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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Measuring the Burgers vector of dislocations with dark-field X-ray microscopy

The subsurface dynamics of dislocations are essential to many properties of bulk crystalline materials. However, it is challenging to characterize a bulk crystal by conventional transmission electron microscopy (TEM) due to the limited penetration depth of electrons. A novel X-ray imaging technique – dark-field X-ray microscopy (DFXM) – was developed to image hierarchical dislocation structures in bulk crystals. While today's DFXM can effectively map the line structures of dislocations, it is still challenging to quantify the Burgers vectors, the key characterization governing the dislocation behaviors. Here, we extend the 'invisibility criterion' formalism from the TEM theory to the geometrical-optics model of DFXM and demonstrate the consistency between DFXM and dark-field TEM using multi-diffraction-peak imaging for a single edge dislocation. Due to the practical difficulty of multi-peak DFXM experiments, we further study how the Burgers vector effect is encoded for a single-peak DFXM experiment. Using the geometrical-optics DFXM simulation, we explore the asymmetry of rocking tilt scans at different rolling tilts and develop a new method to characterize the Burgers vector. The conclusions of this study advance our understanding of the use of DFXM in characterizing individual dislocations, enabling the connection from bulk DFXM imaging to dislocation mechanics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-level parallelization of quantum-chemical calculations

Here, strategies for multiple-level parallelizations of quantum-mechanical calculations are discussed, with an emphasis on using groups of workers for performing parallel tasks. These parallel programming models can be used for a variety ab initio quantum chemistry approaches, including the fragment molecular orbital method and replica-exchange molecular dynamics. Strategies for efficient load balancing on problems of increasing granularity are introduced and discussed. A four-level parallelization is developed based on a multi-level hierarchical grouping, and a high parallel efficiency is achieved on the Theta supercomputer using 131 072 OpenMP threads.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Stable and unstable tiling patterns formed by ABC miktoarm star triblock terpolymers of symmetric interactions

Several discrepancies exist among previous self-consistent field calculations of symmetrically interacting ABC miktoarm star triblock terpolymer melts. Here we address this issue by including all known tiling patterns, as well as several lamellar-type phases known to bound the regions in the parameter space occupied by these patterns. After carefully studying the effect of numerical parameters on the free-energy accuracy, the central region of the phase diagram is constructed in detail; both the (3.4.6.4) pattern and the three-dimensional phase of hierarchical-hexagonal lamellae are found to be stable for the first time. The energetic and entropic contributions to the free-energy density of several phases are analyzed and compared in detail to reveal their stability mechanisms, including the important ( 3 2 .4.3.4) pattern. Comparisons to previous studies of the same model system are present throughout, with our results resolving the aforementioned discrepancies and providing a sound basis for future work to build upon. Published by the American Physical Society 2024

Materials Science↗
DOE OSTI2022

Hot Circumsingle Disks Drive Binary Black Hole Mergers in Active Galactic Nucleus Disks

Abstract Binary black hole (BBH) mergers, particularly those with component masses in the pair-instability gap, may be produced by hierarchical mergers in the disks surrounding Active Galactic Nuclei (AGNs). While the interaction of an embedded BBH with an AGN disk is typically assumed to facilitate a merger, recent high-resolution hydrodynamical simulations challenge this assumption. However, these simulations often have simplified treatments for gas thermodynamics. In this work, we model the possible consequence of various feedback from an embedded BBH with a simple model that maintains an enhanced temperature profile around each binary component. We show that when the minidisks around each BH become hotter than the background by a factor of three, the BBH orbital evolution switches from expansion to contraction. By analyzing the gravitational torque profile, we find that this change in direction is driven by a weakening of the minidisk spirals and their positive torque on the binary. Our results highlight the important role of thermodynamics around BBHs and its effect on their orbital evolution, suggesting that AGN disks could be efficient factories for BBH mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗
DOE OSTI2025

