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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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Analysis of the rotationally-resolved infrared spectrum of CD 3 79 Br and CD 3 81 Br between 900 and 1400 cm -1
We report the infrared spectrum of CD 3 79 Br and CD 3 81 Br between 900 cm -1 and 1400 cm -1 has been analyzed at high resolution. In this region, two fundamental vibrational states, v 2 = 1 and v 5 = 1, an overtone state, v 3 = 2, and a combination state, v 3 = v 6 = 1, have been analyzed for both isotopologues. As found in other halomethane molecules, strong Coriolis resonances couple the v 2 = 1 state to the v 5 = 1 state, and similar resonances were also used to describe the coupling found between the v 5 = 1 state to the v 3 = 2 state. A new determination of the K-dependent constant A 0 was performed for each isotopologue through the use of perturbation-allowed transitions. The values for A 0 determined from the analysis are 2.6001898(26) and 2.6001905(27) for CD 3 79 Br and CD 3 81 Br, respectively. Ab initio calculations were performed to examine the efficacy of high-level theoretical calculations to accurately predict spectroscopic constants. The ab initio calculations had variable success with the largest errors associated with the prediction of the ΔB rotational constants of the interacting states
Augmented Adam-Gibbs model for glass melt viscosity and configuration entropy as functions of temperature and composition
As the temperature of glass melt increases, its structure approaches the state of a simple liquid while the configuration entropy approaches a maximum value. We describe this gradual change using a power law function of inverse temperature. The Adam-Gibbs model for glass viscosity as a function of temperature and glass composition augmented in this way is greatly simplified when applied to common glass families occupying moderate composition regions, such as float glass or nuclear waste glasses, on which properties can be approximated as linear functions of composition. The parsimonious model thus obtained is preferable for use in optimizing glass formulation and mathematical modeling of glass melting and forming. For multicomponent glasses with N viscosity-affecting components, the augmented Adam-Gibbs model requires 2N + 3 adjustable parameters. The model efficacy is demonstrated by fitting the model to a viscosity-temperature-composition dataset for low-activity nuclear waste glasses.
Postirradiation characterization of palladium as an additive for fuel cladding chemical interaction mitigation in metallic fuel
This work describes the microstructural and elemental characterization of irradiated metallic fuels containing palladium as an additive. The use of additives has been proposed to control Fuel-Cladding Chemical Interaction (FCCI) and thus to promote higher fuel utilization (i.e., higher burnup). In this work, Pd has been investigated as a potential additive to metallic fuel to bind lanthanides, impeding their migration and attack on the cladding. The influence of Pd on the microstructure, chemistry and performance of metallic fuel has been characterized via scanning electron microscopy for two metallic fuel designs—namely, annular and solid fuel. Pd was observed to play an important role in the chemistry of the fuel. Indeed, the addition of Pd leads to the formation of new phases. Pd was detected to combine not only with the lanthanides, as intended, but also with Zr, a main element of the fuel matrix. While Pd proved to be effective in preventing lanthanide migration and their attack on the cladding, the Pd-Zr compound may potentially lead to other unexpected fuel-performance issues, such as the formation of low-melting point phases and increased unalloyed U available for FCCI interaction with Fe in the cladding. Even the increase of Zr to 13wt%. did not completely mitigate this adverse phenomenon generated by the Pd-Zr interaction. Furthermore, the efficacy of using this additive needs further investigation.
Accelerated statistical failure analysis of multifidelity TRISO fuel models
Statistical nuclear fuel failure analysis is critical for the design and development of advanced reactor technologies. Although Monte Carlo Sampling (MCS) is a standard method of statistical failure analysis for fuels, the low failure probabilities of some advanced fuel forms and the correspondingly large number of required model evaluations limit its application to low-fidelity (e.g., 1-D) fuel models. In this paper, we present four other statistical methods for fuel failure analysis in Bison, considering tri-structural isotropic (TRISO)-coated particle fuel as a case study. The statistical methods considered are Latin hypercube sampling (LHS), adaptive importance sampling (AIS), subset simulation (SS), and the Weibull theory. Using these methods, we analyzed both 1-D and 2-D representations of TRISO models to compute failure probabilities and the distributions of fuel properties that result in failures. The results of these methods compare well across all TRISO models considered. Overall, SS and the Weibull theory were deemed the most efficient, and can be applied to both 1-D and 2-D TRISO models to compute failure probabilities. Moreover, since SS also characterizes the distribution of parameters that cause TRISO failures, and can consider failure modes not described by the Weibull criterion, it may be preferred over the other methods. Finally, a discussion on the efficacy of different statistical methods of assessing nuclear fuel safety is provided.
