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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 OSTI2025

Structural inversion asymmetry in epitaxial ultrathin films of Bi(111)/InSb(111)B

Bismuth (Bi) films hold potential for spintronic devices due to strong spin-orbit coupling. Understanding the growth, surface states, and interactions with the substrate is key to their functionalization. Large-area high-quality (111) Bi ultrathin films were grown on InSb (111)B substrates by molecular beam epitaxy (MBE). Strong film-substrate interactions epitaxially stabilize the (111) orientation and lead to nonequivalent interface potentials. Analysis of angle-resolved photoemission spectroscopy (ARPES) measurements, employed to characterize the evolution of the surface states with film thickness, indicate a crossing at the $\overline{M}$ point, suggesting a topologically trivial phase in the thin film. In conclusion, the results show the presence of interfacial bonds to the substrate breaks inversion symmetry, preventing the semimetal-to-semiconductor transition predicted for freestanding bismuth layers, highlighting the importance of controlled functionalization and surface passivation of two-dimensional materials.

Inbar, Hadass S. [Univ. of California, Santa Barba↗
DOE OSTI2022

Evaluation of PV Module Packaging Strategies of Monofacial and Bifacial PERC Using Degradation Pathway Network Modeling

As the PV industry is rapidly expanding, it is important to thoroughly investigate the long-term impact of packaging strategies on the performance of PV modules. In this study, the variants in sets differ on the basis of manufacturer (A/B), encapsulant (EVA/POE), rear encapsulant (UV-cutoff/opaque/transparent), module architecture (GB/DG) and cell type (monofacial/bifacial). The minimodules were exposed for 2520 hours in modified damp heat, with or without full spectrum light. Every 504 hours, stepwise electrical characterization techniques were employed to track changes in minimodules. Degradation pathway modeling using network structural equation modeling was employed to study pairwise relationships between variables and service lifetime prediction in minimodules. Through this study, differences in quality control are identified in minimodules made by different manufacturers. Minimodules with UV-cutoff rear encapsulant show relatively better stability, whereas the ones with opaque rear encapsulant show greater power loss. In addition, GB having UV-cutoff rear encapsulation and GB with POE having opaque rear encapsulation were identified to be stable as they lack a best model fit. Here, the primary power loss mechanism in degrading variants is interconnect corrosion.

14 SOLAR ENERGY↗
DOE OSTI2023

Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests

Abstract The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south‐eastern Australia. FMC was estimated using a process‐based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (−12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (−18.5%). In contrast, the largest climate effect on FMC was −6.6% across 48‐years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.

Brown, Tegan P.↗
DOE OSTI2023

Functional soft materials from blue phase liquid crystals

Blue phase (BP) liquid crystals are chiral fluids wherein millions of molecules self-assemble into cubic lattices that are on the order of hundred nanometers. As the unit cell sizes of BPs are comparable to the wavelength of light, they exhibit selective Bragg reflections in the visible. The exploitation of the photonic properties of BPs for technological applications is made possible through photopolymerization, a process that renders mechanical robustness and thermal stability. We review here the preparation and characterization of stimuli-responsive, polymeric photonic crystals based on BPs. We highlight recent studies that demonstrate the promise that polymerized BP photonic crystals hold for colorimetric sensing and dynamic light control. We review using Landau–de Gennes simulations for predicting the self-assembly of BPs and the potential for using theory to guide experimental design. Finally, opportunities for using BPs to synthesize new soft materials, such as highly structured polymer meshes, are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Transcriptional Control of hgcAB by an ArsR-Like Regulator in Pseudodesulfovibrio mercurii ND132

