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NASA NTRS2001

Dynamic Finite Element Predictions for Mars Sample Return Cellular Impact Test #4

The nonlinear finite element program MSC.Dytran was used to predict the impact pulse for (he drop test of an energy absorbing cellular structure. This pre-test simulation was performed to aid in the design of an energy absorbing concept for a highly reliable passive Earth Entry Vehicle (EEV) that will directly impact the Earth without a parachute. In addition, a goal of the simulation was to bound the acceleration pulse produced and delivered to the simulated space cargo container. EEV's are designed to return materials from asteroids, comets, or planets for laboratory analysis on Earth. The EEV concept uses an energy absorbing cellular structure designed to contain and limit the acceleration of space exploration samples during Earth impact. The spherical shaped cellular structure is composed of solid hexagonal and pentagonal foam-filled cells with hybrid graphite-epoxy/Kevlar cell walls. Space samples fit inside a smaller sphere at the enter of the EEV's cellular structure. The material models and failure criteria were varied to determine their effect on the resulting acceleration pulse. Pre-test analytical predictions using MSC.Dytran were compared with the test results obtained from impact test #4 using bungee accelerator located at the NASA Langley Research Center Impact Dynamics Research Facility. The material model used to represent the foam and the proper failure criteria for the cell walls were critical in predicting the impact loads of the cellular structure. It was determined that a FOAMI model for the foam and a 20% failure strain criteria for the cell walls gave an accurate prediction of the acceleration pulse for drop test #4.

Fasanella, Edwin L.↗
NASA NTRS2001

Application of TURBO-AE to Flutter Prediction: Aeroelastic Code Development

The TURBO-AE program has been evaluated by comparing the obtained results to cascade rig data and to prediction made from various in-house programs. A high-speed fan cascade, a turbine cascade, a turbine cascade and a fan geometry that shower flutter in torsion mode were analyzed. The steady predictions for the high-speed fan cascade showed the TURBO-AE predictions to match in-house codes. However, the predictions did not match the measured blade surface data. Other researchers also reported similar disagreement with these data set. Unsteady runs for the fan configuration were not successful using TURBO-AE .

Hoyniak, Daniel↗
NASA NTRS2001

Cranked Arrow Wing (F-16XL-1) Flight Flow Physics with CFD Predictions at Subsonic and Transonic Speeds

The computational fluid dynamics (CFD) modeling used has produced reasonably good global upper-surface pressure coefficient comparisons with measured flight data at both transonic and subsonic speeds at the angles of attack presented. Boundary layer comparisons showed the profiles to be reasonably well predicted inboard and under the primary vortex system. However, the secondary vortex profile was not well predicted either at the anticipated separation point or under the secondary vortex. Moreover, the flight data showed there to be a vortex/boundary-layer interaction that occurred in the vicinity of the secondary vortex. The spanwise distribution of local skin friction measured data was reasonably well predicted, especially away from the wing leading-edge. Lastly, predicted and measured flight-pressures, as well as flight-image data, for the F-16XL-1 airplane are now available via the World Wide Web.

Lamar, John E.↗
NASA NTRS2000

An Assessment of the Predictability of Northern Winter Seasonal Means with the NSIPP 1 AGCM

This atlas assesses the predictability of January-February-March (JFM) means using version 1 of the NASA Seasonal-to-Interannual Prediction Project Atmospheric General Circulation Model (the NSIPP 1 AGCM). The AGCM is part of the NSIPP coupled atmosphere-land-ocean model. For these results, the atmosphere was run uncoupled from the ocean, but coupled with an interactive land model. The results are based on 20 ensembles of nine JFM hindcasts for the period 1980-1999, with sea surface temperature (SST) and sea ice specified from observations. The model integrations were started from initial atmospheric conditions (taken from NCEP/NCAR reanalyses) centered on December 15. The analysis focuses on 200 mb height, precipitation, surface temperature, and sea-level pressure. The results address issues of both predictability and forecast skill. Various signal-to-noise measures are computed to demonstrate the potential for skillful prediction on seasonal time scales under the assumption of a perfect model and perfectly known oceanic boundary forcings. The results show that the model produces a realistic ENSO response in both the tropics and extratropics.

