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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning

This study presents an imaging-based deep learning tool to measure the fuel regression rate in a 2D slab burner experiment for hybrid rocket fuels. The slab burner experiment is designed to verify mechanistic models of reacting boundary layer combustion in hybrid rockets by the measurement of fuel regression rates. A DSLR camera with a high intensity flash is used to capture images throughout the burn and the images are then used to find the fuel boundary to calculate the regression rate. A U-net convolutional neural network architecture is explored to segment the fuel from the experimental images. Here, a Monte-Carlo Dropout process is used to quantify the regression rate uncertainty produced from the network. The U-net computed regression rates are compared with values from other techniques from literature and show error less than 10%. An oxidizer flux dependency study is performed and shows the U-net predictions of regression rates are accurate and independent of the oxidizer flux, when the images in the training set are not over-saturated. Training with monochrome images is explored and is not successful at predicting the fuel regression rate from images with high noise. The network is superior at filtering out noise introduced by soot, pitting, and wax deposition on the chamber glass as well as the flame when compared to traditional image processing techniques, such as threshold binary conversion and spatial filtering. U-net consistently provides low error image segmentations to allow accurate computation of the regression rate of the fuel.

42 ENGINEERING↗
DOE OSTI2023

The emerging field of block copolymer self-assembly-directed quantum materials

The area of functional materials derived from block copolymers (BCPs) as structure-directing agents for various inorganic materials has seen substantial growth as a result of the immense scientific as well as technological promise associated with such hybrid materials. Further, the convergence of BCP self-assembly with the fast-growing field of quantum materials now promises solution-based synthetic approaches to classes of materials that to date are dominated by stringent high-vacuum physical deposition techniques and bulk solid state chemical reactions. Moreover, the ensuing periodic nano- and mesostructures, a characteristic of BCP self-assembly, offer a unique platform for manipulating quantum-level properties, in turn resulting in substantial changes of macroscopic properties. This article will make a case for, review the existing work of, and provide our perspectives on this emerging field of block copolymer self-assembly directed quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Laisk measurements in the nonsteady state: Tests in plants exposed to warming and variable CO2 concentrations

Abstract Light respiration (RL) is an important component of plant carbon balance and a key parameter in photosynthesis models. RL is often measured using the Laisk method, a gas exchange technique that is traditionally employed under steady-state conditions. However, a nonsteady-state dynamic assimilation technique (DAT) may allow for more rapid Laisk measurements. In 2 studies, we examined the efficacy of DAT for estimating RL and the parameter Ci* (the intercellular CO2 concentration where Rubisco's oxygenation velocity is twice its carboxylation velocity), which is also derived from the Laisk technique. In the first study, we compared DAT and steady-state RL and Ci* estimates in paper birch (Betula papyrifera) growing under control and elevated temperature and CO2 concentrations. In the second, we compared DAT-estimated RL and Ci* in hybrid poplar (Populus nigra L. × P. maximowiczii A. Henry “NM6”) exposed to high or low CO2 concentration pre-treatments. The DAT and steady-state methods provided similar RL estimates in B. papyrifera, and we found little acclimation of RL to temperature or CO2; however, Ci* was higher when measured with DAT compared to steady-state methods. These Ci* differences were amplified by the high or low CO2 pre-treatments. We propose that changes in the export of glycine from photorespiration may explain these apparent differences in Ci*.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. The explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973). tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production

One of the forthcoming major challenges in particle physics is the experimental determination of the Higgs trilinear self-coupling. While efforts have largely focused on on-shell double- and single-Higgs production in proton-proton collisions, off-shell Higgs production has also been proposed as a valuable complementary probe. In this article, we design a hybrid neural simulation-based inference (NSBI) approach to construct a likelihood of the Higgs signal incorporating modifications from the Standard Model effective field theory (SMEFT), relevant background processes, and quantum interference effects. It leverages the training efficiency of matrix-element-enhanced techniques, which are vital for robust SMEFT applications, while also incorporating the practical advantages of classification-based methods for effective background estimates. We demonstrate that our NSBI approach achieves sensitivity close to the theoretical optimum and provide expected constraints for the high-luminosity upgrade of the Large Hadron Collider. While we primarily concentrate on the Higgs trilinear self-coupling, we also consider constraints on other SMEFT operators that affect off-shell Higgs production.

