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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of fiber orientation in injection molding: Comparison of glass fiber, carbon fiber and their hybrid composites

Fiber orientation distribution (FOD) in injection-molded panels with respect to distance from the gate was analyzed using X-ray computed tomography (X-CT) for glass fiber (GF), carbon fiber (CF), and hybrid CF/GF (CGF) reinforced nylon 66. To understand the reason behind the FOD with different fiber types, computational fluid dynamics (CFD) and rheology were performed. Samples were extracted at three locations: near the gate, center, and opposite end. Thickness of the layers of typical skin-shell-core type FOD varied with fiber type and location. GF achieved flow direction alignment (in shell) earlier than viscous CF and CGF near the gate, whereas CF showed the highest flow-direction alignment at the center due to shear induced orientation. At the opposite end, GF experienced more backflow than others indicating faster mold filling owing to its lower viscosity. Hybrid CGF exhibited GF-dominated center and CF-dominated end region. The numerical model used to obtain FOD and rheological predictions for the CF and GF composites served to corroborate the trends observed in the experimental trials. The FOD responses across fiber types and location were reflected in their longitudinal and transverse properties. Only GF showed higher longitudinal modulus over transverse modulus near the gate attributed to rapid alignment, whereas CF and CGF exhibited opposite trend. However, fountain flow enhanced the longitudinal modulus over transverse modulus with the distance for all, particularly for CF. This study offers insights into mold filling behavior of different fibers which are critical in optimizing injection molding conditions for tailored final properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Goal-oriented real-time Bayesian inference for linear autonomous dynamical systems with application to digital twins for tsunami early warning

We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity partial differential equation (PDE) models governed by autonomous dynamical systems, leading to large-scale forward problems; (2) they solve a linear inverse problem to assimilate observational data to infer uncertain model components followed by a forward prediction of the evolving dynamics; and (3) the entire end-to-end, data-to-inference-to-prediction computation is carried out without approximation and in real time through a Bayesian framework that rigorously accounts for uncertainties. Several challenges must be overcome to realize this framework, including the large scale of the forward problem, the high dimensionality of the parameter space, and for a class of problems including those we target, the slow decay of the singular values of the parameter-to-observable map. Here we introduce a methodology to overcome these challenges by exploiting the autonomous structure of the forward model to decompose the solution of the inverse problem into a one-time-only offline phase in which the PDE model is solved a limited number of times (equal to the number of sensors), and an online phase that maps well onto GPUs and computes the parameter inference and prediction of quantities of interest in real time, given observational data. Our ultimate goal is to apply this framework to construct digital twins for subduction zones, including Cascadia, to provide early warning for tsunamis generated by megathrust earthquakes. To this end, we demonstrate how our methodology can be used to employ seafloor pressure observations, along with the coupled acoustic–gravity wave equations, to infer the earthquake-induced spatiotemporal seafloor motion (discretized with $\mathscr{O}$ (10 9 ) parameters) and forward predict the tsunami propagation. We present results of an end-to-end inference, prediction, and uncertainty quantification for a representative test problem with $\mathscr{O}$ (10 8 ) inversion parameters for which goal-oriented Bayesian inference is accomplished exactly and in real time, that is, in a matter of seconds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Anisotropic Raman Scattering and Lattice Orientation Identification of 2M-WS 2

Anisotropic materials with low symmetries hold significant promise for next-generation electronic and quantum devices. 2M-WS 2 , which is a candidate for topological superconductivity, has garnered considerable interest. However, a comprehensive understanding of how its anisotropic features contribute to unconventional superconductivity, along with a simple, reliable method to identify its crystal orientation, remains elusive. Here, we combine theoretical and experimental approaches to investigate angle- and polarization-dependent anisotropic Raman modes of 2M-WS 2 . Through first-principles calculations, we predict and analyze the phonon dispersion and lattice vibrations of all Raman modes in 2M-WS 2 . We establish a direct correlation between their anisotropic Raman spectra and high-resolution transmission electron microscopy images. Finally, we demonstrate that anisotropic Raman spectroscopy can accurately determine the crystal orientation and twist angle between two stacked 2M-WS 2 layers. Furthermore, our findings provide insights into the electron–phonon coupling and anisotropic properties of 2M-WS 2 , paving the way for the use of anisotropic materials in advanced electronic and quantum devices.

