Results for “Magnus methods” — Page 2

NASA NTRS2019

Poisson vs. Gaussian statistics for sparse X-ray data: Application to the soft X-ray spectrometer

Reliable results when fitting X-ray data require proper consideration of the statistics involved. We probe the impact of Gaussian versus Poisson statistics at low count levels using both the standard χ^(2) method and maximum likelihood based on Poisson (C) statistics. The difference is studied and quantified through simulated spectra with known properties. We then test the results through analysis of Mn Kα calibration data taken with the flight spare microcalorimeter for the Hitomi soft X-ray spectrometer. Through comparison with simulations, our results show that the χ^(2) method tends to give overly optimistic estimates of the detector energy resolution, in particular when there are few counts. Given an energy resolution of ∼5 eV and a line with about 100 photons, the line width becomes ∼10% lower in the χ^(2) method than in Poisson statistics. This is a consequence of the uncertainties being dominated by counting statistics, and therefore highlights the need to choose the appropriate fit statistic.

Shinya Yamada↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRSDate not supplied

1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRS1982

Higher-Order Panel Method for Aerodynamic Flow Analysis

PANAIR uses high-order panel method to predict inviscid subsonic or supersonic flows about arbitrary configuration. Panel method solves linear partial differential equation numerically by approximating configuration surface with panels on which unknown "singularity strengths" are defined. PANAIR includes advanced software technology as well as advanced aerodynamic technology.

Erickson, L.↗

NASA NTRS2020

NASA Ames Thermophysics Ground Test Facilities Supporting Future Planetary Atmospheric Entry

A review of the current facility capabilities for testing Thermal Protection Systems and quantifying their entry environments at NASA Ames Research Center is presented based on the expected targets of interest to the Planetary Science and Astrobiology communities. While the operational capabilities of these facilities are generally considered sufficient for supporting future missions to targets of interest, expanded ground test capabilities such as larger sample sizes, flight-relevant gas mixtures, dusty environments, and flight-relevant shear/pressure combinations would reduce future entry vehicle design uncertainties and applied margins. These reduced uncertainties may translate into reduced entry vehicle masses, increased robustness, and decreased operational risks during entry phases for science missions. Expanded ground test capabilities would also offer the ability to study material failure modes in environments even more representative of flight than are currently achievable. A list of desired future test capabilities is presented along with suggestions of possible methods of achieving each. The main recommendation of this paper is the undertaking of a detailed study of the benefits and associated costs of each of these expanded capabilities to determine the best future path.

arc jet↗

NASA NTRSDate not supplied

Kalman Filtering and RTS Smoothing for Arc-Jet Sample Edge Tracking

Accurate and temporally consistent measurements of test-article recession are required to characterize material response during arc-jet ablation experiments. However, image-based boundary measurements are often affected by segmentation noise, brightness variations, and frame-to-frame variability. This work extends arcjetCV [1] with filtering methods for tracking the evolving boundaries of hemispherical and ISO-Q test articles. Two boundary-tracking approaches based on Kalman filtering [2] were implemented. The first applies a point-wise Kalman filter followed by a Rauch–Tung–Striebel smoother [3] to individual boundary-point locations. The second applies the same filtering and smoothing framework to a reduced set of geometry-dependent shape parameters. The point-wise method reduces local frame-to-frame fluctuations while preserving spatial details along the detected boundary. The shape-parameter method provides a compact and geometrically constrained estimate of the sample contour. Figure 1 illustrates the two approaches for a hemispherical test article. Both methods improve temporal consistency and support more robust estimation of surface recession from arc-jet imagery. The two filtering approaches provide complementary representations of boundary evolution and improve the reliability of image-based recession measurements during arc-jet experiments.

recession measurement↗

DOE OSTI2023

A gas dynamic perspective on particle lifting in electrostatic discharge-like devices using multiphase particle-in-cell approach

Although particle–laden electrostatic discharges are widely used in laboratories as well as in industrial applications, the mechanism of particle lifting for particles initially at rest in such highly unsteady systems is not well understood. A multiphase gas–particle solver is developed using the multiphase particle-in-cell (MP-PIC) approach to emulate the interaction of a compressible shock-dominated gas phase with the dense particle phase. First, the two-way coupled solver is initially used to study the interaction of a planar traveling shock with a vertical curtain of particulates. The gas and particle phase evolution was found to be in good agreement with a similar experimental study in Ling et al. [Phys. Fluids 24, 113301 (2012)]. Second, the MP-PIC code is used to study the interaction of an expanding blast wave with a thick bed of particles. The simulation considered forces such as quasi-steady drag, pressure-gradient, added-mass, Saffman, and Magnus forces. We observe that the vertical liftoff particles close to the shock impingement point in this configuration are associated with the quasi-steady drag, pressure gradient, and added-mass forces. Also, the Saffman lift and Magnus forces contribute to lifting particles located radially farther away from the shock impingement point. In addition, the study finds a decrease in particle lifting efficiency with decreasing plasma kernel length and shock strength.

