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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Evaluation of the Impact of AIRS Radiance and Profile Data Assimilation in Partly Cloudy Regions

Improvements to global and regional numerical weather prediction have been demonstrated through assimilation of data from NASA s Atmospheric Infrared Sounder (AIRS). Current operational data assimilation systems use AIRS radiances, but impact on regional forecasts has been much smaller than for global forecasts. Retrieved profiles from AIRS contain much of the information that is contained in the radiances and may be able to reveal reasons for this reduced impact. Assimilating AIRS retrieved profiles in an identical analysis configuration to the radiances, tracking the quantity and quality of the assimilated data in each technique, and examining analysis increments and forecast impact from each data type can yield clues as to the reasons for the reduced impact. By doing this with regional scale models individual synoptic features (and the impact of AIRS on these features) can be more easily tracked. This project examines the assimilation of hyperspectral sounder data used in operational numerical weather prediction by comparing operational techniques used for AIRS radiances and research techniques used for AIRS retrieved profiles. Parallel versions of a configuration of the Weather Research and Forecasting (WRF) model with Gridpoint Statistical Interpolation (GSI) are run to examine the impact AIRS radiances and retrieved profiles. Statistical evaluation of a long-term series of forecast runs will be compared along with preliminary results of in-depth investigations for select case comparing the analysis increments in partly cloudy regions and short-term forecast impacts.

Zavodsky, Bradley↗
NASA NTRS2009

Radar Differential Phase Signatures of Ice Orientation for the Prediction of Lightning Initiation and Cessation

The majority of lightning-related casualties typically occur during thunderstorm initiation (e.g., first flash) or dissipation (e.g., last flash). The physics of electrification and lightning production during thunderstorm initiation is fairly well understood. As such, the literature includes a number of studies presenting various radar techniques (using reflectivity and, if available, other dual-polarimetric parameters) for the anticipation of initial electrification and first lightning flash. These radar techniques have shown considerable skill at forecasting first flash. On the other hand, electrical processes and lightning production during thunderstorm dissipation are not nearly as well understood and few, if any, successful techniques have been developed to anticipate the last flash and subsequent cessation of lightning. One promising approach involves the use of dual-polarimetric radar variables to infer the presence of oriented ice crystals in lightning producing storms. In the absence of strong vertical electric fields, ice crystals fall with their largest (semi-major) axis in the horizontal associated with gravitational and aerodynamic forces. In thunderstorms, strong vertical electric fields (100-200 kV m(sup -1)) have been shown to orient small (less than 2 mm) ice crystals such that their semi-major axis is vertical (or nearly vertical). After a lightning flash, the electric field is typically relaxed and prior radar research suggests that ice crystals rapidly resume their preferred horizontal orientation. In active thunderstorms, the vertical electric field quickly recovers and the ice crystals repeat this cycle of orientation for each nearby flash. This change in ice crystal orientation from primarily horizontal to vertical during the development of strong vertical electric fields prior to a lightning flash forms the physical basis for anticipating lightning initiation and, potentially, cessation. Research has shown that radar reflectivity (Z) and other co-polar back-scattering radar measurements like differential reflectivity (Z(sub dr)) typically measured by operational dual-polarimetric radars are not sensitive to these changes in ice crystal orientation. However, prior research has demonstrated that oriented ice crystals cause significant propagation effects that can be routinely measured by most dual-polarimetric radars from X-band (3 cm) to S-band (10 cm) wavelengths using the differential propagation phase shift (often just called differential phase, phi(sub dp)) or its range derivative, the specific differential phase (K(sub dp)). Advantages of the differential phase include independence from absolute or relative power calibration, attenuation, differential attenuation and relative insensitivity to ground clutter and partial beam occultation effects (as long as the signal remains above noise). In research mode, these sorts of techniques have been used to anticipate initial cloud electrification, lightning initiation, and cessation. In this study, we develop a simplified model of ice crystal size, shape, orientation, dielectric, and associated radar scattering and propagation effects in order to simulate various idealized scenarios of ice crystals responding to a hypothetical electric field and their dual-polarimetric radar signatures leading up to lightning initiation and particularly cessation. The sensitivity of the K(sub dp) ice orientation signature to various ice properties and radar wavelength will be explored. Since K(sub dp) is proportional to frequency in the Rayleigh- Gans scattering regime, the ice orientation signatures should be more obvious at higher (lower) frequencies (wavelengths). As a result, simulations at radar wavelengths from 10 cm down to 1 cm (Ka-band) will be conducted. Resonance effects will be considered using the T-matrix method. Since most K(sub dp) Vbased observations have been shown at S-band, we will present ice orientation signatures from C-band (UAH/NASA ARMOR) and X-bd (UAH MAX) dual-polarimetric radars located in Northern Alabama. Issues related to optimal radar scanning for the detection of oriented ice will be discussed. Preliminary suggestions on how these differential phase signatures of oriented ice could contribute to lightning initiation and cessation algorithms will be presented.