2M-WS2↗
DOE OSTI2025

Dislocation‐Driven Formation of Oriented Macroperiodic Metastructures of Curved Single Crystal Lattices in Glass

Abstract Single crystals fabricated in glass by localized heating can develop uniquely deformed lattices stabilized by the surrounding amorphous medium. The development of lattice curvature appears to be intrinsic to the crystal growth process in some systems, while the result of the locally changing crystallography in others. In this work, a model laser‐fabricated rotating lattice Sb 2 S 3 crystal grown in stoichiometric glass is used to demonstrate fabrication of novel macroperiodic metastructures that utilize intrinsic lattice curvature superimposed with subtle crystallographic influences. The limited availability of slip systems drives the lattice curvature magnitude to vary with crystal growth direction, maximizing for lattices aligned with the predominant Burgers vector along with corresponding increases in dislocation density. Misaligned lattice orientations form smaller secondary lattice curvatures arising from misaligned Burgers vectors with further elastic contributions. Over extended crystal growth, these secondary components align the lattice to rotate about either the <001> or <010> crystal axes forming repeating metastructures of lattice orientation with periodicity 20–160 microns in length. The mechanistic approach used in this work may be expanded to other systems with known slip systems to better understand and design macroperiodic metastructures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Crystal Orientation Independent Strong TERS Response in Anisotropic ReS2 on Gold

Rhenium disulfide (ReS2) crystallizes in a distorted 1T' lattice with triclinic symmetry arising from Re–Re dimerization, which produces in-plane anisotropic properties and a dense set of Raman-active modes with mixed atomic displacements. Unlike far-field Raman spectroscopy, which shows strong polarization and orientation dependence, tip-enhanced Raman spectroscopy (TERS) probes out-of-plane Raman tensor components. Here we use 785 nm (1.58 eV) gap-mode TERS to investigate monolayer to four-layer thick ReS2 crystals.. TERS maps and averaged spectra reveal that all modes remain invariant under 90° rotation of the crystal, and the only thickness-dependent feature is the emergence of the multilayer breathing mode. These results demonstrate that TERS yields a crystal-orientation-independent Raman response in ReS2 and provides direct access to Raman tensor components that are not easily accessible in conventional far-field measurements.

Valencia Acuna, Pavel A.↗
DOE OSTI2025

Orientational Disorder of NH3 in Hexammine Magnesium Borohydride

Hexammine magnesium borohydride, Mg(NH3)6(BH4)2, consists of adducted NH3 molecules locked in a matrix of Mg cations and borohydride anions. It is a candidate material for hydrogen storage, with 16.8 wt % hydrogen stored in both the NH3 and borohydride anions. It may also be of interest as an Mg2+-conducting electrolyte in solid-state batteries. Its crystal structure has, until now, eluded a proper structural solution due to ambiguity regarding the NH3 position and behavior. In this work, we show using synchrotron X-ray diffraction that the room-temperature structure can be solved only with a model assuming the orientational disorder of ammonia molecules within the crystal structure. Cooling the sample to 120 K yields additional Bragg peaks, which can be solved only with a unit cell expansion consistent with the freezing of the orientational freedom of ammonia molecules. Using this insight from the structure solution, we performed a full assignment of the vibrational modes in the room-temperature infrared spectrum.

