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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

2016 Wind Technologies Market Report

The U.S. Department of Energy (DOE)’s Wind Technologies Market Report provides an annual overview of trends in the U.S. wind power market. You can find the report, a presentation, and a data file on the Files tab, below. Additionally, several data visualizations are available in the Data Visualizations tab. Highlights of this year’s report include: -Wind power additions continued at a rapid clip in 2016: $13 billion was invested in new wind power plants in 2016. In 2016, wind energy contributed 5.6% of the nation’s electricity supply, more than 10% of total electricity generation in fourteen states, and 29% to 37% in three of those states—Iowa, South Dakota, and Kansas. -Bigger turbines are enhancing wind project performance: Increased blade lengths, in particular, have dramatically increased wind project capacity factors, one measure of project performance. For example, the average 2016 capacity factor among projects built in 2014 and 2015 was 42.6%, compared to an average of 32.1% among projects built from 2004 to 2011 and 25.4% among projects built from 1998 to 2001. -Low wind turbine pricing continues to push down installed project costs: Wind turbine prices have fallen from their highs in 2008, to $800–$1,100/kW. Overall, the average installed cost of wind projects in 2016 was $1,590/kW, down $780/kW from the peak in 2009 and 2010. -Wind energy prices remain low: After topping out at nearly 7¢/kWh for power purchase agreements (PPAs) executed in 2009, the national average price of wind PPAs has dropped to around 2¢/kWh—though this nationwide average is dominated by projects that hail from the lowest-priced Interior region of the country (such as Texas, Iowa, Oklahoma). These prices, which are possible in part due to federal tax support, compare favorably to the projected future fuel costs of gas-fired generation. -The supply chain continued to adjust to swings in domestic demand for wind equipment: Wind sector employment reached a new high of more than 101,000 full-time workers at the end of 2016. For wind projects recently installed in the U.S., domestically manufactured content is highest for nacelle assembly (>90%), towers (65-80%), and blades and hubs (50-70%), but is much lower (<20%) for most components internal to the turbine. -Continued strong growth in wind capacity is anticipated in the near term: With federal tax incentives still available, though declining, various forecasts for the domestic market show expected wind power capacity additions averaging more than 9,000 MW/year from 2017 to 2020.

17 WIND ENERGY↗
NASA NTRS1978

A study of the atmospheric energetics of a six-layer operational forecast model

Verification data for twice-daily analyses of horizontal wind and temperature are compared with 00, 12, 24, 36, 60, and 84 hour forecasts prepared for winter 1975-76 from a six-layer operational forecast model; the comparison focuses on certain energy components in the forecast fields. Significant losses (15 to 20%) of zonal available potential energy and zonal kinetic energy appear beyond the 36-hour forecasts; the zonal kinetic energy loss is associated with a large increase in this parameter during the first 12-hour forecast period, due to a downward and northward movement of the mean jet-stream core. In addition, energy losses in the eddy potential and kinetic energy components are noted in the initialization procedure.

Hauser, R. K.↗
DOE OSTI2022

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. Here, the suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1977

Applications of Seasat to the offshore oil, gas and mining industries

The NASA satellite Seasat-A (to be launched in 1978) has applications to the offshore oil, gas, and mining industries including: (1) improvements in weather and wave forecasting, (2) studies of past wind and wave statistics for planning design requirements, and (3) monitoring ice formation, breakup, and movement in arctic regions. The primary geographic areas which will be monitored by Seasat-A include: the Beaufort Sea, the Labrador Sea, the Gulf of Mexico, the U.S. east coast, West Africa, Equatorial East Pacific, the Gulf of Alaska, and the North Sea. Seasat-A instrumentation used in ocean monitoring consists of a radar altimeter, a radar scatterometer, a synthetic aperture radar, a microwave radiometer, and a visible and infrared radiometer. The future outlook of the Seasat program is planned in three phases: measurement feasibility demonstration (1978-1980), data accessibility/utility demonstration (1980-1983), and operational system demonstration (1983-1985).

Mourad, A. G.↗
NASA NTRS1985

Hurricane Debby - An illustration of the complementary nature of VAS soundings and cloud and water vapor motion winds

The utility of VISSR Atmospheric Sounder (VAS) temperature and moisture soundings and cloud and water vapor motion winds in defining a storm and its surroundings at subsynoptic scales has been examined using a numerical analysis and prognosis system. It is shown that the VAS temperature and moisture data, which specify temperature and moisture well in cloud-free areas, are complemented by cloud and water vapor motion data generated in the cloudy areas. The cloud and water vapor 'winds' provide thermal gradient information for interpolating the soundings across cloudy regions. The loss of analysis integrity due to the reduction of VAS sounding density in the cloudy regions associated with synoptic activity is ameliorated by using cloud and water vapor motion winds. The improvement in numerical forecasts resulting from the addition of these data to the numerical analysis is recorded.

