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At least 1,153 records · Page 64
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NASA NTRS1974

The application of sensitivity analysis to models of large scale physiological systems

A survey of the literature of sensitivity analysis as it applies to biological systems is reported as well as a brief development of sensitivity theory. A simple population model and a more complex thermoregulatory model illustrate the investigatory techniques and interpretation of parameter sensitivity analysis. The role of sensitivity analysis in validating and verifying models, and in identifying relative parameter influence in estimating errors in model behavior due to uncertainty in input data is presented. This analysis is valuable to the simulationist and the experimentalist in allocating resources for data collection. A method for reducing highly complex, nonlinear models to simple linear algebraic models that could be useful for making rapid, first order calculations of system behavior is presented.

Leonard, J. I.↗
NASA NTRSDate not supplied

Dose Error Correction Using Simulation Extrapolation for Community Noise Dose-Response Modeling

The objective of this work is to provide a framework to account and correct for dose error in dose-response modeling due to measurement uncertainty. Error in noise measurements, especially in the case of limited monitoring locations in a community, can lead to an attenuation or misestimation of parameters in dose-response models. This error can result in overpredicted annoyance at lower doses and underpredicted annoyance at higher doses. Simulated data in the present work are based on previous NASA community studies and incorporate a notional design for future studies with the X-59 aircraft. Several populations of different annoyance response sensitivities are included. Simulation extrapolation (SIMEX, Cook and Stefanski 1994) is used to correct for the dose error in a simple, fully pooled logistic regression. Results indicate the negative impact of attenuation is greatly diminished for all amounts of dose error considered, regardless of a population’s annoyance sensitivity. Therefore, SIMEX can help produce a more accurate dose-response relationship.

SIMEX↗
NASA NTRS2004

Comparison of Genotoxic Damage in Monolayer Cell Cultures and Three-Dimensional Tissue-Like Cell Assemblies

Assessing the biological risks associated with exposure to the high-energy charged particles encountered in space is essential for the success of long-term space exploration. Although prokaryotic and eukaryotic cell models developed in our laboratory and others have advanced our understanding of many aspects of genotoxicity, in vitro models are needed to assess the risk to humans from space radiation insults. Such models must be representative of the cellular interactions present in tissues and capable of quantifying I genotoxic damage. Toward this overall goal, the objectives of this study were to examine the effect of the localized microenvironment of cells, cultured as either 2-dimensional (2D) monolayers or 3-dimensional (3D) aggregates, on the rate and type of genotoxic damage resulting from exposure to iron charged particles, a significant portion of space radiation. We used rodent transgenic cell lines containing 50-70 copies of a LacI transgene to provide the enhanced sensitivity required to quantify mutational frequency and type in the 1,100-bp LacI target as well as assessment of DNA,damage to the entire 45-kbp construct. Cultured cells were exposed to high-ener~ir on charged particles at Brookhaven National Laboratory s Alternating Gradient Synchrotron facility for a total dose of 0, 0.1, 0.25,0.5, 1.0, or 2.0 Gy and allowed to recover for 0, 1, or 7 days, after which mutational type and frequency were evaluated. The mutational frequency was found to be higher in 3D samples than in 2D samples at all radiation doses. Mutational frequency also was higher at 7 days after irradiation than immediately after exposure. DNA sequencing of the mutant targets revealed that deletional mutations contributed an increasingly high percentage (up to 27%) of all mutations in cells as the dose was increased from 0.5 to 2 Gy. Several mutants also showed large and complex deletions in multiple locations within the Lac1 target. However, no differences in mutational type were found between the 2D and the 3D samples. These 3D tissue-like model systems can reduce the uncertainty involved in extrapolating risk between in vitro cellular and in vivo models.