08 HYDROGEN↗
DOE OSTI2026

Long-range magnetic order with disordered spin orientations in a high-entropy antiferromagnet

Disorder in magnetic systems typically suppresses long-range order, promoting short-range states such as spin glasses and magnetic clusters. This is particularly prominent in high-entropy materials, characterized by the random distributions of local magnetic entities and exchange interactions. However, in rare exceptions, long-range magnetic order can persist in high-entropy systems, while the microscopic characters and underlying mechanisms remain elusive, especially the magnetic behaviors of individual elements. Here, combining neutron diffraction and resonant soft x-ray scattering, we have conducted an element-specific investigation into the magnetic order of a high-entropy honeycomb-lattice van der Waals material (Mn 1/4 Fe 1/4 Co 1/4 Ni 1/4 )PS 3 . Despite significant atomic disorder, long-range zigzag antiferromagnetic order is observed below 72 K, with all four transition-metal elements participating in a unified phase transition. However, the spin orientations of various elements are distinct, attributed to the competition between single-ion anisotropies and exchange interactions. Our findings showcase a novel form of long-range magnetic order with disordered spin orientations, which is synergically stabilized by distinct magnetic elements in a high entropy magnet, offering a new paradigm for understanding complex magnetic systems.

Shen, Yao [Chinese Academy of Sciences (CAS), Beij↗
DOE OSTI2025

Low damping (111) oriented lithium aluminum ferrite thin films for spin wave applications

Spin wave-based spintronics are an alternative to conventional electronics due to their potential for efficient energy consumption, improved processing speed, and smaller device dimensions. Low damping magnetic insulators provide the medium for efficient propagation of spin waves for information transfer. Here, we have synthesized by pulsed laser deposition epitaxial spinel structure ferrite thin films of Li 0.5 (Al 1.0 Fe 1.5 )O 4 (LAFO) on (111)-oriented MgAl 2 O 4 that support isotropic magnon propagation in the film plane. Our ferromagnetic resonance measurements show low magnetic damping with a typical Gilbert damping parameter of α = 0.006 and weak-spin–orbit coupling with g = 2.02. These films have low effective magnetization, μ o M eff = 20 mT, similar to that of yttrium iron garnet, the gold standard of low loss magnetic insulators. Our findings show that LAFO is a good candidate as a spin wave medium since it can be grown at low temperatures in different crystal orientations.

Takana, Lerato [Stanford Univ., CA (United States)↗
DOE OSTI2025

Orientation-dependent enhanced ionization in acetylene revealed by ultrafast cross-polarized pulse pairs

We investigate the orientation dependence of enhanced ionization (EI) during strong-field-driven nuclear motion in acetylene (C 2 ⁢H 2 ). Here, we both initiate and probe molecular dynamics in acetylene with intense 6-fs cross-polarized pulse pairs, separated by a variable delay. Following multiple ionization by the first pulse, acetylene undergoes simultaneous elongation of the carbon-carbon and carbon-hydrogen bonds, enabling further ionization by the second pulse and the formation of a very highly charged state, [C 2 ⁢H 2 ] 6+ . At small interpulse delays (< 20 fs), this enhancement occurs when the molecule is aligned to the probe pulse. Conversely, at large delays (> 40 fs), formation of [C 2 ⁢H 2 ] 6+ occurs when the molecule is aligned to the pump pulse. By analyzing the polarization and time dependence of sequentially ionized [C 2 ⁢H 2 ] 6+ , we resolve two transient alignments that both contribute to a large increase in the multiple ionization yield. In conclusion, this cross-polarized pulse pair scheme uniquely enables selective probing of deeply bound orbitals, providing new insights on orientation-dependent EI in highly charged hydrocarbons.

Atomic & molecular clusters↗
DOE OSTI2026

A Faster-Than-Real-Time Framework for Reliability-Oriented Simulation of PV Inverters

Physics-of-Failure (PoF) based reliability assessment for photovoltaic (PV) inverters requires long-duration electrical and electrothermal stress histories, yet generating such stress histories with high-fidelity switching models over year long mission profiles is computationally prohibitive. Conventional methods either sacrifice modeling fidelity for speed or require runtimes that are impractical for design iteration and uncertainty studies. To address this bottleneck, this paper presents a High-Performance Computing (HPC) based simulation frame work for faster-than-real-time reliability-oriented simulation. The proposed framework integrates the Average-to-Switching (A2S) method with parallel computing techniques to accelerate switching-level waveform reconstruction. We further introduce optimization strategies, including cluster merging and sensitivity based mission profile screening, to reduce the computational burden. Evaluated using real-world mission profile inputs and a MATLAB/Simulink switching-model reference, the framework reduces the simulation time for a one-year mission from an intractable multi-year duration to approximately 7.3 minutes while maintaining low waveform error. This acceleration provides a practical reliability-oriented simulation engine that can be coupled with component-specific aging models for subsequent PV inverter PoF assessment.

