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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

General Tool for Evaluating High-Contrast Coronagraphic Telescope Performance Error Budgets

The Coronagraph Performance Error Budget (CPEB) tool automates many of the key steps required to evaluate the scattered starlight contrast in the dark hole of a space-based coronagraph. The tool uses a Code V prescription of the optical train, and uses MATLAB programs to call ray-trace code that generates linear beam-walk and aberration sensitivity matrices for motions of the optical elements and line-of-sight pointing, with and without controlled fine-steering mirrors (FSMs). The sensitivity matrices are imported by macros into Excel 2007, where the error budget is evaluated. The user specifies the particular optics of interest, and chooses the quality of each optic from a predefined set of PSDs. The spreadsheet creates a nominal set of thermal and jitter motions, and combines that with the sensitivity matrices to generate an error budget for the system. CPEB also contains a combination of form and ActiveX controls with Visual Basic for Applications code to allow for user interaction in which the user can perform trade studies such as changing engineering requirements, and identifying and isolating stringent requirements. It contains summary tables and graphics that can be instantly used for reporting results in view graphs. The entire process to obtain a coronagraphic telescope performance error budget has been automated into three stages: conversion of optical prescription from Zemax or Code V to MACOS (in-house optical modeling and analysis tool), a linear models process, and an error budget tool process. The first process was improved by developing a MATLAB package based on the Class Constructor Method with a number of user-defined functions that allow the user to modify the MACOS optical prescription. The second process was modified by creating a MATLAB package that contains user-defined functions that automate the process. The user interfaces with the process by utilizing an initialization file where the user defines the parameters of the linear model computations. Other than this, the process is fully automated. The third process was developed based on the Terrestrial Planet Finder coronagraph Error Budget Tool, but was fully automated by using VBA code, form, and ActiveX controls.

Marchen, Luis F.↗
NASA NTRS2016

Evidence Report: Risk of Performance Errors Due to Training Deficiencies

Substantial evidence supports the claim that inadequate training leads to performance errors. Barshi and Loukopoulos (2012) demonstrate that even a task as carefully developed and refined over many years as operating an aircraft can be significantly improved by a systematic analysis, followed by improved procedures and improved training (see also Loukopoulos, Dismukes, & Barshi, 2009a). Unfortunately, such a systematic analysis of training needs rarely occurs during the preliminary design phase, when modifications are most feasible. Training is often seen as a way to compensate for deficiencies in task and system design, which in turn increases the training load. As a result, task performance often suffers, and with it, the operators suffer and so does the mission. On the other hand, effective training can indeed compensate for such design deficiencies, and can even go beyond to compensate for failures of our imagination to anticipate all that might be needed when we send our crew members to go where no one else has gone before. Much of the research literature on training is motivated by current training practices aimed at current training needs. Although there is some experience with operations in extreme environments on Earth, there is no experience with long-duration space missions where crews must practice semi-autonomous operations, where ground support must accommodate significant communication delays, and where so little is known about the environment. Thus, we must develop robust methodologies and tools to prepare our crews for the unknown. The research necessary to support such an endeavor does not currently exist, but existing research does reveal general challenges that are relevant to long-duration, high-autonomy missions. The evidence presented here describes issues related to the risk of performance errors due to training deficiencies. Contributing factors regarding training deficiencies may pertain to organizational process and training programs for spaceflight, such as when training programs are inadequate or unavailable. Furthermore, failure to match between tasks on the one hand, and learning and memory abilities on the other hand is a contributing factor, especially when individuals' relative efficiency with which new information is acquired, and adjustments made in behavior or thinking, are inconsistent with mission demands. Thus, if training deficiencies are present, the likelihood of errors or of the inability to successfully complete a task increases. What's more, the overall risk to the crew, the vehicle, and the mission increases.

