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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Evaluation of Droplet Splashing Algorithm in LEWICE 3.0

The Icing Branch at NASA Glenn Research has developed a computer program to simulate ice formation on the leading edge of an aircraft wing during flight through cold, moist air. As part of the branch's current research, members have developed software known as LEWICE. This program is capable of predicting the formation of ice under designated weather conditions. The success of LEWICE is an asset to airplane manufacturers, ice protection system manufacturers, and the airline industry. Simulations of ice formation conducted in the tunnel and in flight is costly and time consuming. However, the danger of in-flight icing continues to be a concern for both commercial and military pilots. The LEWICE software is a step towards inexpensive and time efficient prediction of ice collection. In the most recent version of the program, LEWICE contains an algorithm for droplet splashing. Droplet splashing is a natural occurrence that causes accumulation of ice on aircraft surfaces. At impingement water droplets lose a portion of their mass to splashing. With part of each droplet joining the airflow and failing to freeze, early versions of LEWICE without the splashing algorithm over-predicted the collection of ice on the leading edge. The objective of my project was to determine whether the revised version of LEWICE accurately reflected the ice collection data obtained from the Icing Research Tunnel (IRT). The experimental data from the IRT was collected by Mark Potapczuk in January, March and July of 2001 and April and December of 2002. Experimental data points were the result of ice tracings conducted shortly after testing in the tunnel. Run sheets, which included a record of velocity, temperature, liquid water content and droplet diameter, served as the input of the LEWICE computer program. Parameters identical to the tunnel conditions were used to run LEWICE 2.0 and LEWICE 3.0. The results from IRT and versions of LEWICE were compared graphically. After entering the raw experimental data and computer output into a spread sheet, I mapped each ice formation onto a clean airfoil. The LEWICE output provided the data points to graphically depict ice formations developed by the program. weather conditions of runs conducted in January 2001, it was evident that the splashing algorithm of LEWICE 3.0 predicts ice formations more accurately than LEWICE 2.0. Especially at conditions with droplet size between 80 and 160 microns, the splashing algorithm of the new LEWICE version compensated for the loss of droplet mass as a result of splashing. In contrast, LEWICE 2.0 consistently over-predicted the mass of the ice in conditions with droplet size exceeding 80 microns. This evidence confirms that changes made to algorithms of LEWICE 3.0 have increased the accuracy of predicting ice collection.

Homenko, Hilary N.↗
NASA NTRS2014

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

Kulkarni, Deepak↗
NASA NTRS2005

A Method for Assessing the Accuracy of a Photogrammetry System for Precision Deployable Structures

The measurement techniques used to validate analytical models of large deployable structures are an integral Part of the technology development process and must be precise and accurate. Photogrammetry and videogrammetry are viable, accurate, and unobtrusive methods for measuring such large Structures. Photogrammetry uses Software to determine the three-dimensional position of a target using camera images. Videogrammetry is based on the same principle, except a series of timed images are analyzed. This work addresses the accuracy of a digital photogrammetry system used for measurement of large, deployable space structures at JPL. First, photogrammetry tests are performed on a precision space truss test article, and the images are processed using Photomodeler software. The accuracy of the Photomodeler results is determined through, comparison with measurements of the test article taken by an external testing group using the VSTARS photogrammetry system. These two measurements are then compared with Australis photogrammetry software that simulates a measurement test to predict its accuracy. The software is then used to study how particular factors, such as camera resolution and placement, affect the system accuracy to help design the setup for the videogrammetry system that will offer the highest level of accuracy for measurement of deploying structures.

