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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantitative approaches for including equity in risk and resilience infrastructure planning analyses

Risk and resilience assessments for critical infrastructure focus on myriad objectives, from natural hazard evaluations to optimizing investments. Although research has started to characterize externalities associated with current or possible future states, incorporation of equity priorities at project inception is increasingly being recognized as critical for planning related activities. However, there is no standard methodology that guides development of equity-informed quantitative approaches for infrastructure planning activities. Here, to address this gap, we introduce a logic model that can be tailored to capture nuances about specific geographies and community priorities, effectively incorporating them into different mathematical approaches for quantitative risk assessments. Specifically, the logic model uses a graded, iterative approach to clarify specific equity objectives as well as inform the development of equations being used to support analysis. We demonstrate the utility of this framework using case studies spanning aviation fuel, produced water, and microgrid electricity infrastructures. For each case study, the use of the logic model helps clarify the ways that local priorities and infrastructure needs are used to drive the types of data and quantitative methodologies used in the respective analyses. The explicit consideration of methodological limitations (e.g., data mismatches) and stakeholder engagements serves to increase the transparency of the associated findings as well as effectively integrate community nuances (e.g., ownership of assets) into infrastructure assessments. Such integration will become increasingly important to ensure that planning activities (which occur throughout the lifecycle of the infrastructure projects) lead to long-lasting solutions to meet both energy and sustainable development goals for communities.

community-based↗
DOE OSTI2020

Process Failure Detection via Recurrence Quantification Analysis in a Slot-Rectangular Spouted Bed

A study was conducted to explore the applicability of recurrence and recurrence quantification analysis (RQA) to the detection of process failures in spouted bed reactor systems. Here, three different potential failure modes were examined in a transparent, cold flow slot-rectangular spouted bed. These being, a simulated air leak from a side wall of the reactor, a simulated gas leak from the top wall of the reactor, and simulated agglomeration of solids via introduction of larger “klinker” particles. Bed pressure drop time history data were collected and analyzed via generation of recurrence plots (RPs) and RQA parameters. In general, the simulated agglomeration case was quite easily detected via ever RQA Parameter examined, whereas the simulated air leaks were detected by only a single RQA parameter.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Influence of Data Acquisition Algorithms on X-ray Phase Contrast Imaging Computed Tomography

X-ray phase contrast imaging (XPCI) is a non-destructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating based Talbot-Lau XPCI system to three- dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Investigating the Origins of Cyclic Variability in Internal Combustion Engines Using Wall-Resolved Large Eddy Simulations

Modern internal combustion engines (ICE) operate at the ragged edge of stable operation characterized by high cycle-to-cycle variations (CCV). A key scientific challenge for ICE is the understanding, modeling, and control of CCV in engine performance, which can contribute to partial burns, misfire, and knock. The objective of this study is to use high-fidelity numerical simulations to improve the understanding of the causes of CCV. Nek5000, a leading high-order spectral element, open source code, is used to simulate the turbulent flow in the engine combustion chamber. Multicycle, wall-resolved large-eddy simulations (LESs) are performed for the General Motors (GM), Transparent Combustion Chamber (TCC-III) optical engine under motored operating conditions. The mean and root-mean-square (rms) of the in-cylinder flow fields at various piston positions are validated using particle image velocimetry (PIV) measurements during the intake and compression strokes. The large-scale flow structures, including the swirl and tumble flow patterns, are analyzed in detail and the causes for cyclic variabilities in these flow features are explained. The energy distribution across the different scales of the flow are quantified using one-dimensional (1D) energy spectra, and the effect of the tumble breakdown process on the energy distribution is examined. Finally, the insights from this study can help us develop improved engine designs with reduced cyclic variabilities in the in-cylinder flow leading to enhanced engine performance.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Optical Flow Diagnostics of Counter Fluidization of Gravity-Driven Moving Packed Bed for a CSP Receiver Section Featuring Staggered Array of Cylindrical Pins

