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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing Basal and Prismatic Planes of Graphitic Materials for Metal Single Atom and Subnanometer Cluster Stabilization

Abstract Supported metal single atom catalysis is a dynamic research area in catalysis science combining the advantages of homogeneous and heterogeneous catalysis. Understanding the interactions between metal single atoms and the support constitutes a challenge facing the development of such catalysts, since these interactions are essential in optimizing the catalytic performance. For conventional carbon supports, two types of surfaces can contribute to single atom stabilization: the basal planes and the prismatic surface; both of which can be decorated by defects and surface oxygen groups. To date, most studies on carbon‐supported single atom catalysts focused on nitrogen‐doped carbons, which, unlike classic carbon materials, have a fairly well‐defined chemical environment. Herein we report the synthesis, characterization and modeling of rhodium single atom catalysts supported on carbon materials presenting distinct concentrations of surface oxygen groups and basal/prismatic surface area. The influence of these parameters on the speciation of the Rh species, their coordination and ultimately on their catalytic performance in hydrogenation and hydroformylation reactions is analyzed. The results obtained show that catalysis itself is an interesting tool for the fine characterization of these materials, for which the detection of small quantities of metal clusters remains a challenge, even when combining several cutting‐edge analytical methods.

Vidal, Mathieu↗
DOE OSTI2025

Synthesis of UO 2 nanoparticles via coulometric titration: influence of electrolytes and ligands on synthesized particle properties

Here, this study investigates the controlled synthesis of uranium oxide nanoparticles (UO 2 NPs) via coulometric titration under ambient conditions, focusing on the impact of electrolyte anions, salt concentrations, and strongly complexing ligands on particle formation and properties. Using dilute solutions of mineral acids (HNO 3 , HCl, and HClO 4 ), we demonstrate that the choice of electrolyte anion affects particle size, polydispersity, and surface charge. Particle characterization using dynamic light scattering and transmission electron microscopy shows that particles synthesized in perchlorate are largest and show a high degree of polydispersity. In comparison, particles synthesized in chloride are smaller and more uniform in size. Synthesis in nitrate yields a wide variety of particle sizes, with the major fraction (>65 %) having a smaller size than that obtained in the other two electrolytes. The presence of more strongly coordinating ligands such as sulfate and acetate modulate hydrolysis and condensation reactions, with sulfate suppressing nanoparticle formation across a wide concentration range ([SO 4 2- ] > 5 mM) and acetate enabling stable colloidal suspensions at [HOAc] ≤ 0.1 M. Synthesis in concentrated electrolytes (2 M NaNO 3 , NaCl, or NaClO 4 ) accelerates reaction kinetics but introduces challenges, as particles showed increased polydispersity and aggregation, and were more prone to oxidation. Electrolyte effects on actinide oxide nanoparticle formation are discussed and a short comparison to established nanoparticle syntheses is drawn. This work underscores the importance of tailoring synthesis parameters to achieve desired nanoparticle properties, providing valuable insights for optimizing UO 2 NP production for various applications.

Actinide hydrolysis↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗
DOE OSTI2022

Consistent responses of vegetation gas exchange to elevated atmospheric CO 2 emerge from heuristic and optimization models

Elevated atmospheric CO 2 concentration is expected to increase leaf CO 2 assimilation rates, thus promoting plant growth and increasing leaf area. It also decreases stomatal conductance, allowing water savings, which have been hypothesized to drive large-scale greening, in particular in arid and semiarid climates. However, the increase in leaf area could reduce the benefits of elevated CO 2 concentration through soil water depletion. The net effect of elevated CO 2 on leaf- and canopy-level gas exchange remains uncertain. To address this question, we compare the outcomes of a heuristic model based on the Partitioning of Equilibrium Transpiration and Assimilation (PETA) hypothesis and three model variants based on stomatal optimization theory. Predicted relative changes in leaf- and canopy-level gas exchange rates are used as a metric of plant responses to changes in atmospheric CO 2 concentration. Both model approaches predict reductions in leaf-level transpiration rate due to decreased stomatal conductance under elevated CO 2 , but negligible (PETA) or no (optimization) changes in canopy-level transpiration due to the compensatory effect of increased leaf area. Leaf- and canopy-level CO 2 assimilation is predicted to increase, with an amplification of the CO 2 fertilization effect at the canopy level due to the enhanced leaf area. The expected increase in vapour pressure deficit (VPD) under warmer conditions is generally predicted to decrease the sensitivity of gas exchange to atmospheric CO 2 concentration in both models. The consistent predictions by different models that canopy-level transpiration varies little under elevated CO 2 due to combined stomatal conductance reduction and leaf area increase highlight the coordination of physiological and morphological characteristics in vegetation to maximize resource use (here water) under altered climatic conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Towards a universal model for carbon dioxide uptake by plants

