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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Human Error: A Concept Analysis

Human error is the subject of research in almost every industry and profession of our times. This term is part of our daily language and intuitively understood by most people however, it would be premature to assume that everyone's understanding of human error s the same. For example, human error is used to describe the outcome or consequence of human action, the causal factor of an accident, deliberate violations,a nd the actual action taken by a human being. As a result, researchers rarely agree on the either a specific definition or how to prevent human error. The purpose of this article is to explore the specific concept of human error using Concept Analysis as described by Walker and Avant (1995). The concept of human error is examined as currently used in the literature of a variety of industries and professions. Defining attributes and examples of model, borderline, and contrary cases are described. The antecedents and consequences of human error are also discussed and a definition of human error is offered.

Hansen, Frederick D.↗
NASA NTRS2011

Error Propagation Analysis in the SAE Architecture Analysis and Design Language (AADL) and the EDICT Tool Framework

This report documents the capabilities of the EDICT tools for error modeling and error propagation analysis when operating with models defined in the Architecture Analysis & Design Language (AADL). We discuss our experience using the EDICT error analysis capabilities on a model of the Scalable Processor-Independent Design for Enhanced Reliability (SPIDER) architecture that uses the Reliable Optical Bus (ROBUS). Based on these experiences we draw some initial conclusions about model based design techniques for error modeling and analysis of highly reliable computing architectures.

LaValley, Brian W.↗
NASA NTRS1985

Integrated analysis of error detection and recovery

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

Shin, K. G.↗
NASA NTRS2000

Low-dimensional Representation of Error Covariance

Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.

Tippett, Michael K.↗
NASA NTRS1982

GP-B error modeling and analysis

Individual source errors and their effects on the accuracy of the Gravity Probe B (GP-B) experiment were investigated. Emphasis was placed on: (1) the refinement of source error identification and classifications of error according to their physical nature; (2) error analysis for the GP-B data processing; and (3) measurement geometry for the experiment.

Hung, J. C.↗
NASA NTRS1993

In-flight measurement of the National Oceanic and Atmospheric Administration (NOAA)-10 static Earth sensor error

Analysis performed in the Goddard Space Flight Center (GSFC) Flight Dynamics Division (FDD) measures error in the static Earth sensor onboard the National Oceanic and Atmospheric Administration (NOAA)-10 spacecraft using flight data. Errors are computed as the difference between Earth sensor pitch and roll angle telemetry and reference pitch and roll attitude histories propagated by gyros. The flight data error determination illustrates the effect on horizon sensing of systemic variation in the Earth infrared (IR) horizon radiance with latitude and season, as well as the effect of anomalies in the global IR radiance. Results of the analysis provide a comparison between static Earth sensor flight performance and that of scanning Earth sensors studied previously in the GSFC/FDD. The results also provide a baseline for evaluating various models of the static Earth sensor. Representative days from the NOAA-10 mission indicate the extent of uniformity and consistency over time of the global IR horizon. A unique aspect of the NOAA-10 analysis is the correlation of flight data errors with independent radiometric measurements of stratospheric temperature. The determination of the NOAA-10 static Earth sensor error contributes to realistic performance expectations for missions to be equipped with similar sensors.

Harvie, E.↗
NASA NTRS1988

Relative error covariance analysis techniques and application

A technique for computing the error covariance of the difference between two estimators derived from different (possibly overlapping) data arcs is presented. The relative error covariance is useful for predicting the achievable consistency between Kalman-Bucy filtered estimates generated from two (not necessarily disjoint) data sets. The relative error covariance analysis technique is then applied to a Venus Orbiter simulation.

Wolff, Peter, J.↗
NASA NTRS2014

Development, Validation, and Application of OSSEs at NASA-GMAO

The GMAO OSSE framework has two general classes of applications. One is to estimate the potential improvements to weather forecasting and analysis by using new proposed instruments that are not as yet built or deployed. This exploits the simulated nature of the OSSE. The other is to assess various aspects of the GMAO data assimilation system. This exploits the availability of truth provided by the OSSE. Two examples of the first class of application will be offered. One concerns deployment of constellations of passive MW sounders on small CUBESATs placed in very low orbits. The other concern is increasing the frequency of radiosonde observations to 4-times daily at all current stations. Several examples of the second class will also be presented. One is an estimation of analysis error characteristics. Another is a comparison between covariances directly determined from explicitly known background errors and those estimated by computing differences between lagged forecasts using the NMC-method prior to tuning. A third is a comparison of effects of model and observation errors on analysis and forecast skill. The last is an examination of spectra of forecast errors in a study of predictability. This latter is an ongoing study.