Particle fluidized beds have the potential to improve the efficiency of heat transfer in concentrated solar receiver furnace for use in next generation concentrated solar power (CSP) plants. This study presents an experimental investigation on the flow characterization of vertically downward moving packed bed with counter fluidization through an array of jets. To control the bubble size and its distribution in the bubbling fluidized bed, an array of cylindrical pin fins was arranged uniformly across the test article. The flow visualization was performed on the surface of transparent glass coated with electrically conductive materials for electrostatic dissipation purposes. The image acquisition was carried out via high-speed camera at a frequency of ~ 1kHz. The acquired images were analyzed in pairs with the help of a modern optical flow algorithm capable of calculating the movement of dense particle flow in the fluidized bed by tracking the light intensity of each predefined window of the frames. A comparison of fluidized beds with plane and pin-finned channels revealed distinct bubble behavior. Pin-finned channels were found to produce a larger number of small-sized bubbles, while plane channels generated fewer but larger bubbles at any given instant. The presence of pin fins was observed to reduce bubble size by preventing bubble merging and splitting larger bubbles when they encountered a pin.

concentrating solar power↗
DOE OSTI2020

Deep Learning for Intelligent Bubble Size Detection in the Spallation Neutron Source Visual Target

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) will undergo proton power upgrade (PPU), increasing the proton beam power from 1.4 MW to 2.8 MW. From 2.8 MW, 2.0 MW will go to the current First Target Station and the rest will go to the future Second Target Station (STS). The First Target Station uses a liquid mercury target that is contained in a 316L stainless steel vessel. The proton beam is pulsed at 60 Hz, with a pulse of about 0.7μs. When the proton beam hits the target, the intense energy deposition leads to a rapid rise in temperature in the mercury. This temperature rise creates pressure waves that propagate through the mercury and cause cavitation erosion. The power upgrade will cause stronger pressure waves that will further increase damage because of cavitation. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at 1.4 MW. However, at higher power, additional mitigation is necessary. Therefore, the 2 MW target vessel will be equipped with swirl bubblers and an additional gas injection port near the nose to inject more gas in the target. To develop a gas injection strategy and design, flow visualization in water with a transparent prototypical target (“visual target”) was performed. Bubble sizes and their spatial distribution in the flow loop are crucial to understanding the effectiveness of the bubbles in mitigating pressure waves. Bubbles were generated in the visual target under varied conditions of input pressures with helium and air. Images were captured using a high-speed camera at varied frame rates at different positions away from the swirl bubbler and different depths in the flow loop under varying lighting conditions. Initially, methods such as circular Hough transforms were applied after a series of images processing to obtain a general distribution of bubble sizes. Bubbles smaller than 500 μm are preferred to effectively mitigate the effect of pressure waves, which demands an accurate bubble detection and sizing system. Intelligent detection and identification of bubble sizes alleviate misdetection and improves accuracies. Employing neural networks, intelligent detection of bubble sizes and their distribution was developed and provides a robust alternative to traditional techniques. Human intervention was employed to label in-focus and out-of-focus bubbles in the set of training images. An object detection network using a pretrained convolutional neural network was created that extracted the features from the training images. Data augmentation was used to improve network accuracy through a random transformation of the original data.

Rasheed, Fayaz↗
DOE OSTI2026

Temperature-dependent dielectric function of tantalum nitride formed by atomic layer deposition for tunnel barriers in Josephson junctions

Here, we report the dielectric functions of insulating tantalum nitride (TaN) films deposited by atomic layer deposition (ALD) on 300 mm Si/SiO 2 substrates to evaluate their suitability as tunnel barriers in tantalum-based Josephson junctions (JJs) for superconducting quantum circuits. Temperature-dependent ellipsometric angles were measured for ALD TaN films with nominal thicknesses of 13 and 25 nm at an incidence angle of 70°, over photon energy ranges of 0.03–0.7 eV (80–300 K) and 0.5–6.5 eV (80–600 K). These data were used to develop a dispersion model for insulating ALD TaN that incorporates a Tauc–Lorentz oscillator with an optical bandgap of 1.5–1.8 eV to describe interband transitions. The extracted dielectric function shows insulating behavior (mid-infrared transparency) at all temperatures and for both film thicknesses tested. In particular, ALD TaN does not exhibit infrared absorption due to free carriers, even at elevated temperatures, consistent with the insulating behavior required for a JJ tunnel barrier. Results from transmission electron microscopy (including selected area electron diffraction) and x-ray diffraction are also discussed. Sputter depth-profile x-ray photoelectron spectroscopy shows an N/Ta ratio of ∼1.2 throughout the film. The lower bandgap, low roughness, and thermal stability of ALD TaN compared to AlO x suggest the possibility of fabricating JJs with thicker barriers while achieving critical current densities required for qubits, enabling improved thickness and composition control, reduced topography, and resistance to aging.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Simple thermal vapor deposition process for and characterization of n-type indium oxysulfide thin films