Moore, Ashley↗
NASA NTRS2001

On the Accuracy of Probabilistic Bucking Load Prediction

The buckling strength of thin-walled stiffened or unstiffened, metallic or composite shells is of major concern in aeronautical and space applications. The difficulty to predict the behavior of axially compressed thin-walled cylindrical shells continues to worry design engineers as we enter the third millennium. Thanks to extensive research programs in the late sixties and early seventies and the contributions of many eminent scientists, it is known that buckling strength calculations are affected by the uncertainties in the definition of the parameters of the problem such as definition of loads, material properties, geometric variables, edge support conditions, and the accuracy of the engineering models and analysis tools used in the design phase. The NASA design criteria monographs from the late sixties account for these design uncertainties by the use of a lump sum safety factor. This so-called 'empirical knockdown factor gamma' usually results in overly conservative design. Recently new reliability based probabilistic design procedure for buckling critical imperfect shells have been proposed. It essentially consists of a stochastic approach which introduces an improved 'scientific knockdown factor lambda(sub a)', that is not as conservative as the traditional empirical one. In order to incorporate probabilistic methods into a High Fidelity Analysis Approach one must be able to assess the accuracy of the various steps that must be executed to complete a reliability calculation. In the present paper the effect of size of the experimental input sample on the predicted value of the scientific knockdown factor lambda(sub a) calculated by the First-Order, Second-Moment Method is investigated.

Arbocz, Johann↗
NASA NTRS1974

Application of ecological, geological and oceanographic ERTS-1 imagery to Delaware's coastal resources management

The author has identified the following significant results. Data from twelve successful ERTS-1 passes over Delaware Bay have been analyzed with special emphasis on coastal vegetation, land use, current circulation, water turbidity and pollution dispersion. Secchi depth, suspended sediment concentration and transmissivity as measured from helicopters and boats were correlated with ERTS-1 image radiance. Multispectral signatures of acid disposal plumes, sediment plumes and slick were investigated. Ten vegetative cover and water discrimination classes were selected for mapping: (1) forest-land; (2) Phragmites communis; (3) Spartina patens and Distichlis spicata; (4) Spartina alterniflora; (5) cropland; (6) plowed cropland; (7) sand and bare sandy soil; (8) bare mud; (9) deep water; and (10) sediment-laden and shallow water. Canonical analysis predicted good classification accuracies for most categories. The actual classification accuracies were very close to the predicted values with 8 of 10 categories classified with greater than 90% accuracy indicating that representative training sets had been selected.

Klemas, V.↗
NASA NTRS1998

Prediction of Unsteady Transitional Layers in Turbomachinery Using Navier Stokes Equations

The objective of the research reported in this presentation is to develop computational techniques for the prediction of unsteady transitional flows associated with the rotor stator interaction in turbomachinery. Three low-Reynolds number turbulence models are incorporated in two unsteady Navier-Stokes codes (one is pressure based and the other is time marching with Runge-Kutta time stepping) and evaluated for accuracy in predicting the onset and the end of unsteady transitional patches due to wake passing. The best model is then used for modification and improvement for the leading edge effect. An existing steady Navier-Stokes code was modified to include pseudo-time stepping, which provided acceleration from 5 to 25 times that of the original code. A systematic validation procedure was implemented to assess the effects of the grid, artificial dissipation, physical, and the pseudo-time step for an accurate prediction of transitional flows resulting from the rotor-stator interaction. The ability of the Navier-Stokes code to predict the unsteady transitional flow on a turbomachinery blade is demonstrated. The unsteady pressure and velocity fields are in good agreement with the experimental data and the prediction from the Euler/boundary layer approach. The numerical solver was able to capture all zones (wake induced transitional strip, wake induced turbulent strip, calmed region, etc.) associated with wake induced transition in a compressor cascade. Another significant step is the assessment of k-epsilon turbulence models, including the leading edge modifications. Best results were obtained from the FLB model. The LB model predicted earlier inception of the transition and shorter transition length. Modification of the k-epsilon model was found to be essential for an accurate prediction of the unsteady transitional flow in a compressor cascade. The CH model failed to predict the unsteady transitional flow. Predicted boundary layer was turbulent from the leading edge, even with the modification of the k-epsilon model near the stagnation point. A comparison of the instantaneous shape factor, the skin friction coefficient, and the momentum thickness indicates that the Navier-Stokes predictions are reasonably good. Interaction between the upstream wake and stator wake results in shedding of unsteady vortices from the trailing edge and increased dissipation in the stator wake and, as a consequence, increased rate of decay of the stator wake. The procedure developed at Penn State should aid the designers of turbomachinery in allowing for the unsteady transitional flows due to rotor-stator interaction.

