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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Integrated Approach to Ancillary PV Component Reliability Assessment (Final Report)

In this project, we have established a nondestructive, generalized methodology that (1) fuses rich field data with advanced ML for proactive reliability forecasting, (2) dramatically reduces experimental iterations via synthetic dataset generation, and (3) achieves unprecedented regression precision in both anomaly detection and component-level degradation assessment—paving the way for truly predictive maintenance of grid-tied PV inverters under diverse outdoor conditions.

14 SOLAR ENERGY↗
NASA NTRS1972

Lifting-surface theory for calculating the loading induced on a wing by a flap

A method is described for using lifting-surface theory to obtain the pressure distribution on a wing with a trailing-edge flap or control surface. The loading has a logarithmic singularity at the flap edges, which may be determined directly by the method of matched asymptotic expansions. Expressions are given for the singular flap loading for various flap hinge line and side edge geometries, both for steady and unsteady flap deflection. The regular part of the flap loading must be obtained by inverting the lifting-surface-theory integral equation relating the pressure and the downwash on the wing: procedures are described to accomplish this for a general wing and flap geometry. The method is applied to several example wings, and the results are compared with experimental data. Theory and test correlate well.

Johnson, W. A.↗
NASA NTRS1977

Inversion of solar extinction data from the Apollo-Soyuz Test Project Stratospheric Aerosol Measurement (ASTP/SAM) experiment

The inversion methods are reported that have been used to determine the vertical profile of the extinction coefficient due to the stratospheric aerosols from data measured during the ASTP/SAM solar occultation experiment. Inversion methods include the onion skin peel technique and methods of solving the Fredholm equation for the problem subject to smoothing constraints. The latter of these approaches involves a double inversion scheme. Comparisons are made between the inverted results from the SAM experiment and near simultaneous measurements made by lidar and balloon born dustsonde. The results are used to demonstrate the assumptions required to perform the inversions for aerosols.

Pepin, T. J.↗
NASA NTRS1985

Thundercloud current determination from measurements at the earth's surface

For the analysis of the actual dominant mechanism or mechanisms of electrification in a given storm cell, it is of great importance to make observations which can be used to determine the configuration and the magnitudes of the currents and impedances of the generator systems in active thunderclouds. Krider and Musser (1982) have measured Maxwell currents below Florida thunderclouds and shown that their patterns are consistent with simple generator geometries. It is, therefore, of great interest to study the extent to which a set of Maxwell current measurements can be inverted to obtain the generator currents and locations. Nisbet (1983) has developed an electrical model for active thunderclouds which solves equations for current densities and electric fields. The present investigation is concerned with the extent to which such a model can be used to derive data about the cloud generator magnitudes and locations, taking into account also the conductivity profile from ground-based measurements or estimates of the Maxwell current density.

Nisbet, J. S.↗
DOE OSTI2012

Microgrid and Inverter Control and Simulator Software

A collection of software that can simulate the operation of an inverter on a microgrid or control a real inverter. In addition, it can simulate the control of multiple nodes on a microgrid." Application: Simulation of inverters and microgrids; control of inverters on microgrids." The MMI submodule is designed to control custom inverter hardware, and to simulate that hardware. The INVERTER submodule is only the simulator code, and is of an earlier generation than the simulator in MMI. The MICROGRID submodule is an agent-based simulator of multiple nodes on a microgrid which presents a web interface. The WIND submodule produces movies of wind data with a web interface.

Source record↗
DOE OSTI2019

Towards Improved Real-Time Observability of Behind-Meter PhotoVoltaic Systems: A Data-Driven Approach

The stochasticity in energy supply introduced by increased PV penetration in grid distribution systems can lead to frequent and sudden supply-demand imbalances, thereby causing voltage and frequency fluctuations. Enhanced observability, in real-time, of grid attributes such as real and reactive power flows, power injections/consumption and voltages is becoming increasingly essential to ensure smooth grid operation under these new large-scale PV integration scenarios. Two significant challenges prevent grid operators from achieving this objective: 1) Computational complexity of quasi-static timeseries simulations, and 2) Partial observability of behind-meter grid assets. In this work, we take a step towards this objective by enhancing the observability of smart inverter equipped PV systems located behind AMI meters. Smart inverters convert the DC output of PV systems into real and reactive power, the ratio of which is governed by a power factor setting. This in turn is governed by the real-time voltage at its terminal, as per a pre-set volt/var curve. While it is possible to infer the power factor setting - thereby, real and reactive power injection of the PV system - at each time interval by quasi-static time series simulations, such methods are not scalable to large grid sizes. We propose to reduce the reliance on simulations by developing a deep neural network based data-driven model which predicts the time dependent power factors of the PV systems in the grid. The model uses a small amount of simulated data to learn the dependence of power factor on 1) load and apparent PV power injection (root mean square of real and reactive power) data, and 2) AMI net-load data (for BTM PV assets). Our results show that our model can predict power factors with mean absolute error of less than 6.2% under full observability scenario and under 8.6% under the partial observability scenario. Thus, our data-driven model can reduce the amount of simulations required to compute real and reactive injections from PV systems in the distribution system with or without complete information.