Suarez, Max J.↗
NASA NTRS2001

Solar Activity Forecasting for use in Orbit Prediction

Orbital prediction for satellites in low Earth orbit (LEO) or low planetary orbit depends strongly on exospheric densities. Solar activity forecasting is important in orbital prediction, as the solar UV and EUV inflate the upper atmospheric layers of the Earth and planets, forming the exosphere in which satellites orbit. Geomagnetic effects also relate to solar activity. Because of the complex and ephemeral nature of solar activity, with different cycles varying in strength by more than 100%, many different forecasting techniques have been utilized. The methods range from purely numerical techniques (essentially curve fitting) to numerous oddball schemes, as well as a small subset, called 'Precursor techniques.' The situation can be puzzling, owing to the numerous methodologies involved, somewhat akin to the numerous ether theories near the turn of the last century. Nevertheless, the Precursor techniques alone have a physical basis, namely dynamo theory, which provides a physical explanation for why this subset seems to work. I discuss this solar cycle's predictions, as well as the Sun's observed activity. I also discuss the SODA (Solar Dynamo Amplitude) index, which provides the user with the ability to track the Sun's hidden, interior dynamo magnetic fields. As a result, one may then update solar activity predictions continuously, by monitoring the solar magnetic fields as they change throughout the solar cycle. This paper ends by providing a glimpse into what the next solar cycle (#24) portends.

Schatten, Kenneth↗
NASA NTRS2001

Predictability of Zonal Means During Boreal Summer

This study examines the predictability of seasonal means during boreal summer. The results are based on ensembles of June-July-August (JJA) simulations (started in mid May) carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTS) and sea ice for the years 1980-1999. We find that the predictability of the JJA extra-tropical height field is primarily in the zonal mean component of the response to the SST anomalies. This contrasts with the cold season (January-February-March) when the predictability of seasonal means in the boreal extratropics is primarily in the wave component of the El Nino/Southern Oscillation (ENSO) response. Two patterns dominate the interannual variability of the ensemble mean JJA zonal mean height field. One has maximum variance in the tropical/subtropical upper troposphere, while the other has substantial variance in middle latitudes of both hemispheres. Both are symmetric with respect to the equator. A regression analysis suggests that the tropical/subtropical pattern is associated with SST anomalies in the far eastern tropical Pacific and the Indian Ocean, while the middle latitude pattern is forced by SST anomalies in the tropical Pacific just east of the dateline. The two leading zonal height patterns are reproduced in model runs forced with the two leading JJA SST patterns of variability. A comparison with observations shows a signature of the middle latitude pattern that is consistent with the occurrence of dry and wet summers over the United States. We hypothesize that both patterns, while imposing only weak constraints on extratropical warm season continental-scale climates, may play a role in the predilection for drought or pluvial conditions.

Schubert, Siegfried↗
NASA NTRS2001

Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective

A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.

Dungan, Jennifer L.↗
NASA NTRS2001

The NASA Seasonal-to-Interannual Prediction Project (NSIPP)

The goal of the project is to develop an assimilation and forecast system based on a coupled atmosphere-ocean-land-surface-sea-ice model capable of using a combination of satellite and in situ data sources to improve the prediction of ENSO and other major S-I signals and their global teleconnections. The objectives of this annual report are to: (1) demonstrate the utility of satellite data, especially surface height surface winds, air-sea fluxes and soil moisture, in a coupled model prediction system; and (2) aid in the design of the observing system for short-term climate prediction by conducting OSSE's and predictability studies.

Rienecker, Michele↗
NASA NTRS2001

Turbulence Modeling Effects on the Prediction of Equilibrium States of Buoyant Shear Flows

The effects of turbulence modeling on the prediction of equilibrium states of turbulent buoyant shear flows were investigated. The velocity field models used include a two-equation closure, a Reynolds-stress closure assuming two different pressure-strain models and three different dissipation rate tensor models. As for the thermal field closure models, two different pressure-scrambling models and nine different temperature variance dissipation rate, Epsilon(0) equations were considered. The emphasis of this paper is focused on the effects of the Epsilon(0)-equation, of the dissipation rate models, of the pressure-strain models and of the pressure-scrambling models on the prediction of the approach to equilibrium turbulence. Equilibrium turbulence is defined by the time rate (if change of the scaled Reynolds stress anisotropic tensor and heat flux vector becoming zero. These conditions lead to the equilibrium state parameters. Calculations show that the Epsilon(0)-equation has a significant effect on the prediction of the approach to equilibrium turbulence. For a particular Epsilon(0)-equation, all velocity closure models considered give an equilibrium state if anisotropic dissipation is accounted for in one form or another in the dissipation rate tensor or in the Epsilon(0)-equation. It is further found that the models considered for the pressure-strain tensor and the pressure-scrambling vector have little or no effect on the prediction of the approach to equilibrium turbulence.

