation in open-circuit voltage and some degradation in series resistance. However, detailed mechanisms can differ from module to module. Here, we study increases in local series resistance that occur over long-term field deployment, indicated by cell areas where the photoluminescence intensity does not degrade but the electroluminescence degrades significantly. To directly measure the local series resistance, we have cored out the local electroluminescence-degraded area, and we measured the sheet resistance by 4-point-probe and local nm-scale resistance using scanning spreading resistance microscopy (SSRM). The results by 4-point-probe show scattered sheet resistance that can be caused, for example, by nonuniform current paths through the transparent conductive oxide layer, the a-Si:H emitter, or the near-junction c-Si inversion layer. In contrast, the SSRM results indicate a relatively uniform and non-degraded resistivity on smaller nanometer spatial scales. SSRM is an atomic force microscopy-based two-terminal resistance mapping technique that measures the local resistance in nm-volume beneath the probe. The consistent resistances measured on the control and degraded samples can exclude the degradation of transparent conductive oxide resistance.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

The Science of Scientific Software Development and Use

Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them is often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. The US Department of Energy Office of Science (DOE-SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. In December 2021, the DOE-SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: software has become an essential part of modern science that impacts new discovery, policy, and technological development. To have full confidence in science delivered via software, we must improve the processes and tools that help us create and use it, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work. The full workshop report will be available at https://doi.org/10.2172/1846009.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Steel Foil Improves Performance Of Blasting Caps

Blasting caps, which commonly include deep-drawn aluminum cups, give significantly higher initiation performance by application of steel foils on output faces. Steel closures 0.005 in. (0.13 mm) thick more effective than aluminum. Caps with directly bonded steel foil produce fragment velocities of 9,300 ft/s (2.8 km/s) with large craters and unpredictable patterns to such degree that no attempts made to initiate explosions. Useful in military and aerospace applications and in specialized industries as mining and exploration for oil.

Bement, Laurence J.↗
NASA NTRS2023

Fault Detection and Diagnosis in Spacecraft Electrical Power Systems

The ability to accurately identify and isolate failures in the electrical power system (EPS) is critical to ensure the reliability of spacecraft. This paper proposes a novel solution to the problem of fault detection and diagnosis in direct current (DC) electric power systems for spacecraft. Autonomous operation becomes essential during deep space missions that lack the ability to monitor and control the spacecraft from ground locations. The current state of EPS fault supervision is insufficient to guarantee highly reliable operation. To solve this issue, a combination of model-based and knowledge-based techniques are used in a hierarchical framework to improve the diagnostic performance of the system. Noise, disturbances, and modeling errors are considered in the design of the fault detection system. Practical considerations related to spacecraft flight hardware and software are accounted for in the system design for flight applications. To assess the functionality of the design, a wide array of failures are simulated in a series of experiments. The experiments showed that the technique improved the capability of the autonomous system by increasing the number of fault types diagnosed. The significance of this study is to provide a framework capable of advanced diagnostics of an EPS with little to no interaction from human operators.

Autonomous Power Systems↗
DOE OSTI2021

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Correction of lunar seismograms for instrumental and near-surface effects and constraints on the velocity structure of the lunar interior

Long-period lunar seismograms were studied with the aim of identifying consistent sets of direct shear and secondary wave arrivals, thus constraining the velocities in the lunar mantle and the depths of the velocity discontinuities. Two velocity models were used to locate the natural impacts and the shallow moonquakes and to obtain the travel time residuals. Seismic sections were made of the radial, transverse, and vertical components of ground motion for impacts, shallow, and deep moonquakes in order to search for consistent sets of secondary wave arrivals. No conclusive set of secondary arrivals could be recognized on the seismic sections and thus the velocities and depths of the velocity discontinuities cannot be severely constrained by secondary arrivals. It is likely that the crust is thinner than 50 km and that a first-order discontinuity separates the upper and lower crust at a depth of between 20 and 30 km.