14 SOLAR ENERGY↗
NASA NTRS2012

NASA Tech Briefs, October 2012

Topics discussed include: Detection of Chemical Precursors of Explosives; Detecting Methane From Leaking Pipelines and as Greenhouse Gas in the Atmosphere; Onboard Sensor Data Qualification in Human-Rated Launch Vehicles; Rugged, Portable, Real-Time Optical Gaseous Analyzer for Hydrogen Fluoride; A Probabilistic Mass Estimation Algorithm for a Novel 7-Channel Capacitive Sample Verification Sensor; Low-Power Architecture for an Optical Life Gas Analyzer; Online Cable Tester and Rerouter; A Three-Frequency Feed for Millimeter-Wave Radiometry; Capacitance Probe Resonator for Multichannel Electrometer; Inverted Three-Junction Tandem Thermophotovoltaic Modules; Fabrication of Single Crystal MgO Capsules; Inflatable Hangar for Assembly of Large Structures in Space; Mars Aqueous Processing System; Hybrid Filter Membrane; Design for the Structure and the Mechanics of Moballs; Pressure Dome for High-Pressure Electrolyzer; Cascading Tesla Oscillating Flow Diode for Stirling Engine Gas Bearings; Compact, Low-Force, Low-Noise Linear Actuator; Ultra-Compact Motor Controller; Extreme Ionizing-Radiation-Resistant Bacterium; Wideband Single-Crystal Transducer for Bone Characterization; Fluorescence-Activated Cell Sorting of Live Versus Dead Bacterial Cells and Spores; Nonhazardous Urine Pretreatment Method; Laser-Ranging Transponders for Science Investigations of the Moon and Mars; Ka-Band Waveguide Three-Way Serial Combiner for MMIC Amplifiers; Structural Health Monitoring with Fiber Bragg Grating and Piezo Arrays; Low-Gain Circularly Polarized Antenna with Torus-Shaped Pattern; Stereo and IMU- Assisted Visual Odometry for Small Robots; Global Swath and Gridded Data Tiling; GOES-R: Satellite Insight; Aquarius iPhone Application; Monitoring of International Space Station Telemetry Using Shewhart Control Charts; Theory of a Traveling Wave Feed for a Planar Slot Array Antenna; Time Manager Software for a Flight Processor; Simulation of Oxygen Disintegration and Mixing With Hydrogen or Helium at Supercritical Pressure; A Superfluid Pulse Tube Refrigerator Without Moving Parts for Sub-Kelvin Cooling; Sapphire Viewports for a Venus Probe; The Mobile Chamber; Electric Propulsion Induced Secondary Mass Spectroscopy; and Radiation-Tolerant DC-DC Converters.

Source record↗
DOE OSTI2022

Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders

Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Efficient 3-D velocity model building using joint inline and crossline plane-wave wave-equation migration velocity analyses

SUMMARY Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and backprojects residual CIG moveouts into wavefield wave paths with a reflection tomographic kernel. However, 3-D PWEMVA is very computationally expensive because 3-D reflection tomographic inversion requires at least five 3-D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3-D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3-D velocity model update through 1-D spline interpolation of 2-D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wave paths, and the method can take less than 1 per cent of the computational cost of full 3-D PWEMVA. To construct unaliased plane waves for our joint inline and crossline PWEMVA, we develop a 3-D data interpolation method in the frequency–wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3-D synthetic seismic data set and a land 3-D field seismic data set acquired at the Farnsworth CO2-EOR (enhanced oil recovery) field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Liu, Xuejian↗
DOE OSTI2023

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.

