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NASA NTRS2006

Effect of Heat on Space-Time Correlations in Jets

Measurements of space-time correlations of velocity, acquired in jets from acoustic Mach number 0.5 to 1.5 and static temperature ratios up to 2.7 are presented and analyzed. Previous reports of these experiments concentrated on the experimental technique and on validating the data. In the present paper the dataset is analyzed to address the question of how space-time correlations of velocity are different in cold and hot jets. The analysis shows that turbulent kinetic energy intensities, lengthscales, and timescales are impacted by the addition of heat, but by relatively small amounts. This contradicts the models and assumptions of recent aeroacoustic theory trying to predict the noise of hot jets. Once the change in jet potential core length has been factored out, most one- and two-point statistics collapse for all hot and cold jets.

Bridges, James↗

NASA NTRS2007

A Coupled GCM-Cloud Resolving Modeling System, and a Regional Scale Model to Study Precipitation Processes

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud-resolving models (CRMs) agree with observations better than traditional single-column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a superparameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage, and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA satellite and field campaign cloud related datasets can provide initial conditions as well as validation for both the MMF and CRMs. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM), and it has started production runs with two years results (1998 and 1999). Also, at Goddard, we have implemented several Goddard microphysical schemes (2ICE, several 31CE), Goddard radiation (including explicitly calculated cloud optical properties), and Goddard Land Information (LIS, that includes the CLM and NOAH land surface models) into a next generatio11 regional scale model, WRF. In this talk, I will present: (1) A brief review on GCE model and its applications on precipitation processes (microphysical and land processes), (2) The Goddard MMF and the major difference between two existing MMFs (CSU MMF and Goddard MMF), and preliminary results (the comparison with traditional GCMs), and (3) A discussion on the Goddard WRF version (its developments and applications).

Tao, Wei-Kuo↗

NASA NTRS2006

A Coupled GCM-Cloud Resolving Modeling System, and A Regional Scale Model to Study Precipitation Processes

Tao, Wei-Kuo↗

NASA NTRS2008

Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

Brown, Molly E.↗

NASA NTRS2016

Preliminary Results From a Heavily Instrumented Engine Ice Crystal Icing Test in a Ground Based Altitude Test Facility

Preliminary results from the heavily instrumented ALF502R-5 engine test conducted in the NASA Glenn Research Center Propulsion Systems Laboratory are discussed. The effects of ice crystal icing on a full scale engine is examined and documented. This same model engine, serial number LF01, was used during the inaugural icing test in the Propulsion Systems Laboratory facility. The uncommanded reduction of thrust (rollback) events experienced by this engine in flight were simulated in the facility. Limited instrumentation was used to detect icing on the LF01 engine. Metal temperatures on the exit guide vanes and outer shroud and the load measurement were the only indicators of ice formation. The current study features a similar engine, serial number LF11, which is instrumented to characterize the cloud entering the engine, detect/characterize ice accretion, and visualize the ice accretion in the region of interest. Data were acquired at key LF01 test points and additional points that explored: icing threshold regions, low altitude, high altitude, spinner heat effects, and the influence of varying the facility and engine parameters. For each condition of interest, data were obtained from some selected variations of ice particle median volumetric diameter, total water content, fan speed, and ambient temperature. For several cases the NASA in-house engine icing risk assessment code was used to find conditions that would lead to a rollback event. This study further helped NASA develop necessary icing diagnostic instrumentation, expand the capabilities of the Propulsion Systems Laboratory, and generate a dataset that will be used to develop and validate in-house icing prediction and risk mitigation computational tools. The ice accretion on the outer shroud region was acquired by internal video cameras. The heavily instrumented engine showed good repeatability of icing responses when compared to the key LF01 test points and during day-to-day operation. Other noticeable observations are presented.

