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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Layer Time Control for Large Scale Additive Manufacturing Using High Performance Computing

This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.

42 ENGINEERING↗
DOE OSTI2023

A deep learning model for automatic analysis of cavities in irradiated materials

Transmission electron microscopy (TEM) is a commonly used technique in materials science for defect investigation. Quantitative analysis of defects is important for understanding the properties of a material, but manual analysis of TEM micrographs can be time-consuming and prone to error, especially when the defects have irregular shapes rather than spherical shapes. Many existing methods or deep learning models do not handle a wide range of sizes for the same object type within a single image. In this work, we present a framework that enables users to train an instance segmentation model called Mask R- CNN on any microstructure dataset, perform multi-detection on the same image at different scales, and obtain properties (e.g., size, area) of the objects based on the desired shape (e.g., circle, ellipse, rectangle). Additionally, we have developed a parallel detection module that uses multiple GPUs to increase the efficiency of the object detection process. We demonstrate the capabilities of our framework using a set of TEM images of cavities with different shapes, size distributions, and background contrasts. Finally, we show that the performance of our model in terms of density, size, and swelling of the cavities is comparable to the human average and that our model achieves the highest recall value compared to existing methods due to the use of image multi-rescaling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Monitoring Preferential Flow of Water in Sand Using Thermoacoustics Wave Imaging

Abstract Accurate predictions of fluid flow, mass transport, and reaction rates critically impact the efficiency and reliability of subsurface exploration and sustainable use of subsurface resources. Quantitative dynamical sensing and imaging can play a pivotal role in the ability to make such predictions. Geophysical thermoacoustic technology has the potential to provide the aforementioned capabilities, since it builds upon the principle that electromagnetic and mechanical wave fields can be coupled through a thermodynamic process. In this letter, we present laboratory experiments featuring the efficacy of thermoacoustic imaging in the monitoring of preferential flow of water in porous media. Our laboratory experimental equipment can be readily packaged in a form factor that fits in a borehole, and the use of multiple acoustic transducers—which can be combined with volumetric coding techniques—has the potential to provide quasi‐real‐time imaging (0.5 Hz video rate) of regions in close proximity (a few meters) of an open field well.

Geology↗
DOE OSTI2021

Hyperdimensional Imaging Contrast Using an Optical Fiber

Fluorescence properties of a molecule can be used to study the structural and functional nature of biological processes. Physical properties, including fluorescence lifetime, emission spectrum, emission polarization, and others, help researchers probe a molecule, produce desired effects, and infer causes and consequences. Correlative imaging techniques such as hyperdimensional imaging microscopy (HDIM) combine the physical properties and biochemical states of a fluorophore. Here we present a fiber-based imaging system that can generate hyper-dimensional contrast by combining multiple fluorescence properties into a single fluorescence lifetime decay curve. Fluorescence lifetime imaging microscopy (FLIM) with controlled excitation polarization and temporally dispersed emission can generate a spectrally coded, polarization-filtered lifetime distribution for a pixel. This HDIM scheme generates a better contrast between different molecules than that from individual techniques. This setup uses only a single detector and is simpler to implement, modular, cost-efficient, and adaptable to any existing FLIM microscope. We present higher contrast data from Arabidopsis thaliana epidermal cells based on intrinsic anthocyanin emission properties under multiphoton excitation. This work lays the foundation for an alternative hyperdimensional imaging system and demonstrates that contrast-based imaging is useful to study cellular heterogeneity in biological samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

The Wormholes Within: A Study of 1, 3, 5–Triamino–2, 4, 6–Trinitrobenzene Crystal Morphology by Micro and Nano–Scale X–Ray Computed Tomography and its Effects Upon the Shock Sensitivity of Detonation

