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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Modeling stratified flames with and without shear using multiple mapping conditioning

A stochastic sparse particle approach is coupled with an artificial thickening flame (ATF) model for large eddy simulations (LES) to predict a series of turbulent premixed-stratified flames with and without shear and stratification. The thickened reaction progress variable serves as reference variable for the multiple mapping conditioning (MMC) mixing model which emulates turbulent mixing of the stochastic particles. The key feature of MMC is to enforce localness in this reference space when particle pairs are mixed and prevents unphysical mixing of burnt and unburnt fluid across the flame front. We apply MMC-ATF to three flames of a series of turbulent stratified flames and validate the method by comparison with experimental data. The new measurements feature increased accuracy in comparison to previously published data of the same flames due to a better signal-to-noise ratio and a setup which is less prone to beam steering. All flame locations are well predicted by the LES-ATF approach and an analysis of the MMC particle statistics demonstrates that MMC preserves the flamelet-like behaviour in regions where the experiments show low scatter around the flamelet solution. Predicted (local) deviations from the flamelet-solution are comparable to deviations observed in the measurements and variations in the flame structure due to differences in stratification and shear are reasonably well captured by the method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

A Model Predictive Control Based Generator Start-Up Optimization Strategy for Restoration With Microgrids as Black-Start Resources

Microgrids (MGs) can operate in an islanded mode and serve as black-start resources for power system restoration (PSR). Here, a model predictive control (MPC) based generator start-up optimization strategy for PSR is proposed utilizing MGs as black-start resources. First, the generator start-up sequence (GSUS) optimization is formulated as a mixed integer linear programming. Then, the uncertainties of MG black-start resources (MBSRs) are modeled by discretizing the probability distribution of the forecast errors, and representative scenarios for MBSRs extracted by formulating the probability mass transportation problem. Third, the generator start-up optimization strategy considering MBSRs is proposed utilizing the MPC technique, in which the optimization objective is to maximize the energy capability of the power systems and minimize the load curtailment of the MGs in each looking-ahead interval. Simulations on the IEEE 118 bus system with MGs and Zhejiang provincial power system in China verify that the introduced strategy for PSR can successfully restore the power system and effectively determine the optimal GSUS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Assessing Adaptive Mesh Refinement (AMR) in a Forced Shallow-Water Model with Moisture

Two forced shallow-water flow scenarios are explored in a 2D fourth-order finite-volume dynamical core with adaptive mesh refinement (AMR) to investigate AMR’s ability to track and resolve complex evolving features. Traditional shallow-water test cases are mainly characterized by large-scale smooth flows that do not effectively test the multiscale abilities of variable-resolution and AMR models to resolve sharp gradients and small-scale flow filaments. Therefore, adding forcing mechanisms to the shallow-water system to model key atmospheric processes adds complexity and creates small-scale phenomena. These can serve as foci for dynamic grid refinement while remaining simple enough to study the numerical design of a model’s dynamical core. The first shallow-water flow scenario represents a strengthening, tropical cyclone–like, vortex that is driven by a Betts–Miller-like convection scheme. The second shallow-water test is built upon a barotropically unstable jet with an added Kessler-like warm rain scheme that leads to precipitating frontal zones. The key feature of both tests is that there is significant sensitivity to the model grid while converging (structurally) at high resolution. Both test cases are investigated for a series of uniform resolutions and a variety of AMR tagging criteria. The AMR simulations demonstrate that grid refinement can resolve local features without requiring global high-resolution meshes. However, the results are sensitive to the refinement criteria. Criteria that trigger refinement early in a simulation reproduce the uniform-resolution reference solutions most reliably. Finally, in contrast, AMR criteria that delay refinement for several days require careful tuning of the AMR thresholds to improve results compared with uniform-resolution simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A2e High Fidelity Modeling: Strategic Planning Meetings

Atmosphere to electrons (A2e) is a multi-year U.S. Department of Energy (DOE) research initiative targeting significant reductions in the cost of wind energy through an improved understanding of the complex physics governing wind flow into and through whole wind farms. Better insight into the flow physics of large multi-turbine arrays will address the plant-level energy losses, is likely to reduce annual operational costs by hundreds of millions of dollars, and will improve project financing terms to more closely resemble traditional capital projects. In support of this initiative, two planning meetings were convened, bringing together professionals from universities, national laboratories, and industry to discuss wind plant modeling challenges, requirements, best practices, and priorities. This report documents the combined work of the two meetings and serves as a key part of the foundation for the A2e/HFM effort for predictive modeling of whole wind plant physics.

