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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Ultrafast laser induced electronic and structural modifications in bulk fused silica

Ultrashort laser pulses can modify the inner structure of fused silica, generating refractive index changes varying from soft positive (type I) light guiding forms to negative (type II) values with void presence and anisotropic sub-wavelength modulation. We investigate electronic and structural material changes in the type I to type II transition via coherent and incoherent secondary light emission reflecting free carrier behavior and post-irradiation material relaxation in the index change patterns. Using phase contrast microscopy, photoluminescence, and Raman spectroscopy, we determine in a space-resolved manner defect formation, redistribution and spatial segregation, and glass network reorganization paths in conditions marking the changeover between type I and type II photoinscription regimes. We first show characteristic patterns of second harmonic generation in type I and type II traces, indicating the collective involvement of free carriers and polarization memory. Second, incoherent photoemission from resonantly and non-resonantly excited defect states reveals accumulation of non-bridging oxygen hole centers (NBOHCs) in positive index domains and oxygen deficiency centers (ODCs) with O{sub 2}{sup −} ions segregation in void-like regions and in the nanostructured domains, reflecting the interaction strength. Complementary Raman investigations put into evidence signatures of the different environments where photo-chemical densification (bond rearrangements) and mechanical effects can be indicated. NBOHCs setting in before visible index changes serve as precursors for subsequent compaction build-up, indicating a scenario of cold, defect-assisted densification for the soft type I irradiation regime. Additionally, we observe hydrodynamic effects and severe bond-breaking in type II zones with indications of phase transition. These observations illuminate densification paths in fused silica in low power irradiation regimes, and equally in energetic ranges, characterized by the onset of thermo-mechanical effects.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Electric field control of magnetism using BiFeO{sub 3}-based heterostructures

Conventional CMOS based logic and magnetic based data storage devices require the shuttling of electrons for data processing and storage. As these devices are scaled to increasingly smaller dimensions in the pursuit of speed and storage density, significant energy dissipation in the form of heat has become a center stage issue for the microelectronics industry. By taking advantage of the strong correlations between ferroic orders in multiferroics, specifically the coupling between ferroelectric and magnetic orders (magnetoelectricity), new device functionalities with ultra-low energy consumption can be envisioned. In this article, we review the advances and highlight challenges toward this goal with a particular focus on the room temperature magnetoelectric multiferroic, BiFeO{sub 3}, exchange coupled to a ferromagnet. We summarize our understanding of the nature of exchange coupling and the mechanisms of the voltage control of ferromagnetism observed in these heterostructures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

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

Charged particle tracking in real-time using a full-mesh data delivery architecture and associative memory techniques

We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. Here, the porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ~95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ECRAM Materials, Devices, Circuits and Architectures: A Perspective

Abstract Non‐von‐Neumann computing using neuromorphic systems based on two‐terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the “write” and “read” operations using a “gate” electrode to tune the conductance state through charge‐transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in‐depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low‐power neuromorphic computing are discussed.

Talin, A. Alec↗
NASA NTRS2013

Miga Aero Actuator and 2D Machined Mechanical Binary Latch

Shape memory alloy (SMA) actuators provide the highest force-to-weight ratio of any known actuator. They can be designed for a wide variety of form factors from flat, thin packages, to form-matching packages for existing actuators. SMA actuators can be operated many thousands of times, so that ground testing is possible. Actuation speed can be accurately controlled from milliseconds to position and hold, and even electronic velocity-profile control is possible. SMA actuators provide a high degree of operational flexibility, and are truly smart actuators capable of being accurately controlled by onboard microprocessors across a wide range of voltages. The Miga Aero actuator is a SMA actuator designed specifically for spaceflight applications. Providing 13 mm of stroke with either 20- or 40-N output force in two different models, the Aero actuator is made from low-outgassing PEEK (polyether ether ketone) plastic, stainless steel, and nickel-titanium SMA wires. The modular actuator weighs less than 28 grams. The dorsal output attachment allows the Aero to be used in either PUSH or PULL modes by inverting the mounting orientation. The SPA1 actuator utilizes commercially available SMA actuator wire to provide 3/8-in. (approx. =.1 cm) of stroke at a force of over 28 lb (approx. = .125 N). The force is provided by a unique packaging of the single SMA wire that provides the output force of four SMA wires mechanically in parallel. The output load is shared by allowing the SMA wire to slip around the output attachment end to adjust or balance the load, preventing any individual wire segment from experiencing high loads during actuation. A built-in end limit switch prevents overheating of the SMA element following actuation when used in conjunction with the Miga Analog Driver [a simple MOSFET (metal oxide semiconductor field-effect transistor) switching circuit]. A simple 2D machined mechanical binary latch has been developed to complement the capabilities of SMA wire actuators. SMA actuators typically perform ideally as latch-release devices, wherein a spring-loaded device is released when the SMA actuator actuates in one direction. But many applications require cycling between two latched states open and closed.

