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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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Susceptibility modeling, prediction of radio frequency interference from satellites, operational radio frequency interference control, and international regulations are considered. The existing satellite interference prediction program DSIP2 is emphasized. A summary status evaluation and recommendations for future work are given.
The paper considers the modeling and analysis of the JPL Precision Pointing and Tracking System, a gyro stabilized platform pointing system using optical target tracking. Sensor and actuator models are described which realistically represent actual hardware, and a practical controller and state estimator is developed through modern control techniques. The effect of the actuator nonlinearities on the system performance evaluation is discussed along with the modeling of the noise sources in the gyro. Finally, the limitations of implementing the control algorithms on the spacecraft computer, and the resulting tradeoffs are presented.
The proposed research addresses all three focus areas, but primarily (2): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model /component/ parameterization selection)
The research performed this summer was a continuation of work performed during the 1995 NASA/ASEE Summer Fellowship. The focus of the work was to expand previously generated predictive models for liquid oxygen (LOX) loading into the external fuel tank of the shuttle. The models which were developed using a block diagram simulation language known as VisSim, were evaluated on numerous shuttle flights and found to well in most cases. Once the models were refined and validated, the predictive methods were integrated into the existing Rockwell software propulsion advisory tool (PAT). Although time was not sufficient to completely integrate the models developed into PAT, the ability to predict flows and pressures in the orbiter section and graphically display the results was accomplished.
This research is aimed at producing a fundamental new research tool for characterizing the source strength of the most important compound controlling the hemispheric and global scale distribution of tropospheric ozone. Specifically, this effort seeks to demonstrate the proof-of-concept of a new general purpose laser-induced fluorescence based spectrometer for making airborne eddy-correlation flux measurements of nitric oxide (NO) and other reactive nitrogen compounds. The new all solid-state laser technology being used in this advanced sensor will produce a forerunner of the type of sensor technology that should eventually result in highly compact operational systems. The proof-of-concept sensor being developed will have over two orders-of-magnitude greater sensitivity than present-day instruments. In addition, this sensor will offer the possibility of eventual extension to airborne eddy-correlation flux measurements of nitrogen dioxide (NO2) and possibly other compounds, such as ammonia (NH3), peroxyradicals (HO2), nitrateradicals (NO3) and several iodine compounds (e.g., I and IO). Demonstration of the new sensor's ability to measure NO fluxes will occur through a series of laboratory and field tests. This proof-of-concept demonstration will show that not only can airborne fluxes of important ultra-trace compounds be made at the few parts-per-trillion level, but that the high accuracy/precision measurements currently needed for predictive models can also. These measurement capabilities will greatly enhance our current ability to quantify the fluxes of reactive nitrogen into the troposphere and significantly impact upon the accuracy of predictive capabilities to model O3's distribution within the remote troposphere. This development effort also offers a timely approach for producing the reactive nitrogen flux measurement capabilities that will be needed by future research programs such as NASA's planned 1999 Amazon Biogeochemistry and Atmospheric Chemistry Experimental portion of LBA.
A two-level model has previously been proposed for approximating the performance of a Viterbi decoder which encounters data received with periodically varying signal-to-noise ratio. Such cyclically gapped data is obtained from the Very Large Array (VLA), either operating as a stand-alone system or arrayed with Goldstone. This approximate model predicts that the decoder error rate will vary periodically between two discrete levels with the same period as the gap cycle. It further predicts that the length of the gapped portion of the decoder error cycle for a constraint length K decoder will be about K-1 bits shorter than the actual duration of the gap. The two-level model for Viterbi decoder performance with gapped data is subjected to detailed validation tests. Curves showing the cyclical behavior of the decoder error burst statistics are compared with the simple square-wave cycles predicted by the model. The validity of the model depends on a parameter often considered irrelevant in the analysis of Viterbi decoder performance, the overall scaling of the received signal or the decoder's branch-metrics. Three scaling alternatives are examined: optimum branch-metric scaling and constant branch-metric scaling combined with either constant noise-level scaling or constant signal-level scaling. The simulated decoder error cycle curves roughly verify the accuracy of the two-level model for both the case of optimum branch-metric scaling and the case of constant branch-metric scaling combined with constant noise-level scaling. However, the model is not accurate for the case of constant branch-metric scaling combined with constant signal-level scaling.