Carey, L.D.↗
NASA NTRS1982

Medium-range objective predictions of thunderstorms on the McIDAS/CSIS interactive computer system

Until recently, all operational meteorological data has been made available to forecasters in a variety of different forms. Predictions based upon these different data formats have been complicated by the inability of forecasters to easily assimilate, in real-time, all data to provide an optimum decision regarding future weather occurrences. By March 1980, a joint NASA/NOAA effort had been initiated to develop the Centralized Storm Information System (CSIS). The primary objectives of this joint project are related to an improvement of the overall severe storm forecast and warning procedure and to a demonstration of the operational utility of techniques developed within the applied research community. CSIS is to utilize the Man Computer Interactive Data Access System (McIDAS). The present investigation is concerned with one of the first attempts to employ the CSIS system for the evaluation of a new research technique involving the prediction of thunderstorms over a forecast period of 12-48 hours.

Wilson, G. S.↗
NASA NTRS1987

Variational four-dimensional analysis using quasi-geostrophic constraints

A variational four-dimensional analysis technique using quasi-geostrophic models as constraints is examined using gridded fields as data. The analysis method uses a standard iterative nonlinear minimization technique to find the solution to the constraining forecast model which best fits the data as measured by a predefined functional. The minimization algorithm uses the derivative of the functional with respect to each of the initial condition values. This derivative vector is found by inserting the weighted differences between the model solution and the inserted data into a backwards integrating adjoint model. The four-dimensional analysis system was examined by applying it to fields created from a primitive equations model forecast and to fields created from satellite retrievals. The results show that the technique has several interesting characteristics not found in more traditional four-dimensional assimilation techniques. These features include a close fit of the model solution to the observations throughout the analysis interval and an insensitivity to the frequency of data insertion or the amount of data. The four-dimensional analysis technique is very versatile and can be extended to more complex problems with little theoretical difficulty.

Derber, John C.↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2022

Multi-service battery energy storage system optimization and control

Battery energy storage systems (BESS) have become fundamental part of modern power systems due to their capability to provide multiple grid services. As the renewable penetration increases, BESS procurement is also expected to increase where it is envisioned to play a systematic and strategical role in power systems planning and operation. Hence, in this paper we present a multiple grid service procurement and operation for BESS - ranging from energy arbitrage, reserve/regulation services, power factor correction, and demand management. The proposed framework considers an optimal multi-temporal dimension, designed to be operable for both planning and real-time operation. Moreover, non-linearity inherent to BESS services and uncertainty associated to market forecasts variables are addressed using techniques such as polyhedral norms and robust optimization approaches. Here, the developed model is tested using a utility-scaled BESS and the obtained results show the effectiveness of the systematic BESS multi-service planning and operation approach.

25 ENERGY STORAGE↗
DOE OSTI2025

Evaluating Sea Breezes and Associated Convective Cloud Evolution in the Model Gray Zone

