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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Unique Observational Constraints on the Seasonal and Longitudinal Variability of the Earth’s Planetary Albedo and Cloud Distribution Inferred From EPIC Measurements

Thorough comparison to observations is key to developing a credible climate model forecasting capability. Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation provide a unique observational perspective that permits a more reliable model/data comparison than is possible with the otherwise available satellite data. The uniqueness is in the DSCOVR satellite’s viewing geometry, which enables continuous viewing of the Earth’s sunlit hemisphere from its Lissajous orbit around the Lagrangian L1 point. The key instrument is the Earth Polychromatic Imaging Camera (EPIC), which views the Earth’s sunlit hemisphere with 1024-by-1024-pixel imagery in 10 narrow spectral bands from 317 to 780 nm, acquiring up to 22 high spatial resolution images per day. The additional feature is that the frequency of EPIC image acquisition is nearly identical to that of the climate GCM data generation scheme where climate data for the entire globe are ‘instantaneously’ calculated at 1-h radiation time-step intervals. Implementation of the SHS (Sunlit Hemisphere Sampling) EPIC-view geometry for the in-line GCM output data sampling establishes a precise self-consistency in the space-time data sampling between EPIC observational and GCM output data generation and sampling. The remaining problem is that the GCM generated data are radiative fluxes, while the EPIC measurements are backscatter-dependent radiances. Radiance to flux conversion is a complex problem with no simple way to convert GCM radiative fluxes into spectral radiances. The more expedient approach is to convert the EPIC spectral radiances into broadband radiances by MODIS/CERES-based regression relationships and then into solar radiative fluxes using the CERES angular distribution models. Averaging over the sunlit hemisphere suppresses the meteorological weather noise, but preserves the intra-seasonal larger scale variability. Longitudinal slicing by the Earth’s rotation permits a self-consistent model/data comparison of the longitudinal model/data differences in the variability of the reflected solar radiation. Ancillary EPIC Composite data provide additional cloud property information for climate model diagnostics. Comparison of EPIC-derived seasonal and longitudinal variability of the Earth’s planetary albedo with the GISS ModelE2 results shows systematic overestimate of cloud reflectivity over the Pacific Ocean with corresponding underestimates over continental land areas.

DSCOVR↗
DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems: Preprint

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗
NASA NTRS2015

Forecasting Space Weather Events for a Neighboring World

Shortly after NASA's Mars Atmosphere and Volatile EvolutioN mission (MAVEN) spacecraft entered Mars' orbit on 21 September 2014, scientists glimpsed the Martian atmosphere's response to a front of solar energetic particles (SEPs) and an associated coronal mass ejection (CME). In response to some solar flares and CMEs, streams of SEPs burst from the solar atmosphere and are further accelerated in the interplanetary medium between the Sun and the planets. These particles deposit their energy and momentum into anything in their path, including the Martian atmosphere and MAVEN particle detectors. MAVEN scientists had been alerted to the likely CME-Mars encounter by a space weather prediction system that had its origins in space weather forecasting for Earth but now forecasts space weather for Earth's neighboring planets. The two Solar Terrestrial Relations Observatory spacecraft and Solar Heliospheric Observatory observed a CME on 26 September, with a trajectory that suggested a Mars intercept. A computer model developed for solar wind prediction, the Wang-Sheeley-Arge-Enlil cone model [e.g., Zheng et al., 2013; Parsons et al., 2011], running in real time at the Community Coordinated Modeling Center (CCMC) located at NASA Goddard since 2006, showed the CME propagating in the direction of Mars (Figure 1). According to MAVEN particle detectors, the disturbance and accompanying SEP enhancement at the leading edge of the CME reached Mars at approximately 17 hours Universal Time on 29 September 2014. Such SEPs may have a profound effect on atmospheric escape - they are believed to be a possible means for driving atmospheric loss. SEPs can cause loss of Mars' upper atmosphere through several loss mechanisms including sputtering of the atmosphere. Sputtering occurs when atoms are ejected from the atmosphere due to impacts with energetic particles.