Gummin, Mark A.↗
DOE OSTI2014

Renin–angiotensin–aldosterone system related gene polymorphisms and urinary total arsenic is related to chronic kidney disease

A recent study demonstrated that an increased risk of chronic kidney disease (CKD) was associated with high urinary total arsenic levels. However, whether genomic instability is related to CKD remains unclear. An association between CKD and genetic polymorphisms of regulation enzymes of the renin–angiotensin–aldosterone system (RAAS), such as angiotensin-converting enzyme (ACE), angiotensinogen (AGT), angiotensin II type I receptor (AT1R), and aldosterone synthase (CYP11B2) has not been shown. The aim of the present study was to investigate the relationship between arsenic, genetic polymorphisms of RAAS enzymes and CKD. A total of 233 patients and 449 age- and gender-matched controls were recruited from the Taipei Medical University Hospital, Taipei Municipal Wan Fang Hospital and the Shin Kong Wu Ho-Su Memorial Hospital. Concentrations of urinary arsenic were determined by a high-performance liquid chromatography-linked hydride generator, and atomic absorption spectrometry. Polymorphisms of ACE(I/D), AGT(A[− 20]C), (T174M), (M235T), AT1R(A1166C) and CYP11B2(C[− 344]T) were examined by polymerase chain reaction and restriction fragment length polymorphism. Subjects carrying the CYP11B2 TT genotype had a higher odds ratio (OR), 1.39 (0.96–2.01), of CKD; while those with the AGT(A[− 20]C) CC genotype had an inverse OR of CKD (0.20 (0.05–0.81)), and a high-risk genotype was defined as A/A + A/C for AGT(A[− 20C]) and T/T for CYP11B2(C[− 344]T). The trend test showed a higher OR for CKD in patients who had either high urinary total arsenic levels or carried the high-risk genotype, or both, compared to patients with low urinary total arsenic levels, who carried the low-risk genotype, and could also be affected by the hypertension or diabetes status. - Highlights: • AGT(− 20 C) and CYP11B2(− 344 T) genotypes were significantly associated with CKD. • Combined effect of high-risk genotypes and high urinary total arsenic on OR of CKD. • Combined effect on the CKD was modified by the hypertension and diabetes status.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

An implantable instrument for studying the long-term flight biology of migratory birds

The design of an instrument deployed in a project studying the high altitude Himalayan migrations of bar-headed geese (Anser indicus) is described. The electronics of this archival datalogger measured 22 × 14 × 6.5 mm, weighed 3 g, was powered by a ½AA-sized battery weighing 10 g and housed in a transparent biocompatible tube sealed with titanium electrodes for electrocardiography (ECG). The combined weight of 32 g represented less than 2% of the typical bodyweight of the geese. The primary tasks of the instrument were to continuously record a digitised ECG signal for heart-rate determination and store 12-bit triaxial accelerations sampled at 100 Hz with 15% coverage over each 2 min period. Measurement of atmospheric pressure provided an indication of altitude and rate of ascent or descent during flight. Geomagnetic field readings allowed for latitude estimation. These parameters were logged twice per minute along with body temperature. Data were stored to a memory card of 8 GB capacity. Instruments were implanted in geese captured on Mongolian lakes during the breeding season when the birds are temporarily flightless due to moulting. The goal was to collect data over a ten month period, covering both southward and northward migrations. This imposed extreme constraints on the design's power consumption. Raw ECG can be post-processed to obtain heart-rate, allowing improved rejection of signal interference due to strenuous activity of locomotory muscles during flight. Accelerometry can be used to monitor wing-beat frequency and body kinematics, and since the geese continued to flap their wings continuously even during rather steep descents, act as a proxy for biomechanical power. The instrument enables detailed investigation of the challenges faced by the geese during these arduous migrations which typically involve flying at extreme altitudes through cold, low density air where oxygen availability is significantly reduced compared to sea level.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Relation of polymorphism of arsenic metabolism genes to arsenic methylation capacity and developmental delay in preschool children in Taiwan