Le Marshall, J. F.↗
NASA NTRS1989

Chemistry and transport in a three-dimensional stratospheric model - Chlorine species during a simulated stratospheric warming

The distributions in the stratosphere of a variety of chemical species were calculated for a 6-day period during the February 1979 stratospheric major warming, using winds derived from a spectral forecast model which included O(x), NO(x), HO(x), and ClO(x) chemistries as well as longitudinally varying reaction rate coefficients and photolysis rates for these molecules. The results obtained indicate a particular importance of chemistry and transport for the Cl-containing species ClO, ClONO2, HCl, and HOCl. Dynamical effects dominate the variability of HCl, while diurnal effects dominate that of ClONO2 and ClO. The effects of strong planetary wave activity may be seen in terms of large longitudinal variability of the total HCl and ClONO columns in the stratosphere; in the middle and high northern latitudes, it is sufficiently large to exceed the diurnal variability of the column.

Kaye, Jack A.↗
NASA NTRS1989

Optimized variational analysis scheme of single Doppler radar wind data

A computer scheme for extracting singularities has been developed and applied to single Doppler radar wind data. The scheme is planned for use in real-time wind and singularity analysis and forecasting. The method, known as Doppler Operational Variational Extraction of Singularities is outlined, focusing on the principle of local symmetry. Results are presented from the application of the scheme to a storm-generated gust front in Oklahoma on May 28, 1987.

Sasaki, Yoshi K.↗
NASA NTRS1992

Hurricane risk assessment to rollback or ride out a cost versus loss decision making approach

The potential exists that a hurricane striking the Kennedy Space Center while a Space Shuttle is on the pad. Winds in excess of 74.5 knots could cause the failure of the holddown bolts bringing about the catastrophic loss of the entire vehicle. Current plans call for the rollback of the shuttle when winds of that magnitude are forecast to strike the center. As this is costly, a new objective method for making rollback/rideout decisions based upon Bayesian Analysis and economic cost versus loss is presented.

Wohlman, Richard A.↗
NASA NTRS1993

The gust-front detection and wind-shift algorithms for the Terminal Doppler Weather Radar system

The Federal Aviation Administration's (FAA) Terminal Doppler Weather Radar (TDWR) system was primarily designed to address the operational needs of pilots in the avoidance of low-altitude wind shears upon takeoff and landing at airports. One of the primary methods of wind-shear detection for the TDWR system is the gust-front detection algorithm. The algorithm is designed to detect gust fronts that produce a wind-shear hazard and/or sustained wind shifts. It serves the hazard warning function by providing an estimate of the wind-speed gain for aircraft penetrating the gust front. The gust-front detection and wind-shift algorithms together serve a planning function by providing forecasted gust-front locations and estimates of the horizontal wind vector behind the front, respectively. This information is used by air traffic managers to determine arrival and departure runway configurations and aircraft movements to minimize the impact of wind shifts on airport capacity. This paper describes the gust-front detection and wind-shift algorithms to be fielded in the initial TDWR systems. Results of a quantitative performance evaluation using Doppler radar data collected during TDWR operational demonstrations at the Denver, Kansas City, and Orlando airports are presented. The algorithms were found to be operationally useful by the FAA airport controllers and supervisors.

Hermes, Laurie G.↗
NASA NTRS1994

Forecast skill of a high-resolution real-time mesoscale model designed for weather support of operations at Kennedy Space Center and Cape Canaveral Air Station

NASA funded Mesoscale Environmental Simulations and Operations (MESO), Inc. to develop a version of the Mesoscale Atmospheric Simulation System (MASS). The model has been modified specifically for short-range forecasting in the vicinity of KSC/CCAS. To accomplish this, the model domain has been limited to increase the number of horizontal grid points (and therefore grid resolution) and the model' s treatment of precipitation, radiation, and surface hydrology physics has been enhanced to predict convection forced by local variations in surface heat, moisture fluxes, and cloud shading. The objective of this paper is to (1) provide an overview of MASS including the real-time initialization and configuration for running the data pre-processor and model, and (2) to summarize the preliminary evaluation of the model's forecasts of temperature, moisture, and wind at selected rawinsonde station locations during February 1994 and July 1994. MASS is a hydrostatic, three-dimensional modeling system which includes schemes to represent planetary boundary layer processes, surface energy and moisture budgets, free atmospheric long and short wave radiation, cloud microphysics, and sub-grid scale moist convection.

Taylor, Gregory E.↗
NASA NTRS2008

Will We Soon Have a Geostationary Microwave Sounder and What Can We Do with It?

