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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Role of Dysprosium Doping on Structural, Optical, Magnetic and Electrical Properties of ZnO Nanorods

Well-defined Dy-doped ZnO rods were synthesized by hydrothermal route. Various studies were carried out on the synthesized rods like x-ray diffraction to confirm their hexagonal wurtzite structure; TEM, HRTEM and selected area electron diffraction to establish their rodlike structure; energy-dispersive x-ray spectroscopy analysis confirming appropriate substitution of dopant; UV-Vis spectra, which showed decrease in band gap with dopant concentration and photoluminescence study furnishing information regarding the presence of various types of defects. Further, magnetic studies were done using VSM to confirm roomtemperature ferromagnetic behaviour of both for undoped and doped ZnO rods and, the electrical studies were performed using four probe method, which confirmed that the ‘variation in resistivity with dopant concentration’ was related to crystallinity of the samples. UV-Vis spectra showed effect of dopant concentration on band gap and photoluminescence study confirms induced ferromagnetic behaviour due to Dy doping.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

An airfoil-based synthetic actuator disk model for wind turbine aerodynamic and structural analysis

Here, this study introduces an airfoil-based refinement technique to enhance the Actuator Disk Model (ADM) for improved wind turbine aerodynamic load prediction and structural simulation in conjunction with Large Eddy Simulations of the wind flow. While ADM offers higher computational efficiency than the more detailed but resource-intensive Actuator Line Model (ALM), it traditionally lacks the resolution needed to capture the localized blade forces accurately. To address this limitation, we introduce a refinement technique that uses airfoil-specific data and employs interpolation-based grid point refinement, achieving ALM-comparable accuracy while preserving ADM's efficiency. Unlike conventional ADM that provides only rotor-disk averaged forces, our synthetic method tracks transient aerodynamic load variations over multiple blade revolutions, allowing us to calculate the distributions of maximum and minimum loads during typical cycles. Applied to the NREL 5 MW reference turbine, our enhanced ADM accurately predicts key aerodynamic parameters (angle of attack, axial velocity, lift, drag, axial and tangential forces along the blades) as well as structural responses (blade tip deflection, maximum stress, and stress concentration). Our results show that the tip deflection ranges from 2.33m (3.69 % of blade length) to 4.28m (6.79 %), with maximum stress concentration occurring near the blade root. This research demonstrates that a refined synthetic ADM approach can serve as a computationally efficient alternative for both aerodynamic analysis and structural simulation of wind turbine blades subjected to realistic wind fields.

17 WIND ENERGY↗
DOE OSTI2020

Ultra-Fast Electron Microscopic Imaging of Single Molecules With a Direct Electron Detection Camera and Noise Reduction

Time-resolved imaging of molecules and materials made of light elements is an emerging field of transmission electron microscopy (TEM), and the recent development of direct electron detection cameras, capable of taking as many as 1,600 fps, has potentially broadened the scope of the time-resolved TEM imaging in chemistry and nanotechnology. However, such a high frame rate reduces electron dose per frame, lowers the signal-to-noise ratio (SNR), and renders the molecular images practically invisible. Here, we examined image noise reduction to take the best advantage of fast cameras and concluded that the Chambolle total variation denoising algorithm is the method of choice, as illustrated for imaging of a molecule in the 1D hollow space of a carbon nanotube with ~1 ms time resolution. Through the systematic comparison of the performance of multiple denoising algorithms, we found that the Chambolle algorithm improves the SNR by more than an order of magnitude when applied to TEM images taken at a low electron dose as required for imaging at around 1,000 fps. Open-source code and a standalone application to apply Chambolle denoising to TEM images and video frames are available for download.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning

Residual-based adaptive strategies are widely used in scientific machine learning yet remain largely heuristic. We introduce a variational framework that formalizes these methods through convex transformations of the residual, where different transformations correspond to distinct objective functionals. For instance, exponential weights target uniform error minimization, while linear weights recover quadratic error minimization. This perspective reveals adaptive weighting as a means of selecting sampling distributions that optimize a primal objective, directly linking discretization choices to error metrics. This principled approach yields three key benefits: it enables systematic design of adaptive schemes, reduces discretization error by lowering estimator variance, and enhances learning dynamics by improving gradient signal-to-noise ratio. Extending the framework to operator learning, we demonstrate substantial performance gains across diverse optimizers and architectures. Our results provide a theoretical perspective for residual-based adaptivity and establish a foundation for principled discretization and training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Tantalum sound velocity under shock compression

