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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Plasma Treatment to Remove Carbon from Indium UV Filters

The sounding rocket experiment FIRE (Far-ultraviolet Imaging Rocket Experiment) will improve the science community fs ability to image a spectral region hitherto unexplored astronomically. The imaging band of FIRE (.900 to 1,100 Angstroms) will help fill the current wavelength imaging observation hole existing from approximately equal to 620 Angstroms to the GALEX band near 1,350 Angstroms. FIRE is a single-optic prime focus telescope with a 1.75-m focal length. The bandpass of 900 to 1100 Angstroms is set by a combination of the mirror coating, the indium filter in front of the detector, and the salt coating on the front of the detector fs microchannel plates. Critical to this is the indium filter that must reduce the flux from Lymanalpha at 1,216 Angstroms by a minimum factor of 10(exp -4). The cost of this Lyman-alpha removal is that the filter is not fully transparent at the desired wavelengths of 900 to 1,100 Angstroms. Recently, in a project to improve the performance of optical and solar blind detectors, JPL developed a plasma process capable of removing carbon contamination from indium metal. In this work, a low-power, low-temperature hydrogen plasma reacts with the carbon contaminants in the indium to form methane, but leaves the indium metal surface undisturbed. This process was recently tested in a proof-of-concept experiment with a filter provided by the University of Colorado. This initial test on a test filter showed improvement in transmission from 7 to 9 percent near 900 with no process optimization applied. Further improvements in this performance were readily achieved to bring the total transmission to 12% with optimization to JPL's existing process.

Greer, Harold F.↗
NASA NTRS2014

Adaptable Iterative and Recursive Kalman Filter Schemes

Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.

Zanetti, Renato↗
NASA NTRS2014

Methods to Fabricate and Improve Stand-alone and Integrated Filters

Embodiments of the invention provide for fabricating a filter, for electromagnetic radiation, in at least three ways, including (1) fabricating integrated thin film filters directly on a detector; (2) fabricating a free standing thin film filter that may be used with a detector; and (3) treating an existing filter to improve the filter's properties.

Greer, Frank↗
NASA NTRS2014

Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms

This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. In certain situations filtering algorithms such as the ensemble Kalman filter (EnKF) and ensemble transform Kalman filter (ETKF) yield updated ensembles that conserve mass but are negative, even though the actual states must be nonnegative. In such situations if negative values are set to zero, or a log transform is introduced, the total mass will not be conserved. In this study, mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate non-negativity constraints. Simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. In two examples, an update that includes a non-negativity constraint is able to properly describe the transport of a sharp feature (e.g., a triangle or cone). A number of implementation questions still need to be addressed, particularly the need to develop a computationally efficient quadratic programming update for large ensemble.

EnKF↗
NASA NTRS2008

Compact Planar Microwave Blocking Filter

This paper proposes a technique to design a compact planar broadband microwave blocking filter. The filter is constructed from multiple sections of bandstop filters with means to control radiation loss. As a result, the filter has small physical size and provides multi-decade of suppression frequency bandwidth. The total length is less than half-wavelength long at the 3-dB corner frequency of 1.45 GHz. The experimental results show that the microstrip microwave blocking filter can provide an attenuation of more than 70 dB from 7 GHz to 39.5GHz with the total DC capacitance of les than 23 pF.

U-yen, Kongpop↗
NASA NTRS2019

High-Order Finite-Difference Nonlinear Filter Methods for Subsonic Turbulence Simulation with Stochastic Forcing

Numerical stability of high-order filter schemes developed by Yee & Sjogreen is tested on three-dimensional turbulence simulations with stochastic forcing and their performance is compared with that of TVD and WENO schemes. The best­ performing filter method employs an eighth-order central base scheme with the Kennedy & Gruber skew-symmetric splitting of the inviscid flux derivative, a wavelet-based local flow sensor, a nonlinear filter utilizing the dissipative portion of seventh-order \VENO scheme, and an explicit third - or fourth-order Runge­-Kutta time integration. We show that the filter scheme is more computational]y efficient and provides a wider spectral bandwidth compared to the seventh-order WENO scheme. The method also demonstrates robust long-time integration for moderately compressible turbulence. In contrast, the fifth- and seventh­ order WENO schemes show non-trivial evolution of the velocity and density power spectra. over a. few dozen dynamical times, where both TVD and filter schemes recover a so lid statistically stationary turbulent state