High-performance Computing↗
DOE OSTI2025

ASDF: A Compiler for Qwerty, a Basis-Oriented Quantum Programming Language

Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents ASDF, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that ASDF produces circuits with comparable cost to prior circuit-oriented compilers.

Adams, Austin J [Georgia Tech]↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2026

BOS Gas Detection Pipeline (Integrated System for Optical Hydrogen Detection Using Background Oriented Schlieren and Machine Learning) [SWR-26-007]

This software is the world's first integrated background oriented schlieren and machine learning-based leak detection system. The system provides real time visualization of gas leaks and machine learning interpenetration of leak severity. The software is supplemented by SWR-25-177, "gpu_piv (Graphics Processing Unit Accelerated Background Oriented Schlieren Algorithm", also developed by the National Laboratory of the Rockies. SEE DOECODE ID 182832.

Palin, Ian [National Laboratory of the Rockies (NL↗
DOE OSTI2025

Tailoring copper current collector roughness and crystallographic orientation to improve lithium plating

Secondary lithium metal batteries are of great interest due to a high theoretical energy density, but rechargeability is limited by the formation of lithium dendrites that lead to internal short circuits and catastrophic cell failure. Mitigating dendrites through uniform current distribution at the current collector is a promising solution to enabling long cycle life of lithium metal electrodes. Here, the impact of copper crystal orientation and roughness on electrochemically deposited lithium morphology is studied. For 50 cycles, the capacity retention from greatest to least followed polycrystalline copper 95 ± 1 % (MC-PC) ≥ 94.55 ± 0.01 % Cu (110) > 93.0 ± 0.4 % Cu(111) > 75.8 ± 0.1 % Cu(100). Cu(100) performed the worst in all cases with shorts occurring frequently before reaching 50 cycles. Cryo-scanning electron microscopy images showed that after 50 cycles, MC-PC Cu had the best lithium morphology, but at early cycles, Cu(111) formed the densest lithium layer. MC-PC Cu had the most uniform lithium nucleation which may have been caused by increased roughness relative to the single crystal samples. When polycrystalline Cu (F-PC) roughness was systematically controlled, the cumulative Coulombic efficiency increased from about 0.3–0.5 with increasing roughness from an Ra of about 10–40 nm. Lastly, plating rates were studied. Slower rates of 0.25 mA/cm 2 exhibited denser lithium morphology in all cases as compared to 0.5 and 1.0 mA/cm 2 , but the relative porosity of Li deposited on the different current collectors depended on the rate applied.

Copper↗
DOE OSTI2025

Synthesis and Self-Assembly of Monodisperse Graphene Nanoribbons: Access to Submicron Architectures with Long-Range Order and Uniform Orientation

Fabricating organic semiconducting materials into large-scale well-organized architectures is critical for building high performance molecular electronics. While graphene nanoribbons (GNRs) hold enormous promise for various device applications, their assembly into a well-structured monolayer or multilayer architecture poses a substantial challenge. Here we report the preparation of length-defined monodisperse GNRs via the integrated iterative binomial synthesis (IIBS) strategy and their self-assembly into submicron-architectures with long-range order, uniform orientation as well as regular layers. Further, the use of short alkyl side chains benefits forming stable multi-layers through interlocking structures. By changing the length and backbone shapes of these monodisperse GNRs, various three-dimensional assemblies including multilayer stripes, monolayer stripes, and nanowires, can be achieved, leading to different photophysical properties and band gaps. The discovery of these intriguing self-assembly behaviors of length-defined GNRs is expected to open the door for various future applications.

