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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scientific Computational Imaging Code (SCICO)

Scientific Computational Imaging Code (SCICO) is a Python package for solving the inverse problems that arise in scientific imaging applications. Its primary focus is providing methods for solving ill-posed inverse problems by using an appropriate prior model of the reconstruction space. SCICO includes a growing suite of operators, cost functionals, regularizers, and optimization routines that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. SCICO is built on top of JAX rather than NumPy, enabling GPU/TPU acceleration, just-in-time compilation, and automatic gradient functionality, which is used to automatically compute the adjoints of linear operators. An example of how to solve a multi-channel tomography problem with SCICO is shown in Figure 1. The SCICO source code is available from GitHub, and pre-built packages are available from PyPI. It has extensive online documentation, including API documentation and usage examples, which can be run online at Google Colab and binder.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

(Doublon) Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm

In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed, such as the Transient Heatsink Overpower Response capsule (THOR), the Transient Water Irradiation System for TREAT (TWIST), and Sirius. Each has provided valuable data for materials performance and reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments can cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used for code verification, validation and uncertainty quantification. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the verification, validation and uncertainty quantification approach. To overcome this problem, the autocorrected reactimeter algorithm (ACRA) has been developed. This approach infers a time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e., when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for developing an improved version of the ACRA that does not require frozen configurations. To develop this new algorithm, we need a precise and unbiased implementation of the inverse point kinetic equations (IPKEs) as any error in the reactivity evaluation will be propagated into the choice of the optimal spatial correction. Indeed, the previous reactimeter algorithm would use approximations, such as a negligible flux amplitude derivative, to focus on rapidity. For the numerical validation of ACRA, we aim at absolute error under for reactivity derived from signals similar to the one of this study. In this summary, we test eight different IPKE implementations. Each will process a mockup signal built for this study, similar to those that the future ACRA will process. Each reactivity output will be compared to the reference reactivity that has been used to generate the mockup signal. The implementation minimizing the difference with the reference reactivity will be used in the development of a new ACRA formulation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Time-lapse seismic data inversion for estimating reservoir parameters using deep learning

Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

Geochemistry & Geophysics↗
DOE OSTI2019

Quantum material topology via defect engineering

Current approaches of discovering new topological quantum materials, such as (magnetic) Weyl materials, by screening material databases or by rational design suffer from experimental challenges in growing the material in the desired geometry, hampering their potential applications. We take an approach of defect-engineering a topology starting from systems with experimentally well-controlled growth . Using SnTe, an experimentally viable topological crystalline insulator, as our model system, we elucidate the different effects of breaking time-reversal, inversion, and mirror symmetries using magnetic dopant defects, to controllably induce a topological phase transition. To this effect, we compute the thermodynamics, magnetism, and band-structure topology of magnetically doped SnTe using a full ab initio approach. In this process, we have discovered that Cr-doped SnTe is a Weyl semimetal. We have computed its classic Fermi-arc experimental signature on the (001) surface and also showed that it has a large anamolous Hall conductivity (AHC) of ~ 250 Ω - 1 cm - 1 . We predict a large Curie temperature ( T c ) of 62 K even at 3.3 % doping, with both T c and AHC being tunable with Cr concentration, suggesting potential for room-temperature applications. Our study in general paves the way for defect-designing room-temperature topological quantum phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

An inverse vulcanized conductive polymer for Li–S battery cathodes

Polymers with a broad range of properties, structural diversity, and mechanical flexibility have been adopted in all aspects of Li–S batteries. However, currently explored polymers for Li–S cathodes suffer from low conductivity, low S content, and poor cycling performance. In this first-principles study, we theoretically design a new polymer, poly(2-vinyl,1,4-phenylene sulfide), by modifying the conductive poly(1,4-phenylene sulfide) via the vinyl group to enhance its ability to vulcanize with element S. Furthermore, we compare the properties of the experimentally realized sulfur vulcanized polymers via condensation and our designed sulfur vulcanized polymers via crosslinking as Li–S battery cathodes, in terms of gravimetric and specific capacities, as well as structural stability during the lithiation. Overall, we find that our designed polymer possesses better conductivity, higher specific capacity and gravimetric energy density, and better kinetic stability, and restricts the shuttle effect more efficiently than the current experimentally explored one. Also, the cross-linked sulfur compounds can be activated efficiently due to the short transport lengths rather than dissolution in the electrolyte during battery operation. Therefore, we believe our designed polymer cathode is promising for practical applications.