17 WIND ENERGY↗
DOE OSTI2016

Modeling and Depletion Simulations for a High Flux Isotope Reactor Cycle with a Representative Experiment Loading

The purpose of this report is to document a high-fidelity VESTA/MCNP High Flux Isotope Reactor (HFIR) core model that features a new, representative experiment loading. This model, which represents the current, high-enriched uranium fuel core, will serve as a reference for low-enriched uranium conversion studies, safety-basis calculations, and other research activities. A new experiment loading model was developed to better represent current, typical experiment loadings, in comparison to the experiment loading included in the model for Cycle 400 (operated in 2004). The new experiment loading model for the flux trap target region includes full length 252 Cf production targets, 75 Se production capsules, 63 Ni production capsules, a 188 W production capsule, and various materials irradiation targets. Fully loaded 238 Pu production targets are modeled in eleven vertical experiment facilities located in the beryllium reflector. Other changes compared to the Cycle 400 model are the high-fidelity modeling of the fuel element side plates and the material composition of the control elements. Results obtained from the depletion simulations with the new model are presented, with a focus on time-dependent isotopic composition of irradiated fuel and single cycle isotope production metrics.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Explaining Multimodal Deceptive News Prediction Models

Social media plays a valuable role in rapid news dissemination, but it also serves as a vehicle to propagate unverified information. For example, news shared on Facebook or Twitter may actually contain disinformation, propaganda, hoaxes, conspiracies, clickbait or satire. This paper presents an in-depth analysis of the behavior of suspicious news classification models including error analysis and prediction confidence. We consider five deep learning architectures that leverage combinations of text, linguistic and image input signals from tweets. The behavior of these models is analyzed across four suspicious news prediction tasks. Our findings include that models leveraging only the text of tweets outperform those leveraging only the image (by 3-13% absolute in F-measure), and that models that combine image and text signals with linguistic cues e.g., biased and subjective language markers can, but do not always, perform even better. Finally, our main contribution is a series of analyses, in which we characterize text and image traits of our classes of suspicious news and analyze patterns of errors made by the various models to inform the design of future deceptive news prediction models.

deep learning, social media, deception↗
DOE OSTI2018

Interface mechanics of carbon fibers with radially-grown carbon nanotubes

An atomistic modeling framework to investigate the interface/interphase of composite architecture with carbon fibers containing radially-grown carbon nanotubes (often called fuzzy fibers) is detailed in this paper. A polymeric functional coating for the carbon fiber surface, which also serves as a substrate for the CNT growth, is explicitly modeled. The tensile and transverse moduli of the fuzzy fiber/epoxy interphase is computed from virtual deformation simulations and compared to experimental values reported in literature, in order to validate the nanoscale model. Furthermore, the effect of the polymer substrate is studied by modeling the local interphase mechanics. Various modes of virtual loading provide the cohesive behavior of the local substrate/epoxy interphase. Conclusions are presented by comparing the material response of the interphase with and without the polymeric substrate. In this study, the integration of results from the nanoscale to an atomistically-informed subcell-based continuum level model is also demonstrated.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

The impact of simultaneous inoculation of Pseudomonas aeruginosa, Staphylococcus aureus , and Candida albicans on rodent burn wounds