The search continues for alternative nontoxic n-type electron transport layers in optoelectronic thin-film devices. Indium oxysulfide, In 2 (O,S) 3 , represents one promising material for this application, especially when paired with chalcogenide absorber layers. The ternary nature of the composition allows for electrical conductivity and optical bandgap tuning by tailoring the sulfur to oxygen ratio in the oxysulfide alloy. However, thin films of In 2 (O,S) 3 are typically deposited only by chemical bath deposition or plasma-enhanced atomic layer deposition. Here we report deposition of thin films of In 2 (O,S) 3 in a custom-built thermal reactor using only water vapor and hydrogen sulfide as the coreactants. This advance is enabled by the use of a recently reported, highly reactive indium formamidinate precursor. As shown by x-ray photoelectron spectroscopy, the composition can be tuned from pure In 2 (O,S) 3 to pure In 2 S 3 by varying the ratio of cycles employing water or hydrogen sulfide. The oxygen to the sulfur ratio in the film can be controlled by altering the dose sequence, although films typically contain more sulfur than would be expected naively from the percentage of hydrogen sulfide doses in the deposition recipe. Rutherford backscattering spectrometry confirms the composition is sulfur-rich relative to the dosing ratio. Structural characterization indicates films are relatively amorphous in nature. Electrically, these films offer reasonably constant electron mobility at different O:S ratios, with an electron concentration tunable over 4 orders of magnitude. These oxysulfide films possess a higher indirect bandgap than their oxygen-free indium sulfide counterparts, indicating higher transmittance to blue light. These indium oxysulfide films may be suitable candidates for electron transport layers in thin-film solar cells where their wider bandgap might result in higher optical transparency and thus short circuit current density, while the tunability of their conduction band offset with an absorber layer may result in higher open circuit voltage.

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

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2024

Temperature-induced degradation of GaN HEMT: An in situ heating study

High-power electronics, such as GaN high electron mobility transistors (HEMTs), are expected to perform reliably in high-temperature conditions. This study aims to gain an understanding of the microscopic origin of both material and device vulnerabilities to high temperatures by real-time monitoring of the onset of structural degradation under varying temperature conditions. This is achieved by operating GaN HEMT devices in situ inside a transmission electron microscope (TEM). Electron-transparent specimens are prepared from a bulk device and heated up to 800 °C. High-resolution TEM (HRTEM), scanning TEM (STEM), energy-dispersive x-ray spectroscopy (EDS), and geometric phase analysis (GPA) are performed to evaluate crystal quality, material diffusion, and strain propagation in the sample before and after heating. Gate contact area reduction is visible from 470 °C accompanied by Ni/Au intermixing near the gate/AlGaN interface. Elevated temperatures induce significant out-of-plane lattice expansion at the SiNx/GaN/AlGaN interface, as revealed by geometry-phase GPA strain maps, while in-plane strains remain relatively consistent. Exposure to temperatures exceeding 500 °C leads to almost two orders of magnitude increase in leakage current in bulk devices in this study, which complements the results from our TEM experiment. The findings of this study offer real-time visual insights into identifying the initial location of degradation and highlight the impact of temperature on the bulk device’s structure, electrical properties, and material degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Laser-induced damage characteristics of fused silica surfaces polished to different depths using fluid jet polishing

Deterministic finishing methods of optical components for high-peak-power laser applications that can meet the requirements for high laser damage resistance are not sufficiently developed to meet all needs. This is especially the case for ultraviolet (UV) laser applications. Fused silica is the material of choice for optics operating at UV wavelengths owing to its intrinsically large bandgap, high transparency, and excellent uniformity. Here we report on the laser damage behavior of fused silica surfaces finished by fluid jet polishing (FJP) as a function of removal depth. Fused silica test substrates were processed by FJP to depths ranging from 0.7 μm to 18 μm. Laser damage testing was conducted on these surfaces at 351 nm and 1-ns pulse lengths for both, 1-on-1 and R-on-1 testing protocols. The results for 1-on-1 testing showed no degradation in the laser-induced damage threshold (LIDT) of the substrates. Instead, a gradual improvement starting at a depth of 2.1 μm was observed and continued to the 18-μm surface. At 18-μm of removal, the LIDT was 16% higher than a surface that was not finished by FJP. For R-on-1 testing, all surfaces treated by FJP demonstrated an improvement in laser damage resistance. At depths greater than 5 μm, the improvements were significantly more pronounced and a 30% increase in the LIDT was realized.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Persistent optical phenomena in oxide semiconductors