Ghosh, Aishik [Univ. of California, Irvine, CA (Un↗
DOE OSTI2024

Effect of Heat Treatment on Microstructure and Mechanical Property of 316L Stainless Steel Produced by Laser Powder Bed Fusion

The advanced non-light water reactor designs (Gen IV reactors), including molten salt/ very high temperature/ sodium-cooled and lead-cooled fast reactors, typically operate at higher temperatures and more extreme radiation conditions than light water reactors. An intrinsic part of the deployment and progress of Gen IV reactor designs is selecting the most suitable structural material for a specific application. Additive manufacturing (AM), a fairly new process of making physical, three-dimensional objects from a computer design file, is going to completely change the way of design, build and certify nuclear systems. It offers a range of opportunities to produce complex geometries from existing materials, offers new routes for processing of previously difficult to process materials, allows for design of new high-performance materials, and finally facilitates hybridization of dissimilar materials. This emerging technology has successfully produced cars, wind turbine blade molds and even live cells. It could also open up big opportunities for the nuclear industry to quickly deploy technologies at a fraction of the cost. So far, AM techniques have been preliminarily applied in the field of nuclear reactors, including the classical parts such as the pressure vessel of a small reactor with 508-III steel, the bottom nozzle of a fuel assembly with 304L steel, the fuel cladding with zirconium alloy and the integrated impeller of a pump and the multi-channel valve body with 316L steel [6,7]. The AM applications for operating nuclear reactors started in auxiliary plant components and have slowly migrated to metallic reactors and core components, but many of these are not safety critical components. Although many parts used for nuclear reactors have been fabricated by AM techniques, practical applications in engineering are still a long way off due to the uncertainty factors focused on the processing, material properties, analysis methods and application standards, which feeds the safety and life-cycle of the nuclear reactor. Due to rapid, repeated heating and cooling during production, a high dislocation density was present in the AM material. This microstructure feature is unstable at elevated temperature while high temperature is one of the typical operation environments for nuclear reactors. Thus, it is important to understand the thermal effect on the microstructure of AM material. The objectives of this study are to investigate the effect of heat treatment on the microstructure and mechanical properties of 316L stainless steel produced by laser powder bed fusion additive manufacturing, and to determine an appropriate heat treatment practice that will be applied to the lightweight AM lattice-structured material with the same chemistry. The heat treatment study consisted of annealing the samples at a temperature range of 800 to 1200 oC with a 50 oC increment for different times (1-24 hours), followed by vacuum or air cooling. Microstructural characterization was carried out by Scanning Electron Microscope (SEM). Grain size and crystallographic orientation were investigated by Electron Backscatter Diffraction (EBSD). Vickers hardness tests with a 0.5 kg load were employed to determine the hardness of samples after different heat treatments. After heat treatment, the random crystallographic orientation was preserved, and the volume fraction of high-angle grain boundaries (grain boundary misorientation =15 oC) remained the same. The dislocation density decreased with annealing temperature due to recovery. The fine subgrain structures in the as-printed specimen were quite stable up to 1200 oC. Minimal recrystallization was observed up to 1200 oC. Recrystallization initiated only after 8.5 hours at 1200 oC. The SEM images did not show obvious dependence of microstructure on cooling rate. The hardness of the specimens decreased with increasing annealing temperature as a result of the decrease in dislocation density. It is interesting to note that the AM material showed very similar hardness to the wrought material when annealing at similar temperature, although the microstructures are very different. Annealing at 1050 oC for 1 hour followed by air cooling was selected as the heat treatment procedure for the lattice designed lightweight AM 316L material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. Here, the explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Coherent Pulse Echo in Hybrid Magnonics with Multimode Phonons

We report the hybridization of magnons and phonons is playing a critical role in the emerging field of hybrid magnonics because it combines the high tunability of magnetism with the long lifetime of mechanics for comprehensive coherent information processing. Recently there has been increasing interest in thin-film bulk acoustic waves because of their long lifetimes at high frequencies. However, the unique multimode nature of such phonon modes has not been exploited as an important resource for coherent information processing. In this Letter we study the simultaneous hybridization of multiple high-overtone bulk acoustic resonances with a magnon and a microwave mode. The demonstrated multimode hybridization allows us to observe coherent pulse echoes, opening opportunities for both fundamental studies and practical applications of hybrid magnonics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Synthesis of a Hybrid Nanostructure of ZnO-Decorated MoS 2 by Atomic Layer Deposition

