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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
NASA NTRS2006

Ice Chemistry Through Quiescent Dust In IC5146

IC 5146 is a nearby (200pc) dark cloud complex in Cygnus. The lack of star formation activity makes it an excellent laboratory for the study of the chemical complexity in the earliest stages of dense molecular cloud evolution. We have used the Spitzer Infrared Spectrometer (IRS) to probe dust along 10 sight-lines toward K-Giant background stars, sampling a range of visual extinction from 2-20mag. Here we present 5-20micron spectra and correlation studies of the 6.0micron water-ice band and 9.7micron silicate absorption band with Av for a sample of our Spitzer program sources. Our IC5146 Spitzer data indicate grain growth and ice formation occurs early in the history of dense cloud formation. Each sight-line observed reveals the 9.7micron amorphous silicate absorption band. The highest Av sightlines show clear detections of ices at 6.0micron (water-ice mixture), 6.85micron (processed ice) and 15.2micron (CO2). However, sight-lines in the low-to-mid Av range provide intriguing variations. We may have the first example of two objects, one with ice features and one without, seen through the same cloud sightline with similar Av approx. 6 and similar silicate band optical depths. Also, the nominally expected linear correlation of Av with silicate band depth does not appear to hold for this cloud (turnover at Av approx.10-12). Both trends imply complexities in the grain growth at one of the earliest stages of dust and ice interaction ever observed. The highest extinction source in our sample, Av=20, reveals the 6.0micron (water), 6.85micron (processed ice), 9.7micron silicate and 15.2micron CO2 ice bands. Until recently, the 6.85micron band had only been detected towards embedded protostellar objects. Two additional quiescent dust sightlines from the C2D results now also indicate the 6.85micron band (Taurus and Serpens) by Knez et al. 2005, demonstrating the role of energetic processing within pristine ices prior to the onset of star formation.

Ennico, Kimberly↗
DOE OSTI2024

STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. Here we achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Detection of Objects Hidden in Highly Scattering Media Using Time-Gated Imaging Methods

Non-intrusive and non-invasive optical imaging techniques has generated great interest among researchers for their potential applications to biological study, device characterization, surface defect detection, and jet fuel dynamics. Non-linear optical parametric amplification gate (NLOPG) has been used to detect back-scattered images of objects hidden in diluted Intralipid solutions. To directly detect objects hidden in highly scattering media, the diffusive component of light needs to be sorted out from early arrived ballistic and snake photons. In an optical imaging system, images are collected in transmission or back-scattered geometry. The early arrival photons in the transmission approach, always carry the direct information of the hidden object embedded in the turbid medium. In the back-scattered approach, the result is not so forth coming. In the presence of a scattering host, the first arrival photons in back-scattered approach will be directly photons from the host material. In the presentation, NLOPG was applied to acquire time resolved back-scattered images under the phase matching condition. A time-gated amplified signal was obtained through this NLOPG process. The system's gain was approximately 100 times. The time-gate was achieved through phase matching condition where only coherent photons retain their phase. As a result, the diffusive photons, which were the primary contributor to the background, were removed. With a large dynamic range and high resolution, time-gated early light imaging has the potential for improving rocket/aircraft design by determining jets shape and particle sizes. Refinements to these techniques may enable drop size measurements in the highly scattering, optically dense region of multi-element rocket injectors. These types of measurements should greatly enhance the design of stable, and higher performing rocket engines.