We used several variations of the shock compression method to measure the longitudinal sound velocity of shocked tantalum over the pressure range 37–363 GPa with a typical uncertainty of 1.0%. These data are consistent with Ta remaining in the bcc phase along the principal Hugoniot from ambient pressure to ≈300 GPa, at which pressure melting occurs. These data also do not support the putative melting phenomena reported below 100 GPa in some static compression experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Hydro-scaling of direct-drive cylindrical implosions at the OMEGA and the National Ignition Facility

Deceleration-phase Rayleigh–Taylor instability (RTI) growth during inertial confinement fusion capsule implosions significantly affects the performance as it mixes cold ablator material into the fuel. Precise measurements of such instability growth are essential for both validating the existing simulation codes and improving our predictive capability. RTI measurements on the inner surface of a spherical shell are limited and are often inferred indirectly at limited convergence. In contrast, cylindrical implosions allow for direct diagnostic access to the converging interface by imaging down the cylinder axis while retaining the effects of convergence. We have performed direct-drive cylindrical implosion experiments at both the OMEGA and the NIF laser facilities using scaled targets. RTI growth is demonstrated to be scale-invariant between the cylindrical targets at OMEGA and similar targets at the NIF, which are scaled up by a factor of three in the radial dimension. Single-mode (m = 0) instability growth factors of ~14 are measured at a convergence ratio (CR) ~ 2.5 with nearly identical mode growth at both scales. The measurements are in agreement with xRAGE radiation-hydrodynamics simulations. In addition, we have developed the Bayesian-inference-engine method to account for the variations in the target alignment, magnification, and the parallax effect in the measurement, allowing a more precise comparison between the experimental data and the simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations

In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub.

Chemistry↗
DOE OSTI2020

Scanning Tunneling Thermometry

The best spatial resolution so far achieved in thermal imaging is several nanometers, much coarser than routinely achieved for other physical properties. Here we propose a method to map electronic temperature variations in operating nanoscale conductors by relying solely upon electrical tunneling current measurements. The proposed measurement scheme involves two scanning probe operations to measure the conductance and thermopower, respectively. These two measurements are shown to determine the local temperature with high accuracy in nanoscale conductors, where the Wiedemann-Franz law holds quite generally. We report the proposed scanning tunneling thermometer, owing to its operation in the tunneling regime, would be capable of mapping temperature variations with subnanometer resolution, thereby enhancing the resolution of scanning thermometry by some 2 orders of magnitude.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Adaptive Quantum Generative Training using an Unbounded Loss Function

We propose a generative quantum learning algorithm using the Adaptive Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss function to be minimized is the maximal quantum Rényi divergence of order two, an unbounded function that mitigates barren plateaus which inhibit training variational circuits. We benchmark this method against other state-of-the-art adaptive algorithms by learning random two-local thermal states. We perform numerical experiments of up to 12 qubits comparing our method learning algorithms that use linear objective functions and show that Rényi-ADAPT is capable of constructing shallow quantum circuits competitive with existing methods, while the gradients remain favorable resulting from the maximal Rényi divergence loss function.

quantum algorithms, quantum machine learning, quan↗
DOE OSTI2022

An Information Theoretic Approach to Identify Dominant Voltage Influencers for Unbalanced Distribution Systems

Smart distribution grid with multiple renewable energy sources can experience random voltage fluctuations due to variable generation, which may result in voltage violations. Traditional voltage control algorithms are inadequate to handle fast voltage variations. Therefore, new dynamic control methods are being developed that can significantly benefit from the knowledge of dominant voltage influencer (DVI) nodes. DVI nodes for a particular node of interest refer to nodes that have a relatively high impact on the voltage fluctuations at that node. Conventional power flow-based algorithms to identify DVI nodes are computationally complex, which limits their use in real-time applications. This paper proposes a novel information theoretic voltage influencing score (VIS) that quantifies the voltage influencing capacity of nodes with DERs/active loads in a three phase unbalanced distribution system. VIS is then employed to rank the nodes and identify the DVI set. VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system. It is shown through experiments that KL divergence and Bhattacharyya distance are effective indicators of DVI nodes with an identifying accuracy of more than 90%. Additionally, the computation burden is also reduced by an order of 5, thus providing the foundation for efficient voltage control.

