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At least 37 records · Page 2

Simulation of Dynamics of PVT Growth: ZnSe

An unsteady model for the simulation of PVT growth process has been developed and applied to growth of ZnSe. The model is capable of tracking the unsteady aspects of the growth process caused by, for example, changes in the system temperature associated with growth, source-depletion, translation of the charge, out-gassing at high temperatures, as well as changes in the source composition during growth. The model predicts the evolution of growth interface morphology as well as the shape of source-gas interface. Thermo-fluid transport in the gas phase is handled by a Low-Mach number unsteady compressible flow formulation. Heat transfer in the crystal, source, and the quartz housing is calculated by the Monte-Carlo technique to capture the variations in the spectral transmittance and index of refraction of the participating media. Simulation results are presented for the growth interface morphology, thermal stresses in the crystal, and growth rate at various stages of growth. The influence of impurity gases on the growth process is also discussed, The simulation results are compared with the experimental observations. The capabilities of the developed tool in addressing other PVT growth processes such as SiC are discussed.

Worlikar, A.↗

Design Issues of GaAs and AlGaAs Delta-Doped p-i-n Quantum-Well APD's

We examine the basic design issues in the optimization of GaAs delta-doped and AlGAs delta-doped quantum-well avalanche photodiode (APD) structures using a theoretical analysis based on an ensemble Monte Carlo simulation. The devices are variations of the p-i-n doped quantum-well structure previously described in the literature. They have the same low-noise, high-gain and high-bandwidth features as the p-i-n doped quantum-well device. However, the use of delta doping provides far greater control or the doping concentrations within each stage possibly enhancing the extent to which the device can be depleted. As a result, it is expected that the proposed devices will operate at higher gain levels (at very low noise) than devices previously developed.

Wang, Yang↗

Understanding Temporal and Spatial Variability of the Lunar Helium Atmosphere Using Simultaneous Observations from LRO, LADEE, and ARTEMIS

Simultaneous measurements of helium in the exosphere of the Moon are made from the Lunar Reconnaissance Orbiter (LRO) Lyman Alpha Mapping Project (LAMP) and the Lunar Atmosphere and Dust Environment Explorer (LADEE) Neutral Mass Spectrometer (NMS) through the entire 5-month span of the LADEE mission. In addition, the ARTEMIS mission monitored the solar wind alpha particle flux to the Moon. Modeling the lunar helium exosphere, we relate the LAMP polar observations to the LADEE equatorial observations. Further, using the ARTEMIS alpha flux in the Monte Carlo model reproduces the temporal variations in helium density. Comparing the LAMP data to the LADEE data shows excellent agreement. Comparing those with the ARTEMIS data reveals that the solar wind alpha flux is the primary driver to variability in the helium exosphere throughout the LADEE mission. Using a decay time for exospheric helium of 5 days, we determine that the solar wind contributes 64 +/- 5% of the helium to the lunar exosphere. The remaining 36 +/- 5% is presumed to come from outgassing of radiogenic helium from the interior of the Moon. Furthermore, the model reproduces the measurements if 63 +/- 6% of the incident alpha particles are converted to thermalized helium atoms through the interaction between the alphas and the lunar surface. However, these values are dependent on both inferred source rates from LAMP and LADEE observations and on the assumed time constant of the exospheric decay rate.

Moon↗

Multi Model Monte Carlo with Python (MXMCPy)

Multi Model Monte Carlo with Python (\mxmc {}) is a software package developed as a general capability for computing the statistics of outputs from an expensive, high-fidelity model by leveraging faster, low-fidelity models for speedup. Motivated by uncertainty propagation problems where classical Monte Carlo (MC) simulation is computationally intractable, various multi-model MC approaches have recently emerged that yield unbiased estimators with significantly reduced variance relative to MC for the same cost. These existing methods include multi-level Monte Carlo (MLMC), multi-fidelity Monte Carlo (MFMC), and approximate control variates (ACV). Given a fixed computational budget and a collection of models with varying cost/accuracy, each method seeks a sample allocation strategy across the models that results in an estimator with optimal variance reduction. \mxmc {} is a versatile tool that enables convenient access to many existing multi-model MC approaches within one modular and extensible package. With \mxmc {}, users can easily compare existing methods to determine the best choice for their particular problem, while developers have a basis for implementing and sharing new variance reduction approaches. This report introduces the \mxmc {} software, providing a summary of the problem-solving workflow for users as well as a brief overview of the code layout for developers.