Horvath, P.↗
DOE OSTI2022

Influence of carrier trapping on radiation detection properties in $\mathrm{CVD}$ grown 4H-SiC epitaxial layers with varying thickness up to 250 µm

We report low doped n-type 4H-SiC epitaxial layers of thickness 50, 150, and 250 µm grown by hot wall chemical vapor deposition were used to fabricate Ni/4H-SiC Schottky radiation detectors. The epitaxial layers were grown on the (0001) face of highly conductive bulk 4H-SiC substrates 8° offcut towards the $\langle 11\bar{2}1\rangle$ direction. The 50, 150, and 250 µm thick epilayer detectors, under optimized settings, showed energy resolutions of 2.0%, 0.78%, and 0.63%, respectively for 5.48 MeV alpha particles. Deep level transient spectroscopy studies showed that the observed variation in the detector resolution is linked to the defect parameters in the devices. Least-squares fitting of the bias dependence of the charge collection efficiency according to a drift-diffusion model, revealed minority carrier diffusion lengths of 16, 10, and 9.2 µm, respectively, implying that the detectors are not limited by minority carrier trapping. The detector performance was observed to be primarily dependent on the concentration and capture cross-sections of the lifetime killing electron traps Z 1/2 and EH 6/7 .

36 MATERIALS SCIENCE↗
DOE OSTI2020

Ionic conductive polymers as artificial solid electrolyte interphase films in Li metal batteries – A review

Lithium (Li) metal has been considered as the ultimate anode material for next-generation rechargeable batteries due to its ultra-high theoretical specific capacity (3860 mAh g -1 ) and the lowest reduction voltage (-3.04 V vs the standard hydrogen electrode). However, the dendritic Li formation, uncontrolled interfacial reactions, and huge volume variations lead to unstable solid electrolyte interphase (SEI) layer, low Coulombic efficiency and hence short cycling lifetime. Designing artificial solid electrolyte interphase (artificial SEI) films on the Li metal electrode exhibits great potential to solve the aforementioned problems and enable Li–metal batteries with prolonged lifetime. Polymer materials with good ionic conductivity, superior processability and high flexibility are considered as ideal artificial SEI film materials. In this review, according to the ionic conductive groups, recent advances in polymeric artificial SEI films are summarized to afford a deep understanding of Li ion plating/stripping behavior and present design principles of high-performance artificial SEI films in achieving stable Li metal electrodes. Perspectives regarding to the future research directions of polymeric artificial SEI films for Li–metal electrode are also discussed. The insights and design principles of polymeric artificial SEI films gained in the current review will be definitely useful in achieving the Li–metal batteries with improved energy density, high safety and long cycling lifetime toward next-generation energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2023

The Effect of DPC Fillers on FEPs Relevant to Disposal of SNF

The US Department of Energy (DOE) is investigating the use of different materials that could be used to fill the void space inside a dual-purpose canister (DPC) loaded with spent nuclear fuel (SNF) just before it is emplaced in a deep geologic repository. The purpose of adding filler material is to maintain subcritical conditions in the repository during the postclosure period, which can span up to 1,000,000 years. Several types of materials have been proposed, including metals, cements, particulates, and glass. Part of this investigation addresses how the presence of filler material inside a DPC will affect the performance of the repository with respect to the repository features; the consequences of events that may occur; and the multiple thermal, hydrologic, chemical, and mechanical processes that may occur in a deep geologic repository over long timescales. This report describes some of the filler materials that have been proposed and studied; identifies 11 features, 6 events, and 25 processes that may be affected by the presence of filler materials; and discusses the effects that may require consideration for each feature, event, or process. The results of this study can be used to direct appropriate research and to develop suitable models if the DOE decides to use fillers to maintain subcritical conditions in DPCs used to dispose of SNF.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1991

Modeling, simulation, and flight characteristics of an aircraft designed to fly at 100,000 feet

