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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Capturing Pressure Oscillations in Numerical Simulations of Internal Combustion Engines

In an earlier publication, the authors compared numerical predictions of the mean cylinder pressure of diesel and dual-fuel combustion, to that of measured pressure data from a medium-speed, large-bore engine. In these earlier comparisons, measured data from a flush-mounted in-cylinder pressure transducer showed notable and repeatable pressure oscillations which were not evident in the mean cylinder pressure predictions from computational fluid dynamics (CFD). In this paper, the authors present a methodology for predicting and reporting the local cylinder pressure consistent with that of a measurement location. Such predictions for large-bore, medium-speed engine operation demonstrate pressure oscillations in accordance with those measured. The temporal occurrences of notable pressure oscillations were during the start of combustion and around the time of maximum cylinder pressure. With appropriate resolutions in time steps and mesh sizes, the local cell static pressure predicted for the transducer location showed oscillations in both diesel and dual-fuel combustion modes which agreed with those observed in the experimental data. Fast Fourier transform (FFT) analysis on both experimental and calculated pressure traces revealed that the CFD predictions successfully captured both the amplitude and frequency range of the oscillations. Furthermore, resolving propagating pressure waves with the smaller time steps and grid sizes necessary to achieve these results required a significant increase in computer resources.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Streamlining the Design-to-Build Transition with Build-Optimization Software Tools

Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. In this paper, we have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. Finally, the herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Nonlinear discrete wavefront shaping for spatiotemporal pulse compression with multicore fibers

Here in this work, we apply an effective discrete phase front focusing method to control the coherent combining and temporal compression of laser pulses by means of nonlinear multicore fibers. We numerically demonstrate the possibility of combining almost entirely the input optical pulses injected in all cores, into the central core of a hexagonal lattice multicore fiber. We investigate the optimal operational conditions for the most effective pulse combining and analyze the influence of a positive input pulse temporal chirp and spatial phase modulation. We demonstrate that a pulse, which has been focused into an arbitrary core of the fiber, also undergoes temporal compression.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

T.Rex Visual Analytics for Transactional Exploration

T.Rex is PNNL's visual analytics tool that specializes in tabular structured data, like you might open with Excel. It's a client-server application, allowing the server to do a lot of the heavy lifting and the client to open spreadsheets with millions of rows. With datasets of that size, especially if you're unfamiliar with the contents, it's very hard to get a good grasp of what's in it using traditional tools. With T.Rex, the multiple views allow you to see categorical, temporal, numerical, relational, and summary data. The interactivity lets you look across your data and see how things relate to each other.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Two-Layer Dynamic Recycling Model (2L-DRM): Learning from Moisture Tracking Models of Different Complexity

Atmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to investigate the origin of moisture resulting in precipitation for particular regions around the globe. Moisture tracking models vary widely in their level of complexity, depending on the number of physical processes represented. Complex models are comprehensive in their physical representation, but computationally much more expensive than simple models, which only focus on specific physical processes and use simplifying assumptions. We present the mathematical derivation of the new two-layer dynamical recycling model (2L-DRM), a simple analytical moisture tracking model that relaxes the vertically-integrated formulation of the original one-layer DRM (Dominguez et al., 2006). By comparing the simple DRM to a very complex moisture tracking model that uses water vapor tracers embedded within the Weather Research and Forecast regional climate model (WRF-WVT) for the North American Monsoon region, we pinpoint the absence of vertical wind shear as the main deficiency in the simple DRM. When comparing both simple models (DRM and 2L-DRM) to the WRF-WVT (which we treat as ‘truth’), the 2L-DRM better captures the spatial extent, the net amount and the temporal variability of precipitation that originates from oceanic and local terrestrial sources. The 2L-DRM is well suited to study the large-scale climatological sources of moisture, and for these applications, performs on-par with the much more complex and computationally demanding WRF-WVT model.

