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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Kinetics and Mechanism of Nickel Oxide Reduction by Methane

Nickel oxide reduction by methane is of particular importance in catalysis, extractive metallurgy, and clean power generation technologies. Despite extensive investigations of the NiO + CH 4 reaction, many questions remain about its kinetics and molecular and structural transformation mechanisms. We discuss the reduction kinetics of bulk polycrystalline NiO by CH 4 using a new calorimetric method. The technique permits rapid, controllable heating of the NiO/Ni wires and continuous data (electrical power, the electrical resistivity of the wire, and temperature) acquisition with a frequency of 10 kHz. The method also allows arresting and preservation of the structure of the sample by programmed termination of electric power in thin specimens. This approach, coupled with ex situ electron microscopy, allows determination of the reaction rate and tracking of the ongoing structural transformations. The mechanism of NiO reduction by methane is suggested based on direct correlations between reaction kinetics and the observed microstructure transformations. The kinetic data treatment revealed that the nucleation-growth model describes the reaction kinetics. Ex situ microscopy observations of reacted specimens showed that the first nuclei rapidly accelerate the process. Pore formation occurs at the nucleation sites, and the porous structure then quickly grows into the bulk NiO. The second type of reduced Ni microstructure, compact layer, can be observed only on the outer surface of the wire. Based on kinetic data and microstructural observations, a mechanism of reduction was suggested according to which porous microstructure forms predominantly by NiO reduction with carbon. The compact layer observed on the outer surface of the wire forms through the diffusion-controlled hydrogen reduction.

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

Constructing a Simulation Surrogate with Partially Observed Output

Gaussian process surrogates are a popular alternative to directly using computationally expensive simulation models. When the simulation output consists of many responses, dimension-reduction techniques are often employed to construct these surrogates. However, surrogate methods with dimension reduction generally rely on complete output training data. This article proposes a new Gaussian process surrogate method that permits the use of partially observed output while remaining computationally efficient. The new method involves the imputation of missing values and the adjustment of the covariance matrix used for Gaussian process inference. The resulting surrogate represents the available responses, disregards the missing responses, and provides meaningful uncertainty quantification. In conclusion, the proposed approach is shown to offer sharper inference than alternatives in a simulation study and a case study where an energy density functional model that frequently returns incomplete output is calibrated.

42 ENGINEERING↗
DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗
DOE OSTI2020

Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

This work introduces a method for learning low-dimensional models from data of high-dimensional black-box dynamical systems. The novelty is that the learned models are exactly the reduced models that are traditionally constructed with classical projection-based model reduction techniques. Thus, the proposed approach learns models that are guaranteed to have the well-studied properties of reduced models known from model reduction, without requiring full knowledge of the governing equations and without requiring the operators of the high-dimensional systems. The key ingredient is a new data sampling scheme to obtain re-projected trajectories of high-dimensional systems that correspond to Markovian dynamics in low-dimensional subspaces. The exact recovery of reduced models from these re-projected trajectories is guaranteed pre-asymptotically under certain conditions for finite amounts of data and for a large class of systems with polynomial nonlinear terms. Numerical results demonstrate that the low-dimensional models learned with the proposed approach match reduced models from traditional model reduction up to numerical errors in practice. In conclusion, the numerical results further indicate that low-dimensional models fitted to re-projected trajectories are predictive even in situations where models fitted to trajectories without re-projection are inaccurate and unstable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Bit Grooming: statistically accurate precision-preserving quantization with compression, evaluated in the netCDF Operators (NCO, v4.4.8+)

Abstract. Geoscientific models and measurements generate false precision (scientifically meaningless data bits) that wastes storage space. False precision can mislead (by implying noise is signal) and be scientifically pointless, especially for measurements. By contrast, lossy compression can be both economical (save space) and heuristic (clarify data limitations) without compromising the scientific integrity of data. Data quantization can thus be appropriate regardless of whether space limitations are a concern. We introduce, implement, and characterize a new lossy compression scheme suitable for IEEE floating-point data. Our new Bit Grooming algorithm alternately shaves (to zero) and sets (to one) the least significant bits of consecutive values to preserve a desired precision. This is a symmetric, two-sided variant of an algorithm sometimes called Bit Shaving that quantizes values solely by zeroing bits. Our variation eliminates the artificial low bias produced by always zeroing bits, and makes Bit Grooming more suitable for arrays and multi-dimensional fields whose mean statistics are important. Bit Grooming relies on standard lossless compression to achieve the actual reduction in storage space, so we tested Bit Grooming by applying the DEFLATE compression algorithm to bit-groomed and full-precision climate data stored in netCDF3, netCDF4, HDF4, and HDF5 formats. Bit Grooming reduces the storage space required by initially uncompressed and compressed climate data by 25–80 and 5–65 %, respectively, for single-precision values (the most common case for climate data) quantized to retain 1–5 decimal digits of precision. The potential reduction is greater for double-precision datasets. When used aggressively (i.e., preserving only 1–2 digits), Bit Grooming produces storage reductions comparable to other quantization techniques such as Linear Packing. Unlike Linear Packing, whose guaranteed precision rapidly degrades within the relatively narrow dynamic range of values that it can compress, Bit Grooming guarantees the specified precision throughout the full floating-point range. Data quantization by Bit Grooming is irreversible (i.e., lossy) yet transparent, meaning that no extra processing is required by data users/readers. Hence Bit Grooming can easily reduce data storage volume without sacrificing scientific precision or imposing extra burdens on users.

