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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HERO WEC V1.0 2024 - WEC-Sim Detailed Simulation Runs and Summary Data

This dataset includes results from simulations of NREL's hydraulic and electric reverse osmosis wave energy converter (HEREO WEC). Simulation runs include 135 wave cases that were based on the updated WEC-Sim model, which is linked below. The data represented in this repository is based on an updated WEC-Sim model using laboratory data to tune and refine the original WEC-Sim model for the V1.0 HERO WEC. The 135 wave cases represent waves with the following wave height and wave period ranges: - Significant Wave Height: 0.25 - 3.75m in 0.25m increments - Wave Period: 5 - 13 sec in 1 sec increments Each run was simulated using a Pierson-Moskowitz irregular wave spectrum with a 100 second ramp time, a total simulation time of 3,100 seconds, and a simulation time-step of 0.005s. A reference table has been included to map each multi condition run (MCR) case with each wave condition. Summary data set includes a spreadsheet and image files with matrices that are associated with data from simulation runs. All matrices cover the same significant wave height and wave periods from the simulation runs, in the same increments. The following matrices are included: - Power Abs: The average absorbed power from the WEC (calculated from anchor reaction force and heave velocity) - Power Hyd: The average hydraulic power output at pump (calculated from pump output flow and pressure) - Power - Hyd ROi: The average hydraulic power measured at the RO system inlet (calculated from RO system pressure and flow (pre-accumulator)) - Flow - Pump out: The average flowrate measured at the pump outlet - Flow - Perm: The average permeate (clean water) production - Flow - RO (pre): The average flowrate measured at the inlet of the RO system before the accumulators - Flow - RO (post): The average flowrate measured after the accumulator bank in the RO system - Pressure - RO: The average pressure measured at the inlet of the RO system This data set has been developed by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
NASA NTRS2006

The Aquarius Scatterometer: An Active System for Measuring Surface Roughness for Sea-Surface Brightness Temperature Correction

The Aquarius scatterometer is a total-power L-band radar system for estimating ocean surface roughness. Its measurements will enable the removal of wind effects from the Aquarius radiometer ocean-surface brightness temperature measurements being used to retrieve ocean salinity. The Aquarius scatterometer is a relatively simple, low-spatial resolution power-detecting radar, without ranging capability. But to meet its science requirement, it must be very stable, with repeatability on the order of 0.1 dB over several days, and calibrated accuracy to this level over several months. Data from this instrument over land as well as ocean areas will be available for a variety of geophysical applications.

scatterometers↗
NASA NTRS1990

Comparison of stratospheric ozone profiles and their seasonal variations as measured by lidar and Stratospheric Aerosol and Gas Experiment during 1988

A ground-based, high power, laser remote sensing system for measurements of stratospheric ozone concentration profiles has been in operation at the Jet Propulsion Laboratory Table Mountain Facility located in southern California, since January 1988. The seasonal variations observed in the ozone profiles, during 1988 and as a function of altitude, are described here. These profiles are compared with those from the Stratospheric Aerosol and Gas Experiment satellite instrument made within a radius of 1000 km from the lidar and also with the zonal mean measurements made in the band 34.4 +/- 5 deg. Comparison with the proposed new CIRA ozone reference model has also been carried out. The seasonal variations, between 25 and 50 km, observed by the two instruments and indicated by the reference model are in good agreement.