25 ENERGY STORAGE↗
DOE OSTI2026

Enhanced Second and Third Harmonic Generations in Metasurfaces Enabled through Point Group Design and Accidental Bound States in the Continuum

Here, we investigate enhanced second‑ and third‑harmonic generation (SHG and THG) in GaAs metasurfaces by combining electromagnetic point‑group designs with accidental bound states in the continuum (BICs). Using group theory, we predict the existence, suppression, and polarization selection rules of SHG and THG for metasurfaces belonging to the D 2h and C 2v point groups. In D 2h metasurfaces, inversion symmetry suppresses SHG while enabling polarization‑selective THG, which is strongly enhanced near accidental BICs with ultrahigh Q‑factors approaching 107. By breaking inversion symmetry to form C 2v metasurfaces through the introduction of nanogaps, SHG is now allowed and is dramatically enhanced near accidental BICs. Importantly, accidental BICs preserve polarization selection rules while providing a robust parameter range for high‑Q resonances, in contrast to quasi‑BICs induced by symmetry breaking. Our results demonstrate that the combination of point‑group theory and accidental‑BIC engineering offers a predictive and robust route toward highly efficient nonlinear metasurfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Adjustable collimation for dark-field proton radiography contrast enhancement

A dark-field imaging condition established through pre- and post-object beam collimation has been tested for a variety of different settings at the proton radiography facility (pRad) at Los Alamos National Laboratory. This technique facilitates imaging of thin samples and materials with small areal density changes with 800 MeV protons that would otherwise appear nearly transparent for high energy protons. In the configuration that was examined, the pre-object (upstream) collimator is utilized to remove protons with large scattering angles prior to the target object. The post-object collimation entails a combination of a conventional collimator, which eliminates protons with large scattering angles after target interaction, and an inverse collimator. The inverse collimator removes unscattered protons traveling on the beam axis and those with smaller scattering angles up to a desired angle. This establishes a dark-field condition by permitting solely protons that exhibit scattering angles from target interaction within a designated range to contribute to the image. This study explores the potential of combining different sized collimators for pre- and post-object collimation. The objective is to optimize contrast for a range of samples with minimal areal density variations. A series of dark-field conditions are explored employing a remote-controlled collimator wheel and the contrast-to-noise ratio, spatial resolution, and proton transmission are compared for those combinations. It is found that the contrast-to-noise ratio can be substantially enhanced for thin samples and low areal density objects, thereby facilitating capturing high-contrast images of objects that were previously invisible to high energy protons.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Interpretive modeling of tungsten divertor leakage during experiments with neon gas seeding

Abstract Many existing and future tokamaks with tungsten divertors operate, or will operate, with low- Z impurity seeding, but the direct effect of these seeded impurities on tungsten Scrape-off-Layer (SOL) transport has not been explored in detail. This paper reports on a DIII-D experiment designed to test how tungsten divertor leakage from the Small-Angle Slot V-Shaped, tungsten-coated divertor is impacted by neon seeding at a variety of injection rates and poloidal injection locations. Measurements from the experiment show an inverse relationship between the neon injection rate and the tungsten core penetration factor. Interpretive modeling is performed with a combination of the SOLPS-ITER and DIVIMP codes to assess the underlying tungsten behavior. The modeling results show that the reduction in tungsten divertor leakage is driven by both an increase in the divertor collisionality as well as a reduction in the ion temperature gradient near the divertor target. Collisions between low- Z impurities and tungsten impurities are found to have a significant impact on the tungsten SOL transport, such that ignoring the low- Z impurity collisional effects on the tungsten transport can result in an overestimate of the divertor leakage by an order-of-magnitude. Given the importance of these localized interactions, neon seeding from the closed, slot-like divertor has a clear advantage in being able to reduce tungsten divertor leakage without the high levels of neon core contamination that occur when seeding from other poloidal locations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Irradiation hardening and ductility loss of Eurofer97 steel variants after neutron irradiation to ITER-TBM relevant conditions