Inefficient arsenic methylation capacity has been associated with developmental delay in children. The present study was designed to explore whether polymorphisms and haplotypes of arsenic methyltransferase (AS3MT), glutathione-S-transferase omegas (GSTOs), and purine nucleoside phosphorylase (PNP) affect arsenic methylation capacity and developmental delay. A case-control study was conducted from August 2010 to March 2014. All participants were recruited from the Shin Kong Wu Ho-Su Memorial Teaching Hospital. In total, 179 children with developmental delay and 88 children without delay were recruited. Urinary arsenic species, including arsenite (As{sup III}), arsenate (As{sup V}), monomethylarsonic acid (MMA{sup V}), and dimethylarsinic acid (DMA{sup V}) were measured using a high-performance liquid chromatography-linked hydride generator and atomic absorption spectrometry. The polymorphisms of AS3MT, GSTO, and PNP were performed using the Sequenom MassARRAY platform with iPLEX Gold chemistry. Polymorphisms of AS3MT genes were found to affect susceptibility to developmental delay in children, but GSTO and PNP polymorphisms were not. Participants with AS3MT rs3740392 A/G + G/G genotype, compared with AS3MT rs3740392 A/A genotype, had a significantly lower secondary methylation index. This may result in an increased OR for developmental delay. Participants with the AS3MT high-risk haplotype had a significantly higher OR than those with AS3MT low-risk haplotypes [OR and 95% CI, 1.59 (1.08–2.34)]. This is the first study to show a joint dose-response effect of this AS3MT high-risk haplotype and inefficient arsenic methylation capacity on developmental delay. Our data provide evidence that AS3MT genes are related to developmental delay and may partially influence arsenic methylation capacity. - Highlights: • AS3MT genotypes were found to affect susceptibility to developmental delay. • AS3MT rs3740392 A/G and G/G genotype had a significantly low SMI (DMA/MMA) index. • AS3MT high-risk haplotype was significantly associated with developmental delay.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Introduction of Chalcogenide Glasses to Additive Manufacturing: Nanoparticle Ink Formulation, Inkjet Printing, and Phase Change Devices Fabrication

Abstract Chalcogenide glasses are one of the most versatile materials that have been widely researched because of their flexible optical, chemical, electronic, and phase change properties. Their application is usually in the form of thin films, which work as active layers in sensors and memory devices. In this work, we investigate the formulation of nanoparticle ink of Ge–Se chalcogenide glasses and its potential applications. The process steps reported in this work describe nanoparticle ink formulation from chalcogenide glasses, its application via inkjet printing and dip-coating methods and sintering to manufacture phase change devices. We report data regarding nanoparticle production by ball milling and ultrasonication along with the essential characteristics of the formed inks, like contact angle and viscosity. The printed chalcogenide glass films were characterized by Raman spectroscopy, X-ray diffraction, energy dispersive spectroscopy and atomic force microscopy. The printed films exhibited similar compositional, structural, electronic and optical properties as the thermally evaporated thin films. The crystallization processes of the printed films are discussed compared to those obtained by vacuum thermal deposition. We demonstrate the formation of printed thin films using nanoparticle inks, low-temperature sintering and proof for the first time, their application in electronic and photonic temperature sensors utilizing their phase change property. This work adds chalcogenide glasses to the list of inkjet printable materials, thus offering an easy way to form arbitrary device structures for optical and electronic applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Combining different 3-D global and regional seismic wave propagation solvers towards box tomography in the deep Earth