Lakshminarayana, B.↗
NASA NTRS1990

Fortified LEWICE with viscous effects

A method for computing the prediction of ice shapes on airfoils and their effects on the airfoil lift and drag coefficients are described. The previously developed LEWICE code has been modified to avoid problems due to multiple stagnation points. The interactive boundary-layer method developed by Cebeci has been incorporated into LEWICE to improve the accuracy of predicting ice shapes as well as to compute the performance characteristics of iced airfoils. The paper also presents ice shapes calculated without and with viscous effects, the consequences for aerodynamic properties, particulary lift and drag, and evaluation of the time steps used in the ice accretion process.

Cebeci, Tuncer↗
NASA NTRS2011

Strike-Slip Fault Patterns on Europa: Obliquity or Polar Wander?

Variations in diurnal tidal stress due to Europa's eccentric orbit have been considered as the driver of strike-slip motion along pre-existing faults, but obliquity and physical libration have not been taken into account. The first objective of this work is to examine the effects of obliquity on the predicted global pattern of fault slip directions based on a tidal-tectonic formation model. Our second objective is to test the hypothesis that incorporating obliquity can reconcile theory and observations without requiring polar wander, which was previously invoked to explain the mismatch found between the slip directions of 192 faults on Europa and the global pattern predicted using the eccentricity-only model. We compute predictions for individual, observed faults at their current latitude, longitude, and azimuth with four different tidal models: eccentricity only, eccentricity plus obliquity, eccentricity plus physical libration, and a combination of all three effects. We then determine whether longitude migration, presumably due to non-synchronous rotation, is indicated in observed faults by repeating the comparisons with and without obliquity, this time also allowing longitude translation. We find that a tidal model including an obliquity of 1.2?, along with longitude migration, can predict the slip directions of all observed features in the survey. However, all but four faults can be fit with only 1? of obliquity so the value we find may represent the maximum departure from a lower time-averaged obliquity value. Adding physical libration to the obliquity model improves the accuracy of predictions at the current locations of the faults, but fails to predict the slip directions of six faults and requires additional degrees of freedom. The obliquity model with longitude migration is therefore our preferred model. Although the polar wander interpretation cannot be ruled out from these results alone, the obliquity model accounts for all observations with a value consistent with theoretical expectations and cycloid modeling.

Rhoden, Alyssa Rose↗
NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗
NASA NTRS1985

Confined turbulent swirling recirculating flow predictions

The capability and the accuracy of the STARPIC computer code in predicting confined turbulent swirling recirculating flows is presented. Inlet flow boundary conditions were demonstrated to be extremely important in simulating a flowfield via numerical calculations. The degree of swirl strength and expansion ratio have strong effects on the characteristics of swirling flow. In a nonswirling flow, a large corner recirculation zone exists in the flowfield with an expansion ratio greater than one. However, as the degree of inlet swirl increases, the size of this zone decreases and a central recirculation zone appears near the inlet. Generally, the size of the central zone increased with swirl strength and expansion ratio. Neither the standard k-epsilon turbulence mode nor its previous extensions show effective capability for predicting confined turbulent swirling recirculating flows. However, either reduced optimum values of three parameters in the mode or the empirical C sub mu formulation obtained via careful analysis of available turbulence measurements, can provide more acceptable accuracy in the prediction of these swirling flows.

Abujelala, M. T.↗
NASA NTRS1980

Analysis of uncertainties in turbine metal temperature predictions

An analysis was conducted to examine the extent to which various factors influence the accuracy of analytically predicting turbine blade metal temperatures and to determine the uncertainties in these predictions for several accuracies of the influence factors. The advanced turbofan engine gas conditions of 1700 K and 40 atmospheres were considered along with those of a highly instrumented high temperature turbine test rig and a low temperature turbine rig that simulated the engine conditions. The analysis showed that the uncertainty in analytically predicting local blade temperature was as much as 98 K, or 7.6 percent of the metal absolute temperature, with current knowledge of the influence factors. The expected reductions in uncertainties in the influence factors with additional knowledge and tests should reduce the uncertainty in predicting blade metal temperature to 28 K, or 2.1 percent of the metal absolute temperature.

