Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “uncertainty analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Statistical optical link budget analysis

In this paper, we describe work on extending statistical analysis methods to optical communications links, with a primary focus on intensity modulated, direct detected photon-counting channel utilizing pulse-position-modulation (PPM). We performed analysis on the relationship between bit error rate (BER) requirement, statistical characteristics of the received signal power and noise power, and the coded performance curves. We presented link analysis results with preliminary uncertainty quantifications of signal power and noise power. We used the Consultative Committee for Space Data Systems (CCSDS) Serially Concatenated convolutionally coded Pulse Position Modulation (SCPPM) prototype software [1] to obtain coded performance curves under different operating conditions with varying signal power, background noise power, code rates,and PPM modulation orders. Comparison with traditional, deterministic analysis shows that extra margin needs to be reserved to compensate for the performance losses caused by uncertainties of the link parameters.

Cheung, Kar-Ming↗
NASA NTRS1975

Dispersion analysis for baseline reference mission 3A using powered explicit guidance

A dispersion analysis considering 3 sigma uncertainties (or perturbations) in platform, vehicle, and environmental parameters was performed for baseline reference mission 3A. Powered explicit guidance (PEG) was used to develop closed loop steering commands for this dispersion analysis. The nominal profile for the dispersion analysis is identical to the nominal profile of Reference 1 ascent trajectory with the exception that generalized linear tangent (GLT) guidance is used in Reference 1. Nominal trajectory differences which result from using PEG instead of GLT were determined and dispersion data were developed using PEG for comparison with similar data developed using GLT guidance.

Snow, L. S.↗
NASA NTRS2007

Accounting for Uncertainties in Strengths of SiC MEMS Parts

A methodology has been devised for accounting for uncertainties in the strengths of silicon carbide structural components of microelectromechanical systems (MEMS). The methodology enables prediction of the probabilistic strengths of complexly shaped MEMS parts using data from tests of simple specimens. This methodology is intended to serve as a part of a rational basis for designing SiC MEMS, supplementing methodologies that have been borrowed from the art of designing macroscopic brittle material structures. The need for this or a similar methodology arises as a consequence of the fundamental nature of MEMS and the brittle silicon-based materials of which they are typically fabricated. When tested to fracture, MEMS and structural components thereof show wide part-to-part scatter in strength. The methodology involves the use of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) software in conjunction with the ANSYS Probabilistic Design System (PDS) software to simulate or predict the strength responses of brittle material components while simultaneously accounting for the effects of variability of geometrical features on the strength responses. As such, the methodology involves the use of an extended version of the ANSYS/CARES/PDS software system described in Probabilistic Prediction of Lifetimes of Ceramic Parts (LEW-17682-1/4-1), Software Tech Briefs supplement to NASA Tech Briefs, Vol. 30, No. 9 (September 2006), page 10. The ANSYS PDS software enables the ANSYS finite-element-analysis program to account for uncertainty in the design-and analysis process. The ANSYS PDS software accounts for uncertainty in material properties, dimensions, and loading by assigning probabilistic distributions to user-specified model parameters and performing simulations using various sampling techniques.

Nemeth, Noel↗
NASA NTRS1987

Challenges in modeling the X-29 flight test performance

Presented are methods, instrumentation, and difficulties associated with drag measurement of the X-29A aircraft. The initial performance objective of the X-29A program emphasized drag polar shapes rather than absolute drag levels. Priorities during the flight envelope expansion restricted the evaluation of aircraft performance. Changes in aircraft configuration, uncertainties in angle-of-attack calibration, and limitations in instrumentation complicated the analysis. Limited engine instrumentation with uncertainties in overall in-flight thrust accuracy made it difficult to obtain reliable values of coefficient of parasite drag. The aircraft was incapable of tracking the automatic camber control trim schedule for optimum wing flaperon deflection during typical dynamic performance maneuvers; this has also complicated the drag polar shape modeling. The X-29A was far enough off the schedule that the developed trim drag correction procedure has proven inadequate. However, good drag polar shapes have been developed throughout the flight envelope. Preliminary flight results have compared well with wind tunnel predictions. A more comprehensive analysis must be done to complete performance models. The detailed flight performance program with a calibrated engine will benefit from the experience gained during this preliminary performance phase.