Artificial Intelligence (AI), reinforcement learni↗
DOE OSTI2012

Advancing the Deployment of Utility-Scale Photovoltaic Plants in the Northeast

As one of the premier research laboratories operated by the Department of Energy, Brookhaven National Laboratory (BNL) is pursuing an energy research agenda that focuses on renewable energy systems and will help to secure the nation's energy security. A key element of the BNL research is the advancement of grid-connected utility-scale solar photovoltaic (PV) plants, particularly in the northeastern part of the country where BNL is located. While a great deal of information has been generated regarding solar PV systems located in mostly sunny, hot, arid climates of the southwest US, very little data is available to characterize the performance of these systems in the cool, humid, frequently overcast climates experienced in the northeastern portion of the country. Recognizing that there is both a need and a market for solar PV generation in the northeast, BNL is pursuing research that will advance the deployment of this important renewable energy resource. BNL's research will leverage access to unique time-resolved data sets from the 37MWp solar array recently developed on its campus. In addition, BNL is developing a separate 1MWp solar research array on its campus that will allow field testing of new PV system technologies, including solar modules and balance of plant equipment, such as inverters, energy storage devices, and control platforms. These research capabilities will form the cornerstone of the new Northeast Solar Energy Research Center (NSERC) being developed at BNL. In this paper, an overview of BNL's energy research agenda is given, along with a description of the 37MWp solar array and the NSERC.

14 SOLAR ENERGY↗
DOE OSTI2016

SU-F-I-76: Fluoroscopic X-Ray Beam Profiles for Spectra Incorporating Copper Filtration

Purpose: The purpose of this investigation is to characterize and quantify X-ray beam profiles for fluoroscopic x-ray beam spectra incorporating spectral (copper) filtration. Methods: A PTW (Freiburg, Germany) type 60016 silicon diode detector and PTW MP3 water tank were used to measure X-ray beam profiles for 60, 80, 100 and 120 kVp x-ray beams at five different copper filtration thicknesses ranging from 0–0.9 mm at 22 and 42 cm fields of view and depths of 1, 5, and 10 cm in both the anode-cathode axis (inplane) and cross-plane directions. All measurements were acquired on a Siemens (Erlangen, Germany) Artis ZeeGo fluoroscope inverted from the typical orientation providing an x-ray beam originating from above the water surface with the water level set at 60 cm from the focal spot. Results: X-ray beam profiles for beam spectra without copper filtration compared well to previously published data by Fetterly et al. [Med Phys, 28, 205 (2001)]. Our data collection benefited from the geometric orientation of the fluoroscope, providing a beam perpendicular to the tank water surface, rather than through a thin side wall as did the previously mentioned study. Profiles for beams with copper filtration were obtained which have not been previously investigated and published. Beam profiles in the anode-cathode axis near the surface and at lower x-ray energy exhibited substantial heel effect, which became less pronounced at greater depth. At higher energy with copper filtration in the beam, the dose falloff out-of-field became less pronounced, as would be anticipated given higher scatter photon energy. Conclusion: The x-ray beam profile data for the fluoroscopic x-ray beams incorporating copper filtration are intended for use as reference data for estimating doses to organs or soft tissue, including fetal dose, involving similar beam qualities or for comparison with mathematical models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Results on Neutrino and Antineutrino Oscillations from the NOvA Experiment

NOvA is a two-detector long-baseline neutrino oscillation experiment using Fermilab’s 700 kW NuMI muon neutrino beam. With a total exposure of 8.85×10$^{20}$+12.33×10$^{20}$ protons on target delivered to NuMI in the neutrino + antineutrino beam mode (78% more antineutrino data than in 2018), the experiment has made a 4.4$\sigma$-significant observation of the $\bar{v}$'e appearance in a $\bar{v}$$\mu$ beam, measured oscillation parameters |Δm$_{32}^{2}$|, sin$^{2}$O$_{23}$, and excluded most values near $\delta$$_{CP}$ = $\pi$/2 for the inverted neutrino mass hierarchy by more than 3$\sigma$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1973

Motor starting a Brayton cycle power conversion system using a static inverter

The power conversion module of a 2- to 15-kWe Brayton engine was motor started using a three-phase, 400-hertz static inverter as the power source. Motor-static tests were conducted for initial gas loop pressures of 10, 14, and 17 N/sq cm (15, 20, and 25 psia) over a range of initial turbine inlet temperatures from 366 to 550 K (200 to 530 F). The data are presented to show the effects of temperature and pressure on the motor-start characteristics of the rotating unit. Electrical characteristics during motoring are also discussed.