58 GEOSCIENCES↗
DOE OSTI2024

Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications

Scientists conduct large-scale simulations to compute derived quantities from primary data. Thus, it is crucial that data compression techniques maintain bounded errors on these derived quantities or quantities of interest (QOI). For many spatiotemporal applications, these QOIs are binary in nature and represent presence or absence of a physical phenomenon. In this work, we propose to use a hybrid approah for differential compression for such applications. We use a neural network (NN) approach to determine regions-of-interest (ROIs) where the binary QOIs are going to be prevalent. This is then used with traditional approaches that compress at a lower level (and higher accuracy) for these ROIs as compared to other regions.

Li, Xiao↗
DOE OSTI2023

Operando High-Energy-Resolution X-ray Spectroscopy of Evolving Cu Nanoparticle Electrocatalysts for CO 2 Reduction

Advances in electrocatalysis research rely heavily on building a thorough mechanistic understanding of catalyst active sites under realistic operating conditions. Only recently have techniques emerged that enable sensitive spectroscopic data collection to distinguish catalytically relevant surface sites from the underlying bulk material under applied potential in the presence of an electrolyte layer. Here, we demonstrate that operando high-energy-resolution fluorescence detected X-ray absorption spectroscopy (HERFD-XAS) is a powerful spectroscopic method which offers critical surface chemistry insights in CO 2 electroreduction with sub-electronvolt energy resolution using hard X-rays. Combined with the high surface area-to-volume ratio of 5 nm copper nanoparticles, operando HERFD-XAS allows us to observe with clear evidence the breaking of chemical bonds between the ligands and the Cu surface as part of the ligand desorption process occurring under electrochemical potentials relevant for the CO 2 reduction reaction (CO 2 RR). In addition, the dynamic evolution of oxidation state and coordination number throughout the operation of the nanocatalyst was continuously tracked. With these results in hand, undercoordinated metallic copper nanograins are proposed to be the real active sites in the CO 2 RR. In conclusion, this work emphasizes the importance of HERFD-XAS compared to routine XAS in catalyst characterization and mechanism exploration, especially in the complicated electrochemical CO 2 RR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Adaptive nonlocal means filtering based on local noise level for CT denoising

Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. Conclusions: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2020

Hypergraph Analytics of Domain Name System Relationships

We report on the use of novel mathematical methods in hypergraph analytice over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech's ActiveDNS repository as hypergraphs allows us to fully represent the interactions between {\em collections} of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities. HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs, acting as a frontend. For the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.

Joslyn, Cliff A.↗
DOE OSTI2021

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Ma, Zhan↗
DOE OSTI2014

Monitoring and Characterization of Miscellaneous Electrical Loads in a Large Retail Environment

Buildings account for 40% of primary energy consumption in the United States (residential 22%; commercial 18%). Most (70% residential and 79% commercial) is used as electricity. Thus, almost 30% of U.S. primary energy is used to provide electricity to buildings. Plug loads play an increasingly critical role in reducing energy use in new buildings (because of their increased efficiency requirements), and in existing buildings (as a significant energy savings opportunity). If all installed commercial building miscellaneous electrical loads (CMELs) were replaced with energy-efficient equipment, a potential annual energy saving of 175 TWh, or 35% of the 504 TWh annual energy use devoted to MELs, could be achieved. This energy saving is equivalent to the annual energy production of 14 average-sized nuclear power plants. To meet DOE's long-term goals of reducing commercial building energy use and carbon emissions, the energy efficiency community must better understand the components and drivers of CMEL energy use, and develop effective reduction strategies. These goals can be facilitated through improved data collection and monitoring methodologies, and evaluation of CMELs energy-saving techniques.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Diagnosing nuclear power plant pipe wall thinning due to flow accelerated corrosion using a passive, thermal non-destructive evaluation method: Feasibility assessment via numerical experiments

Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

New Results from MiniBooNE Charged-Current Quasi-Elastic Anti-Neutrino Data

MiniBooNE anti-neutrino charged-current quasi-elastic (CCQE) data is compared to model predictions. The main background of neutrino-induced events is examined first, where three independent techniques are employed. Results indicate the neutrino flux is consistent with a uniform reduction of {approx}20% relative to the largely uncertain prediction. After background subtraction, the Q{sup 2} shape of {nu}-bar{sub {mu}} CCQE events is consistent with the model parameter M{sub A} = 1.35 GeV determined from MiniBooNE {nu}{sub {mu}} CCQE data, while the normalization is {approx}20% high compared to the same prediction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Alternating-bias assisted annealing of amorphous oxide tunnel junctions