SUMMARY In previous publications, we presented a general framework, which we called ‘box tomography’, that allows the coupling of any two different numerical seismic wave propagation solvers, respectively outside and inside a target region, or ‘box’. The goal of such hybrid wavefield computations is to reduce the cost of computations in the context of full-waveform inversion for structure within the target region, when sources and/or receivers are located at large distances from the box. Previously, we had demonstrated this approach with sources and receivers outside the target region in a 2-D acoustic spherical earth model, and demonstrated and applied this methodology in the 3-D spherical elastic Earth in a continental scale inversion in which all stations were inside the target region. Here we extend the implementation of the approach to the case of a 3-D global elastic earth model in the case where both sources and stations are outside the box. We couple a global 3-D solver, SPECFEM3D_GLOBE, for the computation of the wavefield and Green’s functions in a reference 3-D model, with a regional 3-D solver, RegSEM, for the computation of the wavefield within the box, by means of time-reversal mirrors. We briefly review key theoretical aspects, showing in particular how only the displacement is needed to be stored at the boundary of the box. We provide details of the practical implementation, including the geometrical design of the mirrors, how we deal with different sizes of meshes in the two solvers, and how we address memory-saving through the use of B-spline compression of the recorded wavefield on the mirror. The proposed approach is numerically efficient but also versatile, since adapting it to other solvers is straightforward and does not require any changes in the solver codes themselves, as long as the displacement can be recovered at any point in time and space. We present benchmarks of the hybrid computations against direct computations of the wavefield between a source and an array of stations in a realistic geometry centred in the Yellowstone region, with and without a hypothetical plume within the ‘box’, and with a 1-D or a 3-D background model, down to a period of 20 s. The ultimate goal of this development is for applications in the context of imaging of remote target regions in the deep mantle, such as, for example, Ultra Low Velocity Zones.

Geochemistry & Geophysics↗
DOE OSTI2019

Orientation Relationships upon the Structural Transformation of Monoclinic and Cubic Phases in Silver Sulfide

The orientation relationships between the low-temperature monoclinic semiconductor acanthite α-Ag{sub 2}S and the high-temperature body-centered argentite β-Ag{sub 2}S are determined based on high-temperature X-ray diffraction and high-resolution transmission electron microscopy (HRTEM) data on silver sulfide. It is found that, in contrast to acanthite, the possible distances between silver atoms in cubic argentite are too small to allow Ag atoms to occupy metal sublattice sites with a probability of 1. It is shown that the (010) and (001) atomic planes of acanthite are parallel to the (1 1-bar 0) and (221) planes of argentite, respectively. The found orientation relationships between acanthite and argentite are important for understanding the physical operation of the Ag{sub 2}S/Ag heteronanostructure, which is considered as a potential basis for designing resistive switches and nonvolatile memory devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2012

Active Flash: Performance-Energy Tradeoffs for Out-of-Core Processing on Non-Volatile Memory Devices

In this abstract, we study the performance and energy tradeoffs involved in migrating data analysis into the flash device, a process we refer to as Active Flash. The Active Flash paradigm is similar to 'active disks', which has received considerable attention. Active Flash allows us to move processing closer to data, thereby minimizing data movement costs and reducing power consumption. It enables true out-of-core computation. The conventional definition of out-of-core solvers refers to an approach to process data that is too large to fit in the main memory and, consequently, requires access to disk. However, in Active Flash, processing outside the host CPU literally frees the core and achieves real 'out-of-core' analysis. Moving analysis to data has long been desirable, not just at this level, but at all levels of the system hierarchy. However, this requires a detailed study on the tradeoffs involved in achieving analysis turnaround under an acceptable energy envelope. To this end, we first need to evaluate if there is enough computing power on the flash device to warrant such an exploration. Flash processors require decent computing power to run the internal logic pertaining to the Flash Translation Layer (FTL), which is responsible for operations such as address translation, garbage collection (GC) and wear-leveling. Modern SSDs are composed of multiple packages and several flash chips within a package. The packages are connected using multiple I/O channels to offer high I/O bandwidth. SSD computing power is also expected to be high enough to exploit such inherent internal parallelism within the drive to increase the bandwidth and to handle fast I/O requests. More recently, SSD devices are being equipped with powerful processing units and are even embedded with multicore CPUs (e.g. ARM Cortex-A9 embedded processor is advertised to reach 2GHz frequency and deliver 5000 DMIPS; OCZ RevoDrive X2 SSD has 4 SandForce controllers, each with 780MHz max frequency Tensilica core). Efforts that take advantage of the available computing cycles on the processors on SSDs to run auxiliary tasks other than actual I/O requests are beginning to emerge. Kim et al. investigate database scan operations in the context of processing on the SSDs, and propose dedicated hardware logic to speed up scans. Also, cluster architectures have been explored, which consist of low-power embedded CPUs coupled with small local flash to achieve fast, parallel access to data. Processor utilization on SSD is highly dependent on workloads and, therefore, they can be idle during periods with no I/O accesses. We propose to use the available processing capability on the SSD to run tasks that can be offloaded from the host. This paper makes the following contributions: (1) We have investigated Active Flash and its potential to optimize the total energy cost, including power consumption on the host and the flash device; (2) We have developed analytical models to analyze the performance-energy tradeoffs for Active Flash, by treating the SSD as a blackbox, this is particularly valuable due to the proprietary nature of the SSD internal hardware; and (3) We have enhanced a well-known SSD simulator (from MSR) to implement 'on-the-fly' data compression using Active Flash. Our results provide a window into striking a balance between energy consumption and application performance.