Mcdermid, I. Stuart↗
NASA NTRS1988

Deriving a geocentric reference frame for satellite positioning and navigation

With the advent of Earth-orbiting geodetic satellites, nongeocentric datums or reference frames have become things of the past. Accurate geocentric three-dimensional positioning is now possible and is of great importance for various geodetic and oceanographic applications. While relative positioning accuracy of a few centimeters has become a reality using very long baseline interferometry (VLBI), the uncertainty in the offset of the adopted coordinate system origin from the geocenter is still believed to be on the order of 1 meter. Satellite laser ranging (SLR), however, is capable of determining this offset to better than 10 cm, but this is possible only after years of measurements. Global Positioning System (GPS) measurements provide a powerful tool for an accurate determination of this origin offset. Two strategies are discussed. The first strategy utilizes the precise relative positions that were predetermined by VLBI to fix the frame orientation and the absolute scaling, while the offset from the geocenter is determined from GPS measurements. Three different cases are presented under this strategy. The reference frame thus adopted will be consistent with the VLBI coordinate system. The second strategy establishes a reference frame by holding only the longitude of one of the tracking sites fixed. The absolute scaling is determined by the adopted gravitational constant (GM) of the Earth; and the latitude is inferred from the time signature of the Earth rotation in the GPS measurements. The coordinate system thus defined will be a geocentric Earth-fixed coordinate system.

Malla, R. P.↗
DOE OSTI2020

Optimal PMU Restoration for Power System Observability Recovery After Massive Attacks

Cyber-physically resilient power system operation requires rapid recovery of situational awareness after disastrous scenarios such as massive cyber attacks. In this article, the concept of optimal sequential restoration of Phasor Measurement Units is introduced, aiming at rapid recovery of power system observability in post-attack scenarios. Additionally, the observability recovery problem is formulated as a Mixed Integer Linear Programming problem to maximize the cumulative observability level gained over time during the restoration process. Three alternative objective functions-maximizing the number of observable buses, the number of observable branches, and the total amount of observable power flow-are considered. The effectiveness of the proposed optimal strategy is verified by comparing it with heuristic approaches on the IEEE 57-bus system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Rapid Event Detection via Synchro-Waveform Based Temporal Attention Network in Distributed Grid

Compared with the information collected from phasor measurement units, synchro-waveforms contain high-fidelity disturbances of the grid, which can be a granular and authentic representation of measurements in the modern power system. However, the dynamic changing morphology makes it challenging to effectively capture various disturbance information from the synchro-waveforms. To tackle this issue, this paper proposes a Synchro-waveform based Temporal Attention (STA) network to achieve rapid event detection. First, a multi-scenario distributed model with renewable integration is established to generate synchro-waveforms under various uncertainties. Then, three typical temporal features are extracted directly from the synchro-waveform measurements. Additionally, the lightweight STA network is deployed to identify the most common event types in renewable energy systems via the self-attention based vision transformer module. The results from simulated experiments demonstrate that the proposed approach can achieve rapid and real-time detection within 0.81 ms and over 96.27 % accuracy.

Dong, Yuqing [University of Tennessee (UT)]↗
DOE OSTI2026

Reshaping the Quantum Arrow of Time

While the microscopic laws of physics are often symmetric under time reversal, most natural processes that we observe are not. The emergent asymmetry between typical and time-reversed processes is referred to as the arrow of time. In quantum physics, an arrow of time emerges when a sequence of measurements is performed on a system. We introduce quantum control tools that can yield dynamics more consistent with time flowing backward than forward. The control tools are based on the explicit construction of a Hamiltonian that can replicate the stochastic trajectories of a monitored quantum system. Such a Hamiltonian can reverse the effect of monitoring and, via a feedback process, generate trajectories consistent with a reversed arrow of time. It can also be used to simulate the backward-in-time dynamics of an open quantum system. Finally, we design a feedback-driven continuous measurement engine powered by the energy pumped into the system by the monitoring process. We show that the engine can operate under experimentally realizable conditions with feedback delay and finite-efficiency measurements.