42 ENGINEERING↗
DOE OSTI2021

SSRS (Stochastic Soaring Raptor Simulator)

SSRS (Stochastic Soaring Raptor Simulator) is a generalizable, probabilistic, and predictive tool for wind energy developers, ecologists, wildlife managers and industry consultants to estimate the potential for soaring raptors to interact with operating wind turbines, without the need for site-specific data collection. Rapid expansion of wind energy development across the world has exposed the risk of turbine collisions for birds and bats. The risk to obligate soaring raptors such as golden eagles is of particular concern due to their small population and influence on ecological balance. Golden eagles rely heavily on updrafts to subsidize their flight, putting them in direct conflict with operational wind turbines that utilize the same wind resource. Understanding the behavior of soaring raptors with varying atmospheric conditions is crucial for predicting and mitigating the risk of turbine collision. This software contains a predictive movement model that simulates individual flight paths of golden eagles during updraft-subsidized long-distance flight, including migration. For a given set of atmospheric conditions, the model simulates thousands of eagles at turbine-scale spatial resolution (50m) to produce a relative presence density map. The simulated eagles rely on updrafts to pursue uninterrupted directional flight with minimal energy expenditure, following fluid-flow principles. The simulator includes a stochastic model of eagle behavior and a systematic method of accounting for spatiotemporal variations in atmospheric conditions. This framework only requires publicly available atmospheric data to estimate orographic and thermal updrafts, ensuring general usability.

Sandhu, Rimple↗
DOE OSTI2018

Development of Grade 91 inelastic model for incorporation in ASME Division 5

This report describes a unified viscoplastic model for Grade 91 suitable for use with the Section III, Division 5 design by inelastic analysis procedures in the ASME Boiler and Pressure Vessel Code. The report identifies several key features a successful design inelastic model must capture along with critical aspects of the response of Grade 91 for elevated temperature loading conditions typical of expected reactor operating conditions. An inelastic model for Code design must capture the average response of a material including heat-to-heat and product form variations. This work develops a method for calibrating such a model to a large experimental database using genetic algorithm optimization. Finally, the report compares the final model to several specialized experimental tests to validate that it will be suitable for use in high temperature structural design. The techniques developed here can be applied to the remaining Section III, Division 5, Class A materials to develop a nonmandatory appendix to the Code describing inelastic material models suitable for use with the design by inelastic analysis procedures.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Online Monitoring System for Concrete Structures Affected by Alkali-Silica Reaction

This report presents a comprehensive study of developing ultrasonic wave and acoustic emission (AE) techniques for long-term monitoring of alkali-silica reaction (ASR) development in concrete. Small, medium, and full-scale concrete specimens were cast, conditioned, and monitored for various periods, from at least one year to 2.4 years. The reactive concrete specimens contain coarse or fine reactive aggregates to study the effects of different types of reactive aggregates. Confinements were also designed to simulate the 2-dimensional confinement effects of reinforcement in the shield building of nuclear power plants. The concrete specimens were stored in a environmental chamber with high humidity and high temperature to accelerate the ASR development. The ultrasonic monitoring data shows high sensitivity to ASR development and could detect cracking initiation well before visible surface cracks occurred. However, the linear ultrasonic analysis based on wave velocity is strongly affected by temperature variation. Therefore, a nonlinear ultrasonic method was proposed to measure thermally induced nonlinear acoustic responses of concrete (thermal modulation of ultrasonic wave). The measured nonlinear acoustic parameter shows a high correlation with ASR expansion across specimens with different reactive aggregates and confinement conditions. The same conclusion was obtained from nonlinear resonance tests on small concrete prisms. Compared to the linear acoustic methods, the nonlinear tests show high sensitivities to ASR damage from internal microcracking initiation at the early stage to visible cracks at the late stage of ASR. The attributes of the thermal modulation of nonlinear ultrasonic method include high sensitivity, immunity to temperature effects, and strong correlation with ASR expansion, which demonstrate great potentials of the nonlinear ultrasonic method for diagnosis of ASR damage and prediction of concrete deterioration process. Acoustic emission is a passive sensing technique for damage assessment, and access to only one surface is needed even for thick and heavily reinforced elements such as the walls utilized for nuclear shield building. Additionally, relatively few sensors are required to monitor the progression of the damage process. Results indicate that the AE data can be related to the damage rating index, which is a petrography-based means of assessing damage due to ASR in reinforced concrete. Furthermore, AE is capable of detecting ASR damage long before surface cracking is visually noticeable. Boundary conditions play in important role in the progression of ASR damage and differences in boundary conditions are reflected in the AE data. Entropy based data assessment methods provide a means to assess the damage state, and convolutional neural network based data assessment provided a means to assess the damage state in real-time. Results indicate that artificial neural network models may be used as a means to predict volumetric expansion based on AE data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Lattice calculation of the pion electromagnetic form factor at high momentum transfer