Ten Eurofer97 steel variants, produced by non-standard fabrication-processing routes and modified alloying chemistries, were studied by neutron irradiations in the high flux isotope reactor. The irradiations were performed to ITER-TBM relevant conditions of ~255–350 °C, 2.94–3.24 dpa. We quantified the irradiation-induced degradation of the steels using mechanical property tests. All the steels suffered from irradiation hardening, where a significant increase in Vickers microhardness and yield stress (σ YS ) occurred, accompanied with severe loss of tensile elongation. The extent of hardening was material dependent. For T irr = 300±30 °C, most steels showed σ YS increase in the range of ~30% to as high as ~66%, except for a low temperature tempered steel with σYS increase below 15%. Despite large losses in elongation, most failures were ductile. Significant post-necking ductility was retained with reduction in area (RA) between 65–75%, but <50% for low temperature tempered steels. The ultimate tensile stress to yield stress (σ UTS /σ YS ) ratios decreased significantly after irradiation, highlighting irradiation-induced strain hardening capacity reduction. No major effect of irradiation on the plastic instability stress (σ PIS ) and true fracture stress of the steels was observed. By comparing the tensile stresses in true stress units and with literature, the results suggest that RAFM steel designing should target materials with a large separation between σ PIS and σ YS , to ensure the materials can maintain large work hardening and uniform deformation capability after irradiation. The tensile data of the steels additionally revealed a compelling evidence of an inverse trend between the change in RA and increase in σ YS of the neutron irradiated Eurofer97 type steels.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Atomic Structural Origin of the High Methanol Selectivity over In 2 O 3 –Metal Interfaces: Metal–Support Interactions and the Formation of a InO x Overlayer in Ru/In 2 O 3 Catalysts during CO 2 Hydrogenation

CO 2 hydrogenation to methanol is of great environmental and economic interest due to its potential to reduce carbon emissions and produce valuable chemicals in one single reaction. Compared with the unmodified traditional Cu/ZnO/Al 2 O 3 catalyst, an indium oxide (In 2 O 3 )-based catalyst can double the methanol selectivity from 30–50 to 60–100%. It is worth noting that over catalysts involving various active metals dispersed on indium oxide (M/In 2 O 3 , M = Pd, Ni, Au, etc.), although the methanol yield is boosted, the selectivity remains similar to that of plain In 2 O 3 despite the distinct chemical properties of the added metals. Here, to investigate the phenomena behind this behavior, we used RuO 2 /In 2 O 3 as a test catalyst. The results of ambient pressure photoelectron spectroscopy, in situ X-ray absorption fine structure, and time-resolved X-ray diffraction indicate that the structure of the RuO 2 /In 2 O 3 catalyst is highly dynamic in the presence of a reactive environment. Specifically, under CO 2 hydrogenation conditions, Ru clusters facilitate the reduction of In 2 O 3 to generate In 2 O 3–x aggregates, which encapsulate the Ru systems in a migration driven by thermodynamics. In this way, the Ru O sites for CH 4 production are blocked while creating RuO x –In 2 O 3–x interfacial sites with tunable metal–oxide interactions for selective methanol production. In an inverse oxide/metal configuration, indium oxide has properties not seen in its bulk phase that are useful for the binding and conversion of CO 2 . This work reveals the dynamic nature of In 2 O 3 -based catalysts, providing insights for a rational design of materials for the selective synthesis of methanol.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. 2. Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. We develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. Here we design a generalized preconditioned Crank-Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank-based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two-dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantum Spin-Valley Hall Kink States: From Concept to Materials Design

We propose a general and tunable platform to realize high-density arrays of quantum spin-valley Hall kink (QSVHK) states with spin-valley-momentum locking based on a two-dimensional hexagonal topological insulator. Through the analysis of Berry curvature and topological charge, the QSVHK states are found to be topologically protected by the valley-inversion and time-reversal symmetries. Remarkably, the conductance of QSVHK states remains quantized against both nonmagnetic short- and long-range and magnetic long-range disorder, verified by the Green-function calculations. Based on first-principles results and our fabricated samples, we show that QSVHK states, protected with a gap up to 287 meV, can be realized in bismuthene by alloy engineering, surface functionalization, or electric field, supporting nonvolatile applications of spin-valley filters, valves, and waveguides even at room temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Perovskite-Derivative Valleytronics

Halide perovskites are revolutionizing the renewable energy sector owing to their high photovoltaic efficiency, low manufacturing cost, and flexibility. Their remarkable mobility and long carrier lifetime are also valuable for information technology, but fundamental challenges like poor stability under an electric field prevent realistic applications of halide perovskites in electronics. Here, it is discovered that valleytronics is a promising route to leverage the advantages of halide perovskites and derivatives for information storage and processing. The synthesized all-inorganic lead-free perovskite derivative, Cs 3 Bi 2 I 9 , exhibits strong light-matter interaction and parity-dependent optically addressable valley degree of freedom. In this work, robust optical helicity in all odd-layer-number crystals with inversion symmetry breaking is observed, indicating excitonic coherence extending well beyond 11 layers. The excellent optical and valley properties of Cs 3 Bi 2 I 9 arise from the unique parallel bands, according to first principles calculations. This discovery points to new materials design principles for scalable valleytronic devices and demonstrates the promise of perovskite derivatives beyond energy applications.