The interaction of transparent oxide semiconductors with light is critically important for a range of applications. Persistent effects could be exploited for holographic memory or optically defined circuits. Conversely, they may also be detrimental to device operation. Large, room-temperature persistent photoconductivity (PPC) was discovered in strontium titanate (SrTiO 3 , STO) after annealing in a hydrogen-containing atmosphere. Barium titanate (BaTiO 3 , BTO), a ferroelectric material, was recently found to also exhibit PPC. Room-temperature photodarkening was observed in Cu-doped gallium oxide (β-Ga 2 O 3 ) after exposure to sub-bandgap light. Hydrogen is believed to play a central role in these persistent phenomena. In the proposed model, a photon excites substitutional hydrogen (a proton inside an oxygen vacancy), making the defect unstable. The proton leaves and binds to a host oxygen atom, forming an O-H bond that is observed with infrared spectroscopy. Here, an oxygen vacancy is left behind. Because oxygen vacancies in STO and BTO are shallow donors, this process results in PPC. In β-Ga 2 O 3 :Cu, however, the oxygen vacancy neighbors a Cu acceptor. In that case, photoexcitation results in the rare Cu 3+ state, which absorbs visible light. The effect can be “erased” by annealing at 300-400°C.

Annealing↗
DOE OSTI2023

Generalized analytical and numerical modeling of optical second harmonic generation in anisotropic crystals and complex heterostructures using #SHAARP package

Optical second harmonic generation (SHG) is a nonlinear optical effect widely used for nonlinear optical microscopy and laser frequency conversion. The closed-form analytical solution of the nonlinear optical responses is essential for evaluating the optical responses of new materials whose optical properties are unknown a priori. Many approximations have therefore been employed in the existing analytical approaches, such as slowly varying approximation, weak reflection of the nonlinear polarization, transparent medium, high crystallographic symmetry, Kleinman symmetry, easy crystal orientation along a high-symmetry direction, phase matching conditions and negligible interference among nonlinear waves, which may lead to large errors in the reported material properties. To avoid these approximations, here we have developed an open-source package named Second Harmonic Analysis of Anisotropic Rotational Polarimetry (#SHAARP) for single interface (si) and in multilayers (ml) for homogeneous crystals. The reliability and accuracy are established by experimentally benchmarking with both the SHG polarimetry and Maker fringes predicted from the package using standard materials. SHAARP.si and SHAARP.ml are available through GitHub https://github.com/Rui-Zu/SHAARP and https://github.com/bzw133/SHAARP.ml, respectively.

complex systems↗
DOE OSTI2020

A fivefold UO$_2^{2+}$ node is a path to dodecagonal quasicrystal approximants in coordination polymers [plus supplemental information]

Aperiodic formations continue to focus interest in areas ranging from advanced scientific theories to practical everyday applications. Starting from diverse and tightly bonded intermetallic compounds, this world showed an important breakthrough toward the so-called soft systems of meso/macroscale: liquid crystals, thin films, polymers, proteins, etc. This work opens a route for making bulk quasicrystals (QC) in an unprecedented but very common area, with molecular ligands. Since these systems are, to a large extent, transparent, they extend the possible areas of QC application to previously unreachable corners, e.g., photonics. We combined efficient bridging ligands with uranyl pentagonal bonding centers and, unexpectedly, brought the unique attributes of f-element coordination chemistry to an interdisciplinary area of aperiodic formations. Taking advantage of the planar coordination of uranyl ions, we were able to direct the structure expansion solely in two directions with a characteristic snub square tiling, a predicted but previously unobtainable dodecagonal approximant.

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

Bifacial all-perovskite tandem solar cells

The efficiency of all-perovskite tandem devices falls far below theoretical efficiency limits, mainly because a widening bandgap fails to increase open-circuit voltage. We report on a bifacial all-perovskite tandem structures with an equivalent efficiency of 29.3% under back-to-front irradiance ratio of 30. This increases energy yield and reduces the required bandgap of a wide-bandgap cell. Open-circuit voltage deficit is therefore minimized, although its performance under only front irradiance is not ideal. The bifacial device needs a sputtered rear transparent electrode, which could reduce photon path length and deteriorate stability of Pb-Sn perovskites. Embedding a light-scattering micrometer-sized particle layer into perovskite to trap light, effectively increases absorptance by 5 to 15% in the infrared region. Using a nonacidic hole transport layer markedly stabilizes the hole-extraction interface by avoiding proton-accelerated formation of iodine. These two strategies together increase efficiency of semitransparent Pb-Sn cells from 15.6 to 19.4%, enabling fabrication of efficient bifacial all-perovskite tandem devices.

