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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Neural-net Processed Electronic Holography for Rotating Machines

This report presents the results of an R&D effort to apply neural-net processed electronic holography to NDE of rotors. Electronic holography was used to generate characteristic patterns or mode shapes of vibrating rotors and rotor components. Artificial neural networks were trained to identify damage-induced changes in the characteristic patterns. The development and optimization of a neural-net training method were the most significant contributions of this work, and the training method and its optimization are discussed in detail. A second positive result was the assembly and testing of a fiber-optic holocamera. A major disappointment was the inadequacy of the high-speed-holography hardware selected for this effort, but the use of scaled holograms to match the low effective resolution of an image intensifier was one interesting attempt to compensate. This report also discusses in some detail the physics and environmental requirements for rotor electronic holography. The major conclusions were that neural-net and electronic-holography inspections of stationary components in the laboratory and the field are quite practical and worthy of continuing development, but that electronic holography of moving rotors is still an expensive high-risk endeavor.

Decker, Arthur J.↗
NASA NTRS1998

Model-Trained Neural Networks and Electronic Holography Demonstrated to Detect Damage in Blades

Detect Damage in Blades Electronic holography can show damaged regions in fan blades at 30 frames/sec. The electronic holograms are transformed by finite-element-model-trained artificial neural networks to visualize the damage. The trained neural networks are linked with video and graphics to visualize the bending-induced strain distribution, which is very sensitive to damage. By contrast, it is very difficult to detect damage by viewing the raw, speckled, characteristic fringe patterns. For neural-network visualization of damage, 2 frames or 2 fields are used, rather than the 12 frames normally used to compute the displacement distribution from electronic holograms. At the NASA Lewis Research Center, finite element models are used to compute displacement and strain distributions for the vibration modes of undamaged and cracked blades. A model of electronic time-averaged holography is used to transform the displacement distributions into finite-element-resolution characteristic fringe patterns. Then, a feedforward neural network is trained with the fringe-pattern/strain-pattern pairs, and the neural network, electronic holography, and video are implemented on a workstation. Now that the neural networks have been tested successfully at 30 frames/sec on undamaged and cracked cantilevers, the electronic holography and neural-network processing are being adapted for onsite damage inspection of twisted fan blades and rotormounted blades. Our conclusion is that model-trained neural nets are effective when they are trained with good models whose application is well understood. This work supports the aeromechanical testing portion of the Advanced Subsonic Technology Project.

Decker, Arthur J.↗
DOE OSTI2020

Quantifying leakage fields at ionic grain boundaries using off-axis electron holography

The electrical properties of interfaces in semiconductors and ionic conductors are immensely important in a wide range of applications. Electron holography is ideally suited for the direct measurement of the electrostatic potential of such interfaces. A key challenge with this approach is the contribution of the leakage field from the sample to the observed electron phase shift. This leakage field cannot be a priori independently determined and can cause an overestimation of the phase shift. In this work, we use finite element simulations to compute the three-dimensional electrostatic potential in the vicinity of an interface associated with a given interfacial charge density distribution. We then evaluate the predicted phase shift and demonstrate that the leakage field strongly affects the recovery of the projected interface potential. From the difference between the true potential and uncorrected, recovered potential, we propose a method to correct for this effect. We then demonstrate the application of this methodology to the analysis of experimental off-axis electron holography data acquired from the grain boundaries in lightly doped ceria.

36 MATERIALS SCIENCE↗
NASA NTRS1997

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Decker, Arthur J.↗
NASA NTRS1998

Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture.

Decker, Arthur J.↗
DOE OSTI2020

Curved Three-Dimensional Cobalt Nanohelices for Use in Domain Wall Device Applications

Three-dimensional fabrication of nanostructures opens new possibilities to not only understand the fundamental effects of shape and of the interactions between the nanostructures, but also to develop novel technological applications. Of course, this is of particular importance for magnetic nanostructures, where curvilinear geometry can lead to the emergence of novel topological effects and spin excitations. In this work, we have used the focused electron beam ion deposition (FEBID) method to fabricate three-dimensional magnetic cobalt nanohelices with controlled geometry such as chirality, and curvature. Additionally, using a combination of off-axis electron holography and electron tomography, we are able to determine the way in which the quantitative nanoscale magnetization distribution is related to the 3D morphology and curvature of the nanohelices. These results pave a way forward for development of future 3D magnetic nanostructures to explore novel physics as well as for applications such as magnetic field sensors, spin-wave filters, and magneto-optical devices.

36 MATERIALS SCIENCE↗
NASA NTRS2001

Neural-Net Processed Characteristic Patterns for Measurement of Structural Integrity of Pressure Cycled Components

A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example to flag small changes in the mode shape. The NDE (nondestructive-evaluation) technique is straightforward but in its infancy; its applications are ad-hoc and uncalibrated. Nevertheless previous research has shown that the neural net can detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neural-net processing. The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are presented.