Barshi, Immanuel↗
NASA NTRS1975

Worst error performance of continuous Kalman filters

The worst error performance of estimation filters is investigated for continuous systems in this paper. The pathological performance study, without assuming any dynamical model such as Markov processes for perturbations, except for its bounded amplitude, will give practical and dependable criteria in establishing the navigation and maneuver strategy in deep space missions.

Nishimura, T.↗
NASA NTRS2012

Evidence Report: Risk of Performance Errors Due to Training Deficiencies

The Risk of Performance Errors Due to Training Deficiencies is identified by the National Aeronautics and Space Administration (NASA) Human Research Program (HRP) as a recognized risk to human health and performance in space. The HRP Program Requirements Document (PRD) defines these risks. This Evidence Report provides a summary of the evidence that has been used to identify and characterize this risk. Given that training content, timing, intervals, and delivery methods must support crew task performance, and given that training paradigms will be different for long-duration missions with increased crew autonomy, there is a risk that operators will lack the skills or knowledge necessary to complete critical tasks, resulting in flight and ground crew errors and inefficiencies, failed mission and program objectives, and an increase in crew injuries.

Barshi, Immanuel↗
NASA NTRS2009

A General Tool for Evaluating High-Contrast Coronagraphic Telescope Performance Error Budgets

This paper describes a general purpose Coronagraph Performance Error Budget (CPEB) tool that we have developed under the NASA Exoplanet Exploration Program. The CPEB automates many of the key steps required to evaluate the scattered starlight contrast in the dark hole of a space-based coronagraph. It operates in 3 steps: first, a CodeV or Zemax prescription is converted into a MACOS optical prescription. Second, a Matlab program calls ray-trace code that generates linear beam-walk and aberration sensitivity matrices for motions of the optical elements and line-of-sight pointing, with and without controlled coarse and fine-steering mirrors. Third, the sensitivity matrices are imported by macros into Excel 2007 where the error budget is created. Once created, the user specifies the quality of each optic from a predefined set of PSDs. The spreadsheet creates a nominal set of thermal and jitter motions and combines them with the sensitivity matrices to generate an error budget for the system. The user can easily modify the motion allocations to perform trade studies.

Terrestrial Planet Finder↗
NASA NTRS2016

Wavefront-Error Performance Characterization for the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) Science Instruments

The science instruments (SIs) comprising the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) were tested in three cryogenic-vacuum test campaigns in the NASA Goddard Space Flight Center (GSFC)'s Space Environment Simulator (SES). In this paper, we describe the results of optical wavefront-error performance characterization of the SIs. The wavefront error is determined using image-based wavefront sensing (also known as phase retrieval), and the primary data used by this process are focus sweeps, a series of images recorded by the instrument under test in its as-used configuration, in which the focal plane is systematically changed from one image to the next. High-precision determination of the wavefront error also requires several sources of secondary data, including 1) spectrum, apodization, and wavefront-error characterization of the optical ground-support equipment (OGSE) illumination module, called the OTE Simulator (OSIM), 2) plate scale measurements made using a Pseudo-Nonredundant Mask (PNRM), and 3) pupil geometry predictions as a function of SI and field point, which are complicated because of a tricontagon-shaped outer perimeter and small holes that appear in the exit pupil due to the way that different light sources are injected into the optical path by the OGSE. One set of wavefront-error tests, for the coronagraphic channel of the Near-Infrared Camera (NIRCam) Longwave instruments, was performed using data from transverse translation diversity sweeps instead of focus sweeps, in which a sub-aperture is translated andor rotated across the exit pupil of the system.Several optical-performance requirements that were verified during this ISIM-level testing are levied on the uncertainties of various wavefront-error-related quantities rather than on the wavefront errors themselves. This paper also describes the methodology, based on Monte Carlo simulations of the wavefront-sensing analysis of focus-sweep data, used to establish the uncertainties of the wavefront error maps.

wavefront error↗
NASA NTRS2016

Wavefront-Error Performance Characterization for the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) Science Instruments