Kritsuk, Alexei G.↗
NASA NTRS2019

Effect of Spatial Filtering in Implicit Large-Eddy Simulations of Separated Flows

The relatively high Reynolds number of turbulent flows encountered in various applications puts these problems well beyond the reach of direct numerical simulation (DNS) at present. Meanwhile, lower-fidelity Reynolds-averaged Navier-Stokes (RANS) calculations are known to be not accurate enough in complex problems, such as smooth-body flow separation and other flows involving highly-unsteady phenomena. Hence, given the current infeasibility of DNS and the unsatisfactory performance of RANS, intermediate techniques such as large-eddy simulation (LES) and hybrid RANS-LES, whose fidelity lie between RANS and DNS, have received much attention for application to various problems of practical importance. Modeling of the effect of missing scales on resolved scales, also known as subgrid-scale (SGS) modeling, is an important subject for LES. SGS models can be broadly categorized as explicit or implicit approaches. The explicit approach is based on an SGS model that explicitly appears in the governing equations expressed in the form of so-called “filtered Navier-Stokes equations”, which describe the evolution of the turbulence scales resolved by the LES grid. The effect of the scales unresolved by the grid is represented by the SGS model. The implicit modeling approach, on the other hand, does not employ an explicit model but instead treats the intrinsic dissipation of the numerical discretization scheme as an implicit SGS model. An LES without an explicit SGS model is commonly termed as an implicit LES (ILES). The relative merits of one SGS modeling approach over another is a subject of ongoing debate. We have opted to employ an ILES methodology, based on high-order compact finite-difference and spatial filtering schemes, in our recent investigations of separated flow problems [1, 2]. Further discussion of our preference of ILES over explicit LES is provided in Uzun and Malik [2]. The spatial filtering operation, described in the next section, is treated as an implicit SGS model for the ILES. Some observations made during the course of our recent investigations, which pointed out to excessive numerical dissipation in certain parts of the flowfield, prompted us to take a closer look at the potential effect of the spatial filter on ILES predictions. This technical note is therefore devoted to spatial filter effects in the context of a high Reynolds number, transonic shock-induced separated flow.

Ali Uzun↗
NASA NTRS2019

Using Computational Flow Imaging to Optimize Filtered Rayleigh Scattering Measurements of an Isolator Shock Train

Filtered Rayleigh scattering (FRS) is a laser diagnostic where the intensity of elastically scattered light is measured after it passes through a molecular absorption filter. The filter removes background interference that overwhelms the relatively weak scattering from the gas molecules. However, with a filter, the measured light intensity depends on many of the scattering gas properties, including pressure, density, temperature, and velocity. In this work, CFD simulations of an isolator shock train flow field are input into a physics-based model to predict the values of FRS intensity measurements in a proposed experiment. The goal is to evaluate if a simplistic FRS setup (that utilizes one camera, laser, and absorption filter) can be used to accurately quantify number density despite the fact that the scattered light intensity also depends on other gas properties. It is found that the experimental setup can be optimized such that a linear relationship describes the number density with an average prediction error of 2%. The vast majority of the flow exhibits prediction errors of less than 3%, but small regions of the flow reach up to 11% error. A sensitivity analysis shows that the prediction error increases with the central wavelength of the incident laser light and decreases with the angle between the camera and laser propagation directions. The optimal experimental parameters are chosen based on a compromise between the prediction error, spatial resolution, and the amount of unfiltered light. In the future, the proposed FRS setup will be implemented to acquire new and valuable information on an isolator shock train, a flow field that has been traditionally studied using wall static pressure measurements and path-integrated visualization techniques, such as schlieren and shadowgraphy.

Hunt, Robin L.↗
NASA NTRS2020

Development Requirements for the Exploration PLSS (xPLSS) Thermal Loop Filter

The fluid thermal control loop and auxiliary thermal control loop contain multiple filters including the Thermal Filters (F-448/F-548), which are used to scrub precipitates and biofilms from the cooling loops in order to protect components that may be sensitive to such contamination. The F-448/F-548 filters are comprised of multiple machined piece parts to create the filter housing as well as the removable pressure cap assembly to allow for the thermal filter cartridge replacement.