OSSE↗
NASA NTRS1973

Precision Attitude Determination System (PADS) design and analysis. Two-axis gimbal star tracker

Development of the Precision Attitude Determination System (PADS) focused chiefly on the two-axis gimballed star tracker and electronics design improved from that of Precision Pointing Control System (PPCS), and application of the improved tracker for PADS at geosynchronous altitude. System design, system analysis, software design, and hardware design activities are reported. The system design encompasses the PADS configuration, system performance characteristics, component design summaries, and interface considerations. The PADS design and performance analysis includes error analysis, performance analysis via attitude determination simulation, and star tracker servo design analysis. The design of the star tracker and electronics are discussed. Sensor electronics schematics are included. A detailed characterization of the application software algorithms and computer requirements is provided.

Source record↗
NASA NTRS1980

Symbolic-numeric interface: A review

A survey of the use of a combination of symbolic and numerical calculations is presented. Symbolic calculations primarily refer to the computer processing of procedures from classical algebra, analysis, and calculus. Numerical calculations refer to both numerical mathematics research and scientific computation. This survey is intended to point out a large number of problem areas where a cooperation of symbolic and numerical methods is likely to bear many fruits. These areas include such classical operations as differentiation and integration, such diverse activities as function approximations and qualitative analysis, and such contemporary topics as finite element calculations and computation complexity. It is contended that other less obvious topics such as the fast Fourier transform, linear algebra, nonlinear analysis and error analysis would also benefit from a synergistic approach.

Ng, E. W.↗
NASA NTRS1998

Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle

The use of response surface models and kriging models are compared for approximating non-random, deterministic computer analyses. After discussing the traditional response surface approach for constructing polynomial models for approximation, kriging is presented as an alternative statistical-based approximation method for the design and analysis of computer experiments. Both approximation methods are applied to the multidisciplinary design and analysis of an aerospike nozzle which consists of a computational fluid dynamics model and a finite element analysis model. Error analysis of the response surface and kriging models is performed along with a graphical comparison of the approximations. Four optimization problems are formulated and solved using both approximation models. While neither approximation technique consistently outperforms the other in this example, the kriging models using only a constant for the underlying global model and a Gaussian correlation function perform as well as the second order polynomial response surface models.

Simpson, Timothy W.↗
DOE OSTI2020

A Novel Simulation-Based Framework for Sensor Error Impact Analysis in Smart Building Systems: A Case Study for a Demand-Controlled Ventilation System

Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1992

Orbiter-orbiter and orbiter-lander tracking using same-beam interferometry

Two spacecraft orbiting Mars will subtend a small angle as viewed from Earth. This angle will usually be smaller than the beam width of a single radio antenna. Thus the two spacecraft may be tracked simultaneously by a single Earth-based antenna. The same-beam interferometry (SBI) technique involves using two widely separated antennas, each observing the two spacecraft, to produce a measurement of the angular separation of the two spacecraft in the plane of the sky. The information content of SBI data is thus complementary to the line-of-sight information provided by conventional Doppler data. The inclusion of SBI data with the Doppler data in a joint orbit estimation procedure can desensitize the solution to gravity mismodeling and result in improved orbit determination accuracy. This article presents an overview of the SBI technique, a measurement error analysis, and an error covariance analysis of some examples of the application of SBI to orbit determination. For hypothetical scenarios involving the Mars Observer and the Russian Mars '94 spacecraft, orbit determination accuracy improvements of up to an order of magnitude are predicted, relative to the accuracy that can be obtained by using only Doppler data acquired separately from each spacecraft. Relative tracking between a Mars orbiter and a lander fixed on the surface of Mars is also studied. Results indicate that the lander location may be determined to a few meters, while the orbiter ephemeris may be determined with accuracy similar to the orbiter-orbiter case.