This slide presentation reviews the Geostationary Microwave Sounder (GEO/MW). GEO/MW applications include weather forecasting, hurricane diagnostics, rain, tropospheric wind profiling, and climate research. The presentation also includes information on prototype development, system tests, the notational PATH mission, and data products.

hurricanes↗
NASA NTRS2018

Modeling Key Predictors of Airport Runway Configurations Using Learning Algorithms

Advanced traffic flow management automation will need accurate predictions of airport runway configurations. Terminal area weather and traffic demand are generally considered to be the most significant factors in predicting runway configuration. Weather information is forecasted across multiple features, including wind direction, wind speed, gusts, cloud ceilings, visibility, temperature, and precipitation, among many others. We use machine learning techniques on historical weather and runway data to determine weather features that correlate well with runway configurations. We analyze the predictive capability of weather features using different learning models trained on data from four major U.S. airports: Atlanta (ATL), Washington – Dulles (IAD), New York – Kennedy (JFK), and San Francisco (SFO). Wind direction alone is strongly correlated with runway configurations above all other examined factors, as expected. This correlation is the most significant component of the ~80% prediction accuracy in selecting between the two most frequently used runway configurations. However, individual airports show variations on how well the runway configuration decisions correlate with wind direction. While wind direction was identified as the most significant indicator of configuration decisions in ATL, IAD, and JFK, it did not emerge as such at SFO. Traffic demand was not found to be a strong factor in predicting runway configurations at any of the airports analyzed. In rare instances, when high demand cannot be accommodated within the current configuration, temporary changes are likely to be attributable to demand. However, these occurrences are so limited in number that their overall effect is not sufficient to consider traffic demand as a major indicator of runway configuration at the airports analyzed.

Bilimoria, Karl D.↗
DOE OSTI2022

Mesoscale evaluation of AMPS using AWARE radar observations of a wind and precipitation event over the Ross Island region of Antarctica

Surface, upper-air, and radar observations are used to assess the performance of the Antarctic Mesoscale Prediction System (AMPS) in simulating the mesoscale aspects of a wind and precipitation event over the Ross Island region of Antarctica that spanned January 16–20, 2016. The observations, collected during the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE), provide a unique dataset for evaluating AMPS, especially the radar observations that facilitate a three-dimensional depiction of winds and precipitation. Comparisons of AMPS forecast data with surface meteorology, balloon-sounding, and profiling radar observations at and above sites near McMurdo Station reveal a mixture of similarities and differences. A generally southerly flow is evident at low levels in both the AMPS simulations and observed Doppler radial velocities. AMPS winds are comparable to those observed at the surface and aloft in terms of magnitude, direction, and timing but the strongest simulated southerly flow is displaced eastward relative to the observations. AMPS-simulated reflectivity over the broader Ross Island region is more limited in areal extent and smaller in magnitude than observed by a scanning Doppler radar. Three episodes of surface precipitation are observed near McMurdo Station over the five-day event with peak rates of ~3 mm h -1 and a total accumulation of ~22 mm. However, AMPS produces no surface precipitation at that location over the five-day event due to a low-level dry bias in the forecasts. Herein, the results show the first observationally based three-dimensional understanding of meteorology in the Ross Island region.

AMPS↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS1993

An observing system simulation experiment for the Laser Atmospheric Wind Sounder (LAWS)

The present observing system-simulation experiments evaluate the potential of a Laser Atmospheric Wind Sounder (LAWS) instrument for 5-day forecasting, using a primitive-equation multilevel spectral global circulation model. A 55-deg-inclined and a 98-deg sun-synchronous orbit are examined, by adding LAWS wind profiles into a global 4D data-assimilation system, and comparing both the analyses and forecasts to a control experiment. The 4D data-assimilation system consists of a multivariate optimum interpolation analysis and a nonlinear, normal-mode intialization, using the aforementioned global circulation model.

Rohaly, G. D.↗
NASA NTRS1992

Low-frequency predictability of the Dynamical Extended-Range Forecast Experiment

The objective of the study was to analyze data from the Dynamical Extended-Range Forecast Experiment conducted from January 1986 to March 1987, and to evaluate differences between analysis and forecasts with emphasis on the tropical 30-50-day oscillation. The diagnostic toll used is the projection of analysis and forecast data onto the normal modes of a primitive equation model. Examination of zonal-wind anomalies in the tropics shows that the forecast model predicts propagation of intraseasonal variations more accurately for slow propagation rates. The forecast amplitude is generally weaker than the analyzed amplitude. Analyzed kinetic energy and error fields exhibit similar horizontal scales for internal and external modes. External Rossby-mode components maximize in the extratropics while Rossby internal modes exhibit patterns that extend over the entire globe.

Nogues-Peagle, Julia↗