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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 1,441 records · Page 80
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DOE OSTI2015

Threshold-based queuing system for performance analysis of cloud computing system with dynamic scaling

Cloud computing is promising technology to manage and improve utilization of computing center resources to deliver various computing and IT services. For the purpose of energy saving there is no need to unnecessarily operate many servers under light loads, and they are switched off. On the other hand, some servers should be switched on in heavy load cases to prevent very long delays. Thus, waiting times and system operating cost can be maintained on acceptable level by dynamically adding or removing servers. One more fact that should be taken into account is significant server setup costs and activation times. For better energy efficiency, cloud computing system should not react on instantaneous increase or instantaneous decrease of load. That is the main motivation for using queuing systems with hysteresis for cloud computing system modelling. In the paper, we provide a model of cloud computing system in terms of multiple server threshold-based infinite capacity queuing system with hysteresis and noninstantanuous server activation. For proposed model, we develop a method for computing steady-state probabilities that allow to estimate a number of performance measures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Multimodal microfluidic platform for controlled culture and analysis of unicellular organisms

Modern live-cell imaging approaches permit real-time visualization of biological processes, yet limitations exist for unicellular organism isolation, culturing and long-term imaging that preclude fully understanding how cells sense and respond to environmental perturbations and the link between single-cell variability and whole-population dynamics. Here we present a microfluidic platform that provides fine control over the local environment with the capacity to replace media components at any experimental time point, and provides both perfused and compartmentalized cultivation conditions depending on the valve configuration. In this work, the functionality and flexibility of the platform were validated using both bacteria and yeast having different sizes, motility and growth media. The demonstrated ability to track the growth and dynamics of both motile and non-motile prokaryotic and eukaryotic organisms emphasizes the versatility of the devices, which with further scale-up should enable studies in bioenergy and environmental research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Blobs and drift wave dynamics

The modified Hasegawa-Mima equation retaining all nonlinearities is investigated from the point of view of the formation of blobs. The linear analysis shows that the amplitude of the drift wave packet propagating in the direction of decreasing background plasma density increases and eventually saturates due to nonlinear effects. Nonlinear modification of the time averaged plasma density profile results in the formation of large amplitude modes locked in the radial direction, but still propagating in the poloidal direction, which resembles the experimentally observed chain of blobs propagating in the poloidal direction. Such specific density profiles, causing the locking of drift waves, could form naturally at the edge of tokamak due to a neutral ionization source. Thus, locked modes can grow in situ due to plasma instabilities, e.g., caused by finite resistivity. Furthermore, the modulation instability (in the poloidal direction) of these locked modes can result in a blob-like burst of plasma density.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

A first principles analysis of the effect of hydrogen concentration in hydrogenated amorphous silicon on the formation of strained Si-Si bonds and the optical and mobility gaps

In this paper, we use a model of hydrogenated amorphous silicon generated from molecular dynamics with density functional theory calculations to examine how the atomic geometry and the optical and mobility gaps are influenced by mild hydrogen oversaturation. The optical and mobility gaps show a volcano curve as the hydrogen content varies from undersaturation to mild oversaturation, with largest gaps obtained at the saturation hydrogen concentration. At the same time, mid-gap states associated with dangling bonds and strained Si-Si bonds disappear at saturation but reappear at mild oversaturation, which is consistent with the evolution of optical gap. The distribution of Si-Si bond distances provides the key to the change in electronic properties. In the undersaturation regime, the new electronic states in the gap arise from the presence of dangling bonds and strained Si-Si bonds, which are longer than the equilibrium Si-Si distance. Increasing hydrogen concentration up to saturation reduces the strained bonds and removes dangling bonds. In the case of mild oversaturation, the mid-gap states arise exclusively from an increase in the density of strained Si-Si bonds. Analysis of our structure shows that the extra hydrogen atoms form a bridge between neighbouring silicon atoms, thus increasing the Si-Si distance and increasing disorder in the sample.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Time Matters: A Survival Analysis of Public Electric Vehicle Charging Infrastructure Utilization

The rapid adoption of plug-in electric vehicles (PEVs) places significant demands on public charging infrastructure, making it critical to understand and optimize charger utilization. This study provides one of the most comprehensive analyses of charging behavior to date by applying a survival analysis to a dataset of nearly 16 million level 2 (L2) and direct current (DC) fast charger sessions across the United States from 2017 to 2022. Using Kaplan-Meier curves and log rank tests, our analysis reveals statistically significant and distinct duration patterns influenced by charger type, time of day, and day of the week. We find that L2 charging sessions exhibit high variability tied to venue type, whereas DC sessions are more uniform, typically lasting 30-45 min. This study introduces the operational efficiency score (OES), a metric for standardizing the performance evaluation of charging stations. Our findings offer actionable insights for optimizing charger deployment, developing dynamic pricing strategies to reduce vehicle dwell time, and improving load management for grid operators, ultimately enhancing the efficiency and availability of public charging infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Dynamics of Hydration Water in Sugars and Peptides Solutions