We introduce here the synthesis of hybrid nanostructures comprised of ZnO nanocrystals (NCs) decorating nanosheets and nanowires (NWs) of MoS 2 prepared by atomic layer deposition (ALD). The concentration, size, and surface-to-volume ratio of the ZnO NCs can be systematically engineered by controlling both the number of ZnO ALD cycles and the properties of the MoS 2 substrates, which are prepared by sulfurizing ALD MoO 3 . Analysis of the chemical composition combined with electron microscopy and synchrotron X-ray techniques as a function of the number of ZnO ALD cycles, together with the results of quantum chemical calculations, help elucidate the ZnO growth mechanism and its dependence on the properties of the MoS 2 substrate. The defect density and grain size of MoS 2 nanosheets are controlled by the sulfurization temperature of ALD MoO 3 , and the ZnO NCs in turn nucleate selectively at defect sites on MoS 2 surface and enlarge with increasing ALD cycle numbers. At higher ALD cycle numbers, the coalescence of ZnO NCs contributes to an increase in areal coverage and NC size. Furthermore, the geometry of the hybrid structures can be tuned by changing the dimensionality of the MoS 2 , by employing vertical NWs of MoS 2 as the substrate for ALD ZnO NCs, which in turn leads to improvement of the relevant surface-to-volume ratio. Such materials are expected to find use in newly expanded applications, especially those such as sensors or photo-devices based on a p-n heterojunction which relies on coupling transition metal dichalcogenides (TMDCs) with NCs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Hybrid Integration of GaP Photonic Crystal Cavities with Silicon-Vacancy Centers in Diamond by Stamp-Transfer

Optically addressable solid-state defects are emerging as some of the most promising qubit platforms for quantum networks. Maximizing photon-defect interaction by nanophotonic cavity coupling is key to network efficiency. We demonstrate fabrication of gallium phosphide 1-D photonic crystal waveguide cavities on a silicon oxide carrier and subsequent integration with implanted silicon-vacancy (SiV) centers in diamond using a stamp-transfer technique. The stamping process avoids diamond etching and allows fine-tuning of the cavities prior to integration. After transfer to diamond, we measure cavity quality factors (Q) of up to 8900 and perform resonant excitation of single SiV centers coupled to these cavities. For a cavity with a Q of 4100, we observe a 3-fold lifetime reduction on-resonance, corresponding to a maximum potential cooperativity of C = 2. In conclusion, these results indicate promise for high photon-defect interaction in a platform which avoids fabrication of the quantum defect host crystal.

Purcell enhancement↗
DOE OSTI2021

Dynamic Nuclear Polarization Enhanced Nuclear Spin Optical Rotation

Here, nuclear spin optical rotation (NSOR) has been investigated as a magneto-optical effect, which holds the potential for applications, including hybrid optical-nuclear magnetic resonance (NMR) spectroscopy and gradientless imaging. The intrinsic nature of NSOR renders its detection relatively insensitive, which has prevented it moving from a proof of concept to a method supporting chemical characterizations. In this work, the dissolution dynamic nuclear polarization technique is introduced to provide nuclear spin polarization, increasing the signal-to-noise ratio by several thousand times. NSOR signals of 1 H and 19 F nuclei are observed in a single scan for diluted compounds, which has made this effect suitable for the determination of electronic transitions from a specific nucleus in a large molecule.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

NEML2: An efficient and modular multiphysics constitutive modeling library for hybrid computing environments

This paper presents NEML2, an open-source, high-performance library developed for constitutive material modeling, designed to support the flexible and modular development of models for complex material behavior. Building on the foundational structure of its predecessor, NEML, the NEML2 library introduces significant improvements, including enhanced vectorization, automatic differentiation, and seamless integration with PyTorch, facilitating the application of machine learning techniques in material simulations. NEML2 provides a C++ backend with Python bindings, enabling users to create custom material models that can be executed efficiently on both CPU and GPU platforms. The library also supports coupling with Multiphysics simulation frameworks like MOOSE, making it suitable for realistic simulations involving coupled physical processes. Rigorous quality assurance through unit and regression testing ensures the reliability of results, while the extensible, user-friendly design encourages collaboration and reproducibility across the scientific community. This paper provides an overview of NEML2’s architecture, core features, and applications, highlighting its impact on accelerating material qualification and advancing computational methods in materials science.

GPU↗
DOE OSTI2022

Controlled Growth and Chemical Engineering of FeSe‐Based Superconducting Films

Abstract Chemical engineering tailors functional materials to meet the demands of physical and chemical properties, accelerating materials discovery and design in a multidisciplinary field. However, stereotyped synthetic paradigms in physical and chemical approaches obstruct the effective integration of multiple advantages of respective materials exploration methods. Solid state reaction is often considered separately with wet chemistry synthesis route, such as film growth using physical vapor deposition and hydrothermal methods. Here, the controlled growth of FeSe thin films by physical deposition techniques followed by the chemical engineering to create heterogenous interface in FeSe films via a solution‐based environment is demonstrated. The two‐step route allows an expanded crystal structure and enhanced superconducting transition temperature from 3.6 to 38 K, providing a new strategy to design functional hybrid materials.