We report that we understand heterogeneous-explosive detonation only to the extent that we can first measure the salient chemical and microstructural features. Barring an inordinate amount of trial and error, we can tailor the detonation properties of our HE formulations only to the extent that we understand (at least qualitatively) the same effects. As an insensitive high explosive, 1, 3, 5-triamino-2, 4, 6-trinitrobenzene (TATB) burns more heterogeneously than conventional high explosives, making its detonation properties sensitive to microstructure. The Benziger route can produce various grain morphologies depending on the amount of water used in the amination step. So-called dry-aminated (DA) TATB crystals are riddled with a void structure called wormholes, which are washed out ammonium chloride (NH4Cl) inclusions that form during synthesis. So-called wet-aminated (WA) TATB crystals do not contain NH 4 Cl inclusions but possess a very convoluted external structure. Using both micro and nano-scale CT, we explore three DA-TATB lots that reflect evolutionary processing changes, plus one WA-TATB material. Micro CT imaged crystal collections show the exterior grain structure, revealing its roughness and providing a crude particle size measurement. The DA-TATB lots that test most sensitively in the LANL ECOT corner-turning test are roughest, having the appearance of being “corroded”. Nano-scale CT reveals both internal NH 4 Cl inclusions and void structures. The DA-TATB lots that tested intermediate and high sensitivity in ECOT exhibited similar wormhole structures, which were more extensive than that of the low-sensitivity lot. The least sensitive DA-TATB lot also retained more NH 4 Cl, consistent with reduced connectivity of voids to the surface.

1, 3, 5-triamino-2,4↗
DOE OSTI2023

Quantifying Pore Morphology in Spray-Formed Tantalum Using X-ray Micro-computed Tomography

In order to establish quantitative process–structure–property relationships in thermal spray coatings, a robust framework for defining (micro)structural characteristics is needed. Here, we present a quantitative characterization of the three-dimensional morphology of porosity in spray-formed tantalum samples based on high-resolution X-ray micro-computed tomography. Using synchrotron facilities, we acquired dozens of high-resolution scans, enabling a statistically meaningful comparison across multiple samples, different regions within samples, and spray processes. We quantify the spatial distribution, size, and topology of porous inclusions, with a significant focus on variability across samples and different spray processes (plasma and cold sprayed), as well as sensitivity to image segmentation and resolution. Based on a typical segmentation, we report porosities ranging from 0.9 to 1.7 pct for all samples tested, with significant sensitivity due to image segmentation resulting in estimates as low as 0.6 pct and as high as 4.8 pct. For the complex pore space morphology observed in these materials, we argue that a conventional analysis based on identifying individual pores is not well suited, and propose an alternative approach based on morphological metrics with a rich history in porous media literature, such as spatial correlations, local pore thickness, and scale-dependent sub-sampling. Spatial correlations indicate anisotropic splat structures, but only mildly anisotropic pores. Various measures of pore size show a wide distribution of sizes, ranging from sub-micron to 10-micron length scales. Scale-dependent variations in porosity suggest that representative volumes of several hundred microns are required for convergence of morphological metrics, with larger volumes for cold-spray materials. This work provides a robust quantitative basis for describing three-dimensional pore structure in thermal spray coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optical super-resolution microscopy in polymer science

The advent of optical super-resolution microscopy (OSRM) over two decades ago has transformed light-mediated interrogation of systems down to the nanoscale. This innovative set of approaches to optics breaks the so-called diffraction limit of light (~200 nm), allowing for the first time to use optics in the far field to visualize behavior on nanoscopic length scales. While these visualization tools have rapidly found widespread use in biology and related fields, their implementation in materials and, more specifically, in polymer science has been far slower. To overcome possible barriers and highlight possible future directions, we present in this paper an introduction to OSRM for polymer scientists, beginning with an overview of relevant techniques and existing optical probes. We then show and examine the first examples of OSRM adaptation across major areas of polymer science, including: polymerization and structural characterization, self-assembly and solution behavior, bulk structure and behavior, crystallization, gel structure and behavior, phase transitions, and biofunctionality. We hope and anticipate that the discussions provided in this review will draw the attention of the polymer community to the capacities of these hitherto underexplored optical visualization techniques to further transform polymer science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Development of Lipid-Coated Semiconductor Nanosensors for Recording of Membrane Potential in Neurons