Behravesh, E.↗
NASA NTRS2007

A Comparison of Various Stress Rupture Life Models for Orbiter Composite Pressure Vessels and Confidence Intervals

In conjunction with a recent NASA Engineering and Safety Center (NESC) investigation of flight worthiness of Kevlar Overwrapped Composite Pressure Vessels (COPVs) on board the Orbiter, two stress rupture life prediction models were proposed independently by Phoenix and by Glaser. In this paper, the use of these models to determine the system reliability of 24 COPVs currently in service on board the Orbiter is discussed. The models are briefly described, compared to each other, and model parameters and parameter uncertainties are also reviewed to understand confidence in reliability estimation as well as the sensitivities of these parameters in influencing overall predicted reliability levels. Differences and similarities in the various models will be compared via stress rupture reliability curves (stress ratio vs. lifetime plots). Also outlined will be the differences in the underlying model premises, and predictive outcomes. Sources of error and sensitivities in the models will be examined and discussed based on sensitivity analysis and confidence interval determination. Confidence interval results and their implications will be discussed for the models by Phoenix and Glaser.

Grimes-Ledesma, Lorie↗
NASA NTRS2008

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Narasimhan, Sriram↗
NASA NTRS2000

Intercomparison of Models Representing Direct Shortwave Radiative Forcing by Sulfate Aerosols

The importance of aerosols as agents of climate change has recently been highlighted. However, the magnitude of aerosol forcing by scattering of shortwave radiation (direct forcing) is still very uncertain even for the relatively well characterized sulfate aerosol. A potential source of uncertainty is in the model representation of aerosol optical properties and aerosol influences on radiative transfer in the atmosphere. Although radiative transfer methods and codes have been compared in the past, these comparisons have not focused on aerosol forcing (change in net radiative flux at the top of the atmosphere). Here we report results of a project involving 12 groups using 15 models to examine radiative forcing by sulfate aerosol for a wide range of values of particle radius, aerosol optical depth, surface albedo, and solar zenith angle. Among the models that were employed were high and low spectral resolution models incorporating a variety of radiative transfer approximations as well as a line-by-line model. The normalized forcings (forcing per sulfate column burden) obtained with the several radiative transfer models were examined, and the discrepancies were characterized. All models simulate forcings of comparable amplitude and exhibit a similar dependence on input parameters. As expected for a non-light-absorbing aerosol, forcings were negative (cooling influence) except at high surface albedo combined with small solar zenith angle. The relative standard deviation of the zenith-angle-averaged normalized broadband forcing for 15 models-was 8% for particle radius near the maximum in this forcing (approx. 0.2 microns) and at low surface albedo. Somewhat greater model-to-model discrepancies were exhibited at specific solar zenith angles. Still greater discrepancies were exhibited at small particle radii and much greater discrepancies were exhibited at high surface albedos, at which the forcing changes sign; in these situations, however, the normalized forcing is quite small quite small. Discrepancies among the models arise from inaccuracies in Mie calculations, differing treatment of the angular scattering phase function, differing wavelength and angular resolution, and differing treatment of multiple scattering. These results imply the need for standardized radiative transfer methods tailored to the direct aerosol forcing problem. However, the relatively small spread in these results suggests that the uncertainty in forcing arising from the treatment of radiative forcing of a well-characterized aerosol at well-specified surface albedo is smaller than some of the other sources of uncertainty in estimates of direct forcing by anthropogenic sulfate aerosols and anthropogenic aerosols generally.

Boucher, O.↗
NASA NTRS2016

The Near-Earth Meteoroid Flux, Speed Distribution, and Uncertainty

Meteoroids are known to pose a threat to spacecraft; they can puncture components, disturb spacecraft attitude, and possibly create secondary electrical effects. Accurate environment models are therefore critical for mitigating meteoroid-related risks. While there are several meteoroid environment models available for assessing spacecraft risk, the uncertainties associated with these models are not well understood. Because meteoroid properties are derived from indirect observations such as meteors and impact craters, the uncertainty in the meteoroid flux is potentially quite large. We combine existing meteoroid flux measurements with new radar and optical meteor data to improve our characterization of the meteoroid flux onto the Earth and its velocity distribution. We use data extracted from the NASA all-sky network, the Canadian Automated Meteor Observatory, and the Canadian Meteor Orbit Radar. We improve our characterization of the observed meteoroid speed distribution by incorporating modern descriptions of the ionization efficiency (e.g., Thomas et al., 2016). We also present estimates of the uncertainties associated with our meteoroid flux distribution. Finally, we discuss the implications for spacecraft. Our model is constrained by the cratering rate on the space-facing surface of LDEF, and thus the risk posed to spacecraft by meteoroid-induced physical damage is the least uncertain component of our model. Other sources of risk, however, may vary. For instance, a lower average meteoroid speed would require a higher meteoroid mass flux in order to match the LDEF crater counts, leading to higher predicted rates of attitude disturbances.