Dominguez, Francina↗
DOE OSTI2017

Emissions implications of downscaled electricity generation scenarios for the western United States

This study explores how emissions from electricity generation in the Western Interconnection region of the U.S. might respond in circa 2030 to contrasting scenarios for fuel prices and greenhouse gas (GHG) emissions fees. We examine spatial and temporal variations in generation mix across the region and year using the PLEXOS unit commitment and dispatch model with a production cost model database adapted from the Western Electricity Coordinating Council. Emissions estimates are computed by combining the dispatch model results with unit-specific, emissions-load relationships. Wind energy displaces natural gas and coal in scenarios with relatively expensive natural gas or with GHG fees. Correspondingly, annual emissions of NOx, SO2, and CO2 are reduced by 20-40% in these cases. NOx emissions, which are a concern as a precursor of ground-level ozone, are relatively high and consistent across scenarios during summer, when peak electricity loads occur and wind resources in the region are comparatively weak. Accounting for the difference in start-up versus stabilized NOx emissions rates for natural gas plants had little impact on region-wide emissions estimates due to the dominant contribution from coal-fired plants, but would be more important in the vicinity of the natural gas units.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Sparse chronology strategy for integrating seasonal energy storage in capacity expansion models

Here, this study develops the sparse chronology method to enhance the representative period framework in capacity expansion models, enabling the effective integration of long-duration energy storage modeling. Traditional representative period methods cannot capture the state of charge of seasonal energy storage systems because they do not establish effective inter-day linkages to connect the state of charge between periods. The sparse chronology approach addresses this limitation by establishing inter-day linkages that allow state of charge to shift inter-seasonally. At the same time, it groups identical representative days into partitions, applying constraints sparsely and implicitly to reduce computational load further. Validation results demonstrate that this method successfully simulates long-duration energy storage patterns, achieving close alignment with a continuous yearly benchmark model, with seasonal trends and state of charge cycles clearly represented. The computational load analysis reveals that the sparse chronology method efficiently applies constraints on maximum and minimum state of charge limits within the representative day framework, eliminating the need for detailed constraints on each individual day. By partitioning representative days and constraining only the start and end of each partition, the method significantly decreases computational requirements. Simulation results show that sparse chronology closely approximates the continuous yearly method's accuracy, even with as few as 20 representative days, achieving correlation values with the benchmark of nearly 0.9 in state of charge plots. Furthermore, it maintains computational efficiency, requiring only 4 % of the solver time compared to the continuous yearly method with 20 representative days. This approach allows capacity expansion models to incorporate long-duration energy storage with high temporal, spatial, and technological resolution, enabling more detailed modeling for large-scale power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO 2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.

25 ENERGY STORAGE↗
DOE OSTI2018

Spiky electric and magnetic field structures in flux rope experiments

Magnetic flux ropes are structures that are common in the corona of the sun and presumably all stars. They can be thought of as the building blocks of solar structures. They have been observed in Earth’s magnetotail and near Mars and Venus. When multiple flux ropes are present magnetic field line reconnection, which converts magnetic energy to other forms, can occur when they collide. The structure of multiple magnetic ropes, the interactions between multiple ropes, and their topological properties such as helicity and writhing have been studied theoretically and in laboratory experiments. In this paper, we report on spiky potential and magnetic fields associated with the ropes. We show that the potential structures are chaotic for a range of their temporal half-widths and the probability density function (PDF) of their widths resembles the statistical distribution of crumpled paper. The spatial structure of the magnetic spikes is revealed using a correlation counting method. Computer simulation suggests that the potential structures are the nonlinear end result of an instability involving relative drift between ions and electrons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

1D Atmosphere Models from Inversion of Fe i 630 nm Observations with an Application to Solar Irradiance Studies

Present-day semi-empirical models of solar irradiance (SI) variations reconstruct SI changes measured on timescales greater than a day by using spectra computed in one dimensional atmosphere models (1D models), which are representative of various solar surface features. Various recent studies have pointed out, however, that the spectra synthesized in 1D models do not reflect the radiative emission of the inhomogenous atmosphere revealed by high-resolution solar observations. We aimed to derive observation-based atmospheres from such observations and test their accuracy for SI estimates. We analyzed spectropolarimetric data of the Fe i 630 nm line pair in photospheric regions that are representative of the granular quiet-Sun pattern (QS) and of small- and large-scale magnetic features, both bright and dark with respect to the QS. The data were taken on 2011 August 6, with the CRisp Imaging Spectropolarimeter at the Swedish Solar Telescope, under excellent seeing conditions. We derived atmosphere models of the observed regions from data inversion with the SIR code. We studied the sensitivity of results to spatial resolution and temporal evolution, and discuss the obtained atmospheres with respect to several 1D models. The atmospheres derived from our study agree well with most of the 1D models we compare our results with, both qualitatively and quantitatively (within 10%), except for pore regions. Spectral synthesis computations of the atmosphere obtained from the QS observations return an SI between 400 and 2400 nm that agrees, on average, within 2.2% with standard reference measurements, and within −0.14% with the SI computed on the QS atmosphere employed by the most advanced semi-empirical model of SI variations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Mass Spectrometer Transient Analysis

