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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Joint Topology Identification and State Estimation in Unobservable Distribution Grids

Many distribution system operations (e.g., state estimation, control, fault detection/localization) rely on the assumption that the underlying topology is accurately defined. In general, topology identification is a challenging problem in distribution systems as these systems are unobservable with a very limited number of available measurements. In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. Here, two reformulations of the original MINLP problems are investigated. Firstly, in order to remove the nonlinearity in the MINLP formulation, a mixed integer linear programming (MILP) problem that employs auxiliary variables is derived. Furthermore, to achieve a faster solution, convex relaxation of the original formulation is derived. Finally, using a Markovian model for topology changes, prior information about system topology is used to improve topology identification particularly when a limited amount of measurements is available. Simulation results on IEEE 37-bus test feeder and IEEE 123-bus test feeder illustrate the efficiency and scalability of the proposed approaches from both state estimation and topology identification point of view (even with 30% of available data).

42 ENGINEERING↗
DOE OSTI2020

Much stronger tundra methane emissions during autumn-freeze than spring-thaw

Warming in the Arctic has been more apparent in the non-growing season than in the typical growing season. In this context, methane (CH 4 ) emissions in the non-growing season, particularly in the shoulder seasons, account for a substantial proportion of the annual budget. However, CH 4 emissions in spring and autumn shoulders are often underestimated by land models and measurements due to limited data availability and unknown mechanisms. This study investigates CH 4 emissions during spring thaw and autumn freeze using eddy covariance CH 4 measurements from three Arctic sites with multi-year observations. We find that the shoulder seasons contribute to about a quarter (25.6±2.3%, mean ± standard deviation) of annual total CH 4 emissions. Our study highlights the three to four times higher contribution of autumn freeze CH 4 emission to total annual emission than that of spring thaw. Autumn freeze exhibits significantly higher CH 4 flux (0.88±0.03 mg m -2 h -1 ) than spring thaw (0.48±0.04 mg m -2 h -1 ). The mean duration of autumn freeze (58.94±26.39 days) is significantly longer than that of spring thaw (20.94±7.79 days), which predominates the much higher cumulative CH 4 emission during autumn freeze (1212.31±280.39 mg m -2 yr -1 ) than that during spring thaw (307.39±46.11 mg m -2 yr -1 ). Near-surface soil temperatures cannot completely reflect the freeze-thaw processes in deeper soil layers and appears to have a hysteresis effect on CH 4 emissions from early spring thaw to late autumn freeze. Therefore, it is necessary to consider commonalities and differences in CH 4 emissions during spring thaw versus autumn freeze to accurately estimate CH 4 source from tundra ecosystems for evaluating carbon-climate feedback in Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Tropical root responses to global changes: A synthesis

Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO 2 , and fires. Based on tree species- and community-level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO 2 by increasing root biomass in species-scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Influence of Pressure on Flame-Flow Characteristics of a Reacting Jet in Crossflow

This work experimentally investigates the effects of elevated combustor pressures on the characteristics of a lean premixed reacting methane/air jet injected into a lean vitiated crossflow using a 12.7 mm axial jet. Experiments were conducted in an axially staged combustor, which implements a reacting jet in crossflow (RJIC) configuration and operates over a pressure range of 1–5 atmospheres. Simultaneous CH* chemiluminescence and particle image velocimetry (PIV) are used to study the flow field and flame behavior. The results show that the reacting jet trajectory exhibits greater penetration with elevated pressure, which is a novel finding compared to available data in the literature. However, the flame liftoff point and ignition delay time both decreased with elevated pressure, which was attributed to decreased vorticity along the flame boundary which corresponds to increased Damköhler numbers (Da). Emissions measurements confirm the NOx increase with pressure as reported in the literature for single-stage gas turbine combustors. Concurrently, emission measurements for the staged configuration show the strong NOx benefit of the RJIC system: the data prove a reduction of global outlet emission levels at elevated pressure with the axially staged configuration. Here, the axial emission reduction was attributed to the decreasing liftoff at elevated pressure levels. Hence, the research emphasizes that the flame and emission characteristics are coupled; they are not only dependent on the geometric parameters and momentum flux ratios but are also a function of pressure.