The science instruments (SIs) comprising the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM) were tested in three cryogenic-vacuum test campaigns in the NASA Goddard Space Flight Center (GSFC)'s Space Environment Simulator (SES) test chamber. In this paper, we describe the results of optical wavefront-error performance characterization of the SIs. The wavefront error is determined using image-based wavefront sensing, and the primary data used by this process are focus sweeps, a series of images recorded by the instrument under test in its as-used configuration, in which the focal plane is systematically changed from one image to the next. High-precision determination of the wavefront error also requires several sources of secondary data, including 1) spectrum, apodization, and wavefront-error characterization of the optical ground-support equipment (OGSE) illumination module, called the OTE Simulator (OSIM), 2) F-number and pupil-distortion measurements made using a pseudo-nonredundant mask (PNRM), and 3) pupil geometry predictions as a function of SI and field point, which are complicated because of a tricontagon-shaped outer perimeter and small holes that appear in the exit pupil due to the way that different light sources are injected into the optical path by the OGSE. One set of wavefront-error tests, for the coronagraphic channel of the Near-Infrared Camera (NIRCam) Longwave instruments, was performed using data from transverse translation diversity sweeps instead of focus sweeps, in which a sub-aperture is translated and/or rotated across the exit pupil of the system. Several optical-performance requirements that were verified during this ISIM-level testing are levied on the uncertainties of various wavefront-error-related quantities rather than on the wavefront errors themselves. This paper also describes the methodology, based on Monte Carlo simulations of the wavefront-sensing analysis of focus-sweep data, used to establish the uncertainties of the wavefront-error maps.

image analysis↗
NASA NTRS1981

Statistical model to perform error analysis of curve fits of wind tunnel test data using the techniques of analysis of variance and regression analysis

The considered research had the objective to design a statistical model that could perform an error analysis of curve fits of wind tunnel test data using analysis of variance and regression analysis techniques. Four related subproblems were defined, and by solving each of these a solution to the general research problem was obtained. The capabilities of the evolved true statistical model are considered. The least squares fit is used to determine the nature of the force, moment, and pressure data. The order of the curve fit is increased in order to delete the quadratic effect in the residuals. The analysis of variance is used to determine the magnitude and effect of the error factor associated with the experimental data.

Alston, D. W.↗
NASA NTRS2016

Spectral and Wavefront Error Performance of WFIRST-AFTA Bandpass Filter Coating Prototypes

The Cycle 5 design baseline for the Wide-Field Infrared Survey Telescope Astrophysics Focused Telescope Assets (WFIRST/AFTA) instrument includes a single wide-field channel (WFC) instrument for both imaging and slit-less spectroscopy. The only routinely moving part during scientific observations for this wide-field channel is the element wheel (EW) assembly. This filter-wheel assembly will have 8 positions that will be populated with 6 bandpass filters, a blank position, and a Grism that will consist of a three-element assembly to disperse the full field with an undeviated central wavelength for galaxy redshift surveys. All filter elements in the EW assembly will be made out of fused silica substrates (110 mm diameter) that will have the appropriate bandpass coatings according to the filter designations (Z087, Y106, J129, H158, F184, W149 and Grism). This paper presents and discusses the performance (including spectral transmission and reflected/transmitted wavefront error measurements) of a subset of bandpass filter coating prototypes that are based on the WFC instrument filter compliment. The bandpass coating prototypes that are tested in this effort correspond to the Z087, W149, and Grism filter elements. These filter coatings have been procured from three different vendors to assess the most challenging aspects in terms of the in-band throughput, out of band rejection (including the cut-on and cutoff slopes), and the impact the wavefront error distortions of these filter coatings will have on the imaging performance of the wide-field channel in the WFIRST/AFTA observatory.