space weather↗
DOE OSTI2025

Addressing the Split Incentive Challenge for Enhanced Solar Adoption in Multifamily Rental Properties [Abstract]

The split incentive problem is particularly pronounced in rental markets, where landlords prioritize investments that directly increase property value or rental income. Since energy savings from solar photovoltaic (PV) systems primarily benefit tenants, landlords may perceive little return on investment unless mechanisms exist to recapture some of the financial gains. The primary objective of this project is to develop a publicly available, web-based tool to analyze the U.S. Department of Energy’s ResStock database, which models the U.S. residential building stock. The tool allows users to filter buildings by location, type, HVAC system, square footage, and other characteristics, and outputs typical electric load profiles. By leveraging location-specific electric load data, Fram Energy aims to advance business strategies that address the split incentive barrier and promote the adoption of solar PV installations in rental properties. In addition, a machine learning model will be developed to weigh the marginal contribution of building features across the dataset in predicting electricity demand, supporting guided decision making in forecasting electric load profiles. Lastly, based on each building’s location, load profile, and utility’s electricity rate, an optimized solar photovoltaic array and battery energy storage system will be sized to provide energy arbitrage opportunities.

14 SOLAR ENERGY↗
NASA NTRS2020

Space Weather Architecture Options to Support Human and Robotic Deep Space Exploration

Understanding solar energetic particle (SEP) event characteristics is critical to the proper design of human and robotic space missions. This report details a study of operational response time for monitoring space weather for crewed flights, specifically targeting lunar and Mars missions. The NESC assessment team evaluated the required minimum latency for data streams and forecasts that will directly affect mission operations using a 41-year database (i.e., European Space Agency (ESA) Solar Energetic Particle Environment Modeling (SEPEM) RSDv2.0) of SEP events. The database contained 192 SEP events that resulted in a dose increase above background levels. Of those, 10% were “multiple events,” or events that occurred in quick succession. The analysis provides probabilistic values for time to peak flux and dose rate for the duration of each event. This NESC assessment also evaluated the SEP threshold levels for exploration missions to determine the relevant energy range of required proton measurements.

Solar energetic particle↗
NASA NTRS1975

International Symposium on Remote Sensing of Environment, 10th, University of Michigan, Ann Arbor, Mich., October 6-10, 1975, Proceedings. Volumes 1 & 2

Topics treated include the application of a Fourier transform spectrometer to infrared remote sensing, the performance optimization of a satellite-borne thematic mapper, a data handling system to be integrated with a digital airborne multispectral scanner, infrared thermography for micro- and mesometeorological measurements, satellite interrogated data collection platforms for river and flood forecasting and the automatic measurement of sea surface temperature from a GOES satellite. Solar and atmospheric effects on satellite imagery derived from aircraft reflectance measurements, methods for determining haze levels from multispectral scanner data, restoration of Landsat images by discrete two-dimensional deconvolution and the automatic classification of aircraft and satellite multispectral images using mixed integer programming are also discussed. Individual items are announced in this issue.

Source record↗
NASA NTRS1979

The effects of regional insolation differences upon advanced solar thermal electric power plant performance and energy costs

This paper presents the performance and cost of four 10-MWe advanced solar thermal electric power plants sited in various regions of the continental United States. Each region has different insolation characteristics which result in varying collector field areas, plant performance, capital costs, and energy costs. The paraboloidal dish, central receiver, cylindrical parabolic trough, and compound parabolic concentrator (CPC) comprise the advanced concepts studied. This paper contains a discussion of the regional insolation data base, a description of the solar systems' performances and costs, and a presentation of a range for the forecast cost of conventional electricity by region and nationally over the next several decades.