A Panoramic View of MXenes via an Atomic Coordination‐Based Design Strategy

Two‐dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. Despite extensive research and notable advancements, a fundamental understanding of MXenes’ phase diversity and its connection to their hierarchical precursors, including the intermediate MAX phases and the ancestral bulk phases, remains limited. Here, in this study, it is hypothesized that the atomic coordination environments adopted by transition metal and nonmetallic atoms in their three‐dimensional (3D) bulk precursors may persist in 2D MXenes to govern their phase diversity. Using high‐throughput modeling based on first‐principles density functional theory, a wide range of MXene phases is unveiled and comprehensively evaluate their relative stabilities across a large chemical space. The key to the approach lies in considering various atomic coordination environments drawn from four types of ancestral bulk phases. Through this comprehensive structural library of MXenes, general guiding principles are uncovered, such as a close alignment between the phase stability of MXenes and that of their 3D precursors. These findings introduce a new design strategy in which the atomic coordination environments in bulk phases can serve as reliable predictors for accessing the diverse structural landscape of MXenes.

MXenes↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

eFESTA: Ensemble Feature Exploration with Surface Density Estimates

We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surface features represented as polygon meshes, and no field datasets are required (e.g., scalar fields or vector fields). The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation. An ensemble Feature Exploration method based on Surface densiTy EstimAtes (eFESTA) is then proposed to extract and visualize the major trends of ensemble surface features. For an ensemble of surface features, each surface is first transformed into a density field based on its contribution to the SDE, and the resulting density fields are organized into a hierarchical representation based on the pairwise distances between them. The hierarchical representation is then used to guide visual exploration of the density fields as well as the underlying surface features. We demonstrate the application of our method using isosurface in ensemble scalar fields, Lagrangian coherent structures in uncertain unsteady flows, and streamsurfaces in ensemble fluid flows.

Computational modeling↗
DOE OSTI2020

Evaporative Assembly: Dual-Scale Nanostructures via Evaporative Assembly (Adv. Mater. Interfaces 7/2020)

In article number 1901954, Sunita Srivastava, Surita R. Bhatia, and co-workers demonstrate that evaporative assembly of functionalized nanoparticles can yield striking dual-scale hierarchical structures. Regular microscale stripes of nanoparticle monolayers with hexagonal nanoscale order are obtained on physically and chemically homogeneous substrates through evaporation of a suspension of DNAfunctionalized nanoparticles with a charged shell. The stripe width, spacing and nanoparticle ordering can be controlled by varying nanoparticle concentration and can be described by a simple analytical model. The results indicate that the interplay between “stick-slip” motion of the droplet contact line and Coulombic and steric nanoparticle interactions control the formation of the observed structures. Finally, this work demonstrates a simple cost-effective mask-free method for fabricating nanostructured 2D materials and metasurfaces for applications ranging from energy conversion/storage to optoelectronics and nanophotonics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit

The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.

Sen, Rishav↗
DOE OSTI2023

Data-assimilated time-lapse visco-acoustic full-waveform inversion: Theory and application for injected CO 2 plume monitoring

Continuous seismic monitoring for quantifying CO 2 plume migration and detection of any potential leakages in the subsurface is essential for the security of long-term anthropogenic carbon dioxide geologic storage. Traditional time-lapse full-waveform inversion (TLFWI) methods aim to map the CO 2 distribution by estimating seismic velocity changes, but recent studies find that CO 2 -induced attenuation is an important complement to seismic velocity for tracking the CO 2 plumes and even quantifying the CO 2 saturation. We have developed a novel data-assimilated TLFWI method to construct high-resolution time-lapse velocity and attenuation changes from dense time-lapse monitoring data. This method consists of two theoretical developments: visco-acoustic full-waveform inversion (QFWI) and multiparameter hierarchical matrix-powered extended Kalman filter (mHiEKF). The method is capable of (1) posing temporal constraints to retrieve time-lapse information from dense monitoring data by using mHiEKF, (2) accurately recovering high-spatial-resolution velocity and attenuation perturbations using first-order equation system-based QFWI, and (3) providing the model uncertainty by estimating their model standard deviation. With numerical examples, we first find the effectiveness of the new QFWI on estimating accurate velocity and attenuation models simultaneously. Then, a CO 2 leakage case and a realistic Frio-II CO 2 monitoring case are presented to find the advantages and applicability of our data-assimilated QFWI method for estimating time-lapse changes using dense time-lapse monitoring surveys. Here, by assimilating time-lapse seismic monitoring data over time, our data-assimilated QFWI method can improve the resolution of velocity and attenuation changes and decrease their model uncertainties.