Stepka, F. S.↗
NASA NTRS1992

Predicted aerodynamic characteristics for HL-20 lifting-body using the aerodynamic preliminary analysis system (APAS)

The aerodynamic characteristics of the HL-20 lifting body configuraiton obtained through the APAS and from wind-tunnel tests have been compared. The APAS is considered to be an easy-to-use, relatively simple tool for quick preliminary estimation of vehicle aerodynamics. The APAS estimates are found to be in good agreement with experimental results to be used for preliminary evaluation of the HL-20. The APAS accuracy in predicting aerodynamics of the HL-20 varied over the Mach range. The speed ranges of best agreement were subsonic and hypersonic, while least agreement was in the Mach range from 1.2 to about 2,5.

Cruz, Christopher I.↗
NASA NTRS2014

Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

crop-model ensembles↗
NASA NTRS2023

Evaluating Greenland Surface-Mass-Balance and Firn-Densification Data Using ICESat-2 Altimetry

Surface-mass-balance (SMB) and firn-densification (FD) models are widely used in altimetry studies as a tool to separate atmospheric-driven from ice-dynamics-driven ice-sheet mass changes and to partition observed volume changes into ice-mass changes and firn-air-content changes. Until now, SMB models have been principally validated based on comparison with ice core and weather station data or comparison with widely separated flight radar-survey flight lines. Firn-densification models have been primarily validated based on their ability to match net densification over decades, as recorded in firn cores, and the short-term time-dependent component of densification has rarely been evaluated at all. The advent of systematic ice-sheet-wide repeated ice-surface-height measurements from ICESat-2 (the Ice Cloud, and land Elevation Satellite, 2) allows us to measure the net surface-height change of the Greenland ice sheet at quarterly resolution and compare the measured surface-height differences directly with those predicted by three FD–SMB models: MARv3.5.11 (Modèle Atmosphérique Régional version 3.5.11) and GSFCv1.1 and GSFCv1.2 (the Goddard Space Flight Center FD–SMB models version 1.1 and 1.2). By segregating the data by season and elevation, and based on the timing and magnitude of modelled processes in areas where we expect minimal ice-dynamics-driven height changes, we investigate the models' accuracy in predicting atmospherically driven height changes. We find that while all three models do well in predicting the large seasonal changes in the low-elevation parts of the ice sheet where melt rates are highest, two of the models (MARv3.5.11 and GSFCv1.1) systematically overpredict, by around a factor of 2, the magnitude of height changes in the high-elevation parts of the ice sheet, particularly those associated with melt events. This overprediction seems to be associated with the melt sensitivity of the models in the high-elevation part of the ice sheet. The third model, GSFCv1.2, which has an updated high-elevation melt parameterization, avoids this overprediction.