In the past decade, optical imaging methods have significantly improved our understanding of the information processing principles in the brain. Although many promising tools have been designed, sensors of membrane potential are lagging behind the rest. Semiconductor nanoparticles are an attractive alternative to classical voltage indicators, such as voltage-sensitive dyes and proteins. Such nanoparticles exhibit high sensitivity to external electric fields via the quantum-confine Stark effect. Here we report the development of semiconductor voltage-sensitive nanorods (vsNRs) that self-insert into the neuronal membrane. To facilitate interaction of the nanorods with the membrane, we functionalized their surface with the lipid mixture derived from brain extract. We describe a workflow to detect and process the photoluminescent signal of vsNRs after widefield time-lapse recordings. We also present data indicating that vsNRs are feasible for sensing membrane potential in neurons at a single-particle level. This shows the potential of vsNRs for the detection of neuronal activity with unprecedentedly high spatial and temporal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Structure of the Ecuadorian Upper Plate From a Joint Seismic‐Gravity Inversion

The Ecuadorian portion of the South American subduction zone presents an interesting case study in the structure and complex evolution of an upper plate. There are outstanding questions about its tectonic history, composition, and magmatic processes. While previous studies have employed ambient noise tomography to image the Ecuadorian upper plate, surface wave inversions alone often lack sensitivity at relevant shallow depths. This limitation can be overcome with an independent, complementary data set, such as gravity. We have jointly inverted Rayleigh wave phase velocities and Bouguer gravity anomalies to provide a more detailed seismic velocity model of the Ecuadorian upper plate. Our joint inversion has yielded several key improvements from previous models. First, we observe much shallower slow velocities beneath major basins (the Manabí, Progreso, and Gulf of Guayaquil), better aligning with expected basin structure. Second, we identify a high-velocity block beneath the entire forearc, corresponding to the Piñon Terrane, with velocities suggesting the presence of ultramafic material. Third, we highlight a new narrow swath of slow velocities beneath the Ecuadorian Andes, which closely follows the active volcanoes along the Eastern Cordillera. The extent of these slow velocities coincides with the termination of active arc volcanism and the predicted location of the subducted Carnegie Ridge. The predicted compositions for the mid to lower crust in the region preclude a purely compositional explanation for these velocities, suggesting that some level of partial melt is necessary.

Birkey, Andrew [Univ. of Delaware, Newark, DE (Uni↗
DOE OSTI2025

Local halide heterogeneity drives surface wrinkling in mixed-halide wide-bandgap perovskites

Compositional heterogeneity in wide-bandgap (1.8 - 2.1 eV) mixed-halide perovskites is a key bottleneck in the processing of high-quality solution-processed thin films and prevents their application in efficient multijunction solar cells. Notably, mixed-cation (formamidinium-methylammonium) wide-bandgap perovskite films are prone to form micrometer-scale wrinkles which can interfere with the smooth surfaces ideal for multijunction devices. Here, we study the formation dynamics of wrinkled mixed-halide perovskite films and its impact on the local composition and optoelectronic properties. We use in situ X-ray scattering during perovskite film formation to show that crystallization of bromide-rich perovskites precedes that of mixed-halide phases in wrinkled films cast using an antisolvent-based process. Using nanoscopic X­-ray fluorescence and hyperspectral photoluminescence imaging, we also demonstrate the formation of iodide- and bromide-rich phases in the wrinkled domains. This intrinsic spatial halide segregation results in an increased local bandgap variation and Urbach energy. Morphological disorder and compositional heterogeneity also aggravate the formation of sub-bandgap electronic defects, reducing photostability and accelerating light-induced segregation of iodide and bromide ions in thin films and solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Imaging plasma formation in isolated nanoparticles with ultrafast resonant scattering

We have recorded the diffraction patterns from individual xenon clusters irradiated with intense extreme ultraviolet pulses to investigate the influence of light-induced electronic changes on the scattering response. The clusters were irradiated with short wavelength pulses in the wavelength regime of different 4d inner-shell resonances of neutral and ionic xenon, resulting in distinctly different optical properties from areas in the clusters with lower or higher charge states. The data show the emergence of a transient structure with a spatial extension of tens of nanometers within the otherwise homogeneous sample. Simulations indicate that ionization and nanoplasma formation result in a light-induced outer shell in the cluster with a strongly altered refractive index. The presented resonant scattering approach enables imaging of ultrafast electron dynamics on their natural timescale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Characterization of a 1D-imaging high-energy x-ray backlighter driven by the National Ignition Facility Advanced Radiographic Capability laser