14 SOLAR ENERGY↗
DOE OSTI2024

Strong-Bonding Hole-Transport Layers Reduce Ultraviolet Degradation of Perovskite Solar Cells

The light-emitting diodes (LEDs) used in indoor testing of perovskite solar cells do not expose them to the levels of ultraviolet (UV) radiation that they would receive in actual outdoor use. We report degradation mechanisms of p-i-n-structured perovskite solar cells under unfiltered sunlight and with LEDs. Weak chemical bonding between perovskites and polymer hole-transporting materials (HTMs) and transparent conducting oxides (TCOs) dominate the accelerated A-site cation migration, rather than direct degradation of HTMs. An aromatic phosphonic acid, [2-(9-ethyl-9H-carbazol-3-yl)ethyl]phosphonic acid (EtCz3EPA), enhanced bonding at the perovskite/HTM/TCO region with a phosphonic acid group bonded to TCOs and a nitrogen group interacting with lead in perovskites. A hybrid HTM of EtCz3EPA with strong hole-extraction polymers retained high efficiency and improved the UV stability of perovskite devices, and a champion perovskite minimodule-independently measured by the Perovskite PV Accelerator for Commercializing Technologies (PACT) center-retained operational efficiency of >16% after 29 weeks of outdoor testing.

14 SOLAR ENERGY↗
DOE OSTI2022

Genome Streamlining, Proteorhodopsin, and Organic Nitrogen Metabolism in Freshwater Nitrifiers

Microbial nitrification is a critical process governing nitrogen availability in aquatic systems. Freshwater nitrifiers have received little attention, leaving many unanswered questions about their taxonomic distribution, functional potential, and ecological interactions. Here, we reconstructed genomes to infer the metabolism and ecology of free-living picoplanktonic nitrifiers across the Laurentian Great Lakes, a connected series of five of Earth’s largest lakes. Surprisingly, ammonia-oxidizing bacteria (AOB) related to Nitrosospira dominated over ammonia-oxidizing archaea (AOA) at nearly all stations, with distinct ecotypes prevailing in the transparent, oligotrophic upper lakes compared to Lakes Erie and Ontario. Unexpectedly, one ecotype of Nitrosospira encodes proteorhodopsin, which could enhance survival under conditions where ammonia oxidation is inhibited or substrate limited. Nitrite-oxidizing bacteria (NOB) “Candidatus Nitrotoga” and Nitrospira fluctuated in dominance, with the latter prevailing in deeper, less-productive basins. Genome reconstructions reveal highly reduced genomes and features consistent with genome streamlining, along with diverse adaptations to sunlight and oxidative stress and widespread capacity for organic nitrogen use. Our findings expand the known functional diversity of nitrifiers and establish their ecological genomics in large lake ecosystems. By elucidating links between microbial biodiversity and biogeochemical cycling, our work also informs ecosystem models of the Laurentian Great Lakes, a critical freshwater resource experiencing rapid environmental change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

PETSc TSAdjoint: A Discrete Adjoint ODE Solver for First-Order and Second-Order Sensitivity Analysis

Here, we present a new software system PETSc TSAdjoint for first-order and second order adjoint sensitivity analysis of time-dependent nonlinear differential equations. The derivative calculation in PETSc TSAdjoint is essentially a high-level algorithmic differentiation process. The adjoint models are derived by differentiating the timestepping algorithms and implementing them based on the parallel infrastructure in PETSc. Full differentiation of the library code, including MPI routines, is avoided, and users do not need to derive their own adjoint models for their specific applications. PETSc TSAdjoint can compute the first-order derivative, that is, the gradient of a scalar functional, and the Hessian-vector product, which carries second-order derivative information, while requiring minimal input (a few callbacks) from the users. The adjoint model employs optimal checkpointing schemes in a manner that is transparent to users. Finally, usability, efficiency, and scalability are demonstrated through examples from a variety of applications.

79 ASTRONOMY AND ASTROPHYSICS↗