Galland, Pierre A.↗
DOE OSTI2021

Determination of oxidation rates and volatile oxidation products for HTGR graphite matrix material exposed to steam atmospheres

High-temperature gas-cooled reactors (HTGRs) in operation use tristructural isotropic (TRISO) particles embedded in graphite and carbonized resin matrix to form the fuel element. This graphite matrix material serves as a supportive structural element, heat transfer medium, and neutron moderator. In HTGR designs, fuel compacts are exposed to helium coolant, which facilitates high outlet temperatures (750°C 2 O, 800°C 2 , and H 2 , are quantified in varied oxidant atmospheres using a coupled thermogravimetric analyzer and mass spectrometer. Furthermore, oxidation rates reported here for varied steam (H 2 O [g]) atmospheres are predominantly linear and comparable with literature values in the range of tested temperatures (800–1200°C). Changes in dominant matrix oxidation products from primarily CO to a mixture of CO, CO 2 , and H 2 were observed at higher temperatures (≥1000°C) and steam atmospheres (≥5 kPa pH 2 O). Kinetic data indicates that there was no shift in oxidation regime with chemical oxidation occurring at all temperatures and H2O (g) atmospheres tested. These data provide insight into the oxidation behavior of graphite matrix material and will inform future testing conditions, notably mixed atmospheric conditions, of HTGR fuel elements.

36 MATERIALS SCIENCE↗
NASA NTRS1982

The effect of barriers on wave propagation phenomena: With application for aircraft noise shielding

The frequency spectrum was divided into high and low frequency regimes and two separate methods were developed and applied to account for physical factors associated with flight conditions. For long wave propagation, the acoustic filed due to a point source near a solid obstacle was treated in terms of an inner region which where the fluid motion is essentially incompressible, and an outer region which is a linear acoustic field generated by hydrodynamic disturbances in the inner region. This method was applied to a case of a finite slotted plate modelled to represent a wing extended flap for both stationary and moving media. Ray acoustics, the Kirchhoff integral formulation, and the stationary phase approximation were combined to study short wave length propagation in many limiting cases as well as in the case of a semi-infinite plate in a uniform flow velocity with a point source above the plate and embedded in a different flow velocity to simulate an engine exhaust jet stream surrounding the source.

Mgana, C. V. M.↗
DOE OSTI2021

Biological Mechanisms May Contribute to Soil Carbon Saturation Patterns: Modeling Archive

This Modeling Archive is in support of a TES-SFA publication “Biological Mechanisms May Contribute to Soil Carbon Saturation Patterns” (Craig et al., 2021). We ran and evaluated a multi-assumption soil organic carbon (SOC) model to investigate whether alternative assumptions regarding constraints on soil microbial biomass could lead to soil carbon saturation patterns. We developed this model in the Multi-Assumption Architecture and Testbed (MAAT, https://github.com/walkeranthonyp/MAAT, tag: v1.2.1_Craig2021; Walker et al. 2018). Using MAAT, we embedded three alternative hypotheses in a microbially explicit three-pool SOC model: 1) the efficiency of mineral-associated SOC formation decreases as mineral-associated SOC approaches a maximum value (“Mineral saturation”), 2) the microbial biomass turnover rate increases with increasing microbial biomass (“Density-dependent turnover”), and 3) community carbon use efficiency decreases as microbial biomass increases toward an upper limit (“Density-dependent growth”). We ran a factorial combination of these hypotheses resulting in eight models for three different classes of model (linear decay, Michaelis-Menten decay, or reverse Michaelis-Menten decay), resulting in 24 models, 12 of which are presented or discussed in the related publication. Models were parameterized using values from previous studies with similar models (Wang et al. 2013, Wieder et al. 2014, Li et al. 2014, Georgiou et al. 2017, Hassink and Whitmore 1997) and ran to an approximate steady state (200 years) at six (6) different C input rates corresponding to 0.5, 1, 2, 4, 7, and 10 times the default input value. Further model details are available in the related publication. This archive contains output from three MAAT simulations, and scripts to run these simulations and process and plot the data. Simulations are labeled “lin”, “MM_highKm”, and “RMM_highKm” reflecting factorial runs for linear, Michealis-Menten, and reverse Michaelis-Menten models, respectively. This archive contains: • 3 R scripts prepended with “init_MAAT_” to initialize runs (1 for each simulation), • 1 csv file containing years over which to run simulations (“met_year.csv”), • 1 bash script (.bs) to run MAAT, • 6 XML files that are output from MAAT describing a run (2 for each simulation), • 3 model output csv files prepended by “out_” (1 for each simulation), and • 1 analysis R script for reproducing figures 3 and 4 in Craig et al. 2021. See included user guide (Craig_2021_modeling_archive_20210315.pdf) for file organization details.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

