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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

What we know about the sun's internal rotation from solar oscillations

In this paper, a uniform approach of inversion was used to determine the internal rotation rate of the sun from each of the six available sets of solar oscillation data, which included the data of Duvall et al. (1986), Rhodes et al. (1987, 1990), Tomczyk (1988), Brown and Morrow (1987), and Libbrecht (1989). The technique chosen for inverting the solar oscillation data was the discretized least-squares technique. The results indicate that the rotation rate of the sun in the equatorial plane declines going inward between the surface and 0.6 of the radius and that the polar rate increases going inward (i.e., the surfacelike differential rotation decreases with depth).

Goode, Philip R.↗
DOE OSTI2024

EMT data generation

The integration of inverter-based resources (IBRs) in power systems is accelerating, bringing with it significant benefits such as reduced greenhouse gas emissions, improved grid resilience, and increased energy independence. Despite these advantages, the widespread adoption of IBRs introduces several challenges, including issues related to grid stability, increased operational complexity, and the need for updated regulatory frameworks. To address these challenges, IEEE released Standard 2800 in 2022, which sets forth the necessary interconnection capabilities and performance criteria for IBRs connected to transmission and sub-transmission systems. This standard outlines the performance requirements to ensure the reliable integration of IBRs into the bulk power system. Furthermore, in 2023, the North American Electric Reliability Corporation (NERC) published a reliability guideline for electromagnetic transient (EMT) modeling of BPS-connected IBRs. This guideline provides recommendations for developing EMT model requirements, performing model quality checks, and implementing verification practices specifically for EMT models representing BPS-connected inverter-based resources in reliability studies conducted by transmission planners and planning coordinators. These standards and guidelines have a profound impact on EMT studies for transmission networks, influencing system stability analyses, grid recovery and resynchronization processes, fault ride-through evaluations, protection and coordination strategies, advanced control methodologies, and the inclusion of IBRs in transient models of transmission networks. As a result, the generation of EMT data is crucial for conducting various transient-based studies to understand the impact of IBRs. EMT data generation use cases serve as the basis for scenarios in event detection and identification use cases, providing comprehensive details about EMT data generation for transmission grids with inverter-based resources. These use cases supply sufficient training and validation datasets for subsequent EMT analysis algorithms.

Xia, Qianxue↗
DOE OSTI2026

Electromagnetic Induction (EMI) Data, 2024, Trail Creek, Colorado

This dataset contains Electromagnetic Induction (EMI) data collected at Trail Creek, Colorado, in 2024. EMI surveys were conducted to investigate the spatial distribution of electrical conductivity in the subsurface, providing insights into soil moisture and subsurface geological features. The surveys were performed along multiple transects to capture variations in conductivity influenced by changes in soil composition, moisture content, and underlying geological structures. This dataset complements other geophysical data collected in the region, including Electrical Resistivity Tomography (ERT) and Terrestrial LiDAR Scanning (TLS), providing a detailed understanding of the subsurface and its impact on surface vegetation and hydrological processes. The data are valuable for environmental geophysics, ecological research, and hydrological modeling in mountainous ecosystems. The files include: - data.zip: the raw EMI data (.csv) - inversion.zip: the inverted resistivity model (.csv and .kml) - kriging.zip: the kriging resistivity model (.csv, .tif, .kmz) - flmd.csv: file level metadata file describing all files within this dataset - dd.csv: data dictionary file describing the column headers within CSV files This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

CMD Mini-Explorer↗
NASA NTRS2019

X-57 Mod 2 Motor Thermal Analysis

This work covers the refinement of thermal models from design estimates to actual fabricated performance. Matching the experimental data of the first fully electrified version of the X-57 Maxwell experimental vehicle requires high fidelity thermal analysis to sufficiently capture the electric motor and inverter temperature profiles. Qualification test data of the motors and inverters is used to validate finite element analysis models used to simulate thermal performance over mission transients. An additional thermal-hydraulic models is used to estimate flow characteristics through the propulsor nacelle and component heat sinks. After calibration of the higher order models, overall component sizing and peak temperature constraints can be distilled to reduced order models, to improve modelflexibility and utility.

Chin, Jeffrey C.↗
DOE OSTI2023

DEEPEN: Newberry Volcano MT and Gravity Data - 2022 and 2023 Acquisition and Processing

As part of DEEPEN (DE-risking Exploration of geothermal Plays in magmatic ENvironments), a 3D play fairway analysis (PFA) was conducted at Newberry Volcano in Central Oregon for multiple play types (conventional hydrothermal, superhot EGS, and supercritical). For use in this PFA, combined full tensor broadband magnetotelluric (MT) and gravity data were acquired, processed, and inverted by Enthalpion Energy LLC (Enthalpion) with support from NREL staff. The data collection efforts took place from June 19th to July 24. Data were collected with the goal of gaining an improved understanding of the South Flank and the extent of the magma chamber. This GDR submission includes the raw data, single inversions, joint inversions, resolution matrices, and a report on these processes. More detailed information about the folder structure of the datasets included in this submission may be found on pages 94-97 of Magnetotelluric and Gravity Survey Report.pdf below.

