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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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NASA NTRS1985

Optimal frequency domain textural edge detection filter

An optimal frequency domain textural edge detection filter is developed and its performance evaluated. For the given model and filter bandwidth, the filter maximizes the amount of output image energy placed within a specified resolution interval centered on the textural edge. Filter derivation is based on relating textural edge detection to tonal edge detection via the complex low-pass equivalent representation of narrowband bandpass signals and systems. The filter is specified in terms of the prolate spheroidal wave functions translated in frequency. Performance is evaluated using the asymptotic approximation version of the filter. This evaluation demonstrates satisfactory filter performance for ideal and nonideal textures. In addition, the filter can be adjusted to detect textural edges in noisy images at the expense of edge resolution.

Townsend, J. K.↗
NASA NTRS2020

Terrain Relative Navigation for Guided Descent on Titan

Titan’s dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of 110x110 km and 149x72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3 position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.

Matthies, Larry↗
NASA NTRS2015

Comparison of High-Order and Low-Order Methods for Large-Eddy Simulation of a Compressible Shear Layer

The objective of this work is to compare a high-order solver with a low-order solver for performing Large-Eddy Simulations (LES) of a compressible mixing layer. The high-order method is the Wave-Resolving LES (WRLES) solver employing a Dispersion Relation Preserving (DRP) scheme. The low-order solver is the Wind-US code, which employs the second-order Roe Physical scheme. Both solvers are used to perform LES of the turbulent mixing between two supersonic streams at a convective Mach number of 0.46. The high-order and low-order methods are evaluated at two different levels of grid resolution. For a fine grid resolution, the low-order method produces a very similar solution to the highorder method. At this fine resolution the effects of numerical scheme, subgrid scale modeling, and filtering were found to be negligible. Both methods predict turbulent stresses that are in reasonable agreement with experimental data. However, when the grid resolution is coarsened, the difference between the two solvers becomes apparent. The low-order method deviates from experimental results when the resolution is no longer adequate. The high-order DRP solution shows minimal grid dependence. The effects of subgrid scale modeling and spatial filtering were found to be negligible at both resolutions. For the high-order solver on the fine mesh, a parametric study of the spanwise width was conducted to determine its effect on solution accuracy. An insufficient spanwise width was found to impose an artificial spanwise mode and limit the resolved spanwise modes. We estimate that the spanwise depth needs to be 2.5 times larger than the largest coherent structures to capture the largest spanwise mode and accurately predict turbulent mixing.

large eddy simulations↗
NASA NTRS2015

Comparison of High-Order and Low-Order Methods for Large-Eddy Simulation of a Compressible Shear Layer

The objective of this work is to compare a high-order solver with a low-order solver for performing large-eddy simulations (LES) of a compressible mixing layer. The high-order method is the Wave-Resolving LES (WRLES) solver employing a Dispersion Relation Preserving (DRP) scheme. The low-order solver is the Wind-US code, which employs the second-order Roe Physical scheme. Both solvers are used to perform LES of the turbulent mixing between two supersonic streams at a convective Mach number of 0.46. The high-order and low-order methods are evaluated at two different levels of grid resolution. For a fine grid resolution, the low-order method produces a very similar solution to the high-order method. At this fine resolution the effects of numerical scheme, subgrid scale modeling, and filtering were found to be negligible. Both methods predict turbulent stresses that are in reasonable agreement with experimental data. However, when the grid resolution is coarsened, the difference between the two solvers becomes apparent. The low-order method deviates from experimental results when the resolution is no longer adequate. The high-order DRP solution shows minimal grid dependence. The effects of subgrid scale modeling and spatial filtering were found to be negligible at both resolutions. For the high-order solver on the fine mesh, a parametric study of the spanwise width was conducted to determine its effect on solution accuracy. An insufficient spanwise width was found to impose an artificial spanwise mode and limit the resolved spanwise modes. We estimate that the spanwise depth needs to be 2.5 times larger than the largest coherent structures to capture the largest spanwise mode and accurately predict turbulent mixing.

large eddy simulations↗
NASA NTRS2012

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Armstrong, Jeffrey B.↗
NASA NTRS1973

Frequency modulation television analysis: Distortion analysis

Computer simulation is used to calculate the time-domain waveform of standard T-pulse-and-bar test signal distorted in passing through an FM television system. The simulator includes flat or preemphasized systems and requires specification of the RF predetection filter characteristics. The predetection filters are modeled with frequency-symmetric Chebyshev (0.1-db ripple) and Butterworth filters. The computer was used to calculate distorted output signals for sixty-four different specified systems, and the output waveforms are plotted for all sixty-four. Comparison of the plotted graphs indicates that a Chebyshev predetection filter of four poles causes slightly more signal distortion than a corresponding Butterworth filter and the signal distortion increases as the number of poles increases. An increase in the peak deviation also increases signal distortion. Distortion also increases with the addition of preemphasis.