NASA Tech Briefs, May 2011

Topics covered include: 1) Method to Estimate the Dissolved Air Content in Hydraulic Fluid; 2) Method for Measuring Collimator-Pointing Sensitivity to Temperature Changes; 3) High-Temperature Thermometer Using Cr-Doped GdAlO3 Broadband Luminescence; 4)Metrology Arrangement for Measuring the Positions of Mirrors of a Submillimeter Telescope; 5) On-Wafer S-Parameter Measurements in the 325-508-GHz Band; 6) Reconfigurable Microwave Phase Delay Element for Frequency Reference and Phase-Shifter Applications; 7) High-Speed Isolation Board for Flight Hardware Testing; 8) High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors; 9) 3D Orbit Visualization for Earth-Observing Missions; 10) MaROS: Web Visualization of Mars Orbiting and Landed Assets; 11) RAPID: Collaborative Commanding and Monitoring of Lunar Assets; 12) Image Segmentation, Registration, Compression, and Matching; 13) Image Calibration; 14) Rapid ISS Power Availability Simulator; 15) A Method of Strengthening Composite/Metal Joints; 16) Pre-Finishing of SiC for Optical Applications; 17) Optimization of Indium Bump Morphology for Improved Flip Chip Devices; 18) Measuring Moisture Levels in Graphite Epoxy Composite Sandwich Structures; 19) Marshall Convergent Spray Formulation Improvement for High Temperatures; 20) Real-Time Deposition Monitor for Ultrathin Conductive Films; 21) Optimized Li-Ion Electrolytes Containing Triphenyl Phosphate as a Flame-Retardant Additive; 22) Radiation-Resistant Hybrid Lotus Effect for Achieving Photoelectrocatalytic Self-Cleaning Anticontamination Coatings; 23) Improved, Low-Stress Economical Submerged Pipeline; 24) Optical Fiber Array Assemblies for Space Flight on the Lunar Reconnaissance Orbiter; 25) Local Leak Detection and Health Monitoring of Pressurized Tanks; 26) Dielectric Covered Planar Antennas at Submillimeter Wavelengths for Terahertz Imaging; 27) Automated Cryocooler Monitor and Control System; 28) Broadband Achromatic Phase Shifter for a Nulling Interferometer; 29) Super Dwarf Wheat for Growth in Confined Spaces; 30) Fine Guidance Sensing for Coronagraphic Observatories; 31) Single-Antenna Temperature- and Humidity-Sounding Microwave Receiver; 32) Multi-Wavelength, Multi-Beam, and Polarization-Sensitive Laser Transmitter for Surface Mapping; 33) Optical Communications Link to Airborne Transceiver; 34) Ascent Heating Thermal Analysis on Spacecraft Adaptor Fairings; 35) Entanglement in Self-Supervised Dynamics; 36) Prioritized LT Codes; 37) Fast Image Texture Classification Using Decision Trees; 38) Constraint Embedding Technique for Multibody System Dynamics; 39) Improved Systematic Pointing Error Model for the DSN Antennas; 40) Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks; 41) More-Accurate Model of Flows in Rocket Injectors; 42) In-Orbit Instrument-Pointing Calibration Using the Moon as a Target; 43) Reliability of Ceramic Column Grid Array Interconnect Packages Under Extreme Temperatures; 44) Six Degrees-of-Freedom Ascent Control for Small-Body Touch and Go; and 45) Optical-Path-Difference Linear Mechanism for the Panchromatic Fourier Transform Spectrometer.