The hgcAB gene pair encodes mercury (Hg) methylation capability in a diverse group of microorganisms, but its evolution and transcriptional regulation remain unknown. Working from the possibility that the evolutionary function of HgcAB may not be Hg methylation, we test a possible link to arsenic resistance. Using model Hg methylator Pseudodesulfovibrio mercurii ND132, we evaluated transcriptional control of hgcAB by a putative ArsR encoded upstream and cotranscribed with hgcAB. This regulator shares homology with ArsR repressors of arsenic resistance and S-adenosylhomocysteine (SAH)- responsive regulators of methionine biosynthesis but is distinct from other ArsR/SahR proteins in P. mercurii. Using quantitative PCR (qPCR) and RNA sequencing (RNA-seq) transcriptome analyses, we confirmed this ArsR regulates hgcAB transcription and is responsive to arsenic and SAH. Additionally, RNA-seq indicated a possible link between hgcAB activity and arsenic transformations, with significant upregulation of other ArsRregulated arsenic resistance operons alongside hgcAB. Interestingly, wild-type ND132 was less sensitive to As(V) (but not As(III)) than an hgcAB knockout strain, supporting the idea that hgcAB may be linked to arsenic resistance. Arsenic significantly impacted rates of Hg methylation by ND132; however, responses varied with culture conditions. Differences in growth and metabolic activity did not account for arsenic impacts on methylation. While arsenic significantly increased hgcAB expression, hgcAB gene and transcript abundance was not a good predictor of Hg methylation rates. Taken together, these results support the idea that Hg and As cycling are linked in P. mercurii ND132. Our results may hold clues to the evolution of hgcAB and the controls on Hg methylation in nature. This work reveals a link between microbial mercury methylation and arsenic resistance and may hold clues to the evolution of mercury methylation genes (hgcAB). Microbes with hgcAB produce methylmercury, a strong neurotoxin that readily accumulates in the food web. This study addresses a critical gap in our understanding about the environmental factors that control hgcAB expression. We show that hgcAB expression is controlled by an ArsR-like regulator responsive to both arsenic and S-adenosylhomocysteine in our model organism, Pseudodesulfovibrio mercurii ND132. Exposure to arsenic also significantly impacted Pseudodesulfovibrio mercurii ND132 mercury methylation rates. However, expression of hgcAB was not always a good predictor of Hg methylation rates, highlighting the roles of Hg bioavailability and other biochemical mechanisms in methylmercury production. This study improves our understanding of the controls on hgcAB expression, which is needed to better predict environmental methylmercury production

RNA-seq↗
DOE OSTI2022

A Review of Bayesian Networks for Spatial Data

We report Bayesian networks are a popular class of multivariate probabilistic models as they allow for the translation of prior beliefs about conditional dependencies between variables to be easily encoded into their model structure. Due to their widespread usage, they are often applied to spatial data for inferring properties of the systems under study and also generating predictions for how these systems may behave in the future. We review published research on methodologies for representing spatial data with Bayesian networks and also summarize the application areas for which Bayesian networks are employed in the modeling of spatial data. We find that a wide variety of perspectives are taken, including a GIS-centric focus on efficiently generating geospatial predictions, a statistical focus on rigorously constructing graphical models controlling for spatial correlation, as well as a range of problem-specific heuristics for mitigating the effects of spatial correlation and dependency arising in spatial data analysis. Special attention is also paid to potential future directions for integration of Bayesian networks with spatial processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An approach for the measurement of the bulk temperature of single crystal diamond using an X-ray Free Electron Laser

We present a method to determine the bulk temperature of a single crystal diamond sample at an X-Ray Free Electron Laser using inelastic X-ray scattering. The experiment was performed at the High Energy Density (HED) instrument at the European XFEL GmbH, Germany. The technique, based on inelastic X-ray scattering and the principle of detailed balance, was demonstrated to give accurate temperature measurements, within 8% for both room temperature diamond and heated diamond to 500 K. Here, the temperature was increased in a controlled way using a resistive heater to test theoretical predictions of the scaling of the signal with temperature. The method was tested by validating the energy of the phonon modes with previous measurements made at room temperature using inelastic X-ray scattering and neutron scattering techniques. This technique could be used to determine the bulk temperature in transient systems with a temporal resolution of 50 fs and for which accurate measurements of thermodynamic properties are vital to build accurate equation of state models.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Classification of Ultrasonic Weld Quality using Acoustic Signatures Acquired During Manufacture

Ultrasonic welding is a process is based on generating a solid-state bond between two metals by applying moderate pressure and high intensity sound waves (20-70 kHz frequencies) at their interface. During the solid-state process numerous material, surface, instrument, and environmental factors contribute to bond formation, strength, and durability. The inherent difficulty of measuring and controlling each of these factors has, to date, made predicting bond quality elusive. In this work, a Sonics model MWB20 ultrasonic spot welder with integral base, was used to produce a variety of welds of different metal foils under varying weld conditions. Acoustic measurements were recorded throughout the measurement process. Subsequent analysis of the signals using metrics that approximate the energy dispersed during the weld proved to successfully predict weld quality. Two metrics based on normalized energy differential and Renyi entropy were developed into Python and C++ scripts for direct analysis of weld acoustic data.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Investigations into Balancing Peak-to-Average Power Ratio and Mean Power Extraction for a Two-Body Point-Absorber Wave Energy Converter