We analyzed solute and solvent dynamics of sugars and peptides aqueous solutions using extended epolarized light scattering (EDLS) and broadband dielectric spectroscopies (BDS). Spectra measured with both techniques reveal the same mechanism of rotational diffusion of peptides molecules. In the case of sugars, this solute reorientational relaxation can be isolated by EDLS measurements, whereas its ontribution to the dielectric spectra is almost negligible. In the presented analysis, we characterize the hydration water in terms of hydration number and retardation ratio between relaxation times of hydration and bulk water. Both techniques provide similar estimates of . The retardation imposed on the hydration water by sugars is 3.3 1.3 and involves only water molecules hydrogen-bonded (HB) to solutes ( 3 water molecules per sugar OH-group). In contrast, polar peptides cause longer range erturbations beyond the first hydration shell, and between 2.8 and 8, increasing with the number of chemical groups engaged in HB formation. We demonstrate that chemical heterogeneity and specific HB interactions play a crucial role in hydration dynamics around polar solutes. The obtained results help to disentangle the role of excluded volume and enthalpic contributions in dynamics of hydration water at the interface with biological molecules.

Perticaroli, Stefania↗
DOE OSTI2022

Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. Here, this paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Incremental k-core decomposition: Algorithms and evaluation

A k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. Furthermore, for a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Free-spray characteristics and spray-wall interactions of methanol on a gasoline direct injector under flash-boiling and non-flash-boiling conditions

Methanol is considered a promising alternative fuel for internal combustion engines (ICEs) due to its high-octane number, fast laminar flame speed, and elevated latent heat of vaporization, all of which support higher compression ratios and improved thermal efficiency. However, its substantial latent heat of vaporization also poses cold-start challenges, such as misfire and fuel film deposition. This study aims to investigate methanol spray morphology and spray-wall interaction using the Spray M injector from the Engine Combustion Network within a constant-pressure flow vessel. A recently developed unified numerical framework capable of modeling both flash and non-flash boiling sprays is validated against experimental liquid volume fraction data acquired via 3-D computed tomography. Here, the results reveal that flash boiling significantly alters the spray morphology, leading to smaller droplets and spray collapse due to enhanced air-entrainment-induced turbulence. Quantitative agreement between experiments and simulations confirms this behavior. Coupled 0-D equilibrium and 3-D computational fluid dynamics analyses show that flash boiling accelerates evaporation and reduces fuel residence time, while non-flash conditions maintain a persistent liquid core more susceptible to wall wetting. Wall temperature diagnostics reveal that spray collapse alters heat transfer patterns by shifting cooling effects. Mixture fraction analysis indicates that evaporation is primarily governed by shear-layer turbulence, though deviations from adiabatic equilibrium mixing emerge under low-turbulence conditions. Finally, increasing fuel, ambient, and wall temperatures reduces wall wetting and film thickness, mitigating cold-start risks. These findings enhance the understanding of methanol sprays’ behavior and support its adoption as a viable, alternative fuel for ICEs.

Engine Combustion Network↗
DOE OSTI2026

Spatially resolved phase transformation mapping in 410 stainless steel during additive manufacturing visualized via real-time infrared data

Here, in this study, we demonstrate that spatially resolved cooling curves derived from real-time infrared (IR) thermography during additive manufacturing (AM) can capture spatial variations in phase transformation temperatures through cooling curve analysis (CCA). Using this approach, we show that during laser hot-wire deposition of 410 stainless steel (410SS), the martensite start temperature (M s ) evolves dynamically throughout the build. The M s temperature is spatially nonuniform, ranging from 185 °C to 348 °C, with the lowest values toward the build center and higher values toward the upper region of the deposit. In the lower portion of the build, no M s inflection is detected via CCA, consistent with transformation occurring earlier during thermal cycling followed by tempering during subsequent thermal cycles. These trends in M s are corroborated by characterizing the microstructure by electron backscatter diffraction (EBSD). Traditionally, M s is assumed to be constant, and a single interpass temperature is applied during both deposition and residual stress modeling. Our results demonstrate that IR-derived cooling curves provide a route to spatially and temporally resolved transformation temperature tracking for dynamic interpass control and improved residual-stress modeling.

