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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 109 records · Page 6
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NASA NTRS2020

Designing an Optimal Ensemble Strategy for GMAO S2S Forecast System

GMAO Sub/Seasonal prediction system (S2S) is being readied for a major upgrade to GEOS-S2S Version 3. An important factor in successful extended range forecast is the definition of an ensemble For initialization of the ensemble we propose a combination of lagged and burst initial conditions. We plan to run a relatively large ensemble of 40 members for sub-seasonal forecast (up to 3 months), at which point we sub-sample the ensemble, and continue the forecast with 10 members (up to 12 months). Here we present the results of the extensive testing of various ways to generate the perturbations to the initial conditions and the validation of the stratified sampling strategy we chose.To generate perturbations for the burst ensemble members we used scaled differences of pairs of analysis states separated by 1-10 days, randomly chosen from a corresponding season. We considered perturbing separately only the atmospheric fields or only the ocean or both of the forecast initial conditions. Considering varying separation times between the analysis states, we were able to produce perturbations sampling various modes of variability. Focusing on the ENSO SST indices, we found that all types of perturbations are important for the ensemble spread.Our ensemble size for sub-seasonal forecasts was determined as to maximize the skill of predicting some of the leading modes of boreal winter atmospheric modes, NAO, PNA and AO. It is not feasible to run equally large ensemble for seasonal forecasts. Using a stratified sampling procedure we can identify the emerging directions of error growth. By comparing the stratified ensemble with randomly sampled ensemble of the same size, we were able to show that the former better estimates the mean of the original large ensemble.

Borovikov, Anna↗
NASA NTRS2016

Auralization of NASA N+2 Aircraft Concepts from System Noise Predictions

Auralization of aircraft flyover noise provides an auditory experience that complements integrated metrics obtained from system noise predictions. Recent efforts have focused on auralization methods development, specifically the process by which source noise information obtained from semi-empirical models, computational aeroacoustic analyses, and wind tunnel and flight test data, are used for simulated flyover noise at a receiver on the ground. The primary focus of this work, however, is to develop full vehicle auralizations in order to explore the distinguishing features of NASA's N+2 aircraft vis-à-vis current fleet reference vehicles for single-aisle and large twin-aisle classes. Some features can be seen in metric time histories associated with aircraft noise certification, e.g., tone-corrected perceived noise level used in the calculation of effective perceived noise level. Other features can be observed in sound quality metrics, e.g., loudness, sharpness, roughness, fluctuation strength and tone-to-noise ratio. A psychoacoustic annoyance model is employed to establish the relationship between sound quality metrics and noise certification metrics. Finally, the auralizations will serve as the basis for a separate psychoacoustic study aimed at assessing how well aircraft noise certification metrics predict human annoyance for these advanced vehicle concepts.

Rizzi, Stephen A.↗
NASA NTRS1976

Image processing system performance prediction and product quality evaluation

The author has identified the following significant results. A new technique for image processing system performance prediction and product quality evaluation was developed. It was entirely objective, quantitative, and general, and should prove useful in system design and quality control. The technique and its application to determination of quality control procedures for the Earth Resources Technology Satellite NASA Data Processing Facility are described.

Stein, E. K.↗
NASA NTRS2006

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized and various demand sensitivities can be predicted for future travel. These resulting demand scenarios can be incorporated into future flight schedules, therefore providing a quantifiable demand for flights in the NAS for a range of futures. In addition, new future airline business scenarios are investigated that illustrate when direct flights can replace connecting flights and larger aircraft can be substituted, only when justified by demand.

Viken, Jeff↗
NASA NTRS2009

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Watson, Leela R.↗
NASA NTRS1979

Magnetic storm effects in electric power systems and prediction needs

Geomagnetic field fluctuations produce spurious currents in electric power systems. These currents enter and exit through points remote from each other. The fundamental period of these currents is on the order of several minutes which is quasi-dc compared to the normal 60 Hz or 50 Hz power system frequency. Nearly all of the power systems problems caused by the geomagnetically induced currents result from the half-cycle saturation of power transformers due to simultaneous ac and dc excitation. The effects produced in power systems are presented, current research activity is discussed, and magnetic storm prediction needs of the power industry are listed.

Albertson, V. D.↗
NASA NTRS1983

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Cull, R. C.↗
NASA NTRS2015

System Noise Prediction of the DGEN 380 Turbofan Engine

The DGEN 380 is a small, separate-flow, geared turbofan. Its manufacturer, Price Induction, is promoting it for a small twinjet application in the emerging personal light jet market. Smaller, and producing less thrust than other entries in the industry, Price Induction is seeking to apply the engine to a 4- to 5-place twinjet designed to compete in an area currently dominated by propeller-driven airplanes. NASA is considering purchasing a DGEN 380 turbofan to test new propulsion noise reduction technologies in a relevant engine environment. To explore this possibility, NASA and Price Induction have signed a Space Act Agreement and have agreed to cooperate on engine acoustic testing. Static acoustic measurements of the engine were made by NASA researchers during July, 2014 at the Glenn Research Center. In the event that a DGEN turbofan becomes a NASA noise technology research testbed, it is in the interest of NASA to develop procedures to evaluate engine system noise metrics. This report documents the procedures used to project the DGEN static noise measurements to flight conditions and the prediction of system noise of a notional airplane powered by twin DGEN engines.