15 GEOTHERMAL ENERGY↗
NASA NTRS1989

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some examples are compared to data obtained from testing hardware inverters.

Brush, Andrew S.↗
DOE OSTI2023

PV Inverter Experimental Dataset Version 2 with 100 Percent Power

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 20 kW off-the-shelf grid following PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. The PV inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.88 to 1.09 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59.4 Hz to 60.45 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

14 SOLAR ENERGY↗
DOE OSTI2024

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

The Effects of Stochastic Velocity Variations on Estimating Time Dependent Seismic Moment Tensors: Applications to the Blue Mountain Well Perforation Data

Resolving the time dependent terms in the seismic moment tensor provides important information that can be used to interpret the source process of an explosion, including the separation of isotropic explosion terms from shear forces and potentially isolated force couples. In this report, we detail our method of inverting three component seismic data for the seismic moment tensor. We review possible seismic source models from the simplest isotropic explosion type source to those incorporating the six independent moment tensor terms. The inversion we describe is formulated in the frequency domain, and results in estimates of time dependent moment tensor components. The inversion relies on an accurate estimate of the Green's functions of the Earth. However, given the complexity of the Earth, we explore the effects of inaccuracies in the presumed Earth model used to estimate the Green's functions needed for the inversion. Specifically, we explore the effects of stochastic variations in the Earth models on the inversion results. These tests are synthetic throughout, and show that adding stochastic density/velocity heterogeneity in the presumed Earth model results in reduced amplitude seismic moment tensor estimates, as well as degrading the data misfit. We suggest two mitigation strategies. First, produce a suite of Green's functions using different realizations of the stochastic field within the Earth Model. Secondly, perform the in- version in the power spectral domain, eliminating all phase information. Finally, we analyze actual seismic data collected in winter 2017/2018. The seismic data was collected at in active geothermal well site outside of Winnimucca, NV, and was produced during well stimulation operations. In general, the inversion results were poor, with a high degree of data misfit. We hypothesize that the poor results are a function of a poorly constrained Earth model as well as noisy, high-frequency data being used in the inversion.

58 GEOSCIENCES↗
DOE OSTI2021

Enabling Interoperable SCADA Communications for PV Inverters through Embedded Controllers

The percentage integration of photovoltaic (PV) inverters in the field has increased significantly in the past 5 years. Regardless of the size of the PV plants and the inverters (residential vs. commercial), it is becoming crucial that these devices have the capability to communicate with peers (other smart devices) and with components that are at a hierarchy above the inverters (e.g., supervisory control and data acquisition (SCADA) systems, distributed controllers, and data managers). This project aims to develop a standard SCADA software code for inverters’ embedded controllers that will enable interoperability with other components in the system. To achieve this, the code will be developed using two different protocols: Distributed Network Protocol 3 and International Electrotechnical Commission 61850. The developed code is aimed to be deployed in simple embedded controllers. It will be tested in the National Renewable Energy Laboratory’s (NREL’s) Energy Systems Integration Facility. The tested code will then be made available through Triangle MicroWorks’s (TMW’s) software platform. The primary objectives of this project include training the NREL team with TMW’s embedded controller libraries, developing an interoperable communication code for embedded controllers, successfully testing and deploying the code, and demonstrating the newly developed code in a conference.

14 SOLAR ENERGY↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2018

Response induction coil magnetometers to perturbations in orientation

© 1996-2018 Society of Exploration Geophysicists. All rights reserved. We explore the data collected by a 3-component induction coil magnetometer system with respect to motion of the instruments in earths static magnetic field. The sensitivtiy of the inductiuon coil magnetometer leads to unprecediented accuracy on tilt measurements. We model the signals observed during seismic events as being perturbations in coil orientation. In theory, these perturbations can include ground roll, ocean motion, nearby cultural seismicity, or any other field with a tilting effect. Using data from a magnetic observatory near Parkfield CA we invert several time series of coil data during different levels of seismic activity in an attempt to determine the magnitudes of rotation at which our model accurately describes the coil data. Finally, we explore the transfer function between the coils and nearby seismic instruments (accelerometers, tiltmeters, and velocity seismometers).

Kappler, K↗
DOE OSTI2024

Electron Density Measurements Using USPR (Final Scientific/Technical Report)