Zhao, C. Y.↗
NASA NTRS1994

Automated Performance Prediction of Message Passing Parallel Programs

As the trend toward massively parallel processing continues, the need for tools that can predict scalability trends becomes greater. While high level languages Eke HPF have come into greater use, explicit message-passing programs proliferate, and will probably do so for some time, thanks to the onslaught of standards such as MPI. Much work has been done to create simple models that represent important characteristics of parallel programs, such as latency, network contention, and communication volume. But many of these methods still require a substantial manual effort to represent an application in the model's format. The YAPP ("Yet Another Performance Predictor") tool is an attempt to automate the formation of first-order expressions for completion time, with a minimum of programmer assistance. The content of this paper is as follows: First, we explore the implementation details of YAPP, and illustrate with examples some of the reasons that automatic prediction is difficult. In the following sections, we present the results of four applications, using execution traces on the Intel i860, analyze the error in YAPP's predictions, explain the limitations of our implementation, and mention possible future additions. In particular, we illustrate techniques used to identify pipeline communication patterns, and demonstrate how compiler analysis and regression are combined to automate the prediction process.

Block, Robert J.↗
NASA NTRS2001

A New Ensemble Canonical Correlation Prediction Scheme for Seasonal Precipitation

Department of Mathematical Sciences, University of Alberta, Edmonton, Canada This paper describes the fundamental theory of the ensemble canonical correlation (ECC) algorithm for the seasonal climate forecasting. The algorithm is a statistical regression sch eme based on maximal correlation between the predictor and predictand. The prediction error is estimated by a spectral method using the basis of empirical orthogonal functions. The ECC algorithm treats the predictors and predictands as continuous fields and is an improvement from the traditional canonical correlation prediction. The improvements include the use of area-factor, estimation of prediction error, and the optimal ensemble of multiple forecasts. The ECC is applied to the seasonal forecasting over various parts of the world. The example presented here is for the North America precipitation. The predictor is the sea surface temperature (SST) from different ocean basins. The Climate Prediction Center's reconstructed SST (1951-1999) is used as the predictor's historical data. The optimally interpolated global monthly precipitation is used as the predictand?s historical data. Our forecast experiments show that the ECC algorithm renders very high skill and the optimal ensemble is very important to the high value.

Kim, Kyu-Myong↗
NASA NTRS2001

Transonic Drag Prediction Using an Unstructured Multigrid Solver

This paper summarizes the results obtained with the NSU-3D unstructured multigrid solver for the AIAA Drag Prediction Workshop held in Anaheim, CA, June 2001. The test case for the workshop consists of a wing-body configuration at transonic flow conditions. Flow analyses for a complete test matrix of lift coefficient values and Mach numbers at a constant Reynolds number are performed, thus producing a set of drag polars and drag rise curves which are compared with experimental data. Results were obtained independently by both authors using an identical baseline grid and different refined grids. Most cases were run in parallel on commodity cluster-type machines while the largest cases were run on an SGI Origin machine using 128 processors. The objective of this paper is to study the accuracy of the subject unstructured grid solver for predicting drag in the transonic cruise regime, to assess the efficiency of the method in terms of convergence, cpu time, and memory, and to determine the effects of grid resolution on this predictive ability and its computational efficiency. A good predictive ability is demonstrated over a wide range of conditions, although accuracy was found to degrade for cases at higher Mach numbers and lift values where increasing amounts of flow separation occur. The ability to rapidly compute large numbers of cases at varying flow conditions using an unstructured solver on inexpensive clusters of commodity computers is also demonstrated.

Mavriplis, D. J.↗
NASA NTRS1994

Image Discrimination Models Predict Object Detection in Natural Backgrounds

Object detection involves looking for one of a large set of object sub-images in a large set of background images. Image discrimination models only predict the probability that an observer will detect a difference between two images. In a recent study based on only six different images, we found that discrimination models can predict the relative detectability of objects in those images, suggesting that these simpler models may be useful in some object detection applications. Here we replicate this result using a new, larger set of images. Fifteen images of a vehicle in an other-wise natural setting were altered to remove the vehicle and mixed with the original image in a proportion chosen to make the target neither perfectly recognizable nor unrecognizable. The target was also rotated about a vertical axis through its center and mixed with the background. Sixteen observers rated these 30 target images and the 15 background-only images for the presence of a vehicle. The likelihoods of the observer responses were computed from a Thurstone scaling model with the assumption that the detectabilities are proportional to the predictions of an image discrimination model. Three image discrimination models were used: a cortex transform model, a single channel model with a contrast sensitivity function filter, and the Root-Mean-Square (RMS) difference of the digital target and background-only images. As in the previous study, the cortex transform model performed best; the RMS difference predictor was second best; and last, but still a reasonable predictor, was the single channel model. Image discrimination models can predict the relative detectabilities of objects in natural backgrounds.