Enigine Icing↗

NASA NTRS2016

Preliminary Results From a Heavily Instrumented Engine Ice Crystal Icing Test in a Ground Based Altitude Test Facility

Preliminary results from the heavily instrumented ALF502R-5 engine test conducted in the NASA Glenn Research Center Propulsion Systems Laboratory are discussed. The effects of ice crystal icing on a full scale engine is examined and documented. This same model engine, serial number LF01, was used during the inaugural icing test in the Propulsion Systems Laboratory facility. The uncommanded reduction of thrust (rollback) events experienced by this engine in flight were simulated in the facility. Limited instrumentation was used to detect icing on the LF01 engine. Metal temperatures on the exit guide vanes and outer shroud and the load measurement were the only indicators of ice formation. The current study features a similar engine, serial number LF11, which is instrumented to characterize the cloud entering the engine, detect/ characterize ice accretion, and visualize the ice accretion in the region of interest. Data were acquired at key LF01 test points and additional points that explored: icing threshold regions, low altitude, high altitude, spinner heat effects, and the influence of varying the facility and engine parameters. For each condition of interest, data were obtained from some selected variations of ice particle median volumetric diameter, total water content, fan speed, and ambient temperature. For several cases the NASA in-house engine icing risk assessment code was used to find conditions that would lead to a rollback event. This study further helped NASA develop necessary icing diagnostic instrumentation, expand the capabilities of the Propulsion Systems Laboratory, and generate a dataset that will be used to develop and validate in-house icing prediction and risk mitigation computational tools. The ice accretion on the outer shroud region was acquired by internal video cameras. The heavily instrumented engine showed good repeatability of icing responses when compared to the key LF01 test points and during day-to-day operation. Other noticeable observations are presented.

turbomachinery↗

NASA NTRSDate not supplied

Influence of the Perception Constraint, Installation Effects, and Broadband SelfNoise on Low-Fidelity, Multi-point, UAM Vehicle Perception-Influenced-Design(PID) Optimization

UAM vehicles represent a unique challenge in acoustics because there are a limited number of full-scale datasets that can be used to calibrate and validate prediction models. In addition, acoustics is considered more often in the early design loop as a higher priority concern in UAM vehicle design, whereas with traditional vehicles, acoustics is typically addressed with post-design constraints. This paper will present an approach in design optimization of a proprotor in two flight conditions, hover, and forward flight, with several acoustic objective functions that simulate community response. An adjoint-capable vortex-lattice method (VLM), implemented in VSPAERO, will be used to compute the blade forces and inflow properties required to compute tonal and broadband noise using the second-generation Aircraft NOise Prediction Program (ANOPP2). In addition, a wing is placed near the proprotor to simulate installation effects on a vehicle’s wing. The influence of rotation rate, chord distribution, twist angle, and sweep on installation, broadband noise, and choice of perception constraint from the design optimization will be shown.

acoustics↗

NASA NTRSDate not supplied

Deploying a Self-Supervised Learning Based Model to Search Events Across Space and Time

Motivation - Scientific Study of natural events, phenomena, or disasters require examples which span across time and space. - Machine Learning adaptation is on the rise, but there’s a lack of labeled training datasets that could be used to train or validate the models. - Best case scenario: - There’s an event database that tracks events available through time and space. - Provides all data associated with the events. - Real life scenario: - Some events are better tracked than others. - Scientists need to spend significant time identifying and gathering examples of events from different sources.

Iksha Gurung↗

NASA NTRSDate not supplied

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni-Ti Alloys

Ni-Ti based shape memory alloys (SMAs) have found wide-spread use in aerospace, automotive, biomedical, and commercial applications owing to their favorable properties and ease of operation. Especially important for many NASA applications is the ability to tune the martensitic transformation temperature of Ni-Ti alloys by varying the alloy composition and processing conditions. Recently, researchers at NASA have compiled an extensive database of shape memory properties of materials, including over 8,000 multi-component Ni-Ti alloys containing 37 different alloying elements. Using this dataset, machine learning models are trained to predict transformation temperatures, hysteresis, and transformation strain with extremely low mean absolute errors. These models are used to learn relationships between shape memory behavior and input parameters in the composition and processing space. ML predictions are validated through new experiments. The combination of an extensive experimental dataset and accurate learning models, together, make our approach highly suitable for the rapid discovery and design of novel SMAs with targeted properties. We are not aware of any current approaches capable of predicting SMA transformation behavior over such a wide range of compositions and processing conditions.

