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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Quantized-"Gray-Scale" Electronic Synapses

Proposed array of programmable synaptic connections for electronic neural network applications offers multiple quantized levels of connection strength using only simple, two-terminal, binary microswitch devices. Subgrids in fine grid of programmable resistive connections connected externally in parallel to form coarser synaptic grid. By selection of pattern of connections in each subgrid, connection strength of synaptic node represented by that subgrid set at quantized "gray level". Device structures promise implementations of quantized-"gray-scale" synaptic arrays with very high density.

Lamb, James L.↗
DOE OSTI2023

Execution of computation graphs

A computation graph is accessed. In the computation graph, operations to be performed are represented as interior nodes, inputs to the operations are represented as leaf nodes, and a result of the operations is represented as a root. Selected sets of the operations are combined to form respective kernels of operations. Code is generated execute the kernels of operations. The code is executed to determine the result.

Ravishankar, Mahesh↗
DOE OSTI2026

Oxidation, Oligomerization, Isomerization of Hydrocarbons Using Metal–Organic Frameworks

The selective conversion of hydrocarbons into higher-value fuels and feedstocks is essential to the global energy and chemistry landscape. While porous inorganic materials have enabled significant progress in these transformations, achieving high activity, selectivity, and stability under industrially relevant conditions remains challenging. Metal–organic frameworks (MOFs) are a promising platform to precisely control active-site environments and interrogate structure–function relationships due to their crystallinity, tunability, and porosity. This review highlights relevant hydrocarbon transformations and outlines the general mechanisms for oxidation, oligomerization, and isomerization. Metal node acidity, confinement effects, and active site dispersion are analyzed for their impact on reactivity and selectivity across the three reactions. Lastly, we discuss current limitations in catalyst stability and offer a perspective on integrating reticular chemistry with high-throughput experimentation and machine learning to accelerate the discovery and design of robust, next-generation MOF catalysts.

Catalysts↗
NASA NTRS1988

Browsing schematics: Query-filtered graphs with context nodes

The early results of a research project to create tools for building interfaces to intelligent systems on the NASA Space Station are reported. One such tool is the Schematic Browser which helps users engaged in engineering problem solving find and select schematics from among a large set. Users query for schematics with certain components, and the Schematic Browser presents a graph whose nodes represent the schematics with those components. The query greatly reduces the number of choices presented to the user, filtering the graph to a manageable size. Users can reformulate and refine the query serially until they locate the schematics of interest. To help users maintain orientation as they navigate a large body of data, the graph also includes nodes that are not matches but provide global and local context for the matching nodes. Context nodes include landmarks, ancestors, siblings, children and previous matches.

Ciccarelli, Eugene C.↗
DOE OSTI2020

Copper-zirconia interfaces in UiO-66 enable selective catalytic hydrogenation of CO 2 to methanol

Molecular interactions with both oxides and metals are essential for heterogenous catalysis, leading to remarkable synergistic impacts on activity and selectivity. Here, we show that the direct link between the two phases (and not merely being together) is required to selectively hydrogenate CO 2 to methanol on catalysts containing Cu and ZrO 2 . Materials consisting of isolated Cu particles or atomically dispersed Cu–O–Zr sites only catalyze the reverse watergas shift reaction. In contrast, a metal organic framework structure (UiO-66) with Cu nanoparticles occupying missing-linker defects maximizes the fraction of metallic Cu interfaced to ZrO 2 nodes leading to a material with high adsorption capacity for CO 2 and high activity and selectivity for low-temperature methanol synthesis.

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

Engineering a Cu‐Pd Paddle‐Wheel Metal–Organic Framework for Selective CO 2 Electroreduction

Optimizing the binding energy between the intermediate and the active site is a key factor for tuning catalytic product selectivity and activity in the electrochemical carbon dioxide reduction reaction. Copper active sites are known to reduce CO 2 to hydrocarbons and oxygenates, but suffer from poor product selectivity due to the moderate binding energies of several of the reaction intermediates. Here, we report an ion exchange strategy to construct Cu−Pd paddle wheel dimers within Cu-based metal–organic frameworks (MOFs), [Cu 3-x Pd x (BTC) 2 ] (BTC=benzentricarboxylate), without altering the overall MOF structural properties. Compared to the pristine Cu MOF ([Cu 3 (BTC) 2 ], HKUST-1), the Cu−Pd MOF shifts CO 2 electroreduction products from diverse chemical species to selective CO generation. In situ X-ray absorption fine structure analysis of the catalyst oxidation state and local geometry, combined with theoretical calculations, reveal that the incorporation of Pd within the Cu−Pd paddle wheel node structure of the MOF promotes adsorption of the key intermediate COOH* at the Cu site. This permits CO-selective catalytic mechanisms and thus advances our understanding of the interplay between structure and activity toward electrochemical CO 2 reduction using molecular catalysts.