We characterize convective clouds associated with sea‐breeze circulations (SBC) using multi‐agency observations and multi‐case ensemble model simulations. The focus is on assessing convective cloud lifecycle properties and their merging behavior, as well as the environmental conditions they are embedded in, particularly SBC features. In total, 46 SBC days over the Houston‐Galveston region are selected and simulated using the Weather Research and Forecasting (WRF) model at a gray zone scale with a forecast‐like parameterization setup. Advanced techniques, including change‐point detection, a Lagrangian cloud tracking method, and a newly developed cell merging and splitting detection algorithm, are applied and/or developed for this study. Our findings indicate that the WRF model at 1 km grid spacing well represents the thermodynamic conditions over the region, as well as SBC timing and intensity. However, for the associated convective cells, WRF overestimates the 30‐dBZ echo top height, cell area, and maximum radar reflectivity compared to radar observations. This overestimation is potentially due to under‐resolved entrainment processes, an overestimated merging frequency, and the overestimation of updraft intensity. Furthermore, the model exhibits a deficiency in simulating congestus clouds, showing a more rapid transition from shallow to deep convection compared to observed behavior. Moreover, observations indicate stronger, deeper, and wider clouds when merging happens. Conversely, in simulations, the merging process does not necessarily lead to higher or longer‐lived cells, as many cases experience rapid and frequent merging and splitting which may result in more variance in convective updraft velocity during the convection lifetime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

New Projections for Dark Matter Searches with Paleo-Detectors

Paleo-detectors are a proposed experimental technique to search for dark matter (DM). In lieu of the conventional approach of operating a tonne-scale real-time detector to search for DM-induced nuclear recoils, paleo-detectors take advantage of small samples of naturally occurring rocks on Earth that have been deep underground (≳5 km), accumulating nuclear damage tracks from recoiling nuclei for O(1)Gyr. Modern microscopy techniques promise the capability to read out nuclear damage tracks with nanometer resolution in macroscopic samples. Thanks to their O(1)Gyr integration times, paleo-detectors could constitute nuclear recoil detectors with keV recoil energy thresholds and 100 kilotonne-yr exposures. This combination would allow paleo-detectors to probe DM-nucleon cross sections orders of magnitude below existing upper limits from conventional direct detection experiments. In this article, we use improved background modeling and a new spectral analysis technique to update the sensitivity forecast for paleo-detectors. We demonstrate the robustness of the sensitivity forecast to the (lack of) ancillary measurements of the age of the samples and the parameters controlling the backgrounds, systematic mismodeling of the spectral shape of the backgrounds, and the radiopurity of the mineral samples. Specifically, we demonstrate that even if the uranium concentration in paleo-detector samples is 10−8 (per weight), many orders of magnitude larger than what we expect in the most radiopure samples obtained from ultra basic rock or marine evaporite deposits, paleo-detectors could still probe DM-nucleon cross sections below current limits. For DM masses ≲ 10 GeV/c2, the sensitivity of paleo-detectors could still reach down all the way to the conventional neutrino floor in a Xe-based direct detection experiment.

Baum, Sebastian (ORCID:0000000167929381)↗
NASA NTRS1975

Operational applications of NOAA-VHRR imagery in Alaska

Near-real time operational applications of NOAA satellite enhanced thermal infrared imagery to snow monitoring for river flood forecasts, and a photographic overlay technique of imagery to enhance snowcover are presented. Ground truth comparisons show a thermal accuracy of approximately + or - 1 C for detection of surface radiative temperatures. The application of NOAA imagery to flood mapping is also presented.

Seifert, R. D.↗
NASA NTRS1979

Analyzing water resources

Report on water resources discusses problems in water measurement demand, use, and availability. Also discussed are sensing accuracies, parameter monitoring, and status of forecasting, modeling, and future measurement techniques.

Source record↗
NASA NTRS1981

Impact of VLSI/VHSIC on satellite on-board signal processing

Forecasted improvements in IC fabrication techniques, such as the use of X-ray lithography, are expected to yield submicron circuit feature sizes within the decade of the 1980s. As dimensions decrease, reliability, cost, speed, power consumption and density improvements will be realized which have a significant impact on the capabilities of onboard spacecraft signal processing functions. This will in turn result in increases of the intelligence that may be deployed on spaceborne remote sensing platforms. Among programs oriented toward such goals are the silicon-based Very High Speed Integrated Circuit (VHSIC) researches sponsored by the U.S. Department of Defense, and efforts toward the development of GaAs devices which will compete with silicon VLSI technology for future applications. GaAs has an electron mobility which is five to six times that of silicon, and promises commensurate computation speed increases under low field conditions.