The pion electromagnetic form factor, Fn, is a fundamentally important topic for our understanding of the hadron structure and the transition from perturbative to nonperturbative QCD. JLAB?s experiment E12-06-101 proposes to extend the high quality F? data to Q2=6.0 GeV2 as a part of JLAB?s 12 GeV upgrade. Being motivated by this, we present a lattice QCD calculation of Fn using the method of distillation and the variational approach which significantly reduce the excited state contamination. We study the shape of the vector form factor in the Q2 range from zero to a few GeV2 using Wilson quark formalism on our 203×128 anisotropic lattice configuration with light (up/down) and strange quarks in the sea and 400 MeV pion mass.

Chakraborty, Bipasha↗
DOE OSTI2023

Real-Time Krylov Theory for Quantum Computing Algorithms

Quantum computers provide new avenues to access ground and excited state properties of systems otherwise difficult to simulate on classical hardware. New approaches using subspaces generated by real-time evolution have shown efficiency in extracting eigenstate information, but the full capabilities of such approaches are still not understood. In recent work, we developed the variational quantum phase estimation (VQPE) method, a compact and efficient real-time algorithm to extract eigenvalues on quantum hardware. Here we build on that work by theoretically and numerically exploring a generalized Krylov scheme where the Krylov subspace is constructed through a parametrized real-time evolution, which applies to the VQPE algorithm as well as others. We establish an error bound that justifies the fast convergence of our spectral approximation. We also derive how the overlap with high energy eigenstates becomes suppressed from real-time subspace diagonalization and we visualize the process that shows the signature phase cancellations at specific eigenenergies. We investigate various algorithm implementations and consider performance when stochasticity is added to the target Hamiltonian in the form of spectral statistics. To demonstrate the practicality of such real-time evolution, we discuss its application to fundamental problems in quantum computation such as electronic structure predictions for strongly correlated systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Investigating the Effects of Chemical Mechanism on Soot Formation Under High-Pressure Fuel Pyrolysis

We performed Computational Fluid Dynamics (CFD) simulations using a Reynolds-Averaged Navier-Stokes (RANS) turbulence model of high-pressure spray pyrolysis with a detailed chemical kinetic mechanism encompassing pyrolysis of n-dodecane and formation of polycyclic aromatic hydrocarbons. We compare the results using the detailed mechanism and those found using several different reduced chemical mechanisms to experiments carried out in an optically accessible, high-pressure, constant-volume combustion chamber. Three different soot models implemented in the CONVERGE CFD software are used: an empirical soot model, a method of moments, and a discrete sectional method. There is a large variation in the prediction of the soot between different combinations of chemical mechanisms and soot model. Furthermore, the amount of soot produced from all models is substantially less than experimental measurements. All of this indicates that there is still substantial work that needs to be done to arrive at simulations that can be relied on to accurately predict soot formation.

02 PETROLEUM↗
DOE OSTI2018

Model-independent Exoplanet Transit Spectroscopy

We propose a new data analysis method for obtaining transmission spectra of exoplanet atmospheres and brightness variation across the stellar disk from transit observations. The new method is capable of recovering exoplanet atmosphere absorption spectra and stellar specific intensities without relying on theoretical models of stars and planets. We simultaneously fit both stellar specific intensity and planetary radius directly to transit light curves. This allows stellar models to be removed from the data analysis. Furthermore, we use a data quality weighted filtering technique to achieve an optimal trade-off between spectral resolution and reconstruction fidelity homogenizing the signal-to-noise ratio across the wavelength range. Such an approach is more efficient than conventional data binning onto a low-resolution wavelength grid. We demonstrate that our analysis is capable of reproducing results achieved by using an explicit quadratic limb-darkening equation and that the filtering technique helps eliminate spurious spectral features in regions with strong telluric absorption. The method is applied to the VLT FORS2 observations of the exoplanets GJ 1214 b and WASP-49 b, and our results are in agreement with previous studies. Comparisons between obtained stellar specific intensity and numerical models indicates that the method is capable of accurately reconstructing the specific intensity. The proposed method enables more robust characterization of exoplanetary atmospheres by separating derivation of planetary transmission and stellar specific intensity spectra (that is model-independent) from chemical and physical interpretation.

79 ASTRONOMY AND ASTROPHYSICS↗