Latta, A. F.↗
NASA NTRS2002

Efficient Low Dissipative High Order Schemes for Multiscale MHD Flows

Accurate numerical simulations of complex multiscale compressible viscous flows, especially high speed turbulence combustion and acoustics, demand high order schemes with adaptive numerical dissipation controls. Standard high resolution shock-capturing methods are too dissipative to capture the small scales and/or long-time wave propagations without extreme grid refinements and small time steps. An integrated approach for the control of numerical dissipation in high order schemes for the compressible Euler and Navier-Stokes equations has been developed and verified by the authors and collaborators. These schemes are suitable for the problems in question. Basically, the scheme consists of sixth-order or higher non-dissipative spatial difference operators as the base scheme. To control the amount of numerical dissipation, multiresolution wavelets are used as sensors to adaptively limit the amount and to aid the selection and/or blending of the appropriate types of numerical dissipation to be used. Magnetohydrodynamics (MHD) waves play a key role in drag reduction in highly maneuverable high speed combat aircraft, in space weather forecasting, and in the understanding of the dynamics of the evolution of our solar system and the main sequence stars. Although there exist a few well-studied second and third-order high-resolution shock-capturing schemes for the MHD in the literature, these schemes are too diffusive and not practical for turbulence/combustion MHD flows. On the other hand, extension of higher than third-order high-resolution schemes to the MHD system of equations is not straightforward. Unlike the hydrodynamic equations, the inviscid MHD system is non-strictly hyperbolic with non-convex fluxes. The wave structures and shock types are different from their hydrodynamic counterparts. Many of the non-traditional hydrodynamic shocks are not fully understood. Consequently, reliable and highly accurate numerical schemes for multiscale MHD equations pose a great challenge to algorithm development. In addition, controlling the numerical error of the divergence free condition of the magnetic fields for high order methods has been a stumbling block. Lower order methods are not practical for the astrophysical problems in question. We propose to extend our hydrodynamics schemes to the MHD equations with several desired properties over commonly used MHD schemes.

Sjoegreen, Bjoern↗
NASA NTRS2005

Diverse Applications of Occultation Data in Ozone Assimilation

Ozone profiles from solar occultation instruments provide invaluable information that can be used to evaluate the quality of assimilated ozone fields, from case studies to long-term phenomena. Occultation data can also be applied to include physical constraints while developing components of an assimilation system. Conversely, assimilation of occultation data can help in their validation, and it provides a framework for evaluation of the impact of occultation data on constraining global ozone fields within models. We illustrate these diverse applications by a series of examples using the ozone assimilation system at NASA/Goddard. In a case study, low ozone in the lower stratosphere due to transport of air from the Tropics to northern high latitudes that was captured by assimilation of Aura Microwave Limb Sounder (MLS) and Solar Backscatter Ultraviolet (SBUV) data, was found to agree with SAGE III data. For long-term monitoring, the quality of a multi-year SBUV-only assimilation was evaluated using monthly-mean time series of POAM, HALOE, and SAGE I1 data. We found realism in the representation of the annual cycle in ozone and in some aspects of interannual variability. Assimilation of POAM data was shown to improve the representation of lower stratospheric ozone, especially over Antarctica. More recently, we assimilated ILAS II ozone data in order to help in their validation. Solar occultation data are used to estimate parameters in a new model for forecast error variances that is being developed. These examples demonstrate the importance of occultation data for ozone assimilation, and potential of assimilation to increase the impact and the value of occultation data.

Markowitz, A.↗
NASA NTRS2008

Solar Probe Plus: Report of the Science and Technology Definition Team

Solar Probe+ will be an extraordinary and historic mission, exploring what is arguably the last region of the solar system to be visited by a spacecraft, the Sun s outer atmosphere or corona as it extends out into space. Approaching as close as 9.5 RS* (8.5 RS above the Sun s surface), Solar Probe+ will repeatedly sample the near-Sun environment, revolutionizing our knowledge and understanding of coronal heating and of the origin and evolution of the solar wind and answering critical questions in heliophysics that have been ranked as top priorities for decades. Moreover, by making direct, in-situ measurements of the region where some of the most hazardous solar energetic particles are energized, Solar Probe+ will make a fundamental contribution to our ability to characterize and forecast the radiation environment in which future space explorers will work and live.