Geoffrey F Bomarito↗

Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Analysis

This Technical Publication (TP) is meant to address a number of topics related to the application of Monte Carlo simulation to launch vehicle design and requirements analysis. Although the focus is on a launch vehicle application, the methods may be applied to other complex systems as well. The TP is organized so that all the important topics are covered in the main text, and detailed derivations are in the appendices. The TP first introduces Monte Carlo simulation and the major topics to be discussed, including discussion of the input distributions for Monte Carlo runs, testing the simulation, how many runs are necessary for verification of requirements, what to do if results are desired for events that happen only rarely, and postprocessing, including analyzing any failed runs, examples of useful output products, and statistical information for generating desired results from the output data. Topics in the appendices include some tables for requirements verification, derivation of the number of runs required and generation of output probabilistic data with consumer risk included, derivation of launch vehicle models to include possible variations of assembled vehicles, minimization of a consumable to achieve a two-dimensional statistical result, recontact probability during staging, ensuring duplicated Monte Carlo random variations, and importance sampling.

Hanson, J. M.↗

Characterization of Surface Reflectance Variation Effects on Remote Sensing

The use of Monte Carlo radiative transfer codes to simulate the effects on remote sensing in visible and infrared wavelengths of variables which affect classification is examined. These variables include detector viewing angle, atmospheric aerosol size distribution, aerosol vertical and horizontal distribution (e.g., finite clouds), the form of the bidirectional ground reflectance function, and horizontal variability of reflectance type and reflectivity (albedo). These simulations are used to characterize the sensitivity of observables (intensity and polarization) to variations in the underlying physical parameters both to improve algorithms for the removal of atmospheric effects and to identify techniques which can improve classification accuracy. It was necessary to revise and validate the simulation codes (CTRANS, ARTRAN, and the Mie scattering code) to improve efficiency and accommodate a new operational environment, and to build the basic software tools for acquisition and off-line manipulation of simulation results. Initial calculations compare cases in which increasing amounts of aerosol are shifted into the stratosphere, maintaining a constant optical depth. In the case of moderate aerosol optical depth, the effect on the spread function is to scale it linearly as would be expected from a single scattering model. Varying the viewing angle appears to provide the same qualitative effect as modifying the vertical optical depth (for Lambertian ground reflectance).

Pearce, W. A.↗

Energy storage possibilities of atomic hydrogen

Several recent experiments designed to produce and store macroscopic quantities of atomic hydrogen are discussed. The bulk, ground state properties of atomic hydrogen, deuterium, and tritium systems are calculated assuming that all pair interactions occur via the atomic triplet potential. The conditions required to obtain this system, including inhibition of recombination through the energetically favorable singlet interaction, are discussed. The internal energy, pressure, and compressibility are calculated applying the Monte Carlo technique with a quantum mechanical variational wavefunction. The system studied consisted of 32 atoms in a box with periodic boundary conditions. Results show that atomic triplet hydrogen and deuterium remain gaseous at 0 K; i.e., the internal energy is positive at all molar volumes considered.

Etters, R. D.↗

The effect of normal atmospheric variability on Space Shuttle Orbiter entry performance

The Space Shuttle Orbiter is controlled during entry by a combination of aerodynamic surfaces composed of elevons, a body flap, and a rudder that flares as a speedbrake, and a reaction control system that uses 16 small rocket thrusters. To determine the effects of realistic atmospheric density variations on reentry, a series of Monte Carlo trajectories were generated for each season. The performance parameters analyzed include maximum hinge moments experienced by the aerodynamic control surfaces, the maximum deflections experienced by the aerodynamic control surfaces, a range of angle-of-sideslip values, maximum dynamic pressure, maximum stagnation point heat rate, and state conditions at touchdown. The results of the investigation indicate that the Orbiter guidance and control systems could tolerate atmospheric variability without failures in the control system hardware.