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

Process‐Oriented Calibration of a Turbulence Scheme in the DOE's Global Storm‐Resolving Model Using Machine Learning

A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective regimes: clear‐sky dry convective boundary layer and fair‐weather shallow cumulus clouds from ARM observations. We use perturbed‐parameter ensembles of a doubly periodic version of SCREAM to train surrogates and apply Markov Chain Monte Carlo sampling guided by cost functions based on benchmarking large‐eddy simulations and observations to identify optimized parameter sets that perform well in both regimes. The calibrated SHOC parameters substantially improve boundary‐layer turbulence and cloud boundaries, and modeled cloud fraction and radiative effects align better with observations than the default. These results demonstrate that combining multiple process‐specific convective regimes with machine‐learning surrogates can reduce parametric uncertainties and yield a model more faithful to cloud–turbulence interactions.

58 GEOSCIENCES↗
DOE OSTI2026

Background-Oriented Schlieren Velocimetry of Helium Coolant Flow in Additively Manufactured Channels

High-pressure helium gas cooling is an attractive solution for thermal management of the fusion blanket first wall, as this coolant is chemically and neutronically inert and separable from hydrogenic species. However, due to the low thermal mass of helium, geometric optimization of these channels is required to provide sufficient cooling at manageable flow rates and pumping burdens. Increasingly, analysis and optimization of these coolant channels rely on computational fluid dynamics (CFD) simulations, and these require relevant experimental data for turbulence model validation. Toward this end, a high-pressure helium gas flow visualization system has been employed to image the flow of helium in flow channels with one-sided heating, mimicking the blanket first wall environment. Flow of helium at 4 MPa pressure and flow rates up to 68 g/s (Reynolds number 57 000) is supplied to rectangular channel test sections, with uniform heating applied to the bottom wall of the channel at heat fluxes varied between roughly 50 and 130 kW/m2. A high-speed camera is used to image index of refraction (IOR) gradients in the fluid via background-oriented schlieren (BOS), and temperature and pressure instrumentation are used to characterize thermal-hydraulic performance of each channel. Cross correlation of time-resolved BOS images is then used to calculate time-averaged 2-D helium velocity fields. Flow in additively manufactured (AM) channels is examined in this manner, including both featureless channels and those containing baffling as a heat transfer enhancement. The flow distribution seen in the featureless case differs significantly from that seen in prior simulations, whereas the flow in the baffled case shows the predicted behavior of flow forced along the heated wall. This augmented flow distribution is seen to increase the heat transfer coefficient in the baffled test section. Here, strategies are discussed for ongoing and future validation of these simulations, with the aim of model deployment for blanket cooling design and optimization.

Additive manufacturing↗
DOE OSTI2024

A high fidelity and user-friendly equation-oriented optimization model for carbon capture using a novel water-lean solvent

Research Triangle Institute (RTI) International and SLB have developed a novel water-lean solvent technology for carbon capture, demonstrating low specific reboiler duty (SRD) values at capture rates exceeding 90%. At the Technology Centre Mongstad (TCM) pilot plant, the technology achieved an SRD of 2.55 GJ/t-CO2 at 95% capture, utilizing an intercooler and a 5°C temperature approach in the lean/rich solvent cross exchanger. To meet varying carbon capture targets for Front End Engineering and Design (FEED) studies and to enable real-time optimization and advanced process control, an efficient optimization model is required. This model needs to minimize energy demand for a given capture rate and determine optimal operating parameters in response to fluctuating flue gas conditions. While an existing Aspen Plus simulation model, developed by RTI and SLB, accurately matches TCM plant data, its sequential modular (SM) strategy is too slow for real-time applications due to recycle streams and tight heat integration inherent in solvent-based carbon capture processes. Although an equation-oriented (EO) modeling strategy is more suitable for optimizing these processes, its adoption has been limited by several factors: feature limitations in Aspen Plus EO mode (e.g., lack of balance block support), a less user-friendly interface for variable identification and loop solving, complex troubleshooting of convergence issues, and the necessity for accurate initial values.

carbon capture↗