Plastic deformation of samples compressed to Mbar pressures at high strain rates at the National Ignition Facility (NIF) forms the basis of ongoing material strength experiments in conditions relevant to meteor impacts, geophysics, armor development, and inertial confinement fusion. Hard x-ray radiography is the primary means of measuring the evolution of these samples, typically employing a slit-collimated high-Z microdot driven by the NIF laser to generate >40 keV x rays [E. Gumbrell et al., Rev. Sci. Instrum. 89, 10G118 (2018) and C. M. Huntington et al., Rev. Sci. Instrum. 89, 10G121 (2018)]. Alternatively, a dysprosium “micro-flag” target driven by the Advanced Radiographic Capability laser (~2 kJ, 10 ps) can deliver significantly higher spatiotemporal resolution [M. P. Hill et al., Rev. Sci. Instrum. 92, 033535 (2021)], especially in high-opacity samples. Here, initial experiments revealed problematic brightness and spectral gradients from this source, but by radiographing a set of diamond-turned, 105 µm-thick Pb test objects and supported by simulations using the 3D Monte Carlo code GEANT4, these geometry-dependent gradients across the field of view are quantified and mitigation strategies are assessed. In addition to significantly enhancing the modulation transfer function compared to the existing system, image stacking from multiple layers of image plate is shown to almost double the signal to noise ratio that will reduce uncertainties in future dynamic strength experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A combined TEM and SAXS study of the growth and self-assembly of ultrathin Pt nanowires

Ultrathin Pt nanowires possess high activity for various electrocatalytic applications. However, little work has focused on understanding their growth mechanisms. Herein, we utilize a combination of time-dependent, ex situ transmission electron microscopy (TEM) and small angle x-ray scattering (SAXS) techniques to observe the growth process in addition to associated surfactant-based interactions. TEM images indicate that initially nanoparticles are formed within 30 s; these small 'seed' particles quickly elongate to form ultrathin nanowires after 2 min. These motifs remain relatively unchanged in size and shape up to 480 min of reaction. Complementary SAXS data suggests that the initial nanoparticles, which are coated by a surfactant bilayer, arrange into a bcc superlattice. With increasing reaction time, the bcc lattice disappears as the nanoparticles grow into nanowires, which then self-assemble into a columnar hexagonal structure in which the individual nanowires are covered by a CTAB monolayer. The hexagonal structure eventually degrades, thereby leading to the formation of lamellar stacking phases comprised of surfactant bilayers. To the best of our knowledge, this is the first time that SAXS has been used to monitor the growth and self-assembly of Pt nanowires. In conclusion, these insights can be used to better understand and rationally control the formation of anisotropic motifs of other metallic nanostructures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Effect of detachment on Magnum-PSI ELM-like pulses: direct observations and qualitative results

Abstract Conditions similar to those at the end of the divertor leg in a tokamak were replicated in the linear plasma machine Magnum-PSI. The neutral pressure in the target chamber is then increased to cause the target to transition from an attached to a detached state. Superimposed to this steady state regime, edge localised mode (ELM)-like pulses are reproduced, resulting in a sudden increase in plasma temperature and density, such that the heat flux increases transiently by half an order of magnitude. Visible light emission, target thermography, and Thomson scattering are used to demonstrate that the higher the neutral pressure the more energy is removed from the ELM-like pulse in the volume. If the neutral pressure is sufficiently high, the ELM-like pulse can be prevented from affecting the target and the plasma energy is fully dissipated in the volume instead (ID 4 in table 1). The visible light images allow the division of the ELM-plasma interaction process of ELM energy dissipation into 3 ‘stages’ ranging from no dissipation to full dissipation (the target plasma is detached). In the second publication related to this study, spectroscopic data is analysed with a Bayesian approach, to acquire insights into the significance of molecular processes in dissipating the plasma energy and particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