Shape memory alloys↗

NASA NTRS1987

Evolution of the Southern Oscillation as observed by the Nimbus-7 ERB experiment

The Nimbus-7 satellite has been in a 955-km, sun-synchronous orbit since October 1978. The Earth Radiation Budget (ERB) experiment has taken approximately 8 years of high-quality data during this time, of which seven complete years have been archived at the National Space Science Data Center. A final reprocessing of the wide-field-of-view channel dataset is underway. Error analyses indicate a long-term stability of 1 percent better over the length of the data record. As part of the validation of the ERB measurements, the archived 7-year Nimbus-7 ERB dataset is examined for the presence and accuracy of interannual variations including the Southern Oscillation signal. Zonal averages of broadband outgoing longwave radiation indicate a terrestrial response of more than 2 years to the oceanic and atmospheric manifestations of the 1982-83 El Nino/Southern Oscillation (ENSO) event, especially in the tropics. This signal is present in monthly and seasonal averages and is shown here to derive primarily from atmospheric responses to adjustments in the Pacific Ocean. The calibration stability of this dataset thus provides a powerful new tool to examine the physics of the ENSO phenomena.

Ardanuy, Philip E.↗

NASA NTRS2020

Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Mapping aboveground forest biomass is central for assessing the global carbon balance. However, current large-scale maps show strong disparities, despite good validation statistics of their underlying models. Here, we attribute this contradiction to a flaw in the validation methods, which ignore spatial autocorrelation (SAC) in data, leading to overoptimistic assessment of model predictive power. To illustrate this issue, we reproduce the approach of large-scale mapping studies using a massive forest inventory dataset of 11.8 million trees in central Africa to train and validate a random forest model based on multispectral and environmental variables. A standard nonspatial validation method suggests that the model predicts more than half of the forest biomass variation, while spatial validation methods accounting for SAC reveal quasi-null predictive power. This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.

biomass↗

NASA NTRS2018

Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar

Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world's biodiversity hotspots, Madagascar's lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model's ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with r(squared) values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m(squared)/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and -1 t Cha(exp -1)year(exp -1), consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests.

Forest Productivity↗

NASA NTRS2022

Colorado Eastern Plains Agriculture: Rangeland Monitoring to Inform Grazing Management in Eastern Colorado

Adaptive management on cattle ranches requires rangeland managers to decide the location and duration of the cattle grazing activity across different pastures. Biodiversity, forage availability, and cattle health are all affected by rangeland management. Virtual fencing is a tool that rangeland managers can use to potentially increase biodiversity and improve ranching operations. NASA DEVELOP and Colorado State University (CSU) collaborated with the Nature Conservancy (TNC), and Red Top Ranch to demonstrate the efficacy of virtual fencing. We sought to identify annual and monthly biomass patterns on the ranch through the creation of monthly max biomass productivity maps. We utilized a dataset from the Agricultural Research Service (ARS) to calculate biomass on the ranch. To validate our remotely-sensed results, we compared model-predicted biomass values to field-collected biomass clipping data and an additional biomass dataset from the Rangeland Analysis Platform (RAP). We used satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Landsat 9 –OLI-2, and Sentinel-2 MultiSpectral Instrument (MSI) for 2021 and 2022. We found that there was heterogeneity in biomass across the ranch, with higher biomass on the western side. The highest peak of biomass was in the summer months, with a smaller peak in mid-September. The ARS biomass dataset had a significant relationship with RAP for 2021. ARS biomass did not have a significant relationship with the biomass field data collected in 2022. The results of our study are aimed to support rotation management, ranch production, biodiversity, and conservation management.

cattle↗

NASA NTRS2020

High Spectral Resolution Datasets of in Situ Aquatic Inherent and Apparent Optical Properties

Light emerging from natural water bodies and measured by remote sensing radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities to characterize aquatic ecosystems. Such upcoming missions include NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem PACE Mission; the NASA Surface Biology and Geology observable mission; and NASA Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) - Next Generation airborne missions. In anticipation of these missions, we present a dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP/AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland water, estuaries and oceans. Specific in situ measurements include particulate absorption, particulate attenuation, non-algal particulate or detritus absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, total backscatter, remote sensing reflectance, and irradiance reflectance. The dataset can be downloaded from PANGAEA: (https://www.pangaea.de/TBD ).