Aircraft Design↗
NASA NTRS2015

System Noise Prediction of the DGEN 380 Turbofan Engine

The DGEN 380 is a small, separate-flow, geared turbofan. Its manufacturer, Price Induction, is promoting it for a small twinjet application in the emerging personal light jet market. Smaller, and producing less thrust than other entries in the industry, Price Induction is seeking to apply the engine to a 4- to 5-place twinjet designed to compete in an area currently dominated by propeller-driven airplanes. NASA is considering purchasing a DGEN 380 turbofan to test new propulsion noise reduction technologies in a relevant engine environment. To explore this possibility, NASA and Price Induction have signed a Space Act Agreement and have agreed to cooperate on engine acoustic testing. Static acoustic measurements of the engine were made by NASA researchers during July, 2014 at the Glenn Research Center. In the event that a DGEN turbofan becomes a NASA noise technology research testbed, it is in the interest of NASA to develop procedures to evaluate engine system noise metrics. This report documents the procedures used to project the DGEN static noise measurements to flight conditions and the prediction of system noise of a notional airplane powered by twin DGEN engines.

Aircraft Design↗
NASA NTRSDate not supplied

Evaluation of TAO Observation System on ENSO Predictions from the GMAO S2S Forecast System

Since the early 1990’s, the Tropical Atmosphere Ocean (TAO) mooring array has been instrumental for observing Kelvin and Rossby wave propagation which is key for El Nino/Southern Oscillation (ENSO) prediction. However, recent funding and programmatical issues have highlighted the need to rigorously assess the impact of the TAO observing system on ENSO predictions. Therefore, we evaluate the TAO using data assimilation observation denial experiments (also known as Observing System Evaluation or OSE experiments). This presentation will evaluate the TAO observing impact on both reanalyses and prediction of the big 2015 El Nino. We have completed reanalyses for July 2014-Dec 2015 for both the CONTROL experiment (that assimilates all available data) and the NOTAO experiment (that is identical to the CONTROL but withholds all TAO observations) using the best available NASA GEOS-S2S V3 seasonal prediction system. Validation of these reanalyses shows that TAO assimilation generally improves comparisons of temperature and salinity versus gridded in situ observations. Temperature is universally improved above the thermocline near the equator, but the biggest improvement is found in the eastern Pacific, just below the thermocline, where the variation of the thermocline defines ENSO events. In addition, a surprising result is that even with relatively few observations, salinity is improved throughout the equatorial region except near 120oW near the surface. ENSO forecasts were performed that were initialized from these CONTROL and NOTAO reanalyses. For the 9-month forecasts which were initialized in January, July, and October 2015, the NINO3.4 SST shows that the CONTROL forecasts are warmer and closer to observations than the NOTAO forecasts. We will show that upwelling and shoaling of the mixed layer amplifies the ENSO signal due to TAO assimilation. Prior to (after) April 2015, this upwelling is caused by relatively stronger Rossby (Kelvin) waves in the CONTROL than in the NOTAO experiments.

E. Hackert↗
NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗
NASA NTRS1972

Investigation of the utility of mean square approximation systems and in system response predictions

A method is presented for estimating the variability of a system's natural frequencies arising from the variability of the system's parameters. The only information required to obtain the estimates is the member variability, in the form of second order properties, and the natural frequencies and mode shapes of the mean system. Several examples are worked out in detail to illustrate how the method is applied.

Bogdanoff, J. L.↗
NASA NTRS2018

Seasonal Predictability of Cloud Droplet Number Concentration

Aerosol emissions modify the properties of clouds hence impacting climate. The aerosol indirect effect may have offset part of the global warming caused by anthropogenic greenhouse gas emissions during the industrial era. It however remains unclear whether the same effect is significant over time scales relevant for seasonal and weather climate prediction. Answering such a question has been difficult since most weather prediction systems lack a proper representation of the aerosol evolution and transport and their interaction with clouds. Even in advanced systems it is not clear to what extent cloud microphysical properties are predictable over subseasonal to seasonal time scales. Such an issue is addressed in this study. We use a set of 30 year, four ensemble member, 9 month lead hindcast simulations of the NASA GEOS seasonal prediction system (GEOS-S2S) to study the predictability of cloud droplet number concentration in warm stratocumulus clouds. The latest version GEOS-S2S system implements interactive aerosol as well as a two moment cloud microphysics scheme therefore it is suitable for studying the aerosol indirect effect on climate. Long term retrievals from the MODIS (Moderate Resolution Imaging Spectroradiometer) are used to validate the model predictions and assess its skill in predicting cloud droplet number concentration.

Barahona, Donifan↗
NASA NTRS2007

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

Watson, Leela R.↗
NASA NTRS1961

Guidance and Control During Direct-Descent Parabolic Reentry

The results of studies of four reentry guidance and control techniques for the energy management of vehicles returning to the earth at escape speeds are compared in this paper. The reentry trajectories are constrained to those of direct descent, that is, where the vehicle does not leave that portion of the atmosphere where useful aerodynamic forces are available after its initial entry. The guidance techniques compared are: (1) a piloted simulator study reference trajectory techniques; 2) An automatic controller using reference trajectory techniques; 3) A predictor system employing linear prediction (perturbation) techniques; and 4) A repetitive prediction system employing rapid-time computer techniques.

Foudriat, Edwin C.↗
NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