Powell, R. W.↗

Influence of Subpixel Scale Cloud Top Structure on Reflectances from Overcast Stratiform Cloud Layers

Recent observational studies have shown that satellite retrievals of cloud optical depth based on plane-parallel model theory suffer from systematic biases that depend on viewing geometry, even when observations are restricted to overcast marine stratus layers, arguably the closest to plane parallel in nature. At moderate to low sun elevations, the plane-parallel model significantly overestimates the reflectance dependence on view angle in the forward-scattering direction but shows a similar dependence in the backscattering direction. Theoretical simulations are performed that show that the likely cause for this discrepancy is because the plane-parallel model assumption does not account for subpixel, scale variations in cloud-top height (i.e., "cloud bumps"). Monte Carlo simulation, comparing ID model radiances to radiances from overcast cloud field with 1) cloud-top height variation, but constant cloud volume extinction; 2) flat tops but horizontal variations in cloud volume extinction; and 3) variations in both cloud top height and cloud extinction are performed over a approximately equal to 4 km x 4 km domain (roughly the size of an individual GAC AVHRR pixel). The comparisons show that when cloud-top height variations are included, departures from 1D theory are remarkably similar (qualitatively) to those obtained observationally. In contrast, when clouds are assumed flat and only cloud extinction is variable, reflectance differences are much smaller and do not show any view-angle dependence. When both cloud-top height and cloud extinction variations are included, however, large increases in cloud extinction variability can enhance reflectance difference. The reason 3D-1D reflectance differences are more sensitive to cloud-top height variations in the forward-scattering direction (at moderate to low, sun elevations) is because photons leaving the cloud field in that direction experience fewer scattering events (low-order scattering) and are restricted to the topmost portions of the cloud. While reflectance deviations from 1D theory are much larger for bumpy clouds than for flat clouds with variable cloud extinction, differences in cloud albedo are comparable for these two cases.

Loeb, N. G.↗

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Non-NASA Center↗

Improving Multi-Model Trajectory Simulation Estimators using Model Selection and Tuning

Multi-model Monte Carlo methods have been demonstrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation among the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Preliminary results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC. The challenges and potential benefits to exploring a fully iterative and comprehensive optimization strategy in future work are highlighted.

uncertainty quantification↗

Solid-propellant rocket motor ballistic performance variation analyses

Results are presented of research aimed at improving the assessment of off-nominal internal ballistic performance including tailoff and thrust imbalance of two large solid-rocket motors (SRMs) firing in parallel. Previous analyses using the Monte Carlo technique were refined to permit evaluation of the effects of radial and circumferential propellant temperature gradients. Sample evaluations of the effect of the temperature gradients are presented. A separate theoretical investigation of the effect of strain rate on the burning rate of propellant indicates that the thermoelastic coupling may cause substantial variations in burning rate during highly transient operating conditions. The Monte Carlo approach was also modified to permit the effects on performance of variation in the characteristics between lots of propellants and other materials to be evaluated. This permits the variabilities for the total SRM population to be determined. A sample case shows, however, that the effect of these between-lot variations on thrust imbalances within pairs of SRMs is minor in compariosn to the effect of the within-lot variations. The revised Monte Carlo and design analysis computer programs along with instructions including format requirements for preparation of input data and illustrative examples are presented.

Sforzini, R. H.↗

Bidirectional Spectral Reflectance of Earth Resources: Influence of Scene Complexity and Atmospheric Effects on Remote Sensing

Practical methods for remote sensing when scene complexity and atmospheric effects modify intrinsic reflective properties are developed. The radiation history from ground to space of light reflected from individual leaves is initially multiply scattered within the crop canopy, whose geometry provides a controlling influence, then scattered and attenuated as a result of transmission through the Earth's atmosphere. The experimental and theoretical tools for studying these effects quantitatively are under development. A new radiative transfer code which uses Fourier transforms to solve the 3-D equation of transfer was developed. The initial version permits inhomogeneous non-Lambertian surfaces but assumes horizontal uniformity for the atmosphere. The computational results are in excellent agreement with Monte Carlo calculations. Laboratory apparatus to study the variation of spectral reflectance of individual leaves as a function of illumination incidence angle and reflection angle was used. These data can then be used in models to determine canopy scattering effects. Stress tests by observing leaf reflectance at 0.9 microns as a function of time following clipping from the stem was performed. A reflectance increase due to loss of water has been observed.