Experimental results of conductive inserts to reduce nuclear fuel temperature during nuclear volumetric heating
Advanced fuel designs that incorporate thinner fuel UO2 pellets interspaced by high thermal conductivity inserts have been proposed, with the primary goals of reducing peak centerline temperatures and temperature gradients across fuel pellets and enhancing heat transfer from the fuel to the coolant. An initial series of experiments has been performed on this design, including laboratory experiments and a series of experiments using the Idaho National Laboratory (INL) Transient Reactor Test (TREAT) Facility, the latter of which compared thermal gradient driven fracture of standard pellet designs with that in the proposed advanced fuel design. Although reducing fracture is not the primary objective of the new fuel design, the lower thermal gradients are expected to reduce fracture, so it can serve as an indicator of the thermal behavior of this fuel in the reactor. The in-reactor tests were conducted at multiple linear heat generation rates and confirm the expected result that fracture in both the standard and advanced fuel pellets occurs during the first ramp to power in standard light-water reactor conditions. Post-irradiation examination of the experiment material was performed and included quantification of the extent of fracture in the fuel pellets. It was found that the advanced-design pellets reduce the extent of fracture in a statistically significant way. This confirms the expected behavior predicted by two-dimensional axisymmetric models of this experiment. Here in this study is an important first experimental confirmation of the efficacy of the proposed inserts for achieving their desired effect on the thermal behavior of the fuel.
First-principles investigation of lanthanides diffusion in HCP zirconium via vacancy-mediated transport
The diffusion of lanthanide fission products plays an important role in the growth of the fuel-cladding chemical interaction (FCCI) region in metallic fuels. The use of a Zr interdiffusion barrier may mitigate the transport of lanthanides from the fuel to the cladding, but the efficacy of such a liner is not yet known. In this paper, the stability and vacancy-mediated diffusion of La, Ce, Pr, and Nd in hexagonal close-packed (HCP) Zr is investigated via density functional theory (DFT) calculations and self-consistent mean field (SCMF) analysis. DFT is used to calculate the formation, binding, and migration energies of vacancies and vacancy-solute pairs. The DFT energetics are used in the KineCluE code to calculate the Onsager transport coefficients. La is found to be the fastest diffusing species in HCP Zr and experiences an almost isotropic diffusion behavior. The other three species (Ce, Pr, and Nd) demonstrate anisotropic diffusion where the diffusion in the basal planes is significantly faster than that along the c-axis. Further, the calculated lanthanide diffusivities in HCP Zr are fitted to an Arrhenius relation and the activation energies and prefactors are reported for the first time. Furthermore, the vacancy drag and the segregation tendencies were analyzed using the calculated off-diagonal transport coefficients. According to our vacancy-mediated diffusion model, lanthanides are expected to be enriched at vacancy sinks at low temperatures, while at high temperatures, lanthanides are depleted at sinks and will preferably diffuse into the bulk. The enrichment/depletion transition temperature depends on the diffusion direction (basal or axial) and hence will be controlled by the grain texture and orientation.
A noncollinear density functional theory ansatz for the phononic and thermodynamic properties of α -Pu
Plutonium's phase diagram is host to complex structures and interactions that make the description of its ground state properties elusive. Using all-electron density functional theory calculations, we study the thermodynamic properties of α-Pu. To do this, we build on recent work in the literature by introducing a novel noncollinear magnetic ansatz for α-Pu's ground state. We show that the noncollinear ansatz improves the description of the experimental phonon density of states, heat capacity, and thermal expansion over previous results obtained with a collinear ansatz. Here, despite lacking a bond-equivalent picture of the bonding, these new results for α-Pu, along with recent results on δ-Pu, demonstrate the efficacy of noncollinear ansatzes for the description of plutonium.