Aanstoos, J. V.↗
NASA NTRS1999

Simulated Surface Energy Budgets Over the Southeastern US: The GHCC Satellite Assimilation System and the NCEP Early Eta

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. An advantage of this technique for short-range forecasts (0-48h) is that it does not require a complex land-surface formulation within the atmospheric model. As a result, we can avoid having to specify land surface characteristics such as vegetation resistances, green fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical and horizontal distribution of soil moisture.

Lapenta, William M.↗
DOE OSTI2023

Power Electronics Materials and Bonded Interfaces - Reliability and Lifetime

High temperature operation of wide bandgap devices continue to be a challenge for the power electronics packages. Thermal performance and reliability are important factors that determine the viability of a bonded interface for operation at high temperatures. In this presentation, we present the technical approach and key results from the research on sintered silver, transient liquid phase alloy, and polymeric materials. A lifetime prediction model that incorporates the thermomechanical behavior of sintered silver at 200C was developed. The copper-aluminum transient alloy completed 350 thermal cycles from -40C to 200C and little increase in the defect level was observed. In addition to material research, we initiated a time-series analysis on the scanning acoustic microscope images of eutectic solder to explore statistical forecasting methods and machine learning techniques. Initial results that report the accuracy of a few different statistical models are presented.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗
NASA NTRSDate not supplied

Volume Raycasting of GNSS Signals through Ground Structure Lidar for UAV Navigational Guidance and Safety Estimation

Autonomous UAS navigation at low altitudes is often hindered by degradation of GNSS position estimates. The line of sight from the UAS to orbital satellites may be intersected by foliage (which attenuates the received signal) and by buildings (which block the signal). Since the geometric ray from the presumed UAS position to each GNSS satellite orbital location is predictable, if a 3D survey of ground structures is available, the degree of blockage of each GNSS signal can be estimated. In this study we show raycasting from a UAS location to GNSS satellites at two flight locations: one with overlying structures and bordered by tall trees, and another in an arboreal canyon bordered by tall trees. We confirm the intermittent blockage of satellites in the first location sufficient to lose GNSS position fix. We demonstrate low-altitude GNSS fidelity forecasting via the raycasting method at the second location that can be used to plan navigable flight locations and altitudes. Finally, we match the GNSS signal strength with raycast-derived foliage obstruction depth at hundreds of observation times from 55 recordings collected over 14 days from November 2018 to February 2021 at the second location. This matching confirms that signal attenuation varies with the depth of foliage blockage along a saturating exponential curve, as found in prior continuous-wave RF studies. The exponent and saturation value are species dependent and therefore vary from site to site; once determined empirically, they can be used to characterize foliage along a particular flight path, and refine GNSS fidelity forecasts of flights along that path. The techniques described in this study show the feasibility of a survey method to construct low-altitude navigation safety maps and forecasts.

Navigation↗
NASA NTRS2020

Machine Learning in Heliophysics and space weather forecasting: a white paper of finding and recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology,Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers,expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants

HSR↗
NASA NTRS1981

Applications systems verification and transfer project. Volume 3: Operational applications of satellite snow cover observations in California

Five southern Sierra snowmelt basins and two northern Sierra-Southern Cascade snowmelt basins were used to evaluate the effect on operational water supply forecasting from satellite imagery. Manual photointerpretation techniques were used to obtain SCA and equivalent snow line for the years 1973 to 1979 for the seven test basins using LANDSAT imagery and GOES imagery. The use of SCA was tested operationally in 1977-79. Results indicate the addition of SCA improve the water supply forecasts during the snowmelt phase for these basins where there may be an unusual distribution of snowpack throughout the basin, or where there is a limited amount of real time data available. A high correlation to runoff was obtained when SCA was combined with snow water content data obtained from reporting snow sensors.

Brown, A. J.↗
NASA NTRS1992

The simulation of multispectral composite satellite imagery on 8-bit color workstations

A technique is described which combines up to three channels of imagery into a single 8-bit composite color image. Each image controls a specific color or colors (red, green, or blue) in the resultant color composite image, allowing for improved interpretation of scene characteristics. The technique is a useful tool for the forecaster and satellite image analyst. In addition to providing the capability of displaying multichannel images on an 8-bit desktop color workstation, the technique allows sophisticated image manipulation to be performed.

Griffin, Michael K.↗