cryogenic temperatures↗
NASA NTRS2016

Spectral and Wavefront Error Performance of WFIRST/AFTA Prototype Filters

The Cycle 5 design baseline for the Wide-Field Infrared Survey Telescope Astrophysics Focused Telescope Assets (WFIRSTAFTA) instrument includes a single wide-field channel (WFC) instrument for both imaging and slit-less spectroscopy. The only routinely moving part during scientific observations for this wide-field channel is the element wheel (EW) assembly. This filter-wheel assembly will have 8 positions that will be populated with 6 bandpass filters, a blank position, and a Grism that will consist of a three-element assembly to disperse the full field with an undeviated central wavelength for galaxy redshift surveys. All filter elements in the EW assembly will be made out of fused silica substrates (110 mm diameter) that will have the appropriate bandpass coatings according to the filter designations (Z087, Y106, J129, H158, F184, W149 and Grism). This paper presents and discusses the performance (including spectral transmission and reflectedtransmitted wavefront error measurements) of a subset of bandpass filter coating prototypes that are based on the WFC instrument filter compliment. The bandpass coating prototypes that are tested in this effort correspond to the Z087, W149, and Grism filter elements. These filter coatings have been procured from three different vendors to assess the most challenging aspects in terms of the in-band throughput, out of band rejection (including the cut-on and cutoff slopes), and the impact the wavefront error distortions of these filter coatings will have on the imaging performance of the de-field channel in the WFIRSTAFTA observatory.

cryogenic temperatures↗
NASA NTRS1996

Soft-decision decoding techniques for linear block codes and their error performance analysis

The first paper presents a new minimum-weight trellis-based soft-decision iterative decoding algorithm for binary linear block codes. The second paper derives an upper bound on the probability of block error for multilevel concatenated codes (MLCC). The bound evaluates difference in performance for different decompositions of some codes. The third paper investigates the bit error probability code for maximum likelihood decoding of binary linear codes. The fourth and final paper included in this report is concerns itself with the construction of multilevel concatenated block modulation codes using a multilevel concatenation scheme for the frequency non-selective Rayleigh fading channel.

Lin, Shu↗
NASA NTRS2012

Performance Data Errors in Air Carrier Operations: Causes and Countermeasures

Several airline accidents have occurred in recent years as the result of erroneous weight or performance data used to calculate V-speeds, flap/trim settings, required runway lengths, and/or required climb gradients. In this report we consider 4 recent studies of performance data error, report our own study of ASRS-reported incidents, and provide countermeasures that can reduce vulnerability to accidents caused by performance data errors. Performance data are generated through a lengthy process involving several employee groups and computer and/or paper-based systems. Although much of the airline indUStry 's concern has focused on errors pilots make in entering FMS data, we determined that errors occur at every stage of the process and that errors by ground personnel are probably at least as frequent and certainly as consequential as errors by pilots. Most of the errors we examined could in principle have been trapped by effective use of existing procedures or technology; however, the fact that they were not trapped anywhere indicates the need for better countermeasures. Existing procedures are often inadequately designed to mesh with the ways humans process information. Because procedures often do not take into account the ways in which information flows in actual flight ops and time pressures and interruptions experienced by pilots and ground personnel, vulnerability to error is greater. Some aspects of NextGen operations may exacerbate this vulnerability. We identify measures to reduce the number of errors and to help catch the errors that occur.

countermeasures↗
NASA NTRS1978

Error probability performance of unbalanced QPSK receivers

A simple technique for calculating the error probability performance and associated noisy reference loss of practical unbalanced QPSK receivers is presented. The approach is based on expanding the error probability conditioned on the loop phase error in a power series in the loop phase error and then, keeping only the first few terms of this series, averaging this conditional error probability over the probability density function of the loop phase error. Doing so results in an expression for the average error probability which is in the form of a leading term representing the ideal (perfect synchronization references) performance plus a term proportional to the mean-squared crosstalk. Thus, the additional error probability due to noisy synchronization references occurs as an additive term proportional to the mean-squared phase jitter directly associated with the receiver's tracking loop. Similar arguments are advanced to give closed-form results for the noisy reference loss itself.

Simon, M. K.↗