Entropy production↗
DOE OSTI2026

Detection of Critical Surface Features in PTLs and GDLs for Improved Device Performance and Manufacturing Reliability

High points, or features that protrude above the surface of the material, on porous transport layers (PTLs) and gas diffusion layers (GDLs) can be critical features that may affect the manufacturing process and the performance of the device containing the feature. High points on PTLs and GDLs may stress the membrane of a polymer electrolyte membrane (PEM) during lamination and cell operation of a PEM electrolyzer or fuel cell. Additionally, high points on GDLs may impact the reliability of the manufacturing process. Thus, understanding these critical features and developing procedures to detect them are a key part of developing quality control techniques for PTLs and GDLs. This work evaluates the effectiveness of the Keyence VR6200 benchtop-scale structured light optical profilometer for detection of surface protrusions on PTLs and GDLs. Standard testing procedures for detecting and measuring high points were created for use on both material types. These procedures were evaluated using Gage Repeatability and Reproducibility (Gage R&R), where the repeatability, reproducibility, and effectiveness of the system to detect and measure high points were quantified. We have shown with high statistical power that the system is very effective in detection and measurement of high points, with Gage R&R contributions measured to be 2.2% and 3.6% for PTLs and GDLs, respectively.

36 MATERIALS SCIENCE↗
NASA NTRS1980

Analytical simulation of SPS system performance, volume 3, phase 3

The simulation model for the Solar Power Satellite spaceantenna and the associated system imperfections are described. Overall power transfer efficiency, the key performance issue, is discussed as a function of the system imperfections. Other system performance measures discussed include average power pattern, mean beam gain reduction, and pointing error.

Kantak, A. V.↗
DOE OSTI2024

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Remotely powered line monitor

Improved devices, apparatuses, systems, and methods involving the use of field probes with voltage-controlled variable impedance elements and surface-launched acoustic wave devices, including surface acoustic wave (SAW) devices and SAW sensor-tag wireless interface devices to measure voltage and current in current carrying conductors (CCCs) via measurement of the electric fields and magnetic fields around said CCCs are disclosed. The devices, apparatuses, systems, and methods taught in the present invention include a remotely powered line-mounted measurement unit that requires no connection to electrical ground for operation, and that has no internal energy source, no energy harvesting or power conditioning circuitry, no discrete energy storage components, and no radio. The devices, apparatuses, systems, and methods taught in the present invention also provide for determining the phase (leading or lagging) of the current relative to the voltage in AC power systems, and also provide for measurement of temperature of the conductor.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1982

Laser Doppler velocimetry application in the Langley 0.3-meter Transonic Cryogenic Tunnel

The problems and the potential use of a nonintrusive flow velocity measuring technique in the Langley 0.3 meter transonic cryogenic tunnel (TCT) were investigated. A laser velocimeter (LV) was used. It was concluded that free-stream velocity measurements can be successfully made in the Langley 0.3-m TCT using a low-power (15-mW) LV system. The measured and calculated mean velocities typically agreed within one percent. The overall normalized standard deviation was less than one percent. Tunnel vibration and temperature had no detrimental effects on the optical system. It is recommended that the LV work should be further investigated for future use in the Langley 0.3-m TCT.

Gartrell, L. R.↗
DOE OSTI2022

Machine Learning Using a Simple Feature for Detecting Multiple Types of Events From PMU Data

This paper describes simple and efficient machine learning (ML) methods for efficiently detecting multiple types of power system events captured by PMUs scarcely placed in a large power grid. It uses a single feature from each PMU based on a rectangle area enclosing the event in a given data window. This single feature is sufficient to enable commonly used ML models to detect different types of events quickly and accurately. The feature is used by five ML models on four different data-window sizes. The results indicated a tradeoff between the execution speed and detection accuracy in variety of data-window size choices. Here, the proposed method is insensitive to most data quality issues typical for data from field PMUs, and thus it does not require major data cleansing efforts prior to feature extraction.