Source record↗
NASA NTRS2012

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

Roberts, J. Brent↗
DOE OSTI2021

Supercooled Liquid Water Detection Capabilities from Ka-Band Doppler Profiling Radars: Moment-Based Algorithm Formulation and Assessment

The occurrence of supercooled liquid water in mixed-phase cloud (MPC) affects their cloud microphysical and radiative properties. The prevalence of MPCs in the mid- and high latitudes translates these effects to significant contributions to Earth’s radiative balance and hydrological cycle. The current study develops and assesses a radar-only, moment-based phase partition technique for the demarcation of supercooled liquid water volumes in arctic, MPC conditions. The study utilizes observations from the Ka band profiling radar, the collocated high spectral resolution lidar, and ambient temperature profiles from radio sounding deployments following a statistical analysis of 5.5 years of data (January 2014–May 2019) from the Atmospheric Radiation Measurement observatory at the North Slope of Alaska. The ice/liquid phase partition occurs via a per-pixel, neighborhood-dependent algorithm based on the premise that the partitioning can be deduced by examining the mean values of locally sampled probability distributions of radar-based observables and then compare those against the means of climatologically derived, per-phase probability distributions. Analyzed radar observables include linear depolarization ratio (LDR), spectral width, and vertical gradients of reflectivity factor and radial velocity corrected for vertical air motion. Results highlight that the optimal supercooled liquid water detection skill levels are realized for the radar variable combination of spectral width and reflectivity vertical gradient, suggesting that radar-based polarimetry, in the absence of full LDR spectra, is not as critical as Doppler capabilities. The cloud phase masking technique is proven particularly reliable when applied to cloud tops with an Equitable Threat Score (ETS) of 65%; the detection of embedded supercooled layers remains much more uncertain (ETS = 27%).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

The polarization of the far-infrared radiation from the Galactic center

The first detection of linear polarization of the far-infrared (100-micron) radiation from the about 3-pc-diameter dust ring surrounding the galactic nucleus is reported. The percentage of polarization is between 1 and 2 percent at the three measured positions. It is argued that the polarized radiation is produced by thermal emission from elongated interstellar grains oriented by the local magnetic field. The dust ring is optically thin at 100 microns; therefore the observations sample dust through the entire depth of the cloud and are free of confusing effects due to embedded sources, scattering, or selective absorption. These data provide the first information about the configuration of the magnetic field in the dust ring.

Werner, M. W.↗
DOE OSTI2023

Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure

Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. For chromophores in the condensed phase, the large number of atoms needed to simulate the environment has traditionally prohibited the use of high-level excited-state electronic structure methods. By leveraging transfer learning, we show how to construct machine-learned models to accurately predict the high-level excitation energies of a chromophore in solution from only 400 high-level calculations. Here, we show that when the electronic excitations of the green fluorescent protein chromophore in water are treated using EOM-CCSD embedded in a DFT description of the solvent the optical spectrum is correctly captured and that this improvement arises from correctly treating the coupling of the electronic transition to electric fields, which leads to a larger response upon hydrogen bonding between the chromophore and water.

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

Grassmannian Diffusion Maps--Based Dimension Reduction and Classification for High-Dimensional Data

This work introduces the Grassmannian diffusion maps (GDMaps), a novel nonlinear dimensionality reduction technique that defines the affinity between points through their representation as low-dimensional subspaces corresponding to points on the Grassmann manifold. Here, the method is designed for applications, such as image recognition and data-based classification of constrained high-dimensional data where each data point itself is a high-dimensional object (i.e., a large matrix) that can be compactly represented in a lower-dimensional subspace. The GDMaps is composed of two stages. The first is a pointwise linear dimensionality reduction wherein each high-dimensional object is mapped onto the Grassmann manifold representing the low-dimensional subspace on which it resides. The second stage is a multipoint nonlinear kernel-based dimension reduction using diffusion maps to identify the subspace structure of the points on the Grassmann manifold. To this end, an appropriate Grassmannian kernel is used to construct the transition matrix of a random walk on a graph connecting points on the Grassmann manifold. Spectral analysis of the transition matrix yields low-dimensional Grassmannian diffusion coordinates embedding the data into a low-dimensional reproducing kernel Hilbert space. Further, a novel data classification/recognition technique is developed based on the construction of an overcomplete dictionary of reduced dimension whose atoms are given by the Grassmannian diffusion coordinates. Three examples are considered. First, a "toy" example shows that the GDMaps can identify an appropriate parametrization of structured points on the unit sphere. The second example demonstrates the ability of the GDMaps to revealing the intrinsic subspace structure of high-dimensional random field data. In the last ex- ample, a face recognition problem is solved considering face images subject to varying illumination conditions, changes in face expressions, and occurrence of occlusions. The technique presented high recognition rates (i.e., 95% in the best case) using a fraction of the data required by conventional methods.