The power harnessed by wave energy converters (WECs) in oceans is highly variable and, therefore, has a high peak-to-average power (PTAP) ratio. To minimize the cost of a WEC power take off (PTO) system, it is desirable to reduce the PTAP ratio while maximizing the mean power extracted by WECs. The important issue of how PTAP ratio reduction measures (such as adding an inertia element) can affect the mean power extracted in a reference model has not been thoroughly addressed in the literature. To investigate this correlation, this study focuses on the integration of the U.S. Department of Energy’s Reference Model 3, a two-body point absorber, with a slider-crank WEC for linear-to-rotational conversion. In the first phase of this study, a full-scale numerical model was developed that predicts how PTO system parameters, along with an advanced control algorithm, can potentially affect the proposed WEC’s PTAP ratio as well as the mean power extracted. In the second phase, an appropriate scaled-down model was developed, and extracted power results were successfully validated against the full-scale model. Finally, numerical and hardware-in-the-loop (HIL) simulations based on the scaled-down model were designed and conducted to optimize or make trade-offs between the operational performance and PTAP ratio. The initial results with numerical and HIL simulations reveal that gear ratio, crank radius, and generator parameters substantially impact the PTAP ratio and mean power extracted.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Summary of the IAEA technical meeting on plasma disruptions and their mitigation

Here we summarize the contributions presented at the IAEA technical meeting on plasma disruptions and their mitigation, held virtually, 20–23 July 2020. The meeting brought together more than 120 experts from nuclear fusion research sites worldwide to discuss experimental, theoretical and modelling work in the field of plasma disruptions with special emphasis on developing a solid basis for possible disruption mitigation strategies in ITER and next generation fusion devices. The main topics of the meeting were: (i) disruption consequences, including electromagnetic loads, heat loads, and runaway electrons; (ii) disruption prediction and avoidance, including machine learning and physics-based approaches, and control aspects; and (iii) disruption mitigation, including shattered pellet injection, alternative techniques and general aspects of disruption mitigation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Design, Optimization, and Control of Floating Offshore Wind Farms for Optimal Energy Production (Final report)

The uncertainty and irregularity of ocean waves and the ocean environment is a major factor in the development of commercial scale floating wind turbines as the operation of floating structures in such an environment can lead to irregular and unpredictable loading, fatigue, and ultimately a reduction in the operational life of the turbine system which affects energy production over the lifetime of the turbine. Control solutions that can limit float motions and mitigate stressful events on the structure become essential for extending lifetime and limiting the operational uncertainty of a floating wind turbine. Digital twins are computational replicas of physical systems that operate in parallel with the operation of the physical system. Given advanced knowledge of a systems input, digital twins have the ability to predict the behavior of a system in advance, which can be valuable in the control of that system. In this project, we developed and assessed potential digital twin models developed in house and openly available (OpenFast) for use in the real time control of the six degree of freedom response motions of a floating wind turbine in ocean waves. Coupling these models with near-field real time irregular sea surface (wve) measurement/sensings and prediction models, we used the digital twin to predict how the floating turbine will respond to the incoming waves. Applying this information to a motion control system of the float, one can limit and control float motions to prevent undesirable loading events/large angular motions, thus increasing system life and ultimately contributing to optimizing energy production. Due to the computational intensity of operating a digital twin in real time, we investigated the use of artificial intelligence techniques to speed-up the processes of the digital twin, as well as the wave reconstruction/prediction models. Model tank testing at the University of Rhode Island and University of Maine both validated and demonstrated the developed techniques on simple float geometries and a scale model of the NREL 15 MW reference turbine.

17 WIND ENERGY↗
DOE OSTI2022

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.

Fan, Ming↗
DOE OSTI2022

Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents performance evaluation of a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of sub-transmission and distribution networks, the DERMS software controller, and 84 power hardware solar photovoltaic (PV) inverters, standard communication protocols, and a capacitor bank controller. The DERMS algorithm is also called, Grid-Optimization of Solar (GO-Solar) platform which includes predictive state estimation (PSE) and online multiple objective optimization (OMOO) to dispatch the legacy devices and distributed energy resources (e.g., PV). The voltage regulation performance is evaluated under three scenarios, volt-var smart inverter (baseline), and DERMS control for 100% and 30% of PV. The results show that controlling 30% of PV systems with the GO-Solar platform may provide the best balance of control performance and implementation cost.