42 ENGINEERING↗

NASA NTRS1981

PAN AIR: A computer program for predicting subsonic or supersonic linear potential flows about arbitrary configurations using a higher order panel method. Volume 3: Case manual (version 1.0)

Numerous applications of the PAN AIR computer program system are presented. PAN AIR is user-oriented tool for analyzing and/or designing aerodynamic configurations in subsonic or supersonic flow using a technique generally referred to as a higher order panel method. Problems solved include simple wings in subsonic and supersonic flow, a wing-body in supersonic flow, wing with deflected flap in subsonic flow, design of two-dimensional and three-dimensional wings, axisymmetric nacelle in supersonic flow, and wing-canard-tail-nacelle-fuselage combination in supersonic flow.

Medan, R. T.↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗

NASA NTRS1981

PAN AIR: A Computer Program for Predicting Subsonic or Supersonic Linear Potential Flows About Arbitrary Configurations Using a Higher Order Panel Method: Theory Document (Version 1.1) - Volume 1

An outline of the derivation of the differential equation governing linear subsonic and supersonic potential flow is given. The use of Green's Theorem to obtain an integral equation over the boundary surface is discussed. The engineering techniques incorporated in the PAN AIR (Panel Aerodynamics) program (a discretization method which solves the integral equation for arbitrary first order boundary conditions) are then discussed in detail. Items discussed include the construction of the compressibility transformations, splining techniques, imposition of the boundary conditions, influence coefficient computation (including the concept of the finite part of an integral), computation of pressure coefficients, and computation of forces and moments.

Magnus, Alfred E.↗

NASA NTRSDate not supplied

Magnetic Measurements of Arc Instabilities in Segmented Arc Heater Column

The Aerodynamic Heating Facility (AHF) at NASA Ames Research Center (ARC) generates high enthalpy, supersonic flow environments for ground testing of NASAheat shield materials. This arc jet heater operates by passing a DC arc (<3200A,<2500V) through a gas column (~10cm diameter, 2m length) and ejecting the heatedgas through a supersonic nozzle into a test chamber. The arc plasma is a partially ionized air mixture at low temperature (< 2eV) but needs to be stable for long test times (up to 30min) at high power (< 20MW). Currently, the operational envelope of the facility is limited by arc instabilities including the kink instability. Consequently, it is of interest to characterize these instabilities to better understand facility limits and inform methods to extend stable operational ranges. Due to the high enthalpy and long timescales (i.e., minutes) diagnostics must be non-invasive. This work will cover the development of two sets of magnetic sensors: differential B-dot sensors and tri-axial magnetic hall sensors and will describe initial measurements of arc motion usingthese sensors.

Kink instability↗

NASA NTRSDate not supplied

Magnetic Measurements of Arc Instabilities in Segmented Arc Heater Column

The Aerodynamic Heating Facility (AHF) at NASA Ames Research Center (ARC) generates high enthalpy, supersonic flow environments for ground testing of NASAheat shield materials. This arc jet heater operates by passing a DC arc (<3200 A,<2500 V) through a gas column (~6 cm diameter, 2 m length) and ejecting the heated gas through a supersonic nozzle into a test chamber. The arc plasma is a partially ionized air mixture at low temperature (< 2 eV) but needs to be stable for long test times (up to 30 min) at high power (< 20 MW). Currently, the operational envelope of the facility is limited by arc instabilities including the kink instability. Consequently, it is of interest to characterize these instabilities to better understand facility limits and inform methods to extend stable operational ranges. Due to the high enthalpy and long timescales (i.e., minutes) diagnostics must be non-invasive. This work will cover the development of two sets of magnetic sensors: differential B-dot sensors and tri-axial magnetic hall sensors and will describe initial measurements of arc motion using these sensors.

Kink instability↗

NASA NTRS2024

Defining the Operational Envelope for Air Flows in the Miniature Arc-Jet Research Chamber (mARC II)