A manned real time simulation of a conceptual vehicle, the stratoplane, was developed to study the problems associated with the flight characteristics of a large, lightweight vehicle. Mathematical models of the aerodynamics, mass properties, and propulsion system were developed in support of the simulation and are presented. The simulation was at first conducted without control augmentation to determine the needs for a control system. The unaugmented flying qualities were dominated by lightly damped dutch roll oscillations. Constant pilot workloads were needed at high altitudes. Control augmentation was studied using basic feedbacks. For the longitudinal axis, flight path angle, and pitch rate feedback were sufficient to damp the phugoid mode and to provide good flying qualities. In the lateral directional axis, bank angle, roll rate, and yaw rate feedbacks were sufficient to provide a safe vehicle with acceptable handling qualities. Intentionally stalling the stratoplane to very high angles of attack (deep stall) was studied as a means of enable safe and rapid descent. It was concluded that the deep stall maneuver is viable for this class of vehicle.

Sim, Alex G.↗
NASA NTRS2008

Algorithm Optimally Orders Forward-Chaining Inference Rules

People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

James, Mark↗
NASA NTRS2012

Entry, Descent, and Landing Communications for the 2011 Mars Science Laboratory

The Mars Science Laboratory (MSL), established as the most advanced rover to land on the surface of Mars to date, launched on November 26th, 2011 and arrived to the Martian Gale Crater during the night of August 5th, 2012 (PDT). MSL will investigate whether the landing region was ever suitable to support carbon-based life, and examine rocks, soil, and the atmosphere with a sophisticated suite of tools. This paper addresses the flight system requirement by which the vehicle transmitted indications of the following events using both X-band tones and UHF telemetry to allow identification of probable root causes should a mission anomaly have occurred: Heat-Rejection System (HRS) venting, completion of the cruise stage separation, turn to entry attitude, atmospheric deceleration, bank angle reversal commanded, parachute deployment, heatshield separation, radar ground acquisition, powered descent initiation, rover separation from the descent stage, and rover release. During Entry, Descent, and Landing (EDL), the flight system transmitted a UHF telemetry stream adequate to determine the state of the spacecraft (including the presence of faults) at 8 kbps initiating from cruise stage separation through at least one minute after positive indication of rover release on the surface of Mars. The flight system also transmitted X-band semaphore tones from Entry to Landing plus one minute although since MSL was occulted, as predicted, by Mars as seen from the Earth, Direct-To-Earth (DTE) communications were interrupted at approximately is approx. 5 min after Entry ( approximately 130 prior to Landing). The primary data return paths were through the Deep Space Network (DSN) for DTE and the existing Mars network of orbiting assets for UHF, which included the Mars Reconnaissance Orbiter (MRO), Mars Odyssey (ODY), and Mars Express (MEX) elements. These orbiters recorded the telemetry data stream and returned it back to Earth via the DSN. The paper also discusses the total power received during EDL and the robustness of the telecom design strategy used to ensure EDL communications coverage.

relay operations↗
DOE OSTI2025

Triangle Method for Dense ReLU Layers [SWR-25-72]

This software is an implementation of the methods for initializing and training neural networks to be more efficient per parameter, described more fully below and in the related publication: In theory, depth should make a ReLU network EXPONENTIALLY more efficient by enabling it to produce an exponential number of piecewise linear sections in its output. This reasoning is largely based on the work of mathematicians that have hand-constructed networks that make good use of depth. In practice however, even very deep ReLU networks that have been randomly initialized will behave identically to their shallow counterparts - missing an entire exponential dimension of efficiency. The triangle method is a first attempt at realizing the exponential potential of deep networks. Instead of randomly setting weights, we force pairs of neurons in each layer learn to build triangles (i.e. functions from [0,1] -> [0,1] that look like triangles). This is a very efficient pattern for generating lots of linear pieces because composing two triangular functions doubles the number of pieces with each composition. The triangle method is more than just a different initialization, it is a new paradigm of training. Instead of making direct updates to the matrix weights, we do an extra step of backpropagation to collect the derivatives of the loss function with respect to the shapes of the triangles, training them to tilt left or right. This process essentially holds the networks hand throughout the loss landscape and forces it to always use depth effectively by producing triangular shapes internally. This can produce several orders of magnitude of improvement on convex one-dimensional regression problems. Much more theoretical work is needed to realize its full potential beyond this context, but the implementation in this repository will still work in arbitrary numbers of dimensions. The file Triangle_Method.py is a generalized form of the method that will build each neuron its own custom 1-d convex activation function (with exponential efficiency). Example usage on one dimensional problems can be found in Example_Usage.ipynb and an example of using this in a real neural network can be found in Example_VGG16_CIFAR10.ipynb.