CO2 electroreduction reaction↗
NASA NTRS1987

Space-Subdivision Algorithm for Abstract Trajectories

Computational overhead in testing links between nodes reduced. Computationally efficient algorithm facilitates subdivision of n-dimensional spaces during selection, testing, or optimization of trajectories in those spaces. Reduces cost of computation in such diverse problems as scheduling of interrelated events to avoid conflicts, devising efficient production-line layouts and operating schedules, routing telephone calls, and analyzing the motions of gas particles.

Lyon, Douglas K.↗
NASA NTRS1989

NETRA: A parallel architecture for integrated vision systems. 1: Architecture and organization

Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is considered to be a system that uses vision algorithms from all levels of processing for a high level application (such as object recognition). A model of computation is presented for parallel processing for an IVS. Using the model, desired features and capabilities of a parallel architecture suitable for IVSs are derived. Then a multiprocessor architecture (called NETRA) is presented. This architecture is highly flexible without the use of complex interconnection schemes. The topology of NETRA is recursively defined and hence is easily scalable from small to large systems. Homogeneity of NETRA permits fault tolerance and graceful degradation under faults. It is a recursively defined tree-type hierarchical architecture where each of the leaf nodes consists of a cluster of processors connected with a programmable crossbar with selective broadcast capability to provide for desired flexibility. A qualitative evaluation of NETRA is presented. Then general schemes are described to map parallel algorithms onto NETRA. Algorithms are classified according to their communication requirements for parallel processing. An extensive analysis of inter-cluster communication strategies in NETRA is presented, and parameters affecting performance of parallel algorithms when mapped on NETRA are discussed. Finally, a methodology to evaluate performance of algorithms on NETRA is described.

Choudhary, Alok N.↗
NASA NTRSDate not supplied

Performance Optimization Methods for a Memory-Bound, Unstructured-Grid CFD Application on Massively Parallel GPU Platforms

Computational performance of the FUN3D unstructured-grid computational fluid dynamics (CFD) application on massively parallel GPU environments is memory-bound and highly dependent upon efficient reads from and atomic updates to the irregular cell-, edge-, and node-based data structures. In this talk, we present recent efforts into optimizing select performance-critical kernels on NVIDIA Tesla V100 and A100 GPUs and AMD CDNA MI100 GPUs. A novel use of L2 cache residency controls and asynchronous loads into on-chip shared memory are explored on the A100 GPU for the sparse iterative solver, which is dominated by mixed-precision, sparse matrix vector multiplication. Demonstrations show that these methods improve global memory bandwidth utilization by 13.5% on the A100 GPU. Several techniques are also presented that use registers and/or shared memory to facilitate array transposition and aggregation which combine to reduce the frequency and increase the cache efficiency of floating-point atomic updates to the irregular data structures. These methods are demonstrated to improve the kernel throughput by nearly 500% on select kernels on the AMD MI100 over atomic updates directly to global memory. Overall, both V100 and A100 GPUs outperformed the MI100 GPU on kernels dominated by double-precision atomic updates; however, the techniques demonstrated here reduced the performance gap and improved the MI100 performance.

GPU CPU unstructured CFD memory↗
DOE OSTI2026

Frontier Job-Centric Telemetry Dataset

Comprehensive analysis of high-performance computing (HPC) systems requires linking workload execution to system behavior. This kind of analysis is vital for diagnosing performance issues, managing capacity, detecting anomalous workloads, and understanding how applications interact with system hardware. This job-centric telemetry dataset unifies scheduler job records with node-level measurements, enabling direct association between workloads and their corresponding power, thermal, and performance characteristics. It contains sanitized, scheduler related metadata for 152,400 individual jobs that ran on the Frontier supercomputer and ended on selected days throughout 2024 and 2025, a subpopulation of ~6.8% of the total number of allocated jobs with non-zero run time on the system over that same period. Each is linked with files that contain telemetry time series records of the power utilization and temperature behavior of its allocated nodes and their processors during the run time of the job. Where available, a portion of the job files also contain network performance time series. Jobs are sampled from select days that reflect normal levels of user activity and possess job size distributions with large numbers of leadership class jobs (>20% of Frontier nodes). Jobs in this dataset attempt to best represent successful user workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Taming the fixed-node error in diffusion Monte Carlo via range separation