Decker, A. J.↗
NASA NTRS2004

Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation table in the presence of various sources of laboratory noise is shown. The output of the neural network is called the degradable classification index. The curve was generated by a simultaneous comparison of means, and it shows a peak-to-peak sensitivity of about 100 nm. The following graph uses model generated data from a compressor blade to show that much higher sensitivities are possible when the environment can be controlled better. The peak-to-peak sensitivity here is about 20 nm. The training procedure was modified for the second graph, and the data were subjected to an intensity-dependent transformation called folding. All the measurements for this approach to calibration were optical. The peak-to-peak amplitudes of the vibration modes were measured using heterodyne interferometry, and the modes themselves were recorded using television (electronic) holography.

Decker, Arthur J.↗
DOE OSTI2024

Feld-induced modulation of two-dimensional electron gas at LaAlO 3 /SrTiO 3 interface by polar distortion of LaAlO 3

Since the discovery of two-dimensional electron gas at the LaAlO 3 /SrTiO 3 interface, its intriguing physical properties have garnered significant interests for device applications. Yet, understanding its response to electrical stimuli remains incomplete. Our in-situ transmission electron microscopy analysis of a LaAlO 3 /SrTiO 3 two-dimensional electron gas device under electrical bias reveals key insights. Inline electron holography visualized the field-induced modulation of two-dimensional electron gas at the interface, while electron energy loss spectroscopy showed negligible electromigration of oxygen vacancies. Instead, atom-resolved imaging indicated that electric fields trigger polar distortion in the LaAlO 3 layer, affecting two-dimensional electron gas modulation. This study refutes the previously hypothesized role of oxygen vacancies, underscoring the lattice flexibility of LaAlO 3 and its varied polar distortions under electric fields as central to two-dimensional electron gas dynamics. These findings open pathways for advanced oxide nanoelectronics, exploiting the interplay of polar and nonpolar distortions in LaAlO 3 .

36 MATERIALS SCIENCE↗
NASA NTRS2003

Damage Detection Using Holography and Interferometry

This paper reviews classical approaches to damage detection using laser holography and interferometry. The paper then details the modern uses of electronic holography and neural-net-processed characteristic patterns to detect structural damage. The design of the neural networks and the preparation of the training sets are discussed. The use of a technique to optimize the training sets, called folding, is explained. Then a training procedure is detailed that uses the holography-measured vibration modes of the undamaged structures to impart damage-detection sensitivity to the neural networks. The inspections of an optical strain gauge mounting plate and an International Space Station cold plate are presented as examples.

Decker, Arthur J.↗
DOE OSTI2022

Probing charge density in materials with atomic resolution in real space

The charge distribution in materials at the nanoscale can often explain the origin of macroscopic properties such as localized conductivity or the plasmonic response and illuminate more fundamental changes in the microscopic structure such as changes in chemical bonding characteristics. Previously, direct visualization of the charge density with high spatial resolution was often a missing link in the formation of structure–property relationships, especially in heterogeneous materials systems. Furthermore, recent advancements in microscopy technology have enabled researchers to visualize the charge distribution in materials down to subatomic length scales. In this Technical Review, we discuss the developments in high-resolution real-space charge distribution imaging using diffraction techniques and electron microscopy, with a focus on the recent advancement of four-dimensional scanning transmission electron microscopy, electron holography, and applications to materials interfaces.

Electronic properties and materials↗
DOE OSTI2021

Mesoscale Confinement Effects and Emergent Quantum Interference in Titania Antidot Thin Films

The effect of confinement on electron and ion transport in oxide films is of interest both fundamentally and technologically for the design of next-generation electronic devices. In metal oxides with mobile ions and vacancies, it is the interplay of the different modes of charge transport and the corresponding current–voltage signatures that is of interest. We developed a patterned structure in titania films, with feature sizes of 11–20 nm, that allow us to explore confined transport. In this study, we describe how confinement changes the competing charge transport mechanisms, the patterned antidot array leads to displacement fields and confines the charge density that results in modified and emergent electron transport with an increase in conductivity. This emergent behavior can be described by considering electron interference effects. Characterization of the charge transport with electron holography and impedance spectroscopy, and through comparison with modeling, show that nanoscale confinement is a way to control quantum interference.