Superconducting quantum bits (qubits) rely on ultra-thin, amorphous oxide tunneling barriers that can have significant inhomogeneities and defects as grown. This can result in relatively large uncertainties and deleterious effects in the circuits, limiting the scalability. Finding a robust solution to the junction reproducibility problem has been a long-standing goal in the field. Here, we demonstrate a transformational technique for controllably tuning the electrical properties of aluminum-oxide tunnel junctions. This is accomplished using a low-voltage, alternating-bias applied individually to the tunnel junctions, with which resistance tuning by more than 70% can be achieved. The data indicates an improvement of coherence and reduction of two-level system defects. Transmission electron microscopy shows that the treated junctions are predominantly amorphous, albeit with a more uniform distribution of alumina coordination across the barrier. This technique is expected to be useful for other devices based on ionic amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Pump-probe measurements of the thermal conductivity tensor for materials lacking in-plane symmetry

We previously demonstrated an extension of time-domain thermoreflectance (TDTR) which utilizes offset pump and probe laser locations to measure in-plane thermal transport properties of multilayers. However, the technique was limited to systems of transversely isotropic materials studied using axisymmetric laser intensities. Here, we extend the mathematics so that data reduction can be performed on non-transversely isotropic systems. An analytic solution of the diffusion equation for an N-layer system is given, where each layer has a homogenous but otherwise arbitrary thermal conductivity tensor and the illuminating spots have arbitrary intensity profiles. As a demonstration, we use both TDTR and time-resolved magneto-optic Kerr effect measurements to obtain thermal conductivity tensor elements of <110> α-SiO{sub 2}. We show that the out-of-phase beam offset sweep has full-width half-maxima that contains nearly independent sensitivity to the in-plane thermal conductivity corresponding to the scanning direction. Also, we demonstrate a Nb-V alloy as a low thermal conductivity TDTR transducer layer that helps improve the accuracy of in-plane measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Investigation of industrial-scale carbon dioxide reduction using pulsed electron beams

Carbon dioxide is the most important greenhouse gas contributing to global warming. To help mitigate increasing CO{sub 2} concentrations, we investigate a method of carbon dioxide reduction using high-power electron beams, which can be used on an industrial scale. A series of experiments are conducted in which the reduction of CO{sub 2} is measured for different gas compositions and power deposition rates. An electron beam deposition model is applied to compute reduction rates of CO{sub 2} and energy cost for breaking a CO{sub 2} molecule in flue gas and pure carbon dioxide at atmospheric pressure. For flue gas consisting of 82% N{sub 2}, 6% O{sub 2}, and 12% CO{sub 2}, the calculated energy cost is 85 eV per molecule. In order to dissociate 50% of the CO{sub 2} molecules, beam energy density deposition on the order of 20 J/cm{sup 3} is required. Electron beam irradiation of 12.6 liter gas volume containing 90% CO{sub 2} and 10% CH{sub 4} at beam energy density deposition of 4.2 J/cm{sup 3}, accumulated over 43 shots in a 20 min interval, reduced the CO{sub 2} concentration to 78%. Analogous experiments with a gas mixture containing 11.5% CO{sub 2}, 11.5% CH{sub 4}, and balance of Ar, reduced the CO{sub 2} concentration to below 11% with energy deposition 0.71 J/cm{sup 3}, accumulated over 10 shots in a 5 min interval. The experimental data and the theoretical predictions of CO{sub 2} reduction using pulsed electron beams are in agreement within the experimental error. Other techniques to enhance the removal of CO{sub 2} with pulsed electron beams are also explored, yielding new possible avenues of research.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

Improving I/O Throughput with PRIMACY: Preconditioning ID-Mapper for Compressing Incompressibility

The ability to efficiently handle massive amounts of data is necessary for the continuing development towards exascale scientific data-mining applications and database systems. Unfortunately, recent years have shown a growing gap between the size and complexity of data produced from scientific applications and the limited I/O bandwidth available on modern high-performance computing systems. Utilizing data compression in order to lower the degree of I/O activity offers a promising means to addressing this problem. However, the standard compression algorithms previously explored for such use offer limited gains on both the end-to-end throughput and storage fronts. In this paper, we introduce an in-situ compression scheme aimed at improving end-to-end I/O throughput as well as reduction of dataset size. Our technique, PRIMACY (Preconditioning Id-MApper for Compressing incompressibility), acts as a preconditioner for standard compression libraries by modifying representation of original floating-point scientific data to increase byte-level repeatability, allowing standard loss less compressors to take advantage of their entropy-based byte-level encoding schemes. We additionally present a theoretical model for compression efficiency in high-performance computing environments and evaluate the efficiency of our approach via comparative analysis. Based on our evaluations on 20 real-world scientific datasets, PRIMACY achieved up to 38% and 22% improvements upon standard end-to-end write and read throughputs respectively in addition to a 25% increase in compression ratios paired with 3-to-4-fold improvement in both compression and decompression throughput over general purpose compressors.

Computer Science↗