03 NATURAL GAS↗
DOE OSTI2021

IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools

Hybrid manufacturing machine tools have the potential to be a disruptive technology as they can leverage the benefits of both additive and subtractive manufacturing by incorporating both processes on the same machine while limiting the downsides of the individual processes. Since these machines use two very disparate manufacturing processes and hybrid manufacturing is an emerging technology, it will be useful to monitor data coming from the machine and apply it to improve the manufacturing process, the operation of the machine, and to integrate the machine into the larger digital framework of Industrial Internet of Things (IoT). The present work discusses IoT devices that would be beneficial to add to a hybrid machine tool as well as applications for those devices. The proposed methods discussed in this work have not been experimentally implemented on a hybrid machine tool and so there are no performance data available yet. The hybrid machine tool used as a basis to generate these IoT applications is the Mazak VC-500A/5x AM Hot Wire Deposition, which is a 5-axis machine tool incorporated with a wire feedstock 4kW laser deposition system. Methodologies and applications will be outlined for machine health and process monitoring. Other areas covered include process benchmarking, secure networking options for the proposed IoT framework, and hybrid process improvement. Limitations of these methods and future work for new sensor devices and application areas is also discussed.

Thien, Austen↗
DOE OSTI2023

Rate of Para-Orthohydrogen Conversion in Cryogenic Vortex Tube

Raising efficiency of the cooling process for cryogenic hydrogen and minimizing hydrogen boil-off during storage and transportation of liquid hydrogen are critically important factors for widening implementation of hydrogen-based clean energy systems. One novel approach involves utilization of relatively simple catalyzed vortex tubes, where hydrogen in the primarily para- nuclear spin isomer form is converted into the ortho- form while consuming a significant amount of heat. To design such systems effectively, better understanding is required about catalyst-assisted para-orthohydrogen conversion rates in high-speed vortical flows. In this study, a computational fluid dynamics simulation has been set up to model an experimental system with cryogenic hydrogen vortex tubes. Mesh-verification and a validation study has been conducted first for a non-catalyzed tube. Then, the rate coefficient of the para-orthohydrogen conversion of cryogenic hydrogen has been determined by matching numerical results with experimental data available for a catalyzed vortex tube. Furthermore, the presented information can help design and optimize cryogenic hydrogen cooling within vortex tubes.

08 HYDROGEN↗
DOE OSTI2020

The dark matter interpretation of the 3.5-keV line is inconsistent with blank-sky observations

Observations of nearby galaxies and galaxy clusters have reported an unexpected x-ray emission line around 3.5 kilo–electron volts (keV). Proposals to explain this line include decaying dark matter—in particular, that the decay of sterile neutrinos with a mass around 7 keV could match the available data. If this interpretation is correct, the 3.5-keV line should also be emitted by dark matter in the halo of the Milky Way. We used more than 30 megaseconds of XMM-Newton (X-ray Multi-Mirror Mission) blank-sky observations to test this hypothesis, finding no evidence of the 3.5-keV line emission from the Milky Way halo. We set an upper limit on the decay rate of dark matter in this mass range, which is inconsistent with the possibility that the 3.5-keV line originates from dark matter decay.

Dessert, Christopher↗
DOE OSTI2021

Challenges and opportunities in verification and validation of low temperature plasma simulations and experiments

This paper describes the verification and validation (V&V) framework developed for the stochastic Particle-in-Cell, Direct Simulation Monte Carlo code Aleph. An ideal framework for V&V from the viewpoint of the authors is described where a physics problem is defined, and relevant physics models and parameters to the defined problem are assessed and captured in a Phenomena Identification and Ranking Table (PIRT). Furthermore, numerous V&V examples guided by the PIRT for a simple gas discharge are shown to demonstrate the V&V process applied to a real-world simulation tool with the overall goal to demonstrably increase the confidence in the results for the simulation tool and its predictive capability. Although many examples are provided here to demonstrate elements of the framework, the primary goal of this work is to introduce this framework and not to provide a fully complete implementation, which would be a much longer document. Comparisons and contrasts are made to more usual approaches to V&V, and techniques new to the low-temperature plasma community are introduced. Specific challenges relating to the sufficiency of available data (e.g., cross sections), the limits of ad hoc validation approaches, the additional difficulty of utilizing a stochastic simulation tool, and the extreme cost of formal validation are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Competing ionization and dissociation in the H 2 gerade system