58 GEOSCIENCES↗
DOE OSTI2021

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Harnessing Virtual Power Plants Reliably: Enabling tools for increased observability, controllability, operation, and aggregation of distributed energy resources

Harnessing virtual power plants enhances the integration of distributed energy resources into utility grids for a sustainable energy future. Virtual power plants (VPPs) aggregate DERs to enhance resource adequacy and reduce emissions. U.S. utilities are exploring various technologies to manage DERs effectively. FERC Order 2222 allows DERs to participate in both wholesale and retail markets. Enhancing observability and controllability of behind-the-meter (BTM) DERs is essential for reliable grid operations. A hierarchical control architecture can improve coordination among residential energy resources. Field tests showed nearly 20% energy savings and 30% peak power reduction during grid events. Effective management of DERs requires enhanced situational awareness to prevent grid congestion. Integrating DER management systems (DERMS) with existing planning tools can improve operational security. Near-real-time grid models can validate optimal resource set points against resource uncertainty. Traditional uninterruptible power supplies (UPS) can be upgraded to support grid services and become part of VPPs. Upgrading UPS systems can reduce costs by 75% and unlock significant battery capacity. New battery management systems and grid-aware controllers are essential for optimizing UPS performance. Continued research and development are necessary to address challenges in integrating DERs into utility grids. Encouraging customer participation in pilot programs is vital for the evolution of VPPs. Here, the shift towards price-responsive DERs and VPPs is expected to enhance energy distribution efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Performance Evaluation of Hierarchical Controls for Advanced Distribution Management System-Centered Grid Operations: Preprint

This paper presents a hardware-in-the-loop (HIL) platform to evaluate the performance of voltage regulation using an advanced distribution management system (ADMS) for grid operations to control legacy and grid-edge voltage control devices and coordinate with distributed energy management systems (DERMS) to manage high penetrations of photovoltaics (PV) on a utility distribution system. The HIL platform provides realistic laboratory testing, including accurate modeling (legacy devices, grid-edge devices, and PV) of the real-world distribution system from a utility partner, a real controller (ADMS), software controller DERMS, hardware grid-edge devices, and standard communications protocols. The test results show proper functionalities of the integrated platform and the performance of voltage regulation of the coordinated control systems. Based on laboratory testing, the utility can set up the same grid-automation system to manage DERs, legacy devices, and grid-edge devices to achieve their system-level control and operation objectives (e.g., voltage regulation), thus de-risking potential issues (e.g., instability) for field deployment.

ADMS↗
DOE OSTI2024

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km 2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mechanistic understanding of catalytic conversion of ethanol to 1-butene over 2D-pillared MFI zeolite

Ethanol is an important C2 platform molecule for producing value-added chemicals and distillate hydrocarbon fuels (e.g., jet and diesel). Among these, catalytic upgrading of ethanol to butenes can generate valuable commodity chemicals (e.g., 1-butene) and provide C4 olefin intermediates that can be further upgraded to jet/diesel fuels. 2D zeolites offer hierarchical mesoporous structures, leading to improved mass transport and reduced diffusion length, which can help to address the coking challenges faced by ethanol conversion to hydrocarbons over 3D zeolites. In this study, we investigate the acid-catalyzed conversion of ethanol to 1-butene over the Brønsted acid sites (BAS) in 2D-pillared MFI zeolite (2D-PMFI) using ab initio molecular dynamics (AIMD) simulations, in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), and calorimetric measurements. A detailed thermodynamic analysis, using quasi-harmonic approximation (QHA), on the Gibbs free energy pathway of ethanol conversion shows that the consideration of entropy is critical to accurately capture the detailed thermodynamic profiles. The formation of bulky ethoxy-ethene complex is found to be a potential rate-determining step (RDS), proceeding via stepwise mechanism. The reactivity of 2D-PMFI can be further tuned by manipulating RDS through a careful control of number of BAS and operating temperatures. The accuracy of computational model is confirmed through the comparison of calculated vibrational density of states (VDOS) against the experimental DRIFTS measurements. Overall, our study provides mechanistic insights into ethanol upgrading over the 2D-PMFI and shows the importance of evaluating entropic effects in such confined system.