Thermal performance and energy consumption validation of an occupied local government office building outfitted with ceiling tile phase change materials
Buildings present an opportunity for energy conservation and the modulation of peak energy demand through controlled Heating, Ventilation, and Air Conditioning (HVAC) energy use. The administrative and office building stock in the United States holds potential to achieve energy and demand savings through retrofits such as insulation, weatherization, and thermal energy storage. Specifically, there is a need to validate passive phase change material (PCM) applications in full scale in aging administrative buildings in the US to evaluate the energy benefits. Aim of this study was to conduct a whole building level thermal and energy validation of an operational building and explore an alternative method for evaluating energy efficiency. To accomplish this, the study employed PCMs in the drop ceiling and carry out an energy audit and on-site measurement of HVAC systems' energy demand and consumption. A full-scale EnergyPlus energy model, modeled by the authors, served as a baseline for evaluation. The results show that calibrated model's envelope temperature measures fall within the accepted errors. HVAC energy simulation results also fall within the accepted errors for monthly and hourly pre- and post- PCM retrofit electricity and natural gas data. The novelty of this study is that it employees energy scales per Heating Degree Hour and Cooling Degree Hour, in contrast to the commonly used Heating Degree Days and Cooling Degree Days as reported in the literature to analyze energy savings. These findings underscore the pivotal role of a calibrated model in assessing the efficacy of a singular energy measure, like a PCM-retrofitted ceiling, in an occupied office building.
Demonstrating the viability of Lagrangian in situ reduction on supercomputers
Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.
In situ feature analysis for large-scale multiphase flow simulations
The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. Herein, to address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.
A dynamically bare metal interface enables reversible magnesium electrodeposition at 50 mAh cm −2
Understanding and facilitating pure magnesium nucleation/growth electrodeposition behavior with ultrahigh Coulombic efficiency is complicated by the phenomenon of solid electrolyte interphase (SEI) formation in state-of-the-art, halogen-free magnesium electrolytes. Defining the electrolyte properties necessary to achieve ideal electrodeposition/stripping (E/S) thus remains elusive. Here, in this work, we reveal for the first time, rapid magnesium electrodeposition behavior that forms densely aligned, micron-sized thin platelets by establishing a dynamic bare magnesium/electrolyte interface during high-rate net plating. This effectively “SEI-free” interface allows facile magnesium diffusion and migration in stripping with near-unity E/S efficiency under demanding conditions over long-term cycling. The intrinsic electrolyte stability of the salt/solvent at the molecular level is the key to forming such an interface. The efficacy of the dynamic bare interface and an electrodeposited, free-standing magnesium anode is demonstrated in a high-areal-capacity full cell. These findings provide new design principles and fundamental understanding of interfacial chemistry in multivalent metal batteries.