Curreri, J. S.↗
DOE OSTI2026

Inertia estimation for power grids: A review of methods, challenges, and future prospects

The electric power grid is undergoing a significant transformation, shifting from traditional synchronous generators to inverter-based resources (IBRs) such as solar photovoltaics, wind turbines, and energy storage systems. This evolution leads to a reduction in system inertia, a critical attribute for maintaining frequency stability in response to disturbances. Consequently, the ability to monitor and estimate system inertia has become increasingly essential. This paper provides a comprehensive review of existing inertia estimation methodologies, analyzing them from multiple perspectives, including the types of data utilized, underlying estimation principles, operational modes, and system-wide applicability. A comparative summary table is included to distill commonalities and key characteristics across various studies. In addition, the paper examines practical implementations of inertia estimation across several major power systems worldwide, including the U.S. interconnections, the Nordic power system, and the U.K. grid. Key challenges are identified, particularly in estimating contributions from virtual inertia sources and load-induced inertia in increasingly converter-dominated networks. To address these emerging challenges, the paper proposes an integrated framework for real-time inertia estimation and monitoring. This framework encompasses critical components such as data acquisition, inertia estimation from both synchronous and non-synchronous sources, load-induced effects, optimization techniques, forecasting, and virtual inertia scheduling. Collectively, these elements enable dynamic, system-wide monitoring and adaptive control of grid inertia.

Inertia estimation↗
DOE OSTI2021

Joint inversion of electromagnetic measurements for the determination of water saturation profiles in concrete structures

Highlights: • DC-electrical and dielectric data are combined to estimate concrete saturation degree. • A joint inversion approach of the electromagnetic measurements is proposed. • The sensitivity of the measurements to the saturation model parameters is analysed. • The new joint approach was developed and is applied to synthetic and real data. • The benefits of the joint approach over the inversion of one data type are highlighted. Water saturation profiles in concrete are essential to assess its durability and can be determined using non-destructive techniques, especially the electric and the capacitive methods. In this paper, we propose a new inversion scheme where both resistivity and permittivity measurements are inverted jointly to retrieve the saturation profile. The finite element method is used to model the measurements in 3D, the concrete having a saturation profile with depth, represented by a continuous model taking the form of a Weibull curve with four parameters. A non-linear least-squares optimization based on the Levenberg-Marquardt scheme is developed for the inversion of measurements. Results show that information gathered from both measurements enriches the reconstructed profile, leading to a more reliable estimation of saturation profiles. We believe that the joint inversion method herein developed could lead to the study of more complex phenomena, such as the coupled water-chloride ingress.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Comparing Adjoint Waveform Tomography Models of California Using Different Starting Models

Abstract Adjoint waveform tomography (AWT) sits at the cutting edge of seismic tomography on local, regional, and global scales. However, the choice in starting model may have a significant impact on the final inversion results. In this paper, we present 3 AWT models of California that are based on different starting models. We chose three models that were inverted at different scales: SPiRaL, a global travel‐time tomography model (Simmons et al., 2021, 10.1093/gji/ggab277 ), CSEM_NA, a regional adjoint tomography model of North America and the North Atlantic (Krischer et al., 2018, 10.1029/2017JB015289 ), and WUS256, a regional adjoint tomography model of the western US (Rodgers et al., 2022, https://doi.org/10.1029/2022JB024549 ). We then inverted three AWT models using the same source and receiver set. We ran each model over three period bands: 30–100 s, 25–100 s, and 20–80 s. Once the iterations were finalized, we used five methods of testing model similarity in both the model and data space. We conclude that the choice of starting model has a minimal impact on long wavelength models if an appropriate multi‐scale inversion approach is used.

58 GEOSCIENCES↗
DOE OSTI2025

Active Learning of Microgrid Frequency Dynamics Using Neural Ordinary Differential Equations

Accurate frequency modelling of inverter‐based resource (IBR)‐dominated power systems is crucial for ensuring stable, reliable and resilient operations, particularly given their inherent low‐inertia characteristics and fast dynamics that traditional swing equation‐based models inadequately capture. This paper explores neural ordinary differential equations (Neural ODEs) as a computationally efficient, data‐driven framework for modelling power system frequency dynamics, specifically within microgrids integrating high penetrations of distributed energy resources (DERs). The developed neural ODEs framework incorporates a neural network architecture designed to capture input dynamics. By actively perturbing the system with a known signal, the Python‐based neural ODEs framework was trained using measured system states and inputs, without the need for detailed system information. The framework, tested on a model of the Cordova, AK, microgrid, achieved a goodness of fit ranging from 60% to 99% across different state variables and maintained a mean square error in the 10 -6 p.u. range under square and step excitation signals. The proposed approach demonstrated robustness to measurement noise and initial condition variations while maintaining low computational complexity suitable for real‐time power system control applications. Furthermore, transfer learning enabled the neural ODEs model to adapt to the following changes in system topology or generator dispatch, highlighting its effectiveness for dynamic microgrids with frequently evolving configurations and diverse DERs.

Aryal, Tara [South Dakota State Univ., Brookings, ↗