Burn wound infection often involves a diverse combination of bacterial and fungal pathogens. In this study, we characterize the mixed species burn wound infection by inoculating the burn surface with 1 × 10 3/4/5 CFU of Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans in a 1:1:1 ratio. Using the revised Walker–Mason scald burn rat model, 168 male Sprague-Dawley rats (350–450 g) subject to ~10% TBSA burn injury, with or without inoculation, were evaluated for 11 days after burn. In the wound, P. aeruginosa and S. aureus formed robust biofilms as determined by the bacterial tissue load, ~1 × 10 9 CFU/g, and expression of key biofilm genes. Interestingly, within 3 days C. albicans achieved tissue loads of ~1 × 10 6 CFU/g, but its numbers were significantly reduced beyond the limit of detection in the burn wound by day 7 in partial-thickness injuries and by day 11 in full-thickness injuries. The pathogenic biofilms contributed to burn depth progression, increased release of HMGB-1 into circulation from injured tissue, and significantly elevated the numbers of circulating innate immune cells (Neutrophils, Monocytes, and Basophils). This robust model of multi-species burn wound infection will serve as the basis for the development of new antimicrobials for combating biofilm-based wound infections.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantifying uncertainties due to irreducible three-body forces in deuteron-nucleus reactions

Deuteron-induced nuclear reactions are an essential tool for probing the structure of nuclei as well as astrophysical information such as (n, γ) cross sections. The deuteron-nucleus system is typically described within a Faddeev three-body model consisting of a neutron (n), a proton (p), and the target nucleus (A) interacting through pairwise phenomenological potentials. While Faddeev techniques enable the exact description of the three-body dynamics, their predictive power is limited in part by the omission of irreducible neutron-proton-nucleus three-body force (n–p–A 3BF). Here, our goal is to quantify systematic uncertainties stemming from the reduction of deuteron-nucleus (d + A) dynamics to a picture of three pointlike nuclear clusters interacting via pairwise nucleon-nucleus forces, using as testing grounds d + α scattering and the 6 Li ground state. We particularly focus on quantifying uncertainties arising from the full antisymmetrization of the (A + 2)-body system with the target nucleus fixed in its ground state. We adopt the ab initio no-core shell model coupled with the resonating group method (NCSM/RGM) to compute microscopic n–α and p–α interactions, and use them in a three-body description of the d + α system by means of momentum-space Faddeev-type equations. Simultaneously, we also carry out ab initio calculations of d + α scattering and 6 Li ground state by means of six-body NCSM/RGM calculations to serve as a benchmark for the three-body model predictions given by the Faddeev calculations. By comparing the Faddeev and NCSM/RGM results, we show that the irreducible n–p–α 3BF has a non-negligible effect on bound state and scattering observables alike. Specifically, the Faddeev approach yields a 6 Li ground state that is approximately 600 keV shallower than the one obtained with the NCSM/RGM. Additionally, the Faddeev calculations for d + α scattering yield a 3 + resonance that is located approximately 400 keV higher in energy compared to the NCSM/RGM result. The shape of the d + α angular distributions computed using the two approaches also differ, owing to the discrepancy in the predictions of the 3 + resonance energy. The Faddeev three-body model predictions for d + α scattering and 6 Li using microscopic n–α and p–α potentials differ from those computed microscopically with the NCSM/RGM. These discrepancies are due to the n–p–α 3BF, which arises from two-nucleon exchange terms in the microscopic d–α interaction and are not accounted for in the three-body model Faddeev calculations. This study lays the foundation for future parametrizations of the 3BF due to Pauli exclusion principle effects in improved three-body calculations of deuteron-induced reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

High-accuracy extrapolated ab initio thermochemistry. IV. A modified recipe for computational efficiency

A number of economical modifications to the high-accuracy extrapolated ab initio thermochemistry (HEAT) model chemistry are evaluated. The two resulting schemes, designated as mHEAT and mHEAT+, are designed for efficient and pragmatic evaluation of molecular energies in systems somewhat larger than can be practically studied by the unapproximated HEAT scheme. It is found that mHEAT+ produces heats of formation with nearly subchemical ± 1 kJ/mol) accuracy at a substantially reduced cost relative to the full scheme. Total atomization energies calculated using the new thermochemical recipes are compared to the results of the HEAT-345(Q) model chemistry, and enthalpies of formation for the three protocols are also compared to Active Thermochemical Tables. Finally, a small selection of transition states is studied using mHEAT and mHEAT+, which illuminates some interesting features of reaction barriers and serves as an initial benchmark of the performance of these model chemistries for chemical kinetics applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Chapter 1.12: Solar Radiation Resource Assessment for Renewable Energy Conversion