Additive manufacturing↗
DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Unstable kinetic roughening during the island coalescence stage of sputtered tantalum films

Kinetic roughening of tantalum films during the initial growth stages has been studied by atomic force microscopy, scanning electron microscopy, and dynamic scaling theory. Different from the time-independent scaling behavior for continuous film growth, an intriguing unstable kinetic roughening occurs during island coalescence. In such case, roughness exponent {alpha} increases with growth time, accompanied by lower growth exponent {beta} and higher coarsening exponent {eta}. Detailed analysis of film surface morphology and simple phenomenological models suggests that this unstable behavior is related to the pronounced lateral growth of surface islands, which arises from the combined effect of the formation of grain boundary and the covering of heterogeneous substrate surface.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Femtosecond laser ablation of dielectric materials in the optical breakdown regime: Expansion of a transparent shell

Phase transition pathways of matter upon ablation with ultrashort laser pulses have been considered to be understood long-since for metals and semiconductors. We provide evidence that also certain dielectrics follow the same pathway, even at high pulse energies triggering optical breakdown. Employing femtosecond microscopy, we observe a characteristic ring pattern within the ablating region that dynamically changes for increasing time delays between pump and probe pulse. These transient Newton rings are related to optical interference of the probe beam reflected at the front surface of the ablating layer with the reflection at the interface of the non-ablating substrate. Analysis of the ring structure shows that the ablation mechanism is initiated by a rarefaction wave leading within a few tens of picoseconds to the formation of a transparent thin shell of reduced density and refractive index, featuring optically sharp interfaces. The shell expands and eventually detaches from the solid material at delays of the order of 100 ps.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

VARIABLE REDDENING AND BROAD ABSORPTION LINES IN THE NARROW-LINE SEYFERT 1 GALAXY WPVS 007: AN ORIGIN IN THE TORUS

We report the discovery of an occultation event in the low-luminosity narrow-line Seyfert 1 galaxy WPVS 007 in 2015 February and March. In concert with longer timescale variability, these observations place strong constraints on the nature and location of the absorbing material. Swift monitoring has revealed a secular decrease since ∼2010 accompanied by flattening of the optical and UV photometry that suggests variable reddening. Analysis of four Hubble Space Telescope COS observations since 2010, including a Director’s Discretionary time observation during the occultation, shows that the broad-absorption-line velocity offset and the C iv emission-line width both decrease as the reddening increases. The occultation dynamical timescale, the BAL variability dynamical timescale, and the density of the BAL gas show that both the reddening material and the broad-absorption-line gas are consistent with an origin in the torus. These observations can be explained by a scenario in which the torus is clumpy with variable scale height, and the BAL gas is blown from the torus material like spray from the crest of a wave. As the obscuring material passes into our line of sight, we alternately see high-velocity broad absorption lines and a clear view to the central engine, or low-velocity broad absorption lines and strong reddening. WPVS 007 has a small black hole mass, and correspondingly short timescales, and so we may be observing behavior that is common in BALQSOs, but is not typically observable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH 4 ) to the atmosphere, they represent a large source of uncertainty in the global CH 4 budget due to the complex biogeochemical controls on CH 4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH 4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. In this work we used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH 4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH 4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH 4 emissions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Computational and empirical studies predict Mycobacterium tuberculosis -specific T cells as a biomarker for infection outcome

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2-year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis . We identified T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb -specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

On-Ramp Wireless: Program Final Report

The objective of the project was to develop and demonstrate a centrally managed, wide area wireless distribution grid sensor and faulted circuit indicator system capable of monitoring underground and other hard-to-reach distribution circuits while delivering the reliability, ease of installation, security, battery life and low cost structure required to be a commercially viable product for the electric utility industry. This integrated wireless grid monitoring system enables utilities across the United States to enhance the reliability of their distribution grids, improve performance metrics (i.e., SAIFI, CAIDI, SAIDI), support dynamic load management and predictive maintenance, and enable future remote monitoring applications necessary to achieve the goals of the Smart Grid. This project scope included: conceptual design; trade studies focused on maximizing ease of installation, sensing capabilities, power budget analysis to reduce maintenance costs and time to replace; sensor product development; and field technology demonstration. The trade studies included Radio Frequency (RF) propagation. The RF Trade Study was completed for the 2.4 GHz ISM-band and not extended to 1 GHz band due to availability of suitable radio solutions and associated impacts to overall commercial value of the solution. The technology demonstrations/field trials were performed in partnership with Southern California Edison and San Diego Gas and Electric. The sensing systems included fault indicator and wireless analog sensor devices from Schweitzer Engineering Labs (SEL) and smart transformer monitoring devices from Gridsense. Both SEL and Gridsense worked with ORW during this project to During the demonstration phase, sensors were deployed in multiple locations. Resulting data was analyzed for the following: RF performance, communication systems reliability, sensor system capabilities, and system level analytics relative to the commercial value to the smart grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