Ethanol conversion, 2D-pillared zeolite, MFI, Anha↗
DOE OSTI2021

Characteristics and mechanisms of hydrogen-induced quasi-cleavage fracture of lath martensitic steel

Here this study presents an in-depth characterization of the microstructures, crystallographic orientations, and dislocation characteristics beneath hydrogen-induced quasi-cleavage fracture features of an as-quenched, lath martensitic (α') 22MnB5 steel. The fracture surfaces of gaseous hydrogen-embrittled martensitic specimens were analyzed by a combination of multiple analytical tools: scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), transmission electron microscopy (TEM), and transmission Kikuchi diffraction (TKD). The dominant fracture mode in the hydrogen-affected zones was quasi-cleavage fracture, which involved significant plasticity, evidenced by plastic zones near tear ridges and a high density of dislocations beneath the quasi-cleavage facets. The martensite constituent sizes, variant orientations, and boundaries influenced the quasi-cleavage surface morphologies. Fractography revealed the occurrence of {100} α' -type cleavage across martensitic laths, developing relatively “flat” quasi-cleavage surfaces, in addition to {110} α' -type cracking likely along lath and block boundaries, and fracture along non-cleavage planes. The likelihood of the formation of the relatively “flat” quasi-cleavage surfaces increased with increasing martensitic constituent sizes. Substantial dislocation bands formed on intersecting {112} α' slip planes within a martensite block below the hydrogen-induced cleavage fractures on {100} α' planes. River markings on the quasi-cleavage surfaces were found to originate from the complex, hierarchical lath martensitic microstructure. Steps and ridges on the quasi-cleavage facets generally linked with the various martensitic boundaries, suggesting that they were produced as a result of crack deviations at those boundaries. The fracture paths and martensite quasi-cleavage mechanisms are discussed in the context of the role of hydrogen and Cottrell cleavage model.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Structure–Property Correlation of Hierarchically Porous Carbons for Fluorocarbon Adsorption

Although traditional commercially available porous carbon-fluorocarbon working pairs have shown promising applicability for adsorption cooling, advancements in synthetic carbons may further improve performance. Moreover, insights into structure-property relationships that target higher sorption capacities within these synthesized carbons may guide such materials' future design. We utilized hierarchically porous synthetic carbons (HPCs) with colossal microporous and mesoporous characterized by high surface areas (up to 2689 m 2 /g) and pore volumes (up to 10.31 cm 3 /g) towards fluorocarbon R134a adsorption. This unique pore topology leads to exceptional R134a uptake, ~250 wt.%, outperforming the highest uptake carbon material to date, Maxsorb III (~220 wt.%). Additionally, material characterizations reveal that the outstanding R134a capacity may be attributed to textural properties and oxygen-terminated functional groups more than graphitization of the material. Most importantly, HPCs are efficiently utilized in a two-bed model chiller device, where the performance shows excellent working capacity (105 wt%, ~1.5 times the value of reported carbon materials/R134a). Fluorocarbons adsorption on HPCs also displays fast kinetics (equilibrium time: ~2 min) mainly driven by physical adsorption (Qst: ~27 kJ/mol), characteristic of swiftly reversible behavior adsorption-desorption behaviors. This work provides a fundamental understanding of the applicability of HPCs/R134a working pair for adsorption cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