Huang, Yulong↗
DOE OSTI2021

Interacting fermions in narrow-gap semiconductors with band inversion

Abstract Highly unconventional behavior of the thermodynamic response functions has been experimentally observed in a narrow gap semiconductor samarium hexaboride. Motivated by these observations, we use renormalization group technique to investigate many-body instabilities in the f -orbital narrow gap semiconductors with band inversion in the limit of weak coupling. By projecting out the double occupancy of the f -states we formulate a low-energy theory describing the interacting particles in two hybridized electron- and hole-like bands. The interactions are assumed to be weak and short-ranged. We take into account the difference between the effective masses of the quasiparticles in each band. Upon carrying out the renormalization group analysis we find that there is only one stable fixed point corresponding to the excitonic instability with time-reversal symmetry breaking for small enough mismatch between the effective masses.

Physics↗
DOE OSTI2022

Spin-phonon resonances in nearly polar metals with spin-orbit coupling

In metals in the vicinity of a polar transition, interactions between electrons and soft phonon modes remain to be determined. Here we explore the consequences of spin-orbit assisted electron-phonon coupling on the collective modes of such nearly polar metals in the presence of magnetic field. We find that the soft polar phonon hybridizes with spin-flip electronic excitations of the Zeeman-split bands leading to an anticrossing. The associated energy splitting allows for an unambiguous determination of the strength of the spin-orbit mediated coupling to soft modes in polar metals by spectroscopic experiments. Here, the approach to the polar transition is reflected by the softening of the effective g factor of the hybridized spin-flip mode. Analyzing the static limit, we find that the polar order parameter can be oriented by magnetic field. This provides possibilities for alternative switching protocols in polar metallic materials. We demonstrate that the effects we predict can be observed with current experimental techniques and discuss promising material candidates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Robust Interfacial Effect in Multi-interface Environment through Hybrid Reconstruction Chemistry for Enhanced Energy Storage

Electrochemical-oxidation-driven reconstruction has emerged as an efficient approach for developing advanced materials, but the reconstructed microstructure still faces challenges including inferior conductivity, unsatisfying intrinsic activity, and active-species dissolution. In this report we present hybrid reconstruction chemistry that synergistically couples electrochemical oxidation with electrochemical polymerization (EOEP) to overcome these constraints. During the EOEP process, the metal hydroxides undergo rapid reconstruction and dynamically couple with polypyrrole (PPy), resulting in an interface-enriched microenvironment. We observe that the interaction between PPy and the reconstructed metal center (i.e., Mn > Ni, Co) is strongly correlated. Theoretical calculation results demonstrate that the strong interaction between Mn sites and PPy breaks the intrinsic limitation of MnO 2 , rendering MnO 2 with a metallic property for fast charge transfer and enhancing the ion-adsorption dynamics. Operando Raman measurement confirms the promise of EOEP-treated Mn(OH) 2 (E-MO/PPy) to stably work under a 1.2 V potential window. The tailored E-MO/PPy exhibits a high capacitance of 296 F g –1 at a large current density of 100 A g –1 . Here our strategy presents breakthroughs in upgrading the electrochemical reconstruction technique, which enables both activity and kinetics engineering of electrode materials for better performance in energy-related fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

CoREC: Scalable and Resilient In-memory Data Staging for In-situ Workflows

The dramatic increase in the scale of current and planned high-end HPC systems is leading new challenges, such as the growing costs of data movement and IO, and the reduced mean time between failures (MTBF) of system components. In-situ workflows, i.e., executing the entire application workflows on the HPC system, have emerged as an attractive approach to address data-related challenges by moving computations closer to the data, and staging-based frameworks have been effectively used to support in-situ workflows at scale. However, the resilience of these staging-based solutions has not been addressed, and they remain susceptible to expensive data failures. Furthermore, naive use of data resilience techniques such as n-way replication and erasure codes can impact latency and/or result in significant storage overheads. In this article, we present CoREC, a scalable and resilient in-memory data staging runtime for large-scale in-situ workflows. CoREC uses a novel hybrid approach that combines dynamic replication with erasure coding based on data access patterns. It also leverages multiple levels of replications and erasure coding to support diverse data resiliency requirements. Furthermore, the article presents optimizations for load balancing and conflict-avoiding encoding, and a low overhead, lazy data recovery scheme. We have implemented the CoREC runtime and have deployed with the DataSpaces staging service on leadership class computing machines and present an experimental evaluation in the article. Here, the experiments demonstrate that CoREC can tolerate in-memory data failures while maintaining low latency and sustaining high overall storage efficiency at large scales.

97 MATHEMATICS AND COMPUTING↗