Source record↗
DOE OSTI2023

Distributed Wind-Hybrid Microgrids with Autonomous Controls and Forecasting

Distributed wind-hybrid microgrids have the potential to provide key resilience and economic benefits to both the customers they serve and the utility grids they are connected to. Such microgrids will likely be a key part of the grid of the future, whether connected to large utility grids or linked together in multi-microgrid systems. Through the hybridization of distributed wind and solar photovoltaics, autonomous device-level and system-level controls, battery energy storage systems with smart inverters, and forecasting, these microgrids could maintain local stability and provide grid services - all with renewable power. In the literature, these elements have been considered individually. However, they have not been combined and demonstrated at a high fidelity, which is essential to prove the concept's operation before moving to hardware-in-the-loop and physical demonstrations. In this work, we develop a high-fidelity MATLAB-Simulink model of a real distributed wind-hybrid microgrid that includes all these elements. We demonstrate the microgrid maintaining stability and production in a variety of islanded, grid-connected, and transition scenarios. This includes riding through faults and grid transitions, handling resource variability, and providing grid services. The results demonstrate, at a high fidelity, how distributed wind-hybrid microgrids can operate in an economic and resilient fashion. Finally, we provide recommendations for future research to move advanced distributed wind-hybrid microgrids toward deployment.

ancillary services↗
NASA NTRS2019

Global Evolution of Solar Magnetic Fields and Prediction of Solar Activity Cycles

Prediction of solar activity cycles is challenging because the physical processes inside the Sun involve a broad range of multiscale dynamics that no model can reproduce, and the available observations are highly limited and cover mostly surface layers. Helioseismology makes it possible to probe solar dynamics in the convective zone, but variations in the differential rotation and meridional circulation are currently available for only two solar activity cycles. It has been demonstrated that sunspot observations, which cover over 400 years, can be used to calibrate the Parker-Kleeorin-Ruzmaikin model and that the Ensemble Kalman Filter (EnKF) method can be used to link the model magnetic fields to sunspot observations to make reliable predictions of a following cycle. However, for more accurate predictions, it is necessary to use actual observations of the solar magnetic fields, which are available for only four solar cycles. This raises the question of how limitations in observational data and model uncertainties affect predictive capabilities and implies the need for the development of new forecast methodologies and validation criteria. In this presentation, I will discuss the influence of the limited number of available observations on the accuracy of EnKF estimates of solar cycle parameters.

Kitiashvili, Irina N.↗
DOE OSTI2020

SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of realtime, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m -2 ) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m -2 . For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m -2 for all seven locations. These results, together with the low-latency of the method (1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

42 ENGINEERING↗
NASA NTRS2023

Foreshock Ion Motion Across Discontinuities: Formation of Foreshock Transients

In the ion foreshock, hot flow anomalies (HFAs) and foreshock bubbles (FBs) are two types of foreshock transients that have the strongest fluctuations, which can disturb the magnetosphere-ionosphere system and increase shock acceleration efficiency. They form due to interaction between the foreshock ions and solar wind discontinuities: the direction of the foreshock ion-driven current and whether it decreases or increases the magnetic field strength behind the discontinuity determine whether the transient's formation can be promoted or suppressed. Thus, to predict the HFA and FB formation and forecast their space weather effects, it is necessary to predict the foreshock ion-driven current direction. In this study, we derive analytical equations of foreshock ion velocities within discontinuities to estimate foreshock ion-driven current direction, which provides a quantitative criterion of HFA and FB formation. To validate the criterion, we use Acceleration Reconnection Turbulence & Electrodynamics of Moon's Interaction with the Sun to observe pristine solar wind discontinuities and calculate discontinuity parameters. We use Magnetospheric Multiscale to observe the foreshock ion motion around the discontinuities and show that the data support our model. This study is another step toward a predictive model of HFA and FB formation so that we can forecast their space weather effects at Earth using solar wind observations at lunar orbit or L1.