NIRDye 812: A molecular platform tailored for multimodal bioimaging applications of targeted fluorescence- and photoacoustic-guided surgery
The primary treatment for malignant tumors remains to be surgical removal of the diseased tissue. The presence or absence of residual diseased tissue at the tumor margin is the strongest predictor of postoperative prognosis and recurrence. Accordingly, reliance on the ability of surgeons to visually distinguish diseased tissue from healthy tissue unambiguously in real time is crucial. Near infrared-I (NIRsingle bondI) fluorescence-emitting targeting biomolecular constructs such as anticancer antibody-fluorophore conjugates, namely cetuximab-IRDye® 800CW (CTB-IRDye® 800CW), are FDA-approved for clinical trial usage in the fluorescence-guided resection of diseased tissue due to affording improved direct visualization of tumor tissue when compared to the use of either the unaided eye under standard white light illumination (WLI) surgical techniques or non-targeting fluorophores. Unfortunately, though helpful, CTB-IRDye® 800CW affords limited (i) identification of diseased tissue and (ii) tumor margin delineation, because the immunoconjugate generates suboptimal tumor-to-background ratios (TBRs) as a result of its spectral/photophysical profiles poorly aligning with the fixed optical windows of pre-/clinical setups. As such, CTB-IRDye® 800CW is more prone to affording incomplete resection compared to if TBRs were higher due to otherwise. Here, to aid in accurately identifying deep-seated diseased tissue, photoacoustic (PA) tomography has been implemented alongside CTB-IRDye® 800CW to achieve PA signals that could result in higher TBRs. However, in clinical trial practice, using IRDye® 800CW for PA imaging also yields subpar TBRs due to it affording low PA signals. To overcome such limitations, we developed NIRDye 812, a structurally-modified topological equivalent of IRDye® 800CW, to confer it the capability to yield both higher TBRs and superior PA signal than that of the equivalent CTB-conjugate and fluorophore IRDye® 800CW itself, respectively. To do so, we substituted the oxygen atom at its meso-position with a sulfur atom. CTB-NIRDye 812 demonstrated a red-shifted absorption wavelength at 796 nm and a peak NIR-I fluorescence emission wavelength at 820 nm, which better dovetails with the fixed windows of preinstalled fixed emission filters within commercial pre-/clinical NIR-I fluorescence imaging instruments. Overall, CTB-NIRDye 812 provided a ~ 2-fold increase in TBRs compared to those of CTB-IRDye® 800CW in vivo. Also, NIRDye 812 displayed an ~60% higher PA signal than that of IRDye® 800CW. Collectively, we achieved our goal of improving upon the spectral/photophysical and PA properties of IRDye® 800CW via introducing a subtle modification to its electronic core such that its CTB immunoconjugate could potentially allow for fast track or breakthrough designation by the FDA due to its near-identical structure displaying considerably improved efficacy.
Feature engineering for machine learning enabled early prediction of battery lifetime
Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.
Kinetically versus thermodynamically controlled factors governing elementary pathways of GaP(111) surface oxidation
GaP and related III-V semiconductors have attracted interest as high-efficiency photoelectrochemical electrodes for solar water splitting. However, their efficacy is linked to the presence, identity, and integrity of native surface oxides, which are structurally and chemically complex and evolve during operation. Using ambient pressure X-ray photoelectron spectroscopy (APXPS) coupled with ab initio simulations, we track key chemical motifs expressed during evolution of GaP(111) surface oxides, their associated reaction kinetics, and their correlations with electronic properties. We identify two distinct thermal regimes corresponding to kinetically and thermodynamically controlled oxidation. Below 600 K, exposure to O2 generates kinetically facile Ga-O-Ga configurations, whereas higher temperatures cause activated oxygen to insert into Ga–P bonds as part of a thermodynamically driven transformation into a complex, heterogeneous 3D network of surface POx (1≤x ≤ 4) groups and Ga2O3 species, the latter of which eventually dominates upon depletion of surface phosphorus. Here our study highlights the critical competition between kinetic and thermodynamic factors during GaP oxidation, yielding insights for fabricating stable III-P-based photoelectrodes with precisely engineered surface properties.
Block Gram-Schmidt algorithms and their stability properties
Not Available
Austenitic parent grain reconstruction in martensitic steel using deep learning
In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. Furthermore, this work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.
Location and characterization of heterogeneous phases within Mary Rose wood
Preserving the Mary Rose oak hull for future generations is a major challenge due to the highly heterogeneous nature of waterlogged wooden artifacts, which contain polycrystalline, amorphous, and nanostructured materials that test traditional characterization methods. Effective conservation requires detailed knowledge of the distribution and chemical nature of these species to develop strategies for preventing multiple chemo-mechanical degradation pathways. Here, we apply synchrotron-based computed tomography total scattering methods to the Mary Rose keelson wood that provides valuable position-resolved structural information on multiple embedded species of different length and concentration scales. We identify 5 nm zinc sulfide nanoparticles in the wood, presumably deposits from bacteria operating on the sulfur energy cycle under the anaerobic conditions on the seabed. These are identified as precursors to acid attack on the wood upon removal to an aerobic environment. These insights inform not only next-generation conservation strategies, but also the efficacy and unforeseen issues of previous treatments.