This chapter addresses measurements, modeling, and databases of solar energy potential that may serve as fuel for solar energy conversion systems. Developing innovative designs for capturing and converting solar radiation is only one part of the equation for solar system deployment. Identifying, locating, and prospecting for the appropriate quantity and quality of solar resources to fuel these systems is critical to system designers, investors, financial backers, utilities, governments, and owner/operators. This chapter addresses the fundamentals and state of the art for measuring, modeling, and applying solar radiation resource data to meet decision-making needs.

14 SOLAR ENERGY↗
DOE OSTI2016

Thermal equation of state of hcp-iron: Constraint on the density deficit of Earth's solid inner core: THERMAL EQUATION OF STATE OF HCP-IRON

We conducted high-pressure experiments on hexagonal close packed iron (hcp-Fe) in MgO, NaCl, and Ne pressure-transmitting media and found general agreement among the experimental data at 300 K that yield the best fitted values of the bulk modulus K 0 = 172.7(±1.4) GPa and its pressure derivative K 0 '= 4.79(±0.05) for hcp-Fe, using the third-order Birch-Murnaghan equation of state. Using the derived thermal pressures for hcp-Fe up to 100 GPa and 1800 K and previous shockwave Hugoniot data, we developed a thermal equation of state of hcp-Fe. The thermal equation of state of hcp-Fe is further used to calculate the densities of iron along adiabatic geotherms to define the density deficit of the inner core, which serves as the basis for developing quantitative composition models of the Earth's inner core. We determine the density deficit at the inner core boundary to be 3.6%, assuming an inner core boundary temperature of 6000 K.

58 GEOSCIENCES↗
DOE OSTI2019

Network coding for improving throughput in WDM optical networks with dedicated protection

Network coding (NC) has been recently emerging as an excellent technique for leveraging network efficiency. In optical networks, NC and protection appear to be a good match and indeed, NC could be effectively exploited for greater protection efficiency thanks to the opportunity of combining signals rather than duplication in the traditional approach. Motivated by this observation, we investigate the application of a simple and practical NC technique based on XOR operation to improve the network throughput in survivable optical networks. To this end, we propose the NC-based protection whose speed recovery is on a par with the conventional 1+1 dedicated protection while the capacity usage could be more efficient, paving the way for improving throughput in constrained capacity conditions. A novel mixed integer linear programming model is developed for survivable optical networks design employing the proposed NC-based protection. The model attempts to optimally determine the demands to serve, the routing for served demands and NC assignment among demands aiming at maximizing the network throughput in the constraint of bandwidth limitation. Extensive numerical evaluations on realistic topologies are provided to highlight the advantages of our NC-based protection proposal compared with the conventional dedicated protection in improving the network throughput. Encouraging results are observed when the new protection proposal based on NC could accommodate up to 30% more traffic than the conventional case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description

On the NASA 2020 rover mission to Jezero crater, the remote determination of the texture, mineralogy and chemistry of rocks is essential to quickly and thoroughly characterize an area and to optimize the selection of samples for return to Earth. As part of the Perseverance payload, SuperCam is a suite of five techniques that provide critical and complementary observations via Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), visible and near-infrared spectroscopy (VISIR), high-resolution color imaging (RMI), and acoustic recording (MIC). SuperCam operates at remote distances, primarily 2–7 m, while providing data at sub-mm to mm scales. We report on SuperCam’s science objectives in the context of the Mars 2020 mission goals and ways the different techniques can address these questions. The instrument is made up of three separate subsystems: the Mast Unit is designed and built in France; the Body Unit is provided by the United States; the calibration target holder is contributed by Spain, and the targets themselves by the entire science team. This publication focuses on the design, development, and tests of the Mast Unit; companion papers describe the other units. The goal of this work is to provide an understanding of the technical choices made, the constraints that were imposed, and ultimately the validated performance of the flight model as it leaves Earth, and it will serve as the foundation for Mars operations and future processing of the data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