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

Oxide Two‐Dimensional Electron Gas with High Mobility at Room‐Temperature

Abstract The prospect of 2‐dimensional electron gases (2DEGs) possessing high mobility at room temperature in wide‐bandgap perovskite stannates is enticing for oxide electronics, particularly to realize transparent and high‐electron mobility transistors. Nonetheless only a small number of studies to date report 2DEGs in BaSnO 3 ‐based heterostructures. Here, 2DEG formation at the LaScO 3 /BaSnO 3 (LSO/BSO) interface with a room‐temperature mobility of 60 cm 2 V −1 s −1 at a carrier concentration of 1.7 × 10 13 cm –2 is reported. This is an order of magnitude higher mobility at room temperature than achieved in SrTiO 3 ‐based 2DEGs. This is achieved by combining a thick BSO buffer layer with an ex situ high‐temperature treatment, which not only reduces the dislocation density but also produces a SnO 2 ‐terminated atomically flat surface, followed by the growth of an overlying BSO/LSO interface. Using weak beam dark‐field transmission electron microscopy imaging and in‐line electron holography technique, a reduction of the threading dislocation density is revealed, and direct evidence for the spatial confinement of a 2DEG at the BSO/LSO interface is provided. This work opens a new pathway to explore the exciting physics of stannate‐based 2DEGs at application‐relevant temperatures for oxide nanoelectronics.

2-dimensional electron gas↗
DOE OSTI2022

Electrostatic Asymmetry of Wurtzite Nanocrystals and Resulting Photocatalytic Properties

Efficient light absorption and high energy of charge carriers of zinc cadmium sulfide (ZCS) make this semiconductor attractive for many photocatalytic reactions. Despite marked successes in shape-controlled synthesis of ZCS central to their photocatalytic performance, recombination of charge carriers as they migrate through the nanoscale particles results in losses of excitation energy, markedly reducing the photocatalytic activity of ZCS and other heterogeneous photocatalysts. Here, in this study, we show that the electrostatic asymmetry of nanostructures, previously discovered for nearly spherical nanoparticles, also manifests in wurtzite ZCS with planar geometry. The electrostatic asymmetry assists charge separation and substantially increases the yield of photocatalytic reactions in monocrystalline ZCS. The synthesized ZCS nanorods and nanoplates with identical chemical composition were found to have markedly different photocatalytic activity for evolution of hydrogen in water. Despite much smaller specific surface areas, the ~500 nm wide nanoplates displayed a hydrogen evolution rate 12 times higher than the ~35 nm long nanorods, also outperforming other ZCS photocatalysts. Experimental and computational data indicate that the homo- and heterojunction-free ZCS nanoplates with continuous wurtzite lattice behave essentially as nanoscale dipoles with intrinsic dipole moment as high as 48.39 D per unit cell. Electric-field-directed migration of charge carriers stimulates their localization on opposite parts of the nanoplates. Direct imaging of the intraparticle electrical field using off-axis electron holography confirmed their electrostatic asymmetry. Polarization-enhanced charge separation provides a new pathway to efficient and stable photocatalysts for sustainable energy technologies.

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

Effective Selective Area Doping for GaN Vertical Power Transistors Enabled by Innovative Materials Engineering

GaN vertical power transistors have emerged as promising candidates for future high efficiency high power electronic applications, with the potential to outperform conventional GaN lateral power devices in terms of power, breakdown, and avalanche characteristics. However, the development of current GaN vertical power transistors is seriously hindered by the poor materials performance of selective area doped p-n junctions. A mechanistic understanding of these fundamental materials issues is essential in order to achieve high performance selective area doped p-n junctions and consequently to advance the GaN vertical power transistor technology. To address this challenge, we carried out a comprehensive research program that advance fundamental knowledge in the selective area doping for GaN materials, and which will lead to the development of high performance GaN vertical power transistors. First, we developed innovative fabrication processes, including novel surface etching, surface passivation, and metalorganic chemical vapor deposition (MOCVD) growth, which provided enhanced opportunities for solving the unique challenges of selective area doping in GaN materials. Second, we performed a fundamental materials study using powerful characterization methods including transmission electron microscopy (TEM), ultraviolet (UV-), x-ray and angle-resolved photoelectron spectroscopy (UPS/XPS/ARPES), electron holography, and cathodoluminescence (CL); Third, we investigated several related issues, including Mg incorporation, polarization effects in carrier transport, and non-ideal material effects, which have rarely been explored so far. At the end of this project, we successfully demonstrated (1) fundamental understanding of selective area etching, regrowth, and doping of GaN, and associated knowledge on defects, interface, and breakdown properties. (2) Effective etch and regrowth processing recipes to achieve etch/regrowth GaN p-n diodes with very low leakage of 3.5 nA at 600 V, which meets the ARPA-E target. (2) High performance vertical GaN p-n didoes and vertical junction termination extension (JTE) devices with breakdown voltage of ~ 2 kV, and breakdown electric field of ~ 3.5 MV/cm, which are close to the performance limit of GaN. The successful outcome has resulted in new fundamental understandings in the selective area doping and regrowth process for GaN, which will lead to groundbreaking GaN vertical transistors for high performance next generation power electronics.

24 POWER TRANSMISSION AND DISTRIBUTION↗