Folkner, W. M.↗
NASA NTRS1997

Experimental Investigation of Jet Impingement Heat Transfer Using Thermochromic Liquid Crystals

Jet impingement cooling of a hypersonic airfoil leading edge is experimentally investigated using thermochromic liquid crystals (TLCS) to measure surface temperature. The experiment uses computer data acquisition with digital imaging of the TLCs to determine heat transfer coefficients during a transient experiment. The data reduction relies on analysis of a coupled transient conduction - convection heat transfer problem that characterizes the experiment. The recovery temperature of the jet is accounted for by running two experiments with different heating rates, thereby generating a second equation that is used to solve for the recovery temperature. The resulting solution requires a complicated numerical iteration that is handled by a computer. Because the computational data reduction method is complex, special attention is paid to error assessment. The error analysis considers random and systematic errors generated by the instrumentation along with errors generated by the approximate nature of the numerical methods. Results of the error analysis show that the experimentally determined heat transfer coefficients are accurate to within 15%. The error analysis also shows that the recovery temperature data may be in error by more than 50%. The results show that the recovery temperature data is only reliable when the recovery temperature of the jet is greater than 5 C, i.e. the jet velocity is in excess of 100 m/s. Parameters that were investigated include nozzle width, distance from the nozzle exit to the airfoil surface, and jet velocity. Heat transfer data is presented in graphical and tabular forms. An engineering analysis of hypersonic airfoil leading edge cooling is performed using the results from these experiments. Several suggestions for the improvement of the experimental technique are discussed.

Dempsey, Brian Paul↗
DOE OSTI2022

A Variational Method for Analyzing Vortex Flows in Radar-Scanned Tornadic Mesocyclones. Part III: Sensitivities to Vortex Center Location Errors

Abstract When the vortex center location is estimated from a radar-scanned tornadic mesocyclone, the estimated location is not error-free. This raises an important issue concerning the sensitivities of analyzed vortex flow (VF) fields by the VF-Var (formulated in Part I of this paper series and tested in Part II) to vortex center location errors, denoted by Δ x c . Numerical experiments are performed to address this issue with the following findings: The increase of |Δ x c | from zero to a half of vortex core radius causes large analysis error increases in the vortex core but the increased analysis errors decrease rapidly away from the vortex core especially for dual-Doppler analyses. The increased horizontal-velocity errors in the vortex core are mainly in the Δ x c -normal component, because this component varies much more rapidly than the other component along the Δ x c direction in the vortex core. The vertical variations of Δ x c distort the vertical correlation structure of Δ x c -dislocated VF-dependent background error covariance, which can increase the analysis errors in the vortex core. The dual-Doppler analyses have adequate accuracies outside the vortex core even when |Δ x c | increases to a half of vortex core radius, while single-Doppler analyses can also have adequate accuracies outside the vortex core mainly for the single-Doppler-observed velocity component. The sensitivities to Δ x c are largely unaffected by the vortex slanting. The above findings are important and useful for assessing the accuracies of analyzed VFs for real radar-observed tornadic mesocyclones. Significance Statement When the vortex center location is estimated from a radar-scanned tornadic mesocyclone, the estimated location is not error-free. This raises an issue concerning the sensitivity of analyzed vortex flow (VF) by the VF-Var (formulated in Part I of this paper series and tested with simulated radar observations in Part II) to vortex center location error. This issue and its required investigations are very important for the VF-Var to be applied to real radar-observed tornadic mesocyclones, especially in an operational setting with the WSR-88Ds. Numerical experiments are performed to address this issue. The findings from these experiments are important and useful for assessing the accuracies of VF-Var analyzed VF fields for real radar-observed tornadic mesocyclones.

Xu, Qin↗
NASA NTRS1985

Systematic estimation of forecast and observation error covariances in four-dimensional data assimilation

A two-part algorithm is presented for reliably computing weather forecast model and observational error covariances during data assimilation. Data errors arise from instrumental inaccuracies and sub-grid scale variability, whereas forecast errors occur because of modeling errors and the propagation of previous analysis errors. A Kalman filter is defined as the primary algorithm for estimating the forecast and analysis error convariance matrices. A second algorithm is described for quantifying the noise covariance matrices of any degree to obtain accurate values for the observational error covariances. Numerical results are provided from a linearized one-dimensional shallow-water model. The results cover observational noise covariances, initial instrumental errors and erroneous model values.

Dee, D. P.↗