2D materials↗
DOE OSTI2023

Highly tunable structure-by-design polymer brush membranes for organic solvent nanofiltration

Synthetic polymeric nanofiltration membrane processes offer an alternate low-energy option to energy-intensive distillation to fractionate similar size organic solvents for chemical, petroleum, food and biotechnology industries. Here, in this study, we synthesize and test a new class of structure-by-design hydrophilic polymeric brush membranes, that address the limitations of commercial polymer membranes, are tunable and exhibit commercially relevant filtration performance. Because these brush membranes are grafted by Single Electron Transfer-Living Radical Polymerization (SET-LRP) and replace statistically random phase inversion or interfacial polymerization used for synthesizing commercial polymer membranes, their porous structure can be remodeled by varying their morphology and chemistry. We graft hydroxyethyl methacrylate (HEMA) brush structures with short and long crosslinkers, demonstrate two competing phenomena - pore stiffening and opening - and obtain high selectivity at reasonable permeability for commercially relevant methanol/toluene separation. This new class of stable, tunable, and scalable membranes offers exciting opportunities to reduce energy for separation of organic solvents.

42 ENGINEERING↗
DOE OSTI2025

MTUQ: a framework for estimating moment tensors, point forces, and their uncertainties

SUMMARY We introduce MTUQ, an open-source Python package for seismic source estimation and uncertainty quantification, emphasizing flexibility and operational scalability. MTUQ provides MPI-parallelized grid search and global optimization capabilities, compatibility with 1-D and 3-D Green’s function database formats, customizable data processing, C-accelerated waveform and first-motion polarity misfit functions, and utilities for plotting seismic waveforms and visualizing misfit and likelihood surfaces. Applicability to a range of full- and constrained-moment tensor, point force, and centroid inversion problems is possible via a documented application programming interface, accompanied by example scripts and integration tests. We demonstrate the software using three different types of seismic events: (1) a 2009 intraslab earthquake near Anchorage, Alaska; (2) an episode of the 2021 Barry Arm landslide in Alaska; and (3) the 2017 Democratic People’s Republic of Korea underground nuclear test. With these events, we illustrate the well-known complementary character of body waves, surface waves, and polarities for constraining source parameters. We also convey the distinct misfit patterns that arise from each individual data type, the importance of uncertainty quantification for detecting multimodal or otherwise poorly constrained solutions, and the software’s flexible, modular design.

58 GEOSCIENCES↗
DOE OSTI2024

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Abstract Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates,” which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from “global” chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using “global” surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using “expert-selected” surrogates ( n = 3) instead of “global” surrogates ( n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases. Graphical Abstract

Pu, Shirley (ORCID:0000000201223797)↗
DOE OSTI2022

Training calibration-based counterfactual explainers for deep learning models in medical image analysis

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Generating a stratocumulus-like cloud top in a convection-cloud chamber

Stratocumulus-topped boundary layers play a crucial role in influencing daily weather and earth energy balance. Entrainment at the stratocumulus cloud top affects the cloud’s lifetime, precipitation, and radiative properties, but our understanding remains limited due to the lack of resolution in both field observations and numerical simulations. A recently proposed convection-cloud chamber with detailed control of sidewall temperatures can provide a unique opportunity to explore this mechanism in a laboratory setting. In this work, we use numerical simulations to demonstrate that this design can produce a cloud top that mimics the entrainment interfacial layer in a stratocumulus cloud. Our results show that a steady-state cloud can be formed by cooling the lower portions of the sidewalls and warming the bottom surface, while a temperature inversion at the cloud top can be generated by keeping the upper sidewalls and top surface warmer than the bottom. The turbulent kinetic energy profile and budget are similar to those found in a convective boundary layer, and inhomogeneous mixing near the cloud top can be observed. These findings significantly enhance the scientific value of constructing the tall convection-cloud chamber.

54 ENVIRONMENTAL SCIENCES↗