distributed energy resource management system (DER↗
DOE OSTI2026

Microscopic Dynamics Controls Coupling and Cluster Formation in Brush Particle Solids

Thermodynamics-based models predict the structure of polymer-grafted nanoparticles (PGNs) as well as their assembly behavior based on geometric parameters such as particle size, degree of polymerization, and density of grafted chains. The role of microscopic polymer dynamics, such as the mobility of repeat units in the melt state, in the evolution of the structure and properties remains unknown. Brillouin light spectroscopy (BLS), due to its capability to concurrently discern the local and global elastic properties of PGN assemblies, enables the probing of microscopic processes, such as brush interdigitation, sensitive to the annealing of the assembly. For poly(methyl methacrylate) (PMMA)-grafted silica (SiO 2 ) PGNs in the dry powder state and annealed above the glass transition temperature, BLS revealed fully reversible local elasticity, indicative of limited interdigitation between adjacent PGNs. This contrasts with polystyrene (PS)−SiO 2 analogs that displayed ready (and irreversible) fusion of brush layers during annealing. The retardation of brush interdigitation in PMMA-grafted systems is surprising, given the similar thermomechanical properties of both polymers, and is rationalized as the consequence of higher friction between PMMA repeats compared to PS. Microscopic dynamics thus has a profound impact on the kinetic path of structure (and property) evolution and thus should be considered during the processing of PGNs into functional hybrid materials.

chemical structure↗
DOE OSTI2025

Pseudo-equilibrium theory for extrinsic doping control of the topological semimetal Cd 3 As 2

The standard approach for predicting defect equilibria from first principles assumes that the solid-state system is initially in a thermodynamic equilibrium with the external atomic reservoirs. This “growth step” is then often followed by a temperature quench in a “pseudo-equilibrium” in which some or all defect concentrations are frozen in until only the Fermi level E F remains to be equilibrated. However, this protocol does not account for the possibility of site exchanges which can create important defect redistributions as long as short-range defect migration is kinetically permissible. To model this redistribution, we developed an approach to solve for the non-equilibrium chemical potentials as a function of temperature while maintaining the overall defect stoichiometry. We then apply this approach to the Dirac semimetal Cd 3 As 2 to model extrinsic doping with group 1/11 and 14 elements. Undoped Cd 3 As 2 exhibits an undesirable mismatch between E F and the Dirac point. This unintentional electron doping originates from intrinsic defects and is difficult to overcome through adjustment of synthesis conditions alone. Employing our pseudo-equilibrium modeling, we identify extrinsic doping strategies for realizing doping-balanced Cd 3 As 2 at the relatively low temperatures accessible in thin-film growth of this material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Predictive Self-Healing Seals for Gas Transmission

Damaged elastomeric diaphragms within pneumatic controllers used in the oil and gas industry lead to an unintended release of methane. Self-healing microvascular materials capable of healing various types of damage have been fabricated. These microvascular materials are designed to replace currently available commercial diaphragms found in pneumatic controllers and provide a solution to reduce unintended methane leaks. This project demonstrated the ability of these materials to reduce methane release by more than 80% in lab and pilot settings. Additionally, ML approaches successfully identified leaking systems based on training using synthetic and real data.

03 NATURAL GAS↗
DOE OSTI2024

NSTX-U research advancing the physics of spherical tokamaks

Abstract The objectives of NSTX-U research are to reinforce the advantages of STs while addressing the challenges. To extend confinement physics of low- A , high beta plasmas to lower collisionality levels, understanding of the transport mechanisms that set confinement performance and pedestal profiles is being advanced through gyrokinetic simulations, reduced model development, and comparison to NSTX experiment, as well as improved simulation of RF heating. To develop stable non-inductive scenarios needed for steady-state operation, various performance-limiting modes of instability were studied, including MHD, tearing modes, and energetic particle instabilities. Predictive tools were developed, covering disruptions, runaway electrons, equilibrium reconstruction, and control tools. To develop power and particle handling techniques to optimize plasma exhaust in high performance scenarios, innovative lithium-based solutions are being developed to handle the very high heat flux levels that the increased heating power and compact geometry of NSTX-U will produce, and will be seen in future STs. Predictive capabilities accounting for plasma phenomena, like edge harmonic oscillations, ELMs, and blobs, are being tested and improved. In these ways, NSTX-U researchers are advancing the physics understanding of ST plasmas to maximize the benefit that will be gained from further NSTX-U experiments and to increase confidence in projections to future devices.

74 ATOMIC AND MOLECULAR PHYSICS↗