K A Casey↗

NASA NTRS2018

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗

NASA NTRS1991

Navier-Stokes analysis of a liquid rocket engine disk cavity

This paper presents a Navier-Stokes analysis of hydrodynamic phenomena occurring in the aft disk cavity of a liquid rocket engine turbine. The cavity analyzed in the Space Shuttle Main Engine Alternate Turbopump currently being developed by NASA and Pratt and Whitney. Comparison of results obtained from the Navier-Stokes code for two rotating disk datasets available in the literature are presented as benchmark validations. The benchmark results obtained using the code show good agreement relative to experimental data, and the turbine disk cavity was analyzed with comparable grid resolution, dissipation levels, and turbulence models. Predicted temperatures in the cavity show that little mixing of hot and cold fluid occurs in the cavity and the flow is dominated by swirl and pumping up the rotating disk.

Benjamin, Theodore G.↗

NASA NTRS2004

Characterization of Unsteady Flow Structures Near Leading-Edge Slat: PIV Measurements - Part 1

A comprehensive computational and experimental study has been performed at the NASA Langley Research Center as part of the Quiet Aircraft Technology (QAT) Program to investigate the unsteady flow near a leading-edge slat of a two-dimensional, high-lift system. This paper focuses on the experimental effort conducted in the NASA Langley Basic Aerodynamics Research Tunnel (BART) where Particle Image Velocimetry (PIV) data was acquired in the slat cove and at the slat trailing edge of a three-element, high-lift model at 4, 6, and 8 degrees angle of attack and a freestream Mach Number of 0.17. Instantaneous velocities obtained from PIV images are used to obtain mean and fluctuating components of velocity and vorticity. The data show the recirculation in the cove, reattachment of the shear layer on the slat lower surface, and discrete vortical structures within the shear layer emanating from the slat cusp and slat trailing edge. Detailed measurements are used to examine the shear layer formation at the slat cusp, vortex shedding at the slat trailing edge, and convection of vortical structures through the slat gap. Selected results are discussed and compared with unsteady, Reynolds-Averaged Navier-Stokes (URANS) computations for the same configuration in a companion paper by Khorrami, Choudhari, and Jenkins (2004). The experimental dataset provides essential flow-field information for the validation of near-field inputs to noise prediction tools.

Jenkins, Luther N.↗

NASA NTRS2016

The Vegetation Greenness Trend in Canada and US Alaska from 1984-2012 Landsat Data

To assess the North American high-latitude vegetation response to the rising temperature, we derived NDVI end for 91.2% of the non-water, non-snow land area of Canada and Alaska using the peak-summer Landsat surface reflectance data of 19842012. Our analysis indicated that 29.4% and 2.9 of the land area of Canada and Alaska showed statistically significant positive (greening) and negative (browning) trends respectively, at significance level p b 0.01, after burned forest areas were masked out. The area with greening trend dominated over that with browning trend for all land cover types. The greening occurred primarily in the tundra of western Alaska, along the north coast of Canada and in northeastern Canada; the most intensive and extensive greening occurred in Quebec and Labrador. The browning occurred mostly in the boreal forests of eastern Alaska. The Landsat-based greenness trend is broadly similar to the 8-km GIMMS AVHRR-based trend for all vegetation zones. However, for tundra, the Landsat data indicated much less extensive greening in Alaska North Slope and much more extensive greening in Quebec and Labrador, and substantially less extensive browning trend in the boreal forests that were free of fire disturbances. These differences call for further validation of the Landsat reflectance and the AVHRR NDVI datasets. Correlation study with local environmental factors, such as topography, glacial history and soil condition, will be needed to understand the heterogeneous greenness change at the Landsat scale.

Ju, Junchang↗