We present a comparison of trends in total column ozone from 10 two-dimensional and 4 three-dimensional models and solar backscatter ultraviolet-2 (SBUV/2) satellite observations from the period 1979-2003. Trends for the past (1979-2000), the recent 7 years (1996-2003), and the future (2000-2050) are compared. We have analyzed the data using both simple linear trends and linear trends derived with a hockey stick method including a turnaround point in 1996. If the last 7 years, 1996-2003, are analyzed in isolation, the SBUV/2 observations show no increase in ozone, and most of the models predict continued depletion, although at a lesser rate. In sharp contrast to this, the recent data show positive trends for the Northern and the Southern Hemispheres if the hockey stick method with a turnaround point in 1996 is employed for the models and observations. The analysis shows that the observed positive trends in both hemispheres in the recent 7-year period are much larger than what is predicted by the models. The trends derived with the hockey stick method are very dependent on the values just before the turnaround point. The analysis of the recent data therefore depends greatly on these years being representative of the overall trend. Most models underestimate the past trends at middle and high latitudes. This is particularly pronounced in the Northern Hemisphere. Quantitatively, there is much disagreement among the models concerning future trends. However, the models agree that future trends are expected to be positive and less than half the magnitude of the past downward trends. Examination of the model projections shows that there is virtually no correlation between the past and future trends from the individual models.
With the aid of near-wall asymptotic analysis and results of direct numerical simulation, a new near-wall Reynolds stress model (NNWRS) is formulated based on the SSG high-Reynolds-stress model with wall-independent near-wall corrections. Only one damping function is used for flows with a wide range of Reynolds numbers to ensure that the near-wall modifications diminish away from the walls. The model is able to reproduce complicated flow phenomena induced by complex geometry, such as flow recirculation, reattachment and boundary-layer redevelopment in backward-facing step flow and secondary flow in three-dimensional square duct flow. In simple flows, including fully developed channel/pipe flow, Couette flow and boundary-layer flow, the wall effects are dominant, and the NNWRS model predicts less degree of turbulent anisotropy in the near-wall region compared with a wall-dependent near-wall Reynolds Stress model (NWRS) developed by So and colleagues. The comparison of the predictions given by the two models rectifies the misconception that the overshooting of skin friction coefficient in backward-facing step flow prevalent in those near-wall, models with wall normal is caused by he use of wall normal.
In this paper we characterize and model the cost of rework in a Component Factory (CF) organization. A CF is responsible for developing and packaging reusable software components. Data was collected on corrective maintenance activities for the Generalized Support Software reuse asset library located at the Flight Dynamics Division of NASA's GSFC. We then constructed a predictive model of the cost of rework using the C4.5 system for generating a logical classification model. The predictor variables for the model are measures of internal software product attributes. The model demonstrates good prediction accuracy, and can be used by managers to allocate resources for corrective maintenance activities. Furthermore, we used the model to generate proscriptive coding guidelines to improve programming, practices so that the cost of rework can be reduced in the future. The general approach we have used is applicable to other environments.
The Applied Meteorology Unit (AMU) conducted a year-long evaluation of NCEP's 29-km mesoscale Eta (meso-eta) weather prediction model in order to identify added value to forecast operations in support of the United States space program. The evaluation was stratified over warm and cool seasons and considered both objective and subjective verification methodologies. Objective verification results generally indicate that meso-eta model point forecasts at selected stations exhibit minimal error growth in terms of RMS errors and are reasonably unbiased. Conversely, results from the subjective verification demonstrate that model forecasts of developing weather events such as thunderstorms, sea breezes, and cold fronts, are not always as accurate as implied by the seasonal error statistics. Sea-breeze case studies reveal that the model generates a dynamically-consistent thermally direct circulation over the Florida peninsula, although at a larger scale than observed. Thunderstorm verification reveals that the meso-eta model is capable of predicting areas of organized convection, particularly during the late afternoon hours but is not capable of forecasting individual thunderstorms. Verification of cold fronts during the cool season reveals that the model is capable of forecasting a majority of cold frontal passages through east central Florida to within +1-h of observed frontal passage.