Hodge, W. H.↗
NASA NTRS2007

Data Registration, Match, and Model Component Coupling

A coupling toolkit has been developed to reduce the complexity of model component coupling, in particular among hierarchical model components. The toolkit provides the services of data registration, data matching, data filtering, and model component coupling. In addition, it can generate diagrams to reveal the "producer"-to-"consumer" relations among the components. We have tested this toolkit with the operational NASA Goddard Earth Observing System (GEOS-5), which is built on the Earth System Modeling Framework (ESMF) and consists of several-level Earth system components.

Zhou, Shujia↗
NASA NTRS1986

Shape Determination for Large Static Structures

Parameter and shape estimates updated from new measurements. Involves statistical structural analysis, statistical electromagneticfield analysis, filtering, measurement modeling, and iterative prediction/correction procedures. Estimating algorithms result from generalizations of Kalman statistical-filter theory.

Rodriguez, G.↗
NASA NTRS1985

High-Latitude Filtering in a Global Grid-Point Model Using Model Normal Modes

The aim of high-latitude filtering in the vicinity of the poles is to avoid the excessively short time steps imposed on an explicit time-differencing scheme by linear stability due to fast moving inertia-gravity waves near the poles. The model normal mode expansion toward the problem of high-latitude filtering in a global shallow water model using the same philosophy as that used by Daley for the problem for large timesteps in P.E. models with explicit time integration schemes was applied.

Takacs, L. L.↗
NASA NTRS1977

A comparison of washout filters using a human dynamic orientation model

The Ormsby model of human dynamic orientation, a discrete time computer program, was used to provide a vestibular explanation for observed differences between two washout schemes. These washout schemes, a linear washout and a nonlinear washout, were subjectively evaluated. It was found that the linear washout presented false rate cues, causing pilots to rate the simulation fidelity of the linear scheme much lower than the nonlinear scheme. By inputting these motion histories into the Ormsby model, it was shown that the linear filter causes discontinuities in the pilot's perceived angular velocity, resulting in the sensation of an anomalous rate cue. This phenomenon does not occur with the use of the nonlinear filter.

Riedel, S. A.↗
NASA NTRS2000

Enhancement of Directional Ambiguity Removal Skill in Scatterometer Data Processing Using Planetary Boundary Layer Models

Planetary boundary layer (PBL) models are utilized to enhance directional ambiguity removal skill in scatterometer data processing. The ambiguity in wind direction retrieved from scatterometer measurements is removed with the aid of physical directional information obtained from PBL models. This technique is based on the observation that sea level pressure is scalar and its field is more coherent than the corresponding wind. An initial wind field obtained from the scatterometer measurements is used to derive a pressure field with a PBL model. After filtering small-scale noise in the derived pressure field, a wind field is generated with an inverted PBL model. This derived wind information is then used to remove wind vector ambiguities in the scatterometer data. It is found that the ambiguity removal skill can be improved when the new technique is used properly in conjunction with the median filter being used for scatterometer wind dealiasing at JPL. The new technique is applied to regions of cyclone systems which are important for accurate weather prediction but where the errors of ambiguity removal are often large.

Kim, Young-Joon↗
NASA NTRS2004

Study of Near-Surface Models in Large-Eddy Simulations of a Neutrally Stratified Atmospheric Boundary Layer

Large-eddy simulation (LES) is a widely used technique in armospheric modeling research. In LES, large, unsteady, three dimensional structures are resolved and small structures that are not resolved on the computational grid are modeled. A filtering operation is applied to distinguish between resolved and unresolved scales. We present two near-surface models that have found use in atmospheric modeling. We also suggest a simpler eddy viscosity model that adopts Prandtl's mixing length model (Prandtl 1925) in the vicinity of the surface and blends with the dynamic Smagotinsky model (Germano et al, 1991) away from the surface. We evaluate the performance of these surface models by simulating a neutraly stratified atmospheric boundary layer.