A numerically solvable two-dimensional (2D) model, employed by the authors to study the dissociative recombination of H$^+_2$ in the ungerade symmetry, is extended to describe the collision process in the gerade symmetry of H$_2$. In this symmetry, the ionization and dissociation processes are driven primarily by the direct, curve-crossing mechanism. The model is represented by a set of three coupled electronic channels in 2D, in the space of $\textit{s, p, d}$ partial waves of the colliding electron. In this work, we demonstrate that the Born-Oppenheimer properties of the H$_2$ molecule in the relevant range of internuclear distances can be described by such a model. The molecular rotational degrees of freedom are accounted for by the rotational frame transformation. The numerical solution of the model is discussed, and the resulting rovibrationally inelastic and dissociative recombination cross sections are compared with the available data.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Algorithm 1049: The Delaunay Density Diagnostic

Accurate approximation of a real-valued function depends on two aspects of the available data: the density of inputs within the domain of interest and the variation of the outputs over that domain. There are few methods for assessing whether the density of inputs is sufficient to identify the relevant variations in outputs—i.e., the “geometric scale” of the function—despite the fact that sampling density is closely tied to the success or failure of an approximation method. In this article, we introduce a general purpose, computational approach to detecting the geometric scale of real-valued functions over a fixed domain using a deterministic interpolation technique from computational geometry. The algorithm is intended to work on scalar data in moderate dimensions (2–10). Our algorithm is based on the observation that a sequence of piecewise linear interpolants will converge to a continuous function at a quadratic rate (in L 2 norm) if and only if the data are sampled densely enough to distinguish the feature from noise (assuming sufficiently regular sampling). We present numerical experiments demonstrating how our method can identify feature scale, estimate uncertainty in feature scale, and assess the sampling density for fixed (i.e., static) datasets of input–output pairs. Finally, we include analytical results in support of our numerical findings and have released lightweight code that can be adapted for use in a variety of data science settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗
DOE OSTI2020

Assessing Stability of Transition Metal Nitrides in Aqueous Environments: The Case of Molybdenum, Iron, Vanadium and Nickel Nitride

Analysis of the MoN, Mo 2 N, VN, Fe 2 N, Fe 4 N, Ni 3 N stability in the aqueous media was performed based on the thermodynamic data available in HSC Chemistry software. Our analysis show that among all the studied nitrides, cubic Mo 2 N is the most resistant while cubic VN is the least resistant to oxidation and decomposition. The change in temperature up to 100 °C does not significantly influence the formation potentials of transition metal oxides from nitrides; however, pH significantly affects the stability of the studied transition metal nitrides. In the case of Mo 2 N, MoN, and VN changing pH from acidic to neutral and alkaline decreases the stability of the nitrides and the oxide formation potentials shift to more negative values. Additionally, based on thermodynamics, orthorhombic Fe 2 N should be stable only in alkaline conditions, while cubic Fe 4 N and hexagonal Ni 3 N should be the most stable at pH ~ 7 and pH ~ 9. Changing pH from these values decreases the stability of both Fe 4 N and Ni 3 N and the formation of oxy-, hydroxy- species becomes thermodynamically more favorable. Overall, the limited stability of the metal nitrides places severe constraints for their use in aqueous electrocatalytic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Prediction of electron density and pressure profile shapes on NSTX-U using neural networks

A new model for prediction of electron density and pressure profile shapes on NSTX and NSTX-U has been developed using neural networks. The model has been trained and tested on measured profiles from experimental discharges during the first operational campaign of NSTX-U. By projecting profiles onto empirically derived basis functions, the model is able to efficiently and accurately reproduce profile shapes. In order to project the performance of the model to upcoming NSTX-U operations, a large database of profiles from the operation of NSTX is used to test performance as a function of available data. The rapid execution time of the model is well suited to the planned applications, including optimization during scenario development activities, and real-time plasma control. A potential application of the model to real-time profile estimation is demonstrated.