42 ENGINEERING↗
DOE OSTI2025

Turbulent-like flows in quasi two-dimensional dense suspensions of motile colloids

Dense bacterial suspensions exhibit turbulent-like flows at low Reynolds numbers, driven by the activity of the microswimmers. In this study, we develop a model system to examine these dynamics using motile colloids that mimic bacterial locomotion. The colloids are powered by the Quincke instability, which causes them to spontaneously roll in a random-walk pattern when exposed to a square-wave electric field. We experimentally investigate the flow dynamics in dense suspensions of these Quincke random walkers under quasi two-dimensional conditions, where the particle size is comparable to the gap between the electrodes. The results suggest a scaling regime in the energy spectrum ∼k −4 at high wavenumbers, observed consistently across activity levels and particle concentrations. We observe that velocity time correlations decay within a single period of the square-wave field, yet an anti-correlation appears between successive field applications, indicative of a dynamic structural memory of the ensemble.

Luo, Rui [Northwestern Univ., Evanston, IL (United↗
DOE OSTI2023

Effect of image resolution on automated classification of chest X-rays

Deep learning (DL) models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays (CXRs). Recently available public CXR datasets include high resolution images, but state-of-the-art models are trained on reduced size images due to limitations on graphics processing unit memory and training time. As computing hardware continues to advance, it has become feasible to train deep convolutional neural networks on high-resolution images without sacrificing detail by downscaling. This study examines the effect of increased resolution on CXR classification performance. We used the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution CXR images for this study. We applied image downscaling from native resolution to 2048 × 2048 pixels, 1024 × 1024 pixels, 512 × 512 pixels, and 256 × 256 pixels and then we used the DenseNet121 and EfficientNet-B4 DL models to evaluate clinical task performance using these four downscaled image resolutions. We find that while some clinical findings are more reliably labeled using high resolutions, many other findings are actually labeled better using downscaled inputs. We qualitatively verify that tasks requiring a large receptive field are better suited to downscaled low resolution input images, by inspecting effective receptive fields and class activation maps of trained models. Lastly, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, indicating that diverse information is extracted across resolutions.

47 OTHER INSTRUMENTATION↗
NASA NTRS1982

Numerical analysis and FORTRAN program for the computation of the turbulent wakes of turbomachinery rotor blades, isolated airfoils and cascade of airfoils

Turbulent wakes of turbomachinery rotor blades, isolated airfoils, and a cascade of airfoils were investigated both numerically and experimentally. Low subsonic and incompressible wake flows were examined. A finite difference procedure was employed in the numerical analysis utilizing the continuity, momentum, and turbulence closure equations in the rotating, curvilinear, and nonorthogonal coordinate system. A nonorthogonal curvilinear coordinate system was developed to improve the accuracy and efficiency of the numerical calculation. Three turbulence models were employed to obtain closure of the governing equations. The first model was comprised to transport equations for the turbulent kinetic energy and the rate of energy dissipation, and the second and third models were comprised of equations for the rate of turbulent kinetic energy dissipation and Reynolds stresses, respectively. The second model handles the convection and diffusion terms in the Reynolds stress transport equation collectively, while the third model handles them individually. The numerical results demonstrate that the second and third models provide accurate predictions, but the computer time and memory storage can be considerably saved with the second model.

Hah, C.↗