We present an implementation of a propulsor model based on body force method into the OVERFLOW computational fluid dynamics code to model turbofan engines and propulsors of similar type. The model estimates the forces imparted on the fluid by the blade camber surfaces as a body force source terms in the momentum and energy equations over grids that represent the rotor and stator of a fan stage. We tested the implementation on three cases: 1) Source Diagnostics Test (SDT) fan with R4 rotors, which has extensive test data on aerodynamic performance and rotor wake surveys, 2) A stand-alone Aeronaut TF8000 propulsor which is an off-the-shelf propulsor used on model aircraft and 3) The D8 aircraft model with TF8000 propulsors placed in a wind tunnel. Despite missing some of the features in the velocity profiles near the endwalls (i.e. hub and casing surfaces), the OVERFLOW simulations with body force model predicted area-averaged flow speed and total pressure rise through the SDT propulsor within a few percent of the LDV (Laser Doppler Velocimetry) measurements. In the case of TF8000 propulsor on the D8 airframe, the model under-predicted mechanical power coefficient by several percent of the wind tunnel test results when the horizontal force balance condition over the airframe is targeted by tuning rotor speed. By investigating the upstream influence of the rotor swirl, it was found out that the induced swirl velocity effects upstream were relatively small and they rapidly vanished before reaching one fan diameter upstream of the fan face. The body force model provided insights on aerothermodynamics and aeromechanics of boundary layer ingesting propulsor; these insights could not be obtained by using the uniform pressure jump model.
As research advances diverse forms and missions of aircraft, the National Airspace System (NAS) will become increasingly crowded, limiting current communications resources to accommodate aviation operations. Ongoing research proposes a paradigm of airspace communications, such that resources are autonomously and dynamically allocated via intelligent agents; this allocation requires accurate representations of the NAS, including the predicted positions of aircraft. State-of-the-art research emphasizes the importance of a hybrid-recurrent framework for trajectory prediction and compares the impact of commonly considered weather data on prediction accuracy. However, current research has been limited in its scope of efforts, frequently utilizing a unique flight route, architecture, set of weather data, and date range. This article considers the challenges of generalizing hybrid-recurrent predictive models for flight trajectories. Results illustrate an increase in error variance when identical models are trained over a generalized set of flights; this may be mitigated with careful tuning of hyperparameters, both in the network structure and optimization algorithms. Even so, an irreducible vertical error was identified, resulting from the complex takeoff and landing procedures which can not be correlated to functions of weather or additional assumptions of aircraft behavior. Finally, the use of a test route indicates that generalized models still do not possess sufficient knowledge for general aircraft predictions, with mean error increases ranging from 70-500%. These results illustrate the need for continued efforts on improving model versatility, as well as potential limitations for spectrum allocation near airports and other centers.
The objective of this work was to assess the unstart reliability of the HIFiRE Flight 2 system. To do this, a quantification of margins and uncertainties framework was used for comparing model predictions of the predicted combustion induced shock location to the predicted last stable shock location within the isolator. Uncertainty sources included parametric uncertainty in the flight conditions, the heat release model, and turbulence modeling, as well as model verification errors. Additionally, an estimate of model-form uncertainty was established by comparing the model to measured ground test data. A computationally efficient non-intrusive polynomial chaos approach was used to propagate parametric uncertainty through the computational flu-ids dynamics models of both the ground test configuration and the flight vehicle. Compared to direct-connect ground test data, computational fluid dynamics predictions yielded about two duct heights of model-form uncertainty. This was applied to a prediction of flight vehicle unstart margin at the Mach 6.5 flight condition. Building up all of the computational model uncertainty, including parametric uncertainty, verification errors, and the determined model-form uncertainty, a 95% probability level based confidence ratio, or a ratio of total uncertainty to a statistical margin measure, was found to be 0.31 for the flight system.