Terry Z. Liu↗
NASA NTRS2011

Real-Time CME Forecasting Using HMI Active-Region Magnetograms and Flare History

We have recently developed a method of predicting an active region s probability of producing a CME, an X-class Flare, an M-class Flare, or a Solar Energetic Particle Event from a free-energy proxy measured from SOHO/MDI line-of-sight magnetograms. This year we have added three major improvements to our forecast tool: 1) Transition from MDI magnetogram to SDO/HMI magnetogram allowing us near-real-time forecasts, 2) Automation of acquisition and measurement of HMI magnetograms giving us near-real-time forecasts (no older than 2 hours), and 3) Determination of how to improve forecast by using the active region s previous flare history in combination with its free-energy proxy. HMI was turned on in May 2010 and MDI was turned off in April 2011. Using the overlap period, we have calibrated HMI to yield what MDI would measure. This is important since the value of the free-energy proxy used for our forecast is resolution dependent, and the forecasts are made from results of a 1996-2004 database of MDI observations. With near-real-time magnetograms from HMI, near-real-time forecasts are now possible. We have augmented the code so that it continually acquires and measures new magnetograms as they become available online, and updates the whole-sun forecast from the coming day. The next planned improvement is to use an active region s previous flare history, in conjunction with its free-energy proxy, to forecast the active region s event rate. It has long been known that active regions that have produced flares in the past are likely to produce flares in the future, and that active regions that are nonpotential (have large free-energy) are more likely to produce flares in the future. This year we have determined that persistence of flaring is not just a reflection of an active region s free energy. In other words, after controlling for free energy, we have found that active regions that have flared recently are more likely to flare in the future.

Falconer, David↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Future Solar Irradiance Projections Using the NSRDB

Assessing renewable energy resources under future climate scenarios has been highlighted to understand potential impacts of future climate change in renewable generation on the power sector. Climate model projection has been recognized by the renewable energy community as a useful data set to analyze the impacts of future climate change on renewable resources. However, future climate projections generated from general circulation models (GCMs) contain inherent biases that need to be corrected for accurate analysis of future projections of climate variables. In addition, the coarse spatiotemporal resolution of GCMs needs to be improved for regional climate studies. In this work, we develop statistical methods to downscale future projections of global horizontal irradiance (GHI) in a computationally efficient way. Our approach builds statistical downscaling models that correct bias of climate projection of GHI and downscale the future GHI projection from daily-scale to hourly-scale. The National Solar Radiation Database (NSRDB) is used to calibrate the statistical models and validate the downscaled GHI projections across the contiguous United State (CONUS). Preliminary results show that the statistical approach efficiently downscales climate projections of GHI with a nBIAS of 3%, nMAE of 34 % and nRMSE of 46% calculated against NSRDB for CONUS. This study describes the implemented methodology and initial results as well as future research to create high-resolution climate data sets for solar energy applications.

analytical models↗
DOE OSTI2021

A Multi-Stage Stochastic Risk Assessment With Markovian Representation of Renewable Power

Probabilistic forecasts provide a distribution of possible outputs and so can capture the uncertainty and variability of Variable Renewable Energy (VRE). However, taking advantage of uncertainty information has practical challenges that make it difficult to integrate probabilistic forecasting into control room decision-making. This paper proposes a novel use-case for probabilistic forecasts by incorporating them into the hour-ahead operations for situational awareness via a risk-averse multi-stage stochastic program. We employ a Markovian representation of the probabilistic forecasts that enables the formulation of the multi-stage problem and avoids a scenario generation phase. We test the model on a realistically sized system to assess risk and showcase the capability of using probabilistic renewable forecast as input to produce probabilistic output forecasts of future system states. The results show that the model can capture time consistency in the reserves and Area Control Error (ACE) forecast. The solution times are adequate for risk profiling in hour-ahead timescales.

forecasting↗
DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