chatHPC: Empowering HPC users with large language models

The ever-growing number of pre-trained large language models (LLMs) across scientific domains presents a challenge for application developers. While these models offer vast potential, fine-tuning them with custom data, aligning them for specific tasks, and evaluating their performance remain crucial steps for effective utilization. However, applying these techniques to models with tens of billions of parameters can take days or even weeks on modern workstations, making the cumulative cost of model comparison and evaluation a significant barrier to LLM-based application development. To address this challenge, we introduce an end-to-end pipeline specifically designed for building conversational and programmable AI agents on high performance computing (HPC) platforms. Our comprehensive pipeline encompasses: model pre-training, fine-tuning, web and API service deployment, along with crucial evaluations for lexical coherence, semantic accuracy, hallucination detection, and privacy considerations. Here, we demonstrate our pipeline through the development of chatHPC, a chatbot for HPC question answering and script generation. Leveraging our scalable pipeline, we achieve end-to-end LLM alignment in under an hour on the Frontier supercomputer. We propose a novel self-improved, self-instruction method for instruction set generation, investigate scaling and fine-tuning strategies, and conduct a systematic evaluation of model performance. The established practices within chatHPC will serve as a valuable guidance for future LLM-based application development on HPC platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Downregulation of miR-497 promotes tumor growth and angiogenesis by targeting HDGF in non-small cell lung cancer

Highlights: •MiR-497 is down-regulated in NSCLC cells and tissues. •MiR-497 inhibits NSCLC cell growth in vitro. •HDGF is a target gene of miR-497. •MiR-497 inhibits NSCLC cell growth by downregulating HDGF. •miR-497 inhibits tumor growth and angiogenesis in vivo. -- Abstract: MicroRNAs (miRNAs) play important roles in the development of various cancers. MiRNA-497 functions as a tumor-suppressor that is downregulated in several malignancies; however, its role in non-small cell lung cancer (NSCLC) has not been examined in detail. Here, we showed that miR-497 is downregulated in NSCLC tumors and cell lines and its ectopic expression significantly inhibits cell proliferation and colony formation. Integrated analysis identified HDGF as a downstream target of miR-497, and the downregulation of HDGF by miR-497 overexpression confirmed their association. Rescue experiments showed that the inhibitory effect of miR-497 on cell proliferation and colony formation is predominantly mediated by the modulation of HDGF levels. Furthermore, tumor samples from NSCLC patients showed an inverse relationship between miR-497 and HDGF levels, and ectopic expression of miR-497 significantly inhibited tumor growth and angiogenesis in a SCID mouse xenograft model. Our results suggest that miR-497 may serve as a biomarker in NSCLC, and the modulation of its activity may represent a novel therapeutic strategy for the treatment of NSCLC patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Derivation of at-birth neutron energy distribution of americium-beryllium source based on ISO8529 spectral data

In many practical applications, the accuracy of neutron transport calculations depends on an estimate of the energy distribution provided in the source definition. While the energy distribution of neutrons emitted by spontaneous fission sources is well-defined, the spectra of neutrons produced in (α,n) reactions in compound mixtures, such as americium-beryllium (AmBe), depend on the microstructural properties of the source material, which may vary. Although the energy of neutrons produced in nuclear reactions can be calculated from first principles, such computations require detailed knowledge of the key characteristics of the source material, which are often unknown. Here, this study focuses on deriving the at-birth neutron energy distribution for an AmBe source by applying reverse transport calculations to the externally measured high-precision spectrum recommended in the latest revision of the international standard. The derived at-birth spectrum was validated through indirect energy-sensitive neutron emission rate measurements of AmBe sources calibrated at national primary metrology institutions. The resulting at-birth neutron energy distribution serves as essential input data for radiation transport modeling, providing a validated spectrum for broader experimental and computational applications.

AmBe↗