Moorhead, Althea↗
NASA NTRSDate not supplied

IceCube: Demonstration of an 883 GHz Radiometer for Ice Cloud Remote Sensing

IceCube was a technology demonstration of an 883 GHz heterodyne radiometer on a 3U CubeSat for ice cloud characterization. The project was a collaboration between Goddard Space Flight Center, Virginia Diodes Inc., and Wallops Flight Facility. IceCube was launched to the International Space Station (ISS) in April 2017, and was deployed to the orbit in May 2017. The radiometer measured ice cloud emissions from an ISS orbit for over 15 months. IceCube generated the first 883 GHz cloud map over a large operation temperature range of (5 ºC—37 ºC). Cloud ice plays a major role in the cloud precipitation process and Earth’s energy budget. Ice clouds are used in global circulation models as tuning parameters to achieve model agreement with observation at the top of the atmosphere in the radiation budget and at the bottom for precipitation, however, due to a lack of accurate ice cloud measurements large uncertainties exist in these models. Submillimeter wave remote sensing is capable of addressing this issue by measuring cloud ice mass and microphysical properties in the middle-to-upper troposphere. This fills the sensitivity gap not covered by the visible/infrared and microwave sensors [1]. The goal of IceCube was to increase the TRL of a heterodyne 883 GHz radiometer (using commercial parts) from 5 to 7 by validating the performance in a relevant spaceflight environment. The design of the radiometer was driven by frequency of operation, bandwidth, calibration, available power, and thermal environment requirements. The design included a 15 mm aperture off-axis parabolic reflector with a Potter feed horn, an 883 GHz 2nd-harmonic mixer that is fed by a local oscillator chain with a 24.3 GHz dielectric resonator (MLA), followed by a 6 GHz bandwidth centered at 9 GHz intermediate frequency assembly (IFA), receiver interface card, and power distribution unit. The IFA included an internal noise diode calibration to separate the MLA performance from the rest of the system. For this the radiometer had four operational states: antenna, antenna + noise, reference, and reference + noise; each state’s duration was 10 ms. The total power dissipation of the instrument was 5.6 W. The spacecraft had spinning capabilities to provide a cold sky view for calibration. We present the instrument design, ground test results, challenges, and highlight some of the flight measurements.

N Ehsan↗
NASA NTRS2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

mineral dust↗
NASA NTRS2021

Placing the East-West North American Aridity Gradient in a Multi-Century Context

Instrumental records indicate a century-long trend towards drying over western North America and wetting over eastern North America. A continuation of these trends into the future would have significant hydroclimatic and socioeconomic consequences in both the semi-arid Southwest and humid East. Using tree-ring reconstructions and hydrologic simulations of summer soil moisture, we evaluate and contextualize the modern summer aridity gradient within its natural range of variability established over the past 600 years and evaluate the effects of observed and anthropogenic precipitation, temperature, and humidity trends. The 2001–2020 positive (wet east-dry west) aridity gradient was larger than any 20-year period since 1400 CE, preceded by the most negative (wet west-dry east) aridity gradient during 1976-1995, leading to a strong multi-decade reversal in aridity gradient anomalies that was rivaled only by a similar event in the late-16th century. The 2001-2020 aridity gradient was dominated by long-term summer precipitation increases in the Midwest and Northeast, with smaller contributions from more warming in the West than the East and spring precipitation decreases in the Southwest. Multi-model mean climate simulations from CMIP6 experiments suggest anthropogenic climate trends should not have strongly affected the aridity gradient thus far. However, there is high uncertainty due to inter-model disagreement on anthropogenic precipitation trends. The recent strengthening of the observed aridity gradient, its increasing dependence on precipitation variability, and disagreement in modeled anthropogenic precipitation trends reveal significant uncertainties in how water resource availability will change across North America in the coming decades.

drought↗
NASA NTRS2019

Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2(exp) resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2(exp) horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3(exp) in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3(exp). Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

aerosol optical depth↗
NASA NTRS2022

Uncertainty Estimation for SMAP Level-1 Brightness Temperature Assimilation at Different Timescales