Gross primary production (GPP) - the uptake of carbon dioxide (CO 2 ) by leaves, and its conversion to sugars by photosynthesis - is the basis for life on land. Earth System Models (ESMs) incorporating the interactions of land ecosystems and climate are used to predict the future of the terrestrial sink for anthropogenic CO 2 . ESMs require accurate representation of GPP. However, current ESMs disagree on how GPP responds to environmental variations, suggesting a need for a more robust theoretical framework for modelling. Here in this work, we focus on a key quantity for GPP, the ratio of leaf internal to external CO 2 (χ). χ is tightly regulated and depends on environmental conditions, but is represented empirically and incompletely in today's models. We show that a simple evolutionary optimality hypothesis predicts specific quantitative dependencies of χ on temperature, vapour pressure deficit and elevation; and that these same dependencies emerge from an independent analysis of empirical χ values, derived from a worldwide dataset of >3,500 leaf stable carbon isotope measurements. A single global equation embodying these relationships then unifies the empirical light-use efficiency model with the standard model of C 3 photosynthesis, and successfully predicts GPP measured at eddy-covariance flux sites. This success is notable given the equation's simplicity and broad applicability across biomes and plant functional types. Finally, it provides a theoretical underpinning for the analysis of plant functional coordination across species and emergent properties of ecosystems, and a potential basis for the reformulation of the controls of GPP in next-generation ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗
DOE OSTI2022

A scalable planning framework of energy storage systems under frequency dynamics constraints

As the penetration of renewables increases in power systems, the declining system inertia can cause frequency stability issues. Battery energy storage systems (BESSs) respond fast and therefore can relieve the low inertia difficulty but need to be appropriately sized considering the associated cost. This paper presents a novel stochastic optimization model for economically planning BESS capacity while considering the spatial–temporal correlation of wind generation and generator outages under frequency stability constraints, which include the rate-of-change of frequency (RoCoF), frequency nadir (FN), and quasi-steady-state (QSS) frequency. A set of new FN constraints that can be easily linearized is developed. To account for renewable uncertainties, a realistic uncertainty modeling approach, Random Field, is adopted to generate wind generation scenarios by considering both spatial and temporal evolutions of wind speed profiles. The ESS sizing is formulated as a mixed-integer linear programming problem and solved by using a scalable decomposition-and-coordination approach, Surrogate Absolute Value Lagrangian Relaxation (SAVLR). To further improve the scalability and reduce computational burdens, a rolling-horizon-based update is developed and incorporated into SAVLR for providing a practical solution to the long-term planning of very large-scale power systems. Finally, a modified IEEE 118-bus system and the Polish system are used to validate the effectiveness and scalability of the model and solution methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

A kinetic modeling study on octane rating and fuel sensitivity under HCCI conditions

With the development of advanced compression ignition engines such as the homogeneous charge compression ignition (HCCI) and the reactivity controlled compression ignition (RCCI), the long existing research and motor octane rating for fuels, i.e., RON and MON, might be insufficient to describe the fuel reactivity and performance with these new combustion strategies due to their distinctive engine operation conditions. It is also noted that with the stringent regulation for CO2 emission, a detailed knowledge of fuel reactivity is urgently needed for the selection of novel alternative bio-fuels for transportation, and optimization of their engine performance in the future. It is therefore very important to understand the reactivity and sensitivity for general classes of fuels as a function of the operating conditions in these advanced combustion engines. In this modeling study, using validated kinetic mechanism for ignition, we have simulated the performance of different classes of fuels including hydrogen, alkanes (straight and branched), alcohols and potential bio-fuels under typical HCCI engine conditions using detailed chemical kinetics. The crank angle corresponding to 50% total heat release (CA50) is utilized as an indicator for the fuel reactivity. Iso-contours of CA50 are then plotted in the engine operating parameter coordinate, such as the intake temperature, and pressure. It is shown that CA50 value can vary significantly with different engine operating conditions, and that fuels with the same RON/MON even exhibit qualitatively different CA50 iso-contours in specific operating regimes, implying differences in the intrinsic reactivity of different classes of fuels, as well as the insufficiency of RON/MON as comprehensive metrics of fuel reactivity in advanced compression ignition (ACI) strategies. In addition, the operating regime with high and low sensitivity, as well as potential non-monotonic change in fuel reactivity, has been identified by the CA50 iso-contour map for all studied fuels.