Boyer, Mark↗
DOE OSTI2020

SISSO (Sure-independence-screening sparsifying-operator regressor)

Implementation of the SISSO regression algorithm in MATLAB. The SISSO regression algorithm iteratively selects model features from candidates, converging even when the number of possible features is much greater than the number of available data points. Includes a test script that validates the algorithm by replicating the results using data and procedure from https://analytics-toolkit.nomad-coe.eu/hub/user-redirect/notebooks/tutorials/compressed_sensing.ipynb. Python code from the SISSO regressor in 'sisso.py', from the link above, was used as the basis for developing the MATLAB implementation. The SISSO regression algorithm is detailed by the original authors in R. Ouyang, S. Curtarolo, E. Ahmetcik et al., Phys. Rev. Mater. 2, 083802 (2018), R. Ouyang, E. Ahmetcik, C. Carbogno, M. Scheffler, and L. M. Ghiringhelli, J. Phys.: Mater. 2, 024002 (2019).

Gasper, Paul↗
DOE OSTI2021

LCOC (Levelized Cost of Light-Duty EV Charging Calculator)

This code was developed to calculate the levelized cost of light-duty electric vehicle (EV) charging (LCOC) for the paper "Levelized Cost of Charging Electric Vehicles in the United States" by Borlaug et al.(https://doi.org/10.1016/j.joule.2020.05.013). The calculation takes inputs from a variety of publicly available data sources (e.g., utility tariffs from the Utility Rate Database; EV charging station locations from the Alternative Fuels Data Center; energy price forecasts from the Energy Information Administration) and produces the current LCOC, considering when, where, and how light-duty EVs are recharged. In addition, the lifetime fuel cost savings (LFCS) calculation estimates the discounted fuel cost savings for a new battery EV (BEV) or plug-in hybrid EV (PHEV) over a 15-year time horizon using state-level gasoline price and LCOC projections.

Borlaug, Brennan↗
DOE OSTI2023

A python package for analyzing Resilience of Complex Systems (pyRoCS) v.0.0

SAND2024-01040O PyRoCS software synthesizes mathematical equations from several domains—including information theory, ecology, and engineering sciences—to support resilience analysis for complex systems. Resilience is the ability of the complex system being analyzed to withstand, operate through, and recover from a disruption. The complex system can be a physical system such as an electric grid, an organization such as a company, or even a subfunction of an organization. Existing mathematical equations for resilience analysis are found within multiple domains including information theory, biological sciences, and complex systems. This package synthesizes and refactors equations from these various domains to make them more generalizable for application across different types of complex systems relevant for resilience analysis. Users will be able to apply these equations to characterize different components of complex systems based on available data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Verzi, Stephen↗
DOE OSTI2022

Name Normalizer

Our name normalizer and enrichment micro-service address the shortcomings of standard text normalization. Initially the text input is split into tokens. This process takes into account some conventions of formatting. Any dashes present between two tokens preserves the relationship of those two tokens. Conversely a comma between two tokens ensures that the separation between the two tokens is maintained. The order of tokens is also preserved. Standard text normalization (conversion to lowercase, trimming extra white space, and canonicalization) is then applied to the tokens. Once normalized every unique and sequence of tokens is given confidence values by comparing the normalized values to publicly available data.

Bleeker, Amelia↗
DOE OSTI2024

Firm Synthesizer and Supply-chain Simulator (SynthFirm) v1.0

SynthFirm is a large-scale agent-based freight demand model which generates a complete synthetic population of firms in the U.S. and the business-to-business commodity flows between them. Using publicly available data sources as inputs, SynthFirm simulates detailed firm and fleet characteristics, commodity production and consumption, formation of supply chains, and selection of shipping modes, all of which are essential drivers of commodity flow at a disaggregate level.

Xu, Xiaodan↗