John W Hicks↗
NASA NTRS1987

Challenges in modeling the X-29A flight test performance

The paper presents the methods, instrumentation, and difficulties associated with drag measurement of the X-29A aircraft. The initial performance objective of the X-29A program emphasized drag polar shapes rather than absolute drag levels. Priorities during the flight envelope expansion restricted the evaluation of aircraft performance. Changes in aircraft configuration, uncertainties in angle-of-attack calibration, and limitations in instrumentation complicated the analysis. Limited engine instrumentation with uncertainties in overall in-flight thrust accuracy made it difficult to obtain reliable values of coefficient of parasite drag. The aircraft was incapable of tracking the automatic camber control trim schedule for optimum wing flaperon deflection during typical dynamic performance maneuvers; this has also complicated the drag polar shape modeling. The X-29A was far enough off the schedule that the developed trim drag correction procedure has proven inadequate. Despite these obstacles, good drag polar shapes have been developed throughout the flight envelope. Preliminary flight results have compared well with wind tunnel predictions. A more comprehensive analysis must be done to complete the performance models. The detailed flight performance program with a calibrated engine will benefit from the experience gained during this preliminary performance phase.

Hicks, John W.↗
NASA NTRS1976

Dispersion analysis for baseline reference mission 3A with 400,000 foot entry interface altitude

A dispersion analysis considering 3 beta uncertainties in platform, vehicle, and environmental parameters is described. Powered explicit guidance used to develop closed loop steering commands, the nominal profile, and entry interface conditions are discussed. The groundrules and assumptions for the analysis are reviewed. The results presented include dispersion data at specific time slices from liftoff to entry interface, covariance matrices, summary data, and exchange ratios.

Snow, L. S.↗
NASA NTRS2019

Operational and Technical Updates to the Object Reentry Survival Analysis Tool

The Object Reentry Survival Analysis Tool (ORSAT) has been used in the NASA Orbital Debris Program Office for over 25 years to estimate risk due to uncontrolled reentry of spacecraft and rocket bodies. Development over the last 3 years has included: a major change to the treatment of carbon fiber- and glass fiber-reinforced plastics (CFRP and GFRP, respectively); an updated atmospheric model; a new model for computing casualty area around an impacting debris object; and a newly-implemented scheme to determine the breakup altitude of a reentry object. Software also was written to automatically perform parameter sweeps in ORSAT to allow for uncertainty quantification and sensitivity analysis for components with borderline demisability. These updates have improved the speed and fidelity of the reentry analysis performed using ORSAT, and have allowed for improved engineering understanding by estimating the uncertainty for each component’s survivability. A statistical model for initial conditions captures the latitude bias in population density, a large improvement over the previous inclination-based latitude-averaged models. A sample spacecraft has been analyzed with standard techniques using ORSAT 6.2.1 and again using all the updated models; we will demonstrate the variation in the total debris casualty area and overall expectation of casualty.

Ostrom, C.↗
NASA NTRS1976

Uncertainties in derived temperature-height soundings A summary

It is shown that high-resolution low-uncertainty temperature-height profiles cannot be deduced from pressure-height data alone because the relative uncertainty in pressure data cannot be expected to be smaller than 1% or 2% in present-day practical instrumentation. The basic equations relating temperature to pressure-height or density-height data are subjected to error analysis to determine the uncertainty introduced into computed temperature-height values by observed data. The results show that the use of density data is much more preferable than the use of pressure data if atmospheric temperature is to be determined as a function of height with a fine height resolution and a small uncertainty from the height profiles of pressure or density and if pressures and densities can be measured with comparable accuracies.

Minzner, R. A.↗
NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗
NASA NTRS2014

Repeatability Modeling for Wind-Tunnel Measurements: Results for Three Langley Facilities

Data from extensive check standard tests of seven measurement processes in three NASA Langley Research Center wind tunnels are statistically analyzed to test a simple model previously presented in 2000 for characterizing short-term, within-test and across-test repeatability. The analysis is intended to support process improvement and development of uncertainty models for the measurements. The analysis suggests that the repeatability can be estimated adequately as a function of only the test section dynamic pressure over a two-orders- of-magnitude dynamic pressure range. As expected for low instrument loading, short-term coefficient repeatability is determined by the resolution of the instrument alone (air off). However, as previously pointed out, for the highest dynamic pressure range the coefficient repeatability appears to be independent of dynamic pressure, thus presenting a lower floor for the standard deviation for all three time frames. The simple repeatability model is shown to be adequate for all of the cases presented and for all three time frames.