Wu, Tong↗
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2023

All-temperature zinc batteries with high-entropy aqueous electrolyte

Electrification of transportation and rising demand for grid energy storage continue to build momentum around batteries across the globe. However, the supply chain of Li-ion batteries is exposed to the increasing challenges of resourcing essential and scarce materials. Therefore, incentives to develop more sustainable battery chemistries are growing. Here, in this paper, we show an aqueous ZnCl 2 electrolyte with introduced LiCl as supporting salt. Once the electrolyte is optimized to Li 2 ZnCl 4 ∙9H 2 O, the assembled Zn–air battery can sustain stable cycling over the course of 800 hours at a current density of 0.4 mA cm -2 between -60 °C and +80 °C, with 100% Coulombic efficiency for Zn stripping/plating. Even at -60 °C, >80% of room-temperature power density can be retained. Advanced characterization and theoretical calculations reveal a high-entropy solvation structure that is responsible for the excellent performance. The strong acidity allows ZnCl 2 to accept donated Cl - ions to form ZnCl 4 2- anions, while water molecules remain within the free solvent network at low salt concentration or coordinate with Li ions. Our work suggests an effective strategy for the rational design of electrolytes that could enable next-generation Zn batteries.

25 ENERGY STORAGE↗
DOE OSTI2017

Proposed Methodology for a Tour-Based Freight Model

This study is part of the US Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility initiative. It is included in the Multi-Modal pillar of SMART Mobility and is titled: Optimization of Intra-City Freight Movement and New Delivery Methods. The primary purpose of this study is to analyze the energy reduction opportunities provided by new freight modes and technologies. This will be done by developing a tour-based freight model for Columbus, Ohio. This model will be used to analyze the energy and emission savings of alternative technologies including drones, central delivery lockers, and the use of electric vehicles for last-mile freight deliveries. The tour-based model will also be used to examine the growth and distribution of freight traffic and its impacts on the local transportation system. Development of this model will be coordinated with the Mid-Ohio Regional Planning Commission (MORPC), the Metropolitan Planning Organization (MPO) for the Columbus, Ohio area.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Addressing Human and Organizational Factors In Nuclear Industry Modernization: A Sociotechnically-Based Strategic Framework

The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach, based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes (STAMP), human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high level functions: knowledge elicitation, knowledge representation, and cross-functional integration. We illustrated the utility of this approach by applying it to practical problems and case studies. The complete report is available in [1]; this paper is a description of our approach and report contents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Methods and Tools To Assess Robustness of Nuclear Plant Outages

Refueling outages are one of the most challenging phases in a nuclear power plant (NPP) operating cycle. Refueling outages are extremely costly for an NPP due to the large amount of required resources and because of lost revenue due to plant being off the grid. Outage durations have steadily decreased across the industry over that last few decades primarily due to improved planning and coordination, but there are still many plants that struggle to meet the performance metrics accomplished by other utilities. Schedule resilience is one of the issues. NPP outages require scheduling thousands of activities within 30 days on average. Despite detailed planning, once the outage starts, numerous emergent issues typically appear along with schedule delays requiring continuous replanning and adjustment. When schedule disruption occurs during an outage, plant staff make urgent efforts to recover but are often not able to maintain the planned outage duration. These outage delays can cost a utility several million dollars per day. Tools that could help outage schedulers create a more resilient schedule and allow them to optimally reschedule emergent work could significantly reduce outage delays. One key aspect of creating a resilient schedule is to have accurate estimates for activity duration. Another important outage scheduling capability is the ability to schedule emergent work with minimal disruption. This paper focuses on developing tools and methods to support NPPs with outage schedule optimization and it describes the initial development of tools to support outage management that leverage computational and machine learning methods.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Advantages of MgAlOx over gamma-Al2O3 as a support material for potassium-based high temperature lean NOx traps