Soil Moisture Active Passive (SMAP) mission brightness temperature (T(b) ) observations are assimilated into NASA’s Catchment Land Surface Model using an Ensemble Kalman filter to update simulations of surface and root-zone soil moisture. Different time series components of the T(b) observations are assimilated including anomalies, inter-annual variations, and high frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed T(b) is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. In a benchmark experiment, T(b) observations are assimilated using a spatially constant 4 Kelvin (K) observation uncertainty, as in the operational SMAP Level-4 algorithm. All DA experiments exhibit notable skill improvements in most regions. Improvements are largest for the inter-annual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (-) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). No notable difference in skill - neither improvement nor deterioration - is observed between the experiments that use optimized observation uncertainty parameters and the 4 K benchmark experiment. This may be explained by the presence of large observation operator errors, which are analytically shown to have the potential to render post-update uncertainty insensitive to inaccuracies in estimates of the Kalman gain. These results have important implications for the design of soil moisture DA systems, in particular for parameterizing model and observation uncertainties.

Hydrology↗
NASA NTRS2001

Searching for the Beginning of the Ozone Turnaround Using a 22-Year Merged Satellite Data Set

We have used the data from six satellite instruments that measure the total column amount of ozone to construct a consistent merged data set extending from late 1978 into 2000. The keys to constructing a merged data set are to minimize potential drift of individual instruments and to accurately establish instrument-to-instrument offsets. We have used the short-wavelength D-pair measurements (306nm-313nm) of the SBUV and SBUV/2 instruments near the equator to establish a relatively drift-free record for these instruments. We have then used their overlap with the Nimbus 7 and EP TOMS instruments to establish the relative calibration of the various instruments. We have evaluated the drift uncertainty in our merged ozone data (MOD) set by examining both the individual instrument drift uncertainty and the uncertainty in establishing the instrument- to-instrument differences. We conclude that the instrumental drift uncertainty over the 22-year data record is 0.9 %/decade (2-sigma). We have compared our MOD record with 37 ground stations that have a continuous record over that time period. We have a mean drift with respect to the stations of +0.3 %/decade which is within 1-sigma of our uncertainty estimate. Using the satellite record as a transfer standard, we can estimate the capability of the ground instruments to establish satellite calibration. Adding the statistical variability of the station drifts with respect to the satellite to an estimate of the overall drift uncertainty of the world standard instrument, we conclude that the stations should be able to be used to establish the drift of the satellite data record to within and uncertainty of 0.6 %/decade (2-sigma). Adding to this an uncertainty due to the-incomplete global coverage of the stations, we conclude that the station data should be able to establish the global trend with an uncertainty of about 0.7 %/decade, slightly better than for the satellite record. We conclude that merging the two records together gives only a slight improvement in the uncertainty. Keeping them separate gives the greater confidence of two independent measures of the ozone trend and potential recovery. We fit the trend in our MOD record through May of 1991 and then extrapolated forward to see if the data at the end of the record was above the statistical model as a measure of ozone recovery as was done in the last WMO/UNEP assessment report. Because our data set drifts with respect to the ground-stations through May of 1991, we calculated a smaller global trend (-1.1 %/decade) than in the WMO/UNEP report. Our data in 1998 and 1999 was, on average 2 DU above the extrapolated statistical model with a 2-sigma uncertainty of 6 DU. For the combined mid-latitudes of the northern and southern hemispheres, the data was 5 DU above the extrapolated statistical model with a 2-sigma uncertainty of 10 DU. These may be signs of recovery, but they are still statistically insignificant.