UC Davis has fabricated an ultrashort pulse reflectometer (USPR) diagnostic instrument for electron density profile measurements on compact, short duration, magnetically-confined fusion-energy concept devices such as spheromaks and FRCs. The USPR system transmits extremely short duration (~few nsec) chirped waveforms that together span 29 to 75 GHz. These chirped waveforms illuminate and reflect from the target plasma, with each frequency component reflecting from a different density layer (higher frequencies probe deeper into the plasma before reflecting). The reflected waveforms are split into roughly 42 different frequencies; time-of-flight (TOF) measurements made at each frequency with high resolution (~25 psec measurement resolution which corresponds to ~5 mm). These TOF data may then be inverted via software to generate electron density profiles with high time resolution (~10 μsec). At the heart of the system is a field programmable gate array (FPGA) based controller which collects and processes all of the USPR data in addition to generating all of the control signals required for maximum flexibility. The FPGA controller has the software flexibility to be easily reconfigured for different plasma devices, and the entire system sufficiently compact to be easily and quickly transported between devices. A high speed impulse generator was transformed into a set of three ultrashort pulse transmitter chirps using a combination of dispersive waveguide, frequency doublers and high-pass filters. A mm-wave controller was fabricated to sequentially switch between the three chirps, directing the chirps one-by-one to three different mm-wave assemblies spanning 29-75 GHz. Each mm-wave assembly consists of a high power active multiplier chain which converts the transmitter chirp to higher frequencies, and a broadband mixer which downconverts the reflected waveform to the 2-18 GHz range of the UPSR receiver. The 16-channel receiver (shared by all 3 mm-wave assemblies) was fabricated employing custom TOF modules capable of operating at a high 1 MHz sampling rate. Laboratory testing of the full system revealed the presence of unwanted harmonics from the multiplication process, with interference observed in the downconverted reflections at selected frequency channels that could not be completely filtered out. Additional interference effects arising from internal reflections within the mm-wave assemblies were minimized using a high-speed switch which served to “gate out” much of these reflections. The USPR diagnostic was transported and installed onto the HIT-SIU plasma device, becoming operational on 11/08/2022. Although designed to span 3 distinct mm-wave bands, the HIT-SIU plasmas at this time were sufficiently low density such that only the lowest of the three bands was likely to have strong plasma reflections. The system was then set to operate on only the lowest band (assembly #1), with data collected every 1 μsec rather than 3 μsec which would have been the case when cycling through all three bands. Connected to HIT-SIU, time-varying plasma reflections were observed on 9 of 16 possible frequency channels. Close examination of the data collected revealed issues previously unobserved in laboratory testing, associated with (a) reflections from the small aperture horns required for operation within the HIT-SIU device, and (b) a dependence of the recorded TOF with the threshold voltage of a given channel. Plans were made to address each of these issues before undertaking any future campaigns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1984

The p-wave upper mantle structure beneath an active spreading centre - The Gulf of California

Over 1400 seismograms of earthquakes in Mexico are analyzed and data sets for the travel time, apparent phase velocity, and relative amplitude information are utilized to produce a tightly constrained, detailed model for depths to 900 km beneath an active oceanic ridge region, the Gulf of California. The data are combined by first inverting the travel times, perturbing that model to fit the p-delta data, and then performing trial and error synthetic seismogram modelling to fit the short-period waveforms. The final model satisfies all three data sets. The ridge model is similar to existing upper mantle models for shield, tectonic-continental, and arc-trench regimes below 400 km, but differs significantly in the upper 350 km. Ridge model velocities are very low in this depth range; the model 'catches up' with the others with a very large velocity gradient from 225 to 390 km.

Walck, M. C.↗
NASA NTRS1989

Studies in astronomical time series analysis. III - Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data

This paper develops techniques to evaluate the discrete Fourier transform (DFT), the autocorrelation function (ACF), and the cross-correlation function (CCF) of time series which are not evenly sampled. The series may consist of quantized point data (e.g., yes/no processes such as photon arrival). The DFT, which can be inverted to recover the original data and the sampling, is used to compute correlation functions by means of a procedure which is effectively, but not explicitly, an interpolation. The CCF can be computed for two time series not even sampled at the same set of times. Techniques for removing the distortion of the correlation functions caused by the sampling, determining the value of a constant component to the data, and treating unequally weighted data are also discussed. FORTRAN code for the Fourier transform algorithm and numerical examples of the techniques are given.

Scargle, Jeffrey D.↗
NASA NTRS1989

Frequency domain model for analysis of paralleled, series-output-connected Mapham inverters

The Mapham resonant inverter is characterized as a two-port network driven by a selected periodic voltage. The two-port model is then used to model a pair of Mapham inverters connected in series and employing phasor voltage regulation. It is shown that the model is useful for predicting power output in paralleled inverter units, and for predicting harmonic current output of inverter pairs, using standard power flow techniques. Some sample results are compared to data obtained from testing hardware inverters.

Brush, Andrew S.↗
DOE OSTI2026

Empirical Comparison of Machine Learning Approaches for Black-Box Modeling of Power Conversion System Dynamics

Inverter-based resources are key components in modern power systems, but accurately modeling their complex behavior can be challenging. Standard, generic converter models often oversimplify inverter dynamics, leading to significant errors in predicting performance. In this work, we compare several data-driven machine learning (ML) approaches for inverter modeling, performing experiments on power conversion systems, systematically varying input conditions, and recording the resulting voltages and currents. The ML models were then trained on this measured data to capture the inverter's dynamic response and to predict the inverter's output current. A performance comparison between the four ML models under study is conducted, laying the foundation for future work on hardware implementation for real-time inference.

30 DIRECT ENERGY CONVERSION↗