MgAlOx mixed oxides were employed as supports for potassium-based lean NOx traps (LNTs) targeted for high temperature applications. Effects of support compositions, K/Pt loadings, thermal aging and catalyst regeneration on NOx storage capacity were systematically investigated. The catalysts were characterized by XRD, NOx-TPD, TEM, STEM-HAADF and in-situ XAFS. The results indicate that MgAlOx mixed oxides have significant advantages over conventional gamma-Al2O3-supports for LNT catalysts, in terms of high temperature NOx trapping capacity and thermal stability. First, as a basic support, MgAlOx stabilizes stored nitrates (in the form of KNO3) to much higher temperatures than mildly acidic gamma-Al2O3. Second, MgAlOx minimizes Pt sintering during thermal aging, which is not possible for gamma-Al2O3 supports. Notably, combined XRD, in-situ XAFS and STEM-HAADF results indicate that Pt species in the thermally aged Pt/MgAlOx samples are finely dispersed in the oxide matrix as isolated atoms. This strong metal-support interaction stabilizes Pt and minimizes the extent of sintering. However, such strong interactions result in Pt oxidation via coordination with the support so that NO oxidation activity can be adversely affected after aging which, in turn, decreases NOx trapping ability for these catalysts. Interestingly, a high-temperature reduction treatment regenerates essentially full NOx trapping performance. In fact, regenerated Pt/K/MgAlOx catalyst exhibits much better NOx trapping performance than fresh Pt/K/Al2O3 LNTs over the entire temperature range investigated here. In addition to thermal aging, Pt/K loading effects were systemically studied over the fresh samples. The results indicate that NOx trapping is kinetically limited at low temperatures, while thermodynamically limited at high temperatures. A simple conceptual model was developed to explain the Pt and K loading effects on NOx storage. An optimized K loading, which allows balancing between the stability of nitrates and exposed Pt surface, gives the best NOx trapping capability.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

Addressing Human and Organizational Factors in Nuclear Industry Modernization: A Sociotechnically Based Strategic Framework

The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included not only core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes, human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high-level functions: knowledge elicitation, knowledge representation, and cross-functional integration. Finally, we illustrated the utility of this approach by applying it to practical problems and case studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A Distributed OpenCL Framework using Redundant Computation and Data Replication

Applications written solely in OpenCL or CUDA cannot execute on a cluster as a whole. Most previous approaches that extend these programming models to clusters are based on a common idea: designating a centralized host node and coordinating the other nodes with the host for computation. However, the centralized host node is a serious performance bottleneck when the number of nodes is large. In this paper, we propose a scalable and distributed OpenCL framework called SnuCL-D for large-scale clusters. SnuCL-D's remote device virtualization provides an OpenCL application with an illusion that all compute devices in a cluster are confined in a single node. To reduce the amount of control-message and data communication between nodes, SnuCL-D replicates the OpenCL host program execution and data in each node. We also propose a new OpenCL host API function and a queueing optimization technique that significantly reduce the overhead incurred by the previous centralized approaches. To show the effectiveness of SnuCL-D, we evaluate SnuCL-D with a microbenchmark and eleven benchmark applications on a large-scale CPU cluster and a medium-scale GPU cluster.

Kim, Junghyun↗
DOE OSTI2022

Revisiting metastable immiscibility in SiO 2 –Al 2 O 3 : Structure and phase separation of supercooled liquids and glasses

Understanding and controlling liquid–liquid phase separation in aluminosilicates is crucial for optimizing glass properties. However, the metastable nature of aluminosilicates’ phase separation has made it difficult to study experimentally, and uncertainty persists regarding the compositional and temperature extents of the miscibility gap. Here, we present new experimental evidence that suggests a consolute temperature between 1440 and 1590°C and endmember compositions of 7 and 62 mol.% Al 2 O 3 for the phase-separated glasses. Using containerless melt processing, deeply supercooled liquids over the 0–60 mol.% Al 2 O 3 range are probed with in situ small- and wide-angle X-ray scattering, which simultaneously reveals changes in nanoscale density heterogeneity and atomic structure. Correlations between phase separation and atomic coordination environments are compared for liquids and glasses. Pair distribution function analysis shows mean O–(Si + Al) coordination increases with Al 2 O 3 content and decreases with temperature.

36 MATERIALS SCIENCE↗