By combining density-functional theory (DFT) and wave function theory via the range separation (RS) of the interelectronic Coulomb operator, we obtain accurate fixed-node diffusion Monte Carlo (FN-DMC) energies with compact multi-determinant trial wave functions. In particular, we combine here short-range exchange-correlation functionals with a flavor of selected configuration interaction known as configuration interaction using a perturbative selection made iteratively (CIPSI), a scheme that we label RS-DFT-CIPSI. One of the take-home messages of the present study is that RS-DFT-CIPSI trial wave functions yield lower fixed-node energies with more compact multi-determinant expansions than CIPSI, especially for small basis sets. Indeed, as the CIPSI component of RS-DFT-CIPSI is relieved from describing the short-range part of the correlation hole around the electron-electron coalescence points, the number of determinants in the trial wave function required to reach a given accuracy is significantly reduced as compared to a conventional CIPSI calculation. Importantly, by performing various numerical experiments, we evidence that the RS-DFT scheme essentially plays the role of a simple Jastrow factor by mimicking short-range correlation effects, hence avoiding the burden of performing a stochastic optimization. Considering the 55 atomization energies of the Gaussian-1 benchmark set of molecules, we show that using a fixed value of mu = 0.5 bohr(-1) provides effective error cancellations as well as compact trial wave functions, making the present method a good candidate for the accurate description of large chemical systems.

Scemama, Anthony↗
DOE OSTI2020

Structural reversibility of Cu doped NU-1000 MOFs under hydrogenation conditions

The metal-organic framework (MOF), NU-1000, and its metalated counterparts have found proof-of-concept application in heterogeneous catalysis and hydrogen storage amongst others. A vapor-phase technique, akin to atomic layer deposition (ALD), is used to selectively deposit divalent Cu ions on oxo, hydroxo-bridged hexa-zirconium(IV) nodes capped with terminal –OH and -OH 2 ligands. Subsequent reaction with steam yields node-anchored, CuII-oxo,hydroxo clusters. We find that cluster installation via AIM (= ALD In MOFs) is accompanied by an expansion of MOF mesopore (channel) diameter . We investigated the behavior of the cluster-modified material, termed Cu-AIM-NU-1000, to heat treatment up to 325 °C, at atmospheric pressure with a low flow of H2 into the reaction cell. The response under these conditions revealed two important results: (1) Above 200 °C, the initially installed few-metal-ion clusters reduce to neutral Cu atoms. The neutral atoms migrate from the nodes and aggregate into Cu nanoparticles. While the size of particles formed in the MOF interior is constrained by the width of mesopores (ca. 3 nm), those formed on the exterior surface of the MOF can grow as large as ca. 8 nm. (2) Reduction and release of Cu atoms from the MOFs nodes is accompanied NU-1000 undergoes dynamic structural transformation as it reverts back to its original dimension following the release. These results show while the MOF framework itself remains intact at 325 °C in an H 2 atmosphere, the small, AIM-installed Cu II -oxo,hydroxo clusters are stable with respect to reduction and conversion to metallic nanoparticles only up to ~200 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Assessing the Optimality of LinDist3Flow for Optimal Tap Selection of Step Voltage Regulators in Unbalanced Distribution Networks

The adoption of distributed energy resources such as photovoltaics (PVs) has increased dramatically during the previous decade. The increased penetration of PVs into distribution networks (DNs) can cause voltage fluctuations that have to be mitigated. One of the key utility assets employed to this end are step-voltage regulators (SVRs). It is desirable to include tap selection of SVRs in optimal power flow (OPF) routines, a task that turns out to be challenging because the resultant OPF problem is nonconvex with added complexities stemming from accurate SVR modeling. While several convex relaxations based on semi-definite programming (SDP) have been presented in the literature for optimal tap selection, SDP based schemes do not scale well and are challenging to implement in large-scale planning or operational frameworks. This paper deals with the optimal tap selection (OPTS) problem for wye-connected SVRs using linear approximations of power flow equations. Specifically, the LinDist3Flow model is adopted and the effective SVR ratio is assumed to be continuous- enabling the formulation of a problem called LinDist3Flow-OPTS, which amounts to a linear program. The scalability and optimality gap of LinDist3Flow-OPTS are evaluated with respect to existing SDP-based and nonlinear programming techniques for optimal tap selection in three standard feeders, namely, the IEEE 13-bus, 123-bus, and 8500-node DNs. For all DNs considered, LinDist3Flow-OPTS achieves an optimality gap of approximately 1% or less while significantly lowering the computational burden.