Diner, D. J.↗

A Geometric Analysis to Protect Manned Assets from Newly Launched Objects - COLA Gap Analysis

A safety risk was identified for the International Space Station (ISS) by The Aerospace Corporation following the launch of GPS IIR-20 (March 24, 2009), when the spent upper stage of the launch vehicle unexpectedly crossed inside the ISS notification box shortly after launch. This event highlighted a 56-hour vulnerability period following the end of the launch Collision Avoidance (COLA) process where the ISS would be unable to react to a conjunction with a newly launched object. Current launch COLA processes screen each launched object across the launch window to determine if an object's nominal trajectory is predicted to pass within 200 km of the ISS (or any other manned/mannable object), resulting in a launch time closure. These launch COLA screens are performed from launch through separation plus I 00 minutes. Once the objects are in orbit, they are cataloged and evaluated as part of routine on-orbit conjunction assessment processes. However, as the GPS IIR-20 scenario illustrated, there is a vulnerability period in the time line between the end of launch COLA coverage and the beginning of standard on-orbit COLA assessment activities. The gap between existing launch and on-orbit COLA processes is driven by the time it takes to track and catalog a launched object, identify a conjunction, and plan and execute a collision avoidance maneuver. For the ISS, the total time required to accomplish an of these steps is 56 hours. To protect human lives, NASA/JSC has requested that an US launches take additional steps to protect the ISS during this "COLA gap" period. The uncertainty in the state of a spent upper stage can be quite large after all bums are complete and all remaining propellants are expelled to safe the stage. Simply extending the launch COLA process an additional 56 hours is not a viable option as the 3-sigma position uncertainty will far exceed the 200 km miss-distance criterion. Additionally, performing a probability of collision (Pc) analysis over this period is also not practical due to the limiting effects of these large orbit state uncertainties. An estimated upper bound for Pc for a typical spent upper stage if nominally aligned for a direct broadside collision with the ISS is only on the order of 10-6. For a smaller manned object such as a Soyuz capsule, the risk level decreases to an order of 10'8 . In comparison, the Air Force Range policy (AFI 91-217) for launch COLAs would only eliminate launch opportunities when conjunctions with objects exceed a Pc of 10'5 This paper demonstrates a conservative geometry-based methodology that may be used to determine if launch opportunities pose a threat to the ISS during the COLA gap period. The NASA Launch Services Program at Kennedy Space Center has developed this COLA gap analysis method and employed it fQr three NASA missions to identify potential ISS conjunctions and corresponding launch window closures during the 56-hour at-risk period. In the analysis, for each launch opportunity, the nominal trajectory of the spent upper stage and the orbit state of the ISS are propagated over the 56 hour period. Each time the upper stage crosses the orbit plane of the ISS, the relative radial and argument of latitude separations are calculated. A window cutout is identified if these separation differences fall within a mission-specific violation box, which is determined from the evaluation of a Monte Carlo dispersions analysis that quantifies the potential variation in the upper stage radial and argument of latitude differences. This paper details the results of these analyses and their impacts to each mission.

Hametz, Mark E.↗

State Estimation of International Space Station Centrifuge Rotor With Incomplete Knowledge of Disturbance Inputs

This thesis develops a state estimation algorithm for the Centrifuge Rotor (CR) system where only relative measurements are available with limited knowledge of both rotor imbalance disturbances and International Space Station (ISS) thruster disturbances. A Kalman filter is applied to a plant model augmented with sinusoidal disturbance states used to model both the effect of the rotor imbalance and the 155 thrusters on the CR relative motion measurement. The sinusoidal disturbance states compensate for the lack of the availability of plant inputs for use in the Kalman filter. Testing confirms that complete disturbance modeling is necessary to ensure reliable estimation. Further testing goes on to show that increased estimator operational bandwidth can be achieved through the expansion of the disturbance model within the filter dynamics. In addition, Monte Carlo analysis shows the varying levels of robustness against defined plant/filter uncertainty variations.

SINUSOIDAL DISTURBANCE STATES↗

Tool Support for Parametric Analysis of Large Software Simulation Systems

The analysis of large and complex parameterized software systems, e.g., systems simulation in aerospace, is very complicated and time-consuming due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such an analysis with a tool to support envelope assessment: we utilize a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. Additional test-cases, automatically generated from models (e.g., UML, Simulink, Stateflow) improve the coverage. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. The tool has been developed around the Trick simulation environment, which is widely used within NASA. We will present this tool with a GN&C (Guidance, Navigation and Control) simulation of a small satellite system.

Schumann, Johann↗

Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems

A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluations of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variation. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation.

Stengel, Robert F.↗