Kristina Noelle Todd↗
NASA NTRS2020

Design of high-transmission plasmonic wavelength and polarization filters for infrared photodetectors

We have designed plasmonic filters that simultaneously perform wavelength and polarization filtering in the wavelength range 3-5 micrometers. In this work we outline the design and present the simulated results of our filters. The filters consist of sub wavelength slits in a thin metal sheet which is deposited on top of a gallium antimonide (GaSb) substrate and a spacer layer of SiO2. This configuration makes our filters suitable for integration with GaSb based infrared photodetectors and photodetector arrays that are used in space applications. Our work opens up the possibility of using these detector arrays for highly compact Earth-observing infrared polarimeters and multispectral imaging applications.

Wenger, Tobias↗
NASA NTRS2023

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally-distributed single-catchment locations for a 10-year experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF systematically increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results also demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Soil moisture↗
NASA NTRSDate not supplied

Development of Challenge Aerosols for Testing Filters in Spacecraft Air Revitalization Systems

The common means for reducing particle concentrations in air in enclosed spaces, including in space habitats, are source prevention and particle removal by air filters. While air filtration and testing is a well-established discipline and industry, testing and classifying filters according to commonly used standards rely on a test aerosol that is often arbitrary and chosen for the convenience of the test method. In space habitats, the particle size distributions are expected to be quite different than the particle size distributions prescribed in test standards, due to the partial or low gravity environment affecting sedimentation of large particulates like hair or cloth fibers, or the introduction of planetary dust to the pressurized volume. This means that the efficacy of the filter will be quite different in the space habitat than specified according to a prevailing standard. This paper will present a means to specify and generate a “composite” test aerosol that is similar to the measured and reported particle sizes utilizing the International Space Station (ISS) as an example. Application of this test aerosol is expected to yield filter efficiencies and loading effects closer to what one can expect on the ISS and to be useful in determining filter lifetime and replacement cycles. The method can be tuned, within reason, to match other particle size distributions one may encounter, such as the intrusion of Lunar dust for a Lunar lander or habitat.

Filtration↗
NASA NTRSDate not supplied

Hybrid Flush and Synthetic Air Data Filter for Entry Vehicle Atmospheric State Estimation

A hybrid flush/synthetic air data sensing filter utilizing Kalman-Schmidt and Rach-Tung-Striebel smoothers is developed to obtain entry vehicle atmosphere estimates. The filter/smoother blends information from pressure sensors distributed on the heatshield with measurements of the vehicle aerodynamic forces and moments computed from mass properties and inertial measurement unit data, and prior estimates of the atmosphere. The filter produces estimates of the atmospheric conditions along the entry trajectory, and systematic error estimates to reconcile differences between the pressure and aerodynamic data sources. The filter is applied to data acquired during the Mars Science Laboratory and Mars 2020 entry, descent, and landing at Gale crater and at Jezero crater, respectively. The results show that the hybrid filter produces estimates of the freestream flight condition with lower uncertainty than either the flush or synthetic air data algorithms. The filter accomplishes this result by incorporating additional data and computing estimates of systematic error parameters in the pressure data and the aerodynamic model to further reduce the uncertainties.

Christopher D. Karlgaard↗
NASA NTRSDate not supplied

An Efficient Filter for Measurements Corrupted with Cauchy Noise

This paper present a new sequential filter for state estimation using measurements corrupted with Cauchy noise. The new filter retains the familiar structure of the Kalman filter and is computationally efficient. In addition, it does not exhibit computational complexity which grows or varies in time like existing methods. These results are based upon a nearly 50 year old result by Masreliez in which the conditional mean estimator is approximated via linearization of the measurement predictive density. This work derives the new filter, provides discussion regarding practical implementation, and present Monte Carlo analyses to validate and assess the new filter's performance.