Milkert, Max [National Renewable Energy Laboratory↗
DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗
DOE OSTI2021

Sculpting the Plasmonic Responses of Nanoparticles by Directed Electron Beam Irradiation

Spatial confinement of matter in functional nanostructures has propelled these systems to the forefront of nanoscience, both as a playground for exotic physics and quantum phenomena and in multiple applications including plasmonics, optoelectronics, and sensing. In parallel, the emergence of monochromated electron energy loss spectroscopy (EELS) has enabled exploration of local nanoplasmonic functionalities within single nanoparticles and the collective response of nanoparticle assemblies, providing deep insight into associated mechanisms. However, modern synthesis processes for plasmonic nanostructures are often limited in the types of accessible geometry, and materials and are limited to spatial precisions on the order of tens of nm, precluding the direct exploration of critical aspects of the structure-property relationships. Here, the atomic-sized probe of the scanning transmission electron microscope is used to perform precise sculpting and design nanoparticle configurations. Using low-loss EELS, dynamic analyses of the evolution of the plasmonic response are provided. It is shown that within self-assembled systems of nanoparticles, individual nanoparticles can be selectively removed, reshaped, or patterned with nanometer-level resolution, effectively modifying the plasmonic response in both space and energy. This process significantly increases the scope for design possibilities and presents opportunities for unique structure development, which are ultimately the key for nanophotonic design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Unraveling Defect-Dependent Conductivity-Type Switching in CuFe 2 O 4 for Enhanced Photoelectrocatalytic Reduction of Benzaldehyde

Photoelectrocatalytic (PEC) reduction provides a sustainable route for upgrading biomass-derived feedstocks with reduced energy requirements, yet remains largely unexplored beyond hydrogen evolution and CO 2 reduction due to the scarcity of stable photocathodes. Here, we report a defect-engineered CuFe 2 O 4 photocathode that enables directly quantified PEC reduction of benzaldehyde to benzyl alcohol using a singlecomponent, earth-abundant oxide. By controlling annealing temperature and oxygen partial pressure, CuFe 2 O 4 is systematically tuned from n-type to p-type conductivity. Electrochemical measurements, X-ray and ultraviolet photoelectron spectroscopy, and firstprinciples defect calculations collectively show that oxygen-rich annealing conditions suppress deep donor-type oxygen vacancies while stabilizing shallow acceptor-type copper vacancies, resulting in enhanced hole concentration and improved charge transport. In a mixed acetonitrile/water electrolyte employing 1,4-benzoquinone as a redox mediator, the optimized CuFe 2 O 4 photocathode achieves stable photoelectrochemical operation over 18 h under continuous illumination with a benzyl alcohol production rate of 2.57 μmol/h at −0.50 V vs Ag/AgNO 3 , corresponding to a Faradaic efficiency of 51.3%. This PEC approach lowers the required applied potential by ∼1 V compared to traditional electrocatalytic methods, offering a more energy-efficient route for carbonyl reduction. These findings establish CuFe 2 O 4 as a viable photocathode platform for sustainable photoelectrocatalytic organic transformations under mild reaction conditions.

benzaldehyde reduction↗