This software implements a complete preprocessing pipeline for transient mass spectrometry (MS) data collected during TAP (Temporal Analysis of Products) experiments. It is designed to extract chemically meaningful fluxes from overlapping ion signals by applying a calibrated defragmentation matrix and solving the resulting linear system using non-negative least squares (NNLS) regression. The core script, preprocess_mass_spec.py, performs the following operations: Gain correction: Applies amplifier gain scalars derived from inert-packed calibration pulses to normalize signal intensities across AMUs and acquisition settings. Background subtraction: Removes experiment baselines using user-defined time windows, ensuring compatibility with slow-diffusing species and preventing negative values that would interfere with NNLS. Options to subtract before and after defragmentation. Defragmentation: Constructs a fragmentation matrix A from zeroth moments of calibration pulses (equal molar gas:inert mixtures) and solves Ax=b at each time point, where b is the raw MS signal and x is the estimated species flux. The matrix is normalized to inert signals and accounts for instrument-specific fragmentation behavior. Pulse-mode handling: Supports both averaged and individual pulse modes, enabling statistical treatment of fluxes and calculation of standard deviations. Integration and output: Computes zeroth moments (integrated fluxes) and exports time-resolved and integrated data in CSV format, suitable for downstream kinetic modeling. The software is validated using both virtual TAP simulations (VTAP) and experimental data from propane dehydrogenation (PDH) on CrOx/Al2O3 catalysts. It preserves temporal resolution by applying NNLS point-by-point across the pulse duration (typically 6,000+ time slices per pulse), leveraging the linear superposition principle to reconstruct full flux profiles. The defragmented outputs are compatible with kinetic extraction methods such as the G and Y procedures, which are used to derive rate–concentration relationships from TAP data. The details of these validations are discussed in detail in the supporting manuscript and supporting information. Example data and output files are also included. The methodology is robust to experimental noise and drift, with calibration protocols that account for pulse size effects, MS aging, and inert gas normalization. The software is modular, reproducible, and tailored for high-throughput TAP-MS workflows in catalysis research.

Kristy, Stephen [Idaho National Laboratory (INL), ↗
DOE OSTI2025

Towards the next generation of Geospatial Artificial Intelligence

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.

58 GEOSCIENCES↗
DOE OSTI2015

Heat flux expressions that satisfy the conservation laws in atomistic system involving multibody potentials

Heat flux expressions are derived for multibody potential systems by extending the original Hardy's methodology and modifying Admal & Tadmor's formulas. The continuum thermomechanical quantities obtained from these two approaches are easy to compute from molecular dynamics (MD) results, and have been tested for a constant heat flux model in two distinctive systems: crystalline iron and polyethylene (PE) polymer. The convergence criteria and affecting parameters, i.e. spatial and temporal window size, and specific forms of localization function are found to be different between the two systems. The conservation of mass, momentum, and energy are discussed and validated within this atomistic–continuum bridging.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Simultaneous use of camera and probe diagnostics to unambiguously identify and study the dynamics of multiple underlying instabilities during the route to plasma turbulence

We use multiple-tip Langmuir probes and fast imaging to unambiguously identify and study the dynamics of underlying instabilities during the controlled route to fully-developed plasma turbulence in a linear magnetized helicon plasma device. Langmuir probes measure radial profiles of electron temperature, plasma density and potential; from which we compute linear growth rates of instabilities, cross-phase between density and potential fluctuations, Reynold's stress, particle flux, vorticity, time-delay estimated velocity, etc. Fast imaging complements the 1D probe measurements by providing temporally and spatially resolved 2D details of plasma structures associated with the instabilities. We find that three radially separated plasma instabilities exist simultaneously. Density gradient driven resistive drift waves propagating in the electron diamagnetic drift direction separate the plasma into an edge region dominated by strong, velocity shear driven Kelvin-Helmholtz instabilities and a central core region which shows coherent Rayleigh-Taylor modes propagating in the ion diamagnetic drift direction. The simultaneous, complementary use of both probes and camera was crucial to identify the instabilities and understand the details of the very rich plasma dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures

Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid–liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid–liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