Big data↗
NASA NTRS2019

Thermal Design Challenges for In-Flight Exposure to an Electric Propulsion Plasma Plume Environment

The use of electric propulsion to carry out NASA in-space propulsion demands has been increasing. A big part of fulfilling this demand is the Power and Propulsion Element (PPE) for NASA's Lunar Gateway. When built, the PPE will have the largest electric propulsion system to ever fly on a spacecraft, which brings new and difficult challenges. The environment created by the electric propulsion system during on-orbit operation of the thrusters has been shown to be different than those measured during operation in terrestrial vacuum facilities. Understanding the on-orbit environment created by the thrusters and its impacts on the spacecraft is the goal of the Plasma Diagnostic Package (PDP). The PDP is a sensor package, which is being developed by NASA GRC (Glenn Research Center), to fly on the PPE. The PDP will measure different aspects of the thruster plume in order to develop higher fidelity modeling of EP (Electric Power) systems. In order to capture quality measurements of the plume the PDP will need to install sensors in close proximity of it. This poses several unique thermal design challenges. Some of these challenges include: the long duration exposure to plume induced heating, the effects of plume induced erosion and sputter deposition on thermal control surfaces, and the extreme environments of a cis-lunar orbit. This paper looks to define the thermal challenges, explain modeling techniques, and offer design solutions for unique challenges of the PDP mission.

Thermal↗
NASA NTRS1987

Single-state selection system for hydrogen masers

The design and operation of a single-state selection system based on the adiabatic fast-passage technique for atomic state inversion is described. The system uses a variable-pitch dc solenoid and orthogonal four-wire RF coil to create the state-changing magnetic fields, and a half-wavelength state selection magnet to remove the inverted state atoms from the hydrogen beam. The system's performance was determined by measuring maser power as a function of line Q. The system removes more than 85 percent of undesired atoms from the beam. The potential effects of improvements in the maser on its frequency stability have been calculated. The use of single-state selection combined with increased RF coupling to the cavity and increased storage bulb collimation offers the possibility of frequency stability in the 10 to the -17th regime over intervals on the order of 60,000 s.

Mattison, Edward M.↗
DOE OSTI2020

Impedance-Based Prediction of SSR-Generated Harmonics in Doubly-Fed Induction Generators

Doubly-fed induction generators (DFIGs) are prone to subsynchronous resonance (SSR) with series-compensated transmission lines and experiencing SSR-generated harmonics of wide-ranging magnitudes. This paper presents a large-signal impedance-based approach for the prediction of the magnitude of SSR-generated harmonics in DFIGs. Note that the large-signal impedance of a network shows variation in its impedance response with the magnitude of perturbation at its terminals. It is discovered in this paper that the impedance of a series-compen- sated transmission line starts changing with the magnitude of SSR because of the metal-oxide varistor-based protection of series capacitors in the line. The changing line impedance halts the growth of the SSR beyond a certain point, where the DFIG forms a limit-cycle mode and emits harmonics at the SSR frequency. The discovered role of the protection of series capacitors in SSR not only enables the prediction of SSR-generated harmonics but also can be used for the timely detection of an SSR event to avoid system disruption and sudden loss of bulk generation. The large- signal impedance-based prediction of SSR-generated harmonics in DFIG-based wind and hydro generators is demonstrated using numerical simulations of a 2.5-MVA, 690-V DFIG supplying to a series-compensated transmission line.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2022

Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM

A large amount of synchrophasor data in the wide area measurement system (WAMS) needs to be collected and transmitted to the phasor data concentrator, thereby increasing the possibility of being attacked by hackers. The attacked data are therefore hidden into the normal synchrophasor data so that the synchrophasor data based application will be affected. To remedy this problem, an identification framework is proposed to detect the data cyber-attack in WAMS utilizing variational mode decomposition (VMD) and multifusion support vector machine (MSVM). First, VMD is used to transform the attacked data into multiple modal components. Thereafter, a novel MSVM is employed to classify the deterministic features using the proposed linear combined multikernel (LCM). Further, this LCM can fuse multiple types of features, including the time, frequency, and statistical domains of the synchrophasor data. Utilizing the actual data from FNET/GridEye, different experiments are conducted under multiple attack strengths and types. The results demonstrate that the identification framework has higher precision and robustness compared with other conventional classifiers.

97 MATHEMATICS AND COMPUTING↗