Senocak, I.↗
NASA NTRS2016

Model-Based Engine Control Architecture with an Extended Kalman Filter

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

engine control↗
NASA NTRS2016

Model-Based Engine Control Architecture with an Extended Kalman Filter

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

engine control↗
NASA NTRS2018

Verifying Implementation of the Dryden Turbulence Model and MIL-F-8785 Gust Gradient

Turbulence modeling in human-in-the-loop simulation is important to assessing aircraft handling qualities and pilot performance and to provide additional realism for pilot training. In the simulation community, the Dryden turbulence spectra is a popular choice for modeling the linear turbulent gusts because its rational form is efficiently reproduced by passing white noise through linear filters. The MIL-F-8785 gust gradients similarly use additional linear filters to model the gradient of the turbulent gust over the wing, and it represents the gust gradients as perturbations to the air-relative rotational rates. The Cockpit Motion Facility at NASA Langley Research Center (LaRC) models continuous random turbulence using the Dryden one-dimensional spectra and MIL-F-8785 gust gradient. The facility recently reviewed and updated its verification of these models as part of an initiative to improve motion cueing under turbulence. This exercise introduced improved methods for verifying the turbulence models and led to rediscovery of model assumptions that informed improvements to implementation.

Madden, Michael M.↗
NASA NTRS1997

DAC-3 Pointing Stability Analysis Results for SAGE 3 and Other Users of the International Space Station (ISS) Payload Attachment Sites (PAS)

The purpose of this paper is to provide final results of a pointing stability analysis for external payload attachment sites (PAS) on the International Space Station (ISS). As a specific example, the pointing stability requirement of the SAGE III atmospheric science instrument was examined in this paper. The instrument requires 10 arcsec stability over 2 second periods. SAGE 3 will be mounted on the ISS starboard side at the lower, outboard FIAS. In this engineering analysis, an open-loop DAC-3 finite element model of ISS was used by the Microgravity Group at Johnson Space Flight Center to generate transient responses at PAS to a limited number of disturbances. The model included dynamics up to 50 Hz. Disturbance models considered included operation of the solar array rotary joints, thermal radiator rotary joints, and control moment gyros. Responses were filtered to model the anticipated vibration attenuation effects of active control systems on the solar and thermal radiator rotary joints. A pointing stability analysis was conducted by double integrating acceleration transient over a 2 second period. Results of the analysis are tabulated for ISS X, Y, and Z Axis rotations. These results indicate that the largest excursions in rotation during pointing occurred due to rapid slewing of the thermal radiator. Even without attenuation at the rotary joints, the resulting pointing error was limited to less than 1.6 arcsec. With vibration control at the joints, to a maximum 0.5 arcsec over a 2 second period. Based on this current level of model definition, it was concluded that between 0 - 50 Hz, the pointing stability requirement for SAGE 3 will not be exceeded by the disturbances evaluated in this study.

Woods-Vedeler, Jessica A.↗
NASA NTRS2013

Developing a Model Component

The Spaceport Command and Control System (SCCS) Simulation Computer Software Configuration Item (CSCI) is,. responsible for providing simulations to support test and verification of SCCS hardware and software. The Universal Coolant Transporter System (UCTS) is a Space Shuttle Orbiter support piece of the Ground Servicing Equipment (GSE). The purpose of the UCTS is to provide two support services to the Space Shuttle Orbiter immediately after landing at the Shuttle Landing Facility. The Simulation uses GSE Models to stand in for the actual systems to support testing of SCCS systems s:luring their development. As an intern at KSC, my assignment was to develop a model component for the UCTS. I was given a fluid component (drier) to model in Matlab. The drier was a Catch All replaceable core type filter-drier. The filter-drier provides maximum protection for the thermostatic expansion valve and solenoid valve from dirt that may be in the system. The filter-drier also protects the valves from freezing up. I researched fluid dynamics to understand the function of my component. I completed training for UNIX and Simulink to help aid in my assignment. The filter-drier was modeled by determining affects it has on the pressure, velocity and temperature of the system. I used Bernoulli's Equation to calculate the pressure and velocity differential through the dryer. I created my model filter-drier in Simulink and wrote the test script to test the component. I completed component testing and captured test data. The finalized model was sent for peer review for any improvements.

Fields, Christina M.↗