Hemsch, Michael J.↗
NASA NTRS2018

Determination of Uncertainties for Analytically Derived Material Properties to Be Used in Monte Carlo Based Orion Heatshield Sizing

Ablative materials are often used for spacecraft heatshields to protect underlying structures from the extreme environments associated with atmospheric reentry. NASA's Orion EM-1 capsule has been designed to use a molded Avcoat material system. In order to determine the required heatshield thickness, a Monte Carlo approach to the sizing process was proposed. To perform the Monte Carlo simulation, statistical uncertainties on all material property input parameters were required. Obtaining these values for measured properties is straightforward, however input parameters that are derived analytically have historically used uncertainties based on engineering judgment. A MATLAB program was created to use laboratory generated thermogravimetric analysis (TGA) data to calculate uncertainties on the Arrhenius parameters for molded Avcoat. Uncertainties associated with the normalized ablation rate and pyrolysis gas enthalpy were also generated using a wrapper script and the ACE code. These uncertainties could then be tied directly to measured values of individual elemental constituents. The resulting uncertainty values will allow for a probabilistic sizing approach on molded Avcoat with a higher level of confidence in the input parameters.

material property uncertainties↗
NASA NTRS1983

A design methodology for nonlinear systems containing parameter uncertainty

In the present design methodology for nonlinear systems containing parameter uncertainty, a generalized sensitivity analysis is incorporated which employs parameter space sampling and statistical inference. For the case of a system with j adjustable and k nonadjustable parameters, this methodology (which includes an adaptive random search strategy) is used to determine the combination of j adjustable parameter values which maximize the probability of those performance indices which simultaneously satisfy design criteria in spite of the uncertainty due to k nonadjustable parameters.

Young, G. E.↗
NASA NTRS1974

NASTRAN variance analysis and plotting of HBDY elements

The NASTRAN Thermal Analyzer has been intended to do variance analysis and plot the thermal boundary elements. The objective of the variance analysis addition is to assess the sensitivity of temperature variances resulting from uncertainties inherent in input parameters for heat conduction analysis. The plotting capability provides the ability to check the geometry (location, size and orientation) of the boundary elements of a model in relation to the conduction elements. Variance analysis is the study of uncertainties of the computed results as a function of uncertainties of the input data. To study this problem using NASTRAN, a solution is made for both the expected values of all inputs, plus another solution for each uncertain variable. A variance analysis module subtracts the results to form derivatives, and then can determine the expected deviations of output quantities.

Harder, R. L.↗
NASA NTRSDate not supplied

Robust Trajectory Optimization for Guided Powered Descent and Landing

A robust trajectory optimization approach for guidance algorithm gain selection for powered descent and landing is developed. This approach uses a genetic algorithm to determine optimal guidance algorithm parameters while incorporating uncertainty information from linear covariance analysis. The optimal guidance algorithm parameters are determined while accounting for environment, navigation, and vehicle property uncertainty and sensor suite fidelity. As a demonstration of this method, the optimal gains for the fractional polynomial powered descent guidance are found for the braking phase of a robotic lunar landing mission. Scenarios with differing sensor suites and sensor qualities are considered, with objective functions to minimize variability in propellant usage or terminal position. Results show that the optimal guidance algorithm gains for a given trajectory differ based on the sensor suite, and optimal guidance algorithm gains may result in up to 20% performance improvements over the baseline in propellant usage and landed accuracy.

Grace E Calkins↗
NASA NTRS1986

Flat-plate solar array project. Volume 8: Project analysis and integration

Project Analysis and Integration (PA&I) performed planning and integration activities to support management of the various Flat-Plate Solar Array (FSA) Project R&D activities. Technical and economic goals were established by PA&I for each R&D task within the project to coordinate the thrust toward the National Photovoltaic Program goals. A sophisticated computer modeling capability was developed to assess technical progress toward meeting the economic goals. These models included a manufacturing facility simulation, a photovoltaic power station simulation and a decision aid model incorporating uncertainty. This family of analysis tools was used to track the progress of the technology and to explore the effects of alternative technical paths. Numerous studies conducted by PA&I signaled the achievement of milestones or were the foundation of major FSA project and national program decisions. The most important PA&I activities during the project history are summarized. The PA&I planning function is discussed and how it relates to project direction and important analytical models developed by PA&I for its analytical and assessment activities are reviewed.

Mcguire, P.↗