James S McCabe↗
NASA NTRSDate not supplied

Evaluation of Finite Impulse Response Filters for Turbulence Effects on Sonic Booms

Turbulence effects on sonic booms lead to random variability of sonic boom waveforms measured on the ground, complicating the prediction of such waveforms using sonic boom propagation codes that do not account for turbulence effects. The NASA PCBoom software is one such code shown to accurately predict sonic booms above the atmospheric boundary layer but not those on the ground. Analyses of measured sonic booms show that, on average, the turbulence effects result in sonic boom loudness reduction, on average, that increases with propagation distance and turbulence strength. To efficiently account for such effects, a signal processing-based approach has been developed at NASA using finite impulse response filters derived from predicted waveforms by solving a nonlinear parabolic equation. Analyses indicate that the mean loudness reduction obtained using the filters does not increase with propagation distance or with increasing turbulence strength, in disagreement with flight test data. An alternative approach to the filters is currently under development employing the multiple scattering theory (MST) of wave propagation. Available results from the physics-based MST approach are used to evaluate the predictive capability of the filters, suggesting that the filters can severely underestimate the prediction of the MST approach.

sonic booms↗
NASA NTRSDate not supplied

Evaluation of Finite Impulse Response Filters for Turbulence Effects on Sonic Booms

Turbulence effects on sonic booms lead to random variability of sonic boom waveforms measured on the ground, complicating the prediction of such waveforms using sonic boom propagation codes that do not account for turbulence effects. The NASA PCBoom software is one such code shown to accurately predict sonic booms above the atmospheric boundary layer but not those on the ground. Analyses of measured sonic booms show that, on average, the turbulence effects result in sonic boom loudness reduction, on average, that increases with propagation distance and turbulence strength. To efficiently account for such effects, a signal processing-based approach has been developed at NASA using finite impulse response filters derived from predicted waveforms by solving a nonlinear parabolic equation. Analyses indicate that the mean loudness reduction obtained using the filters does not increase with propagation distance or with increasing turbulence strength, in disagreement with flight test data. An alternative approach to the filters is currently under development employing the multiple scattering theory (MST) of wave propagation. Available results from the physics-based MST approach are used to evaluate the predictive capability of the filters, suggesting that the filters can severely underestimate the prediction of the MST approach.

sonic booms↗
NASA NTRSDate not supplied

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Rolf Reichle↗
NASA NTRS2024

AC and DC Fault Management for Megawatt Electrified Aircraft Electrical Powertrains - Task 2: Power Quality Filtering Using Nanocrystalline Soft Magnetic Inductor

The NASA RTAPS program on AC and DC Fault Management for Megawatt Electrified Power Train is a multi-year joint project with Pratt & Whitney (P&W), Collins Aerospace (CA), and RTX Technology Research Center (RTRC). This research program focuses on the high-voltage distribution issues that present a significant technological obstacle in the adoption of Electrified Aviation Propulsion (EAP) systems. One challenge to the adoption of high-voltage distribution systems with power electronic converters is the need for filter elements to limit the generation and propagation of noise, protect the cable systems from premature aging and prevent against excessive heating within subcomponents due to high-frequency induced currents. While increased distribution voltages aide in reducing the cable mass for a fixed power system, the associated mass with the filtering elements for power electronic converter can grow with increasing distribution voltages – thereby mitigating any benefit associated with increasing the distribution system voltage. To enable high-voltage distribution systems with high system specific power densities, new magnetic materials must be developed. Therefore, the second task of the NASA RTAPS program is associated with the design and application of advanced soft magnetic components for Megawatt class electric propulsion systems, specifically the motor drive system. This report covers the collaborative work between NASA Glenn Research Center (GRC), RTRC, P&W and CA in the development of three types of magnetic components over the span of the three-year program. These critical magnetic components are the DC side EMI filter, which limits the propagation of harmful electromagnetic noise to the rise of the distribution system, and the AC side damping with the dv/dt filter, which limits the fast rise time of the power electronic converter output voltage to limit the degradation on the cable/motor insulation systems. Each of these components are investigated from component level design and are optimized at the system level with a combined modelling and testing effort. In the final experimental evaluation of the NASA developed soft magnetic material with a dv/dt filter, a commercial-off-the-shelf (COTS) magnetic core and the GRC magnetic core are optimized and loaded at 320Arms to evaluate their difference in performance. After a run time of 30 minutes in a MW-class motor driver at RTRC, the NASA GRC cores were found to not only offer a lower temperature rise of nearly 25°𝐶, but also a reduction in measured core loss of 25% (12.75W to 9.5W).

Elecrified Aircraft Propulsion↗