linear approximations↗
DOE OSTI2023

ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k -means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Discrete observability and numerical quadrature

The authors consider the problem of approximate observability of a one-dimensional diffusion equation on a finite spatial domain with spatial point measurements. The problem of the optimal selection of the measurement points is considered under three conditions: (1) no preassigned measurement nodes; (2) one preassigned node and; (3) two preassigned nodes. The main observation is that the optimal choice is related to three classical procedures in numerical analysis: (1) Gaussian quadrature; (2) Radau quadrature and; (3) Lobatto quadrature. It is shown that the existence of the Radau and Lobatto quadrature is closely related to classical root locus theory.

Martin, Clyde F.↗
NASA NTRS1993

Structure identification in fuzzy inference using reinforcement learning

In our previous work on the GARIC architecture, we have shown that the system can start with surface structure of the knowledge base (i.e., the linguistic expression of the rules) and learn the deep structure (i.e., the fuzzy membership functions of the labels used in the rules) by using reinforcement learning. Assuming the surface structure, GARIC refines the fuzzy membership functions used in the consequents of the rules using a gradient descent procedure. This hybrid fuzzy logic and reinforcement learning approach can learn to balance a cart-pole system and to backup a truck to its docking location after a few trials. In this paper, we discuss how to do structure identification using reinforcement learning in fuzzy inference systems. This involves identifying both surface as well as deep structure of the knowledge base. The term set of fuzzy linguistic labels used in describing the values of each control variable must be derived. In this process, splitting a label refers to creating new labels which are more granular than the original label and merging two labels creates a more general label. Splitting and merging of labels directly transform the structure of the action selection network used in GARIC by increasing or decreasing the number of hidden layer nodes.

Berenji, Hamid R.↗
NASA NTRS2011

Accurate Adaptive Level Set Method and Sharpening Technique for Three Dimensional Deforming Interfaces

In this paper, we demonstrate improved accuracy of the level set method for resolving deforming interfaces by proposing two key elements: (1) accurate level set solutions on adapted Cartesian grids by judiciously choosing interpolation polynomials in regions of different grid levels and (2) enhanced reinitialization by an interface sharpening procedure. The level set equation is solved using a fifth order WENO scheme or a second order central differencing scheme depending on availability of uniform stencils at each grid point. Grid adaptation criteria are determined so that the Hamiltonian functions at nodes adjacent to interfaces are always calculated by the fifth order WENO scheme. This selective usage between the fifth order WENO and second order central differencing schemes is confirmed to give more accurate results compared to those in literature for standard test problems. In order to further improve accuracy especially near thin filaments, we suggest an artificial sharpening method, which is in a similar form with the conventional re-initialization method but utilizes sign of curvature instead of sign of the level set function. Consequently, volume loss due to numerical dissipation on thin filaments is remarkably reduced for the test problems

Kim, Hyoungin↗
DOE OSTI2022

Assessing the Optimality of LinDist3Flow for Optimal Tap Selection of Step Voltage Regulators in Unbalanced Distribution Networks: Preprint

The adoption of distributed energy resources such as photovoltaics (PVs) has increased dramatically during the previous decade. The increased penetration of PVs into distribution networks (DNs) can cause voltage fluctuations that have to be mitigated. One of the key utility assets employed to this end are step-voltage regulators (SVRs). It is desirable to include tap selection of SVRs in optimal power flow (OPF) routines, a task that turns out to be challenging because the resultant OPF problem is nonconvex with added complexities stemming from accurate SVR modeling. While several convex relaxations based on semi-definite programming (SDP) have been presented in the literature for optimal tap selection, SDP based schemes do not scale well and are challenging to implement in large-scale planning or operational frameworks. This paper deals with the optimal tap selection (OPTS) problem for wye-connected SVRs using linear approximations of power flow equations. Specifically, the LinDist3Flow model is adopted and the effective SVR ratio is assumed to be continuous–enabling the formulation of a problem called LinDist3Flow-OPTS, which amounts to a linear program. The scalability and optimality gap of LinDist3Flow-OPTS are evaluated with respect to existing SDP-based and nonlinear programming techniques for optimal tap selection in three standard feeders, namely, the IEEE 13-bus, 123-bus, and 8500-node DNs. For all DNs considered, LinDist3Flow-OPTS achieves an optimality gap of approximately 1% or less while significantly lowering the computational burden.

linear approximations↗