Ahumada, Albert J., Jr.↗
NASA NTRS2001

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated With Oxidation and Interdiffusion of Overlay-Coated Substrates

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating life based on a concentration dependent failure criterion (e.g., surface solute content drops to 2%). The computer code is written in FORTRAN and employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Nesbitt, James A.↗
NASA NTRS2002

Maneuvering Rotorcraft Noise Prediction: A New Code for a New Problem

This paper presents the unique aspects of the development of an entirely new maneuver noise prediction code called PSU-WOPWOP. The main focus of the code is the aeroacoustic aspects of the maneuver noise problem, when the aeromechanical input data are provided (namely aircraft and blade motion, blade airloads). The PSU-WOPWOP noise prediction capability was developed for rotors in steady and transient maneuvering flight. Featuring an object-oriented design, the code allows great flexibility for complex rotor configuration and motion (including multiple rotors and full aircraft motion). The relative locations and number of hinges, flexures, and body motions can be arbitrarily specified to match the any specific rotorcraft. An analysis of algorithm efficiency is performed for maneuver noise prediction along with a description of the tradeoffs made specifically for the maneuvering noise problem. Noise predictions for the main rotor of a rotorcraft in steady descent, transient (arrested) descent, hover and a mild "pop-up" maneuver are demonstrated.

Brentner, Kenneth S.↗
NASA NTRS2002

Development of a Generic Creep-Fatigue Life Prediction Model

The objective of this research proposal is to further compile creep-fatigue data of steel alloys and superalloys used in military aircraft engines and/or rocket engines and to develop a statistical multivariate equation. The newly derived model will be a probabilistic fit to all the data compiled from various sources. Attempts will be made to procure the creep-fatigue data from NASA Glenn Research Center and other sources to further develop life prediction models for specific alloy groups. In a previous effort [1-3], a bank of creep-fatigue data has been compiled and tabulated under a range of known test parameters. These test parameters are called independent variables, namely; total strain range, strain rate, hold time, and temperature. The present research attempts to use these variables to develop a multivariate equation, which will be a probabilistic equation fitting a large database. The data predicted by the new model will be analyzed using the normal distribution fits, the closer the predicted lives are with the experimental lives (normal line 1 to 1 fit) the better the prediction. This will be evaluated in terms of a coefficient of correlation, R 2 as well. A multivariate equation developed earlier [3] has the following form, where S, R, T, and H have specific meaning discussed later.

Goswami, Tarun↗
NASA NTRS1999

On-Line Fringe Tracking and Prediction at IOTA

The Infrared/Optical Telescope Array (IOTA) is a multi-aperture Michelson interferometer located on Mt. Hopkins near Tucson, Arizona. To enable viewing of fainter targets, an on-line fringe tracking system is presently under development at NASA Ames Research Center. The system has been developed off-line using actual data from IOTA, and is presently undergoing on-line implementation at IOTA. The system has two parts: (1) a fringe tracking system that identifies the center of a fringe packet by fitting a parametric model to the data; and (2) a fringe packet motion prediction system that uses characteristics of past fringe packets to predict fringe packet motion. Combined, this information will be used to optimize on-line the scanning trajectory, resulting in improved visibility of faint targets. Fringe packet identification is highly accurate and robust (99% of the 4000 fringe packets were identified correctly, the remaining 1% were either out of the scan range or too noisy to be seen) and is performed in 30-90 milliseconds on a Pentium II-based computer. Fringe packet prediction, currently performed using an adaptive linear predictor, delivers a 10% improvement over the baseline of predicting no motion.

Wilson, Edward↗
NASA NTRS2002

Climate Prediction Sees Future Despite Chaos: Researchers Outside NASA use NCCS Resources for Studies

The air on this mostly sunny January day is crisp and the wind is blustery. The morning's National Weather Service 6-hour forecast had accurately predicted these conditions for the Baltimore-Washington area and the 2-3 day extended outlook was almost perfect. The previous week, the National Center for Environmental Prediction's (NCEP) 6-10 day temperature and precipitation outlook for the general trends for the' region was correct as well. However, no forecast could have predicted specific details about this day. It is 28.5 F in the sunshine bright enough for dark sunglasses, and windy enough to blow off a hat. Such details are impossible to foresee with any accuracy and are outside the scope of routine weather prediction. Equally difficult is accurately forecasting weather beyond about 2 weeks.

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