42 ENGINEERING↗
NASA NTRS2008

FPGA Coprocessor for Accelerated Classification of Images

An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.

Pingree, Paula J.↗
NASA NTRS2005

Hybrid Systems Diagnosis

This paper reports on an on-going Project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.

McIlraith, Sheila↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multiscale analysis in solids with unseparated scales: fine-scale recovery, error estimation, and coarse-scale adaptivity

There are several engineering applications in which the assumptions of homogenization and scale separation may be violated, in particular, for metallic structures constructed through additive manufacturing. Instead of resorting to direct numerical simulation of the macroscale system with an embedded fine scale, an alternative approach is to use an approximate macroscale constitutive model, but then estimate the model-form error using a posteriori error estimation techniques and subsequently adapt the macroscale model to reduce the error for a given boundary value problem and quantity of interest. Here, we investigate this approach to multiscale analysis in solids with unseparated scales using the example of an additively manufactured metallic structure consisting of a polycrystalline microstructure that is neither periodic nor statistically homogeneous. As a first step to the general nonlinear case, we focus here on linear elasticity in which each grain within the polycrystal is linear elastic but anisotropic.

42 ENGINEERING↗
NASA NTRS1996

Robust, Brillouin Active Embedded Fiber-Is-The-Sensor System in Smart Composite Structures

Extensive review of our proposed sensing scheme, based mainly on the forward Guided Acoustic Wave Brillouin Scattering (GAWBS) with backward stimulated Brillouin scattering (sBs) as an auxiliary scheme for system fault tolerance has been completed during this project period. This preliminary study is conducted for a number of reasons. The most significant reasons lie in the essential capability of the system to measure temperature and pressure. These two measurands have been proposed to be sensed by sBs in our proposal. Temperature and pressure/strain are important measurands in structural monitoring, so that the effectiveness of sensing by sBs needs to be further examined. It has been pointed out initially that sBs shift will be dependent on temperature and pressure/strain simultaneously. The shift versus temperature or strain is linear. Now, the question is how can these two measurands be separated when sBs is used to sense an environment, in which both temperature and strain are changing simultaneously. Typical sBs shift plotted versus strain and varying temperature is shown in Fig. 1. As is clear, a fiber initially stressed will relax with rising temperature. This is verified by a displacement to the right with rising temperature of the sBs shift vs strain curves in the figure. A way to circumvent this ambiguity is by employing two fibers, one pre-stressed and the other is a free fiber. The latter will measure temperature and subtracting data in the latter fiber from those of the former will give us net strain readings. This is a laborious approach, since it involves the use of two identical fibers, and this is hard to accomplish, especially when many sensors are needed. Additional multiplexing of the data stream for data subtraction becomes a necessity.

Yu, Chung↗
DOE OSTI2022

Texture analysis of AGR program matrix materials

We report the fuel form for high-temperature gas-cooled reactors consists of tristructural isotropic (TRISO) particles embedded in a matrix of graphite flake and carbonized resin. The process of overcoating particles prior to compacting yields a circumferential orientation of the graphite flake surrounding the TRISO particles, which is modified to varied extents when overcoated particles are pressed into the final fuel form. As graphite is highly anisotropic, the texture may impact the properties and performance of the fuel. Ellipsometry was used to measure the texture of the matrix for fueled compacts and unfueled “matrix-only” samples. Results indicated local texture related to the spherical particles in compacts associated with overcoating versus a more linear layered structure in “matrix-only” samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