The second-generation 30 kW miniature Arc-jet Research Chamber (mARC II) at NASA Ames Research Center produces high enthalpy flows relevant for entry systems ground testing. The mARC II facility has recently undergone upgrades, including the installation of a new vacuum system to address the issues preventing it from maintaining underexpanded flow under test conditions. In this work, we present data obtained from an Integrated Systems Testing campaign and provide an initial assessment of arc-jet performance following the upgrades. Air is used as the working gas for the standard mARC II arc-heater configuration with two constrictor disks. Seven runs were investigated for five test conditions to assess the lowest achievable stagnation point heat fluxes for air flow rates of 0.15 or 0.25 g s −1 . The heat flux was measured using a water-cooled Gardon gauge (Ø4.76 mm, 3/16" hemispherical) at 70 mm from the nozzle exit plane. The new vacuum system produced test box pressures in the medium (fine) vacuum range (~0.03 torr, 4 Pa) prior to gas addition and successfully maintained underexpanded flow after gas addition. The upgrade yielded a ~4X reduction in heat flux relative to the previous system for the same set test conditions. We report the lowest heat fluxes measured in mARC II to date, ranging from 26 to 81 W cm −2 , for sonic flow enthalpies of 4–14 MJ kg −1 . Bulk enthalpies estimated using an energy balance method (EB 2 ) are reported for the first time using mARC II.Initial data suggests EB 2 generally estimates lower enthalpies than sonic flow methods for mARC II. Lastly, laminar axisymmetric Navier–Stokes simulations were performed using the NASA DPLR code. Numerical heat flux results show good agreement with experiments at low arc powers (3–15% difference at the minimum set arc current), but discrepancy increases with arc power (49% difference at the maximum set arc current).

Pitot tube↗

NASA NTRS2024

Defining the Operational Envelope for Air Flows in the Miniature Arc-Jet Research Chamber (mARC II)

The second-generation 30 kW miniature Arc-jet Research Chamber (mARC II) at NASA Ames Research Center produces high enthalpy flows relevant for entry systems ground testing. The mARC II facility has recently undergone upgrades, including the installation of a new vacuum system to address the issues preventing it from maintaining underexpanded flow under test conditions. In this work, we present data obtained from an Integrated Systems Testing campaign and provide an initial assessment of arc-jet performance following the upgrades. Air is used as the working gas for the standard mARC II arc-heater configuration with two constrictor disks. Seven runs were investigated for five test conditions to assess the lowest achievable stagnation point heat fluxes for air flow rates of 0.15 or 0.25 g s −1 . The heat flux was measured using a water-cooled Gardon gauge (Ø4.76 mm, 3/16" hemispherical) at 70 mm from the nozzle exit plane. The new vacuum system produced test box pressures in the medium (fine) vacuum range (~0.03 torr, 4 Pa) prior to gas addition and successfully maintained underexpanded flow after gas addition. The upgrade yielded a ~4X reduction in heat flux relative to the previous system for the same set test conditions. We report the lowest heat fluxes measured in mARC II to date, ranging from 26 to 81 W cm −2 , for sonic flow enthalpies of 4–14 MJ kg −1 . Bulk enthalpies estimated using an energy balance method (EB 2 ) are reported for the first time using mARC II.Initial data suggests EB 2 generally estimates lower enthalpies than sonic flow methods for mARC II. Lastly, laminar axisymmetric Navier–Stokes simulations were performed using the NASA DPLR code. Numerical heat flux results show good agreement with experiments at low arc powers (3–15% difference at the minimum set arc current), but discrepancy increases with arc power (49% difference at the maximum set arc current).

arc-jet↗

NASA NTRS2010

Optical See-Through Head Mounted Display Direct Linear Transformation Calibration Robustness in the Presence of User Alignment Noise

Augmented Reality (AR) is a technique by which computer generated signals synthesize impressions that are made to coexist with the surrounding real world as perceived by the user. Human smell, taste, touch and hearing can all be augmented, but most commonly AR refers to the human vision being overlaid with information otherwise not readily available to the user. A correct calibration is important on an application level, ensuring that e.g. data labels are presented at correct locations, but also on a system level to enable display techniques such as stereoscopy to function properly [SOURCE]. Thus, vital to AR, calibration methodology is an important research area. While great achievements already have been made, there are some properties in current calibration methods for augmenting vision which do not translate from its traditional use in automated cameras calibration to its use with a human operator. This paper uses a Monte Carlo simulation of a standard direct linear transformation camera calibration to investigate how user introduced head orientation noise affects the parameter estimation during a calibration procedure of an optical see-through head mounted display.

Axholt, Magnus↗

NASA NTRS1970

Calculation of the planar supercritical flow over a NASA supercritical profile

An unsteady finite difference procedure was used to calculate the steady inviscid flow over an 11% thickness ratio NASA supercritical profile of LWP 505 at M sub infinity = 0.80 and alpha = 0. An attempt is made to include the viscous effects using a modified form of Head's entrainment method to calculate the turbulent boundary layer. The attempt to predict the viscous effects using the compressible form of Head's integral method with a modified auxiliary equation for the form factors was unsatisfactory. Though a reasonably separation bubble was established on the lower surface, a grossly exaggerated displacement effect resulted downstream of the shock on the upper surface. There clearly is substantial further effort required to evolve a satisfactory boundary layer procedure, which must then be coupled in a still unproven manner with the inviscid procedure.

Yoshihara, H.↗