Stolarski, Richard S.↗
NASA NTRS2011

A Model-Based Prognostics Approach Applied to Pneumatic Valves

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

Daigle, Matthew J.↗
NASA NTRS2014

Assessment of Satellite Ocean Colour Radiometry and Derived Geophysical Products

Standardization of methods to assess and assign quality metrics to satellite ocean color radiometry and derived geophysical products has become paramount with the inclusion of the marine reflectance and chlorophyll-a concentration (Chla) as essential climate variables (ECV; [1]) and the recognition that optical remote sensing of the oceans can only contribute to climate research if and when a continuous succession of satellite missions can be shown to collectively provide a consistent, long-term record with known uncertainties. In 20 years, the community has made significant advancements toward that objective, but providing a complete uncertainty budget for all products and for all conditions remains a daunting task. In the retrieval of marine water-leaving radiance from observed top-of-atmosphere radiance, the sources of uncertainties include those associated with propagation of sensor noise and radiometric calibration and characterization errors, as well as a multitude of uncertainties associated with the modeling and removal of effects from the atmosphere and sea surface. This chapter describes some common approaches used to assess quality and consistency of ocean color satellite products and reviews the current status of uncertainty quantification in the field. Its focus is on the primary ocean color product, the spectrum of marine reflectance Rrs, but uncertainties in some derived products such as the Chla or inherent optical properties (IOPs) will also be considered.

Ocean↗
NASA NTRS2008

Dynamic Method for Identifying Collected Sample Mass

G-Sample is designed for sample collection missions to identify the presence and quantity of sample material gathered by spacecraft equipped with end effectors. The software method uses a maximum-likelihood estimator to identify the collected sample's mass based on onboard force-sensor measurements, thruster firings, and a dynamics model of the spacecraft. This makes sample mass identification a computation rather than a process requiring additional hardware. Simulation examples of G-Sample are provided for spacecraft model configurations with a sample collection device mounted on the end of an extended boom. In the absence of thrust knowledge errors, the results indicate that G-Sample can identify the amount of collected sample mass to within 10 grams (with 95-percent confidence) by using a force sensor with a noise and quantization floor of 50 micrometers. These results hold even in the presence of realistic parametric uncertainty in actual spacecraft inertia, center-of-mass offset, and first flexibility modes. Thrust profile knowledge is shown to be a dominant sensitivity for G-Sample, entering in a nearly one-to-one relationship with the final mass estimation error. This means thrust profiles should be well characterized with onboard accelerometers prior to sample collection. An overall sample-mass estimation error budget has been developed to approximate the effect of model uncertainty, sensor noise, data rate, and thrust profile error on the expected estimate of collected sample mass.

Carson, John↗
NASA NTRS1977

Ku-band antenna acquisition and tracking performance study, volume 4

The results pertaining to the tradeoff analysis and performance of the Ku-band shuttle antenna pointing and signal acquisition system are presented. The square, hexagonal and spiral antenna trajectories were investigated assuming the TDRS postulated uncertainty region and a flexible statistical model for the location of the TDRS within the uncertainty volume. The scanning trajectories, shuttle/TDRS signal parameters and dynamics, and three signal acquisition algorithms were integrated into a hardware simulation. The hardware simulation is quite flexible in that it allows for the evaluation of signal acquisition performance for an arbitrary (programmable) antenna pattern, a large range of C/N sub O's, various TDRS/shuttle a priori uncertainty distributions, and three distinct signal search algorithms.

Huang, T. C.↗
NASA NTRS1992

Uncertainties in obtaining high reliability from stress-strength models

There has been a recent interest in determining high statistical reliability in risk assessment of aircraft components. The potential consequences are identified of incorrectly assuming a particular statistical distribution for stress or strength data used in obtaining the high reliability values. The computation of the reliability is defined as the probability of the strength being greater than the stress over the range of stress values. This method is often referred to as the stress-strength model. A sensitivity analysis was performed involving a comparison of reliability results in order to evaluate the effects of assuming specific statistical distributions. Both known population distributions, and those that differed slightly from the known, were considered. Results showed substantial differences in reliability estimates even for almost nondetectable differences in the assumed distributions. These differences represent a potential problem in using the stress-strength model for high reliability computations, since in practice it is impossible to ever know the exact (population) distribution. An alternative reliability computation procedure is examined involving determination of a lower bound on the reliability values using extreme value distributions. This procedure reduces the possibility of obtaining nonconservative reliability estimates. Results indicated the method can provide conservative bounds when computing high reliability. An alternative reliability computation procedure is examined involving determination of a lower bound on the reliability values using extreme value distributions. This procedure reduces the possibility of obtaining nonconservative reliability estimates. Results indicated the method can provide conservative bounds when computing high reliability.

Neal, Donald M.↗