Benjamin E. Smith↗
NASA NTRS2010

Multivariate Methods for Prediction of Geologic Sample Composition with Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) uses pulses of laser light to ablate a material from the surface of a sample and produce an expanding plasma. The optical emission from the plasma produces a spectrum which can be used to classify target materials and estimate their composition. The ChemCam instrument on the Mars Science Laboratory (MSL) mission will use LIBS to rapidly analyze targets remotely, allowing more resource- and time-intensive in-situ analyses to be reserved for targets of particular interest. ChemCam will also be used to analyze samples that are not reachable by the rover's in-situ instruments. Due to these tactical and scientific roles, it is important that ChemCam-derived sample compositions are as accurate as possible. We have compared the results of partial least squares (PLS), multilayer perceptron (MLP) artificial neural networks (ANNs), and cascade correlation (CC) ANNs to determine which technique yields better estimates of quantitative element abundances in rock and mineral samples. The number of hidden nodes in the MLP ANNs was optimized using a genetic algorithm. The influence of two data preprocessing techniques were also investigated: genetic algorithm feature selection and averaging the spectra for each training sample prior to training the PLS and ANN algorithms. We used a ChemCam-like laboratory stand-off LIBS system to collect spectra of 30 pressed powder geostandards and a diverse suite of 196 geologic slab samples of known bulk composition. We tested the performance of PLS and ANNs on a subset of these samples, choosing to focus on silicate rocks and minerals with a loss on ignition of less than 2 percent. This resulted in a set of 22 pressed powder geostandards and 80 geologic samples. Four of the geostandards were used as a validation set and 18 were used as the training set for the algorithms. We found that PLS typically resulted in the lowest average absolute error in its predictions, but that the optimized MLP ANN and the CC ANN often gave results comparable to PLS. Averaging the spectra for each training sample and/or using feature selection to choose a small subset of wavelengths to use for predictions gave mixed results, with degraded performance in some cases and similar or slightly improved performance in other cases. However, training time was significantly reduced for both PLS and ANN methods by implementing feature selection, making this a potentially appealing method for initial, rapid-turn-around analyses necessary for Chemcam's tactical role on MSL. Choice of training samples has a strong influence on the accuracy of predictions. We are currently investigating the use of clustering algorithms (e.g. k-means, neural gas, etc.) to identify training sets that are spectrally similar to the unknown samples that are being predicted, and therefore result in improved predictions

Morris, Richard↗
NASA NTRS1990

Heat transfer on accreting ice surfaces

Based on previous observations of glaze ice accretion, a 'Multi-Zone' model with distinct zones of different surface roughness is demonstrated. The use of surface roughness in the LEWICE ice accretion prediction code is examined. It was found that roughness is used in two ways: to determine the laminar to turbulent transition location and to calculate the turbulent heat transfer coefficient. A two zone version of the Multi-Zone model is implemented in the LEWICE code, and compared with experimental heat transfer coefficient and ice accretin results. The analysis of the boundary layer transition, surface roughness, and viscous flow field effects significantly increased the accuracy in predicting heat transfer coefficients. The Multi-Zone model was found to greatly improve the ice accretion prediction for the cases compared.

Yamaguchi, Keiko↗
NASA NTRS1992

Accuracy of critical-temperature sensitivity coefficients predicted by multilayered composite plate theories

An assessment is made of the accuracy of the critical-temperature sensitivity coefficients of multilayered plates predicted by different modeling approaches, based on two-dimensional shear-deformation theories. The sensitivity coefficients considered measure the sensitivity of the critical temperatures to variations in different lamination and material parameters of the plate. The standard of comparison is taken to be the sensitivity coefficients obtained by the three-dimensional theory of thermoelasticity. Numerical studies are presented showing the effects of variation in the geometric and lamination parameters of the plate on the accuracy of both the sensitivity coefficients and the critical temperatures predicted by the different modeling approaches.

Noor, Ahmed K.↗
NASA NTRS2017

Influence of Shear Stiffness Degradation on Crack Paths in Uni-Directional Composite Laminates

Influence of shear stiffness degradation in an element, due to damage, on crack paths in uni-directional laminates has been demonstrated. A new shear stiffness degradation approach to improve crack path prediction has been developed and implemented in an ABAQUS/Explicit frame work using VUMAT. Three progressive failure analysis models, built-in ABAQUS (TradeMark), original COmplete STress Reduction (COSTR) and the modified COSTR damage models have been utilized in this study to simulate crack paths in five unidirectional notched laminates, 15deg, 30deg, 45deg, 60deg and 75deg under uniaxial tension load. Results such as crack paths and load vs. edge displacement curves are documented in this report. Modified COSTR damage model shows better accuracy in predicting crack paths in all the uni-directional laminates compared to the ABAQUS (TradeMark) and the original COSTR damage models.

Satyanarayana, Arunkumar↗