This paper presents a framework for calibrating computational models using data from several and possibly dissimilar validation experiments. The offset between model predictions and observations, which might be caused by measurement noise, model-form uncertainty, and numerical error, drives the process by which uncertainty in the models parameters is characterized. The resulting description of uncertainty along with the computational model constitute a predictor model. Two types of predictor models are studied: Interval Predictor Models (IPMs) and Random Predictor Models (RPMs). IPMs use sets to characterize uncertainty, whereas RPMs use random vectors. The propagation of a set through a model makes the response an interval valued function of the state, whereas the propagation of a random vector yields a random process. Optimization-based strategies for calculating both types of predictor models are proposed. Whereas the formulations used to calculate IPMs target solutions leading to the interval value function of minimal spread containing all observations, those for RPMs seek to maximize the models' ability to reproduce the distribution of observations. Regarding RPMs, we choose a structure for the random vector (i.e., the assignment of probability to points in the parameter space) solely dependent on the prediction error. As such, the probabilistic description of uncertainty is not a subjective assignment of belief, nor is it expected to asymptotically converge to a fixed value, but instead it casts the model's ability to reproduce the experimental data. This framework enables evaluating the spread and distribution of the predicted response of target applications depending on the same parameters beyond the validation domain.
A critical evaluation is presented of the assumptions on which the mathematical model for the geometrical form of a volcano arising from the flow of magma in a porous medium of Lacey et al. (1981) is based. The lack of evidence for an equipotential surface or its equivalent in volcanoes prior to eruption is pointed out, and the preference of volcanic eruptions for low ground is attributed to the local stress field produced by topographic loading rather than a rising magma table. Other difficulties with the model involve the neglect of the surface flow of lava under gravity away from the vent, and the use of the Dupuit approximation for unconfined flow and the assumption of essentially horizontal magma flow. Comparisons of model predictions with the shapes of actual volcanoes reveal the model not to fit lava shield volcanoes, for which the cone represents the solidification of small lava flows, and to provide a poor fit to composite central volcanoes.
A detailed analysis of dynamic stall experiments has led to a set of relatively compact analytical expressions, called synthesized unsteady airfoil data, which accurately describe in the time-domain the unsteady aerodynamic characteristics of stalled airfoils. An analytical research program was conducted to expand and improve this synthesized unsteady airfoil data method using additional available sets of unsteady airfoil data. The primary objectives were to reduce these data to synthesized form for use in rotor airload prediction analyses and to generalize the results. Unsteady drag data were synthesized which provided the basis for successful expansion of the formulation to include computation of the unsteady pressure drag of airfoils and rotor blades. Also, an improved prediction model for airfoil flow reattachment was incorporated in the method. Application of this improved unsteady aerodynamics model has resulted in an improved correlation between analytic predictions and measured full scale helicopter blade loads and stress data.
An ideal backmixed reactor model (CSTR) and a fluidized bed bubbling reactor model (FBBR) were developed for silane pyrolysis. Silane decomposition is assumed to occur via two pathways: homogeneous decomposition and heterogeneous chemical vapor deposition (CVD). Both models account for homogeneous and heterogeneous silane decomposition, homogeneous nucleation, coagulation and growth by diffusion of fines, scavenging of fines by large particles, elutriation of fines and CVD growth of large seed particles. At present the models do not account for attrition. The preliminary comparison of the model predictions with experimental results shows reasonable agreement. The CSTR model with no adjustable parameter yields a lower bound on fines formed and upper estimate on production rates. The FBBR model overpredicts the formation of fines but could be matched to experimental data by adjusting the unkown jet emulsion exchange efficients. The models clearly indicate that in order to suppress the formation of fines (smoke) good gas-solid contacting in the grid region must be achieved and the formation of the bubbles suppressed.
One of the most important uses of acoustic propagation models lies in the area of detection and tracking of vehicles. Propagation models are used to compute transmission losses in performance prediction models and to analyze the results of past experiments. Vehicles can also provide the means for cost effective experiments to measure acoustic propagation conditions over significant ranges. In order to properly correlate the information provided by the experimental data and the propagation models, the following issues must be taken into consideration: the phenomenology of the vehicle noise sources must be understood and characterized; the vehicle's location or 'ground truth' must be accurately reproduced and synchronized with the acoustic data; and sufficient meteorological data must be collected to support the requirements of the propagation models. The experimental procedures and instrumentation needed to carry out propagation experiments are discussed. Illustrative results are presented for two cases. First, a helicopter was used to measure propagation losses at a range of 1 to 10 Km. Second, a heavy diesel-powered vehicle was used to measure propagation losses in the 300 to 2200 m range.