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At least 163 records · Page 9

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

Moore, N. R.↗

Design solutions for the solar cell interconnect fatigue fracture problem

Mechanical fatigue of solar cell interconnects is a major failure mechanism in photovoltaic arrays. A comprehensive approach to the reliability design of interconnects, together with extensive design data for the fatigue properties of copper interconnects, has been published. This paper extends the previous work, developing failure prediction (fatigue) data for additional interconnect material choices, including aluminum and a variety of copper-Invar and copper-steel claddings. An improved global fatigue function is used to model the probability-of-failure statistics of each material as a function of level and number of cycles of applied strain. Life-cycle economic analyses are used to evaluate the relative merits of each material choce. The copper-Invar clad composites demonstrate superior performance over pure copper. Aluminum results are disappointing.

Mon, G. R.↗

Parts and Components Reliability Assessment: A Cost Effective Approach

System reliability assessment is a methodology which incorporates reliability analyses performed at parts and components level such as Reliability Prediction, Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) to assess risks, perform design tradeoffs, and therefore, to ensure effective productivity and/or mission success. The system reliability is used to optimize the product design to accommodate today?s mandated budget, manpower, and schedule constraints. Stand ard based reliability assessment is an effective approach consisting of reliability predictions together with other reliability analyses for electronic, electrical, and electro-mechanical (EEE) complex parts and components of large systems based on failure rate estimates published by the United States (U.S.) military or commercial standards and handbooks. Many of these standards are globally accepted and recognized. The reliability assessment is especially useful during the initial stages when the system design is still in the development and hard failure data is not yet available or manufacturers are not contractually obliged by their customers to publish the reliability estimates/predictions for their parts and components. This paper presents a methodology to assess system reliability using parts and components reliability estimates to ensure effective productivity and/or mission success in an efficient manner, low cost, and tight schedule.

Lee, Lydia↗

The Challenger tragedy was caused by an Apollo mistake, terminating risk analysis

NASA’s view of risk changed between early Apollo and the Space Shuttle. Risk was a known serious problem at the beginning of Apollo and the risk estimates were disturbingly high. To avoid public concern, risk analysis was discontinued. Risk analysis was avoided in Shuttle, leading to an unnecessarily risky design. The immediate cause of the Challenger tragedy was the mistaken decision to launch in cold weather. The fundamental cause was the high risk of the Shuttle design. Before Challenger, management thought and testified that the probability of an accident was 1 in 100,000. After Challenger, Probabilistic Risk Analysis (PRA) found a roughly 1 in 100 chance of a Shuttle failure. The recent Orion design uses the safer Apollo approach, with a hardened capsule, launch abort escape, and the crew placed above the rocket tanks and engines. During Apollo it was estimated that, “assuming all elements from propulsion to rendezvous and life support were done as well or better than ever before, that 30 astronauts would be lost before 3 were returned safely to the Earth.” The chance of astronaut survival was only 10%. After the Apollo 1 tragedy, the awareness of risk led to an intense focus on achieving safety. “The only possible explanation for the astonishing success – no losses in space and on time – was that every participant at every level in every area far exceeded the norm of human capabilities.” During Apollo, a NASA PRA found that the chance of success was “less than 5 percent.” The NASA Administrator felt that “the numbers could do irreparable harm,” and discontinued numerical risk assessment. This led to decreasing understanding of risk. The head of Apollo reliability and safety decided, “Statistics don’t count for anything,” and that risk is reduced by “attention taken in design.” The great and initially unexpected success of Apollo appeared to validate the neglect of PRA. Continuing to neglect the mathematical estimation of risk led Shuttle into a high risk design that produced tragic results. The initial design of the Shuttle emphasized increasing capability and reducing cost without analysis or even mention of risk. A retired NASA official stated, “some NASA people began to confuse desire with reality. … One result was to assess risk in terms of what was thought acceptable without regard for verifying the assessment. … Note that under such circumstances real risk management is shut out.” Not computing risk led to removing launch abort, removing crew escape, selecting less reliable Solid Rocket Boosters, placing the crew compartment next to the rocket boosters, and accepting more stressed shielding tile designs. Accepting these specific risks directly caused the shuttle disasters. The Challenger tragedy is frequently taught as a case of management failure. The focus is on the Challenger launch decision hours before, which is a dramatic example of bad management. However, the true cause of the Challenger disaster occurred decades earlier in the Apollo era. When the easily predictable failures occurred, failure investigations focused on how they might have been avoided. The Shuttle was cancelled after the space station was completed because of its high risk. The ultimate cause of the Shuttle tragedies was the choice by the Apollo-era NASA administrator to avoid a negative public reaction to realistic risk analysis.

Harry W. Jones↗

Space Shuttle Main Engine structural analysis and data reduction/evaluation. Volume 1: Aft Skirt analysis

Using the ANSYS finite element program, a global model of the aft skirt and a detailed nonlinear model of the failure region was made. The analysis confirmed the area of failure in both STA-2B and STA-3 tests as the forging heat affected zone (HAZ) at the aft ring centerline. The highest hoop strain in the HAZ occurs in this area. However, the analysis does not predict failure as defined by ultimate elongation of the material equal to 3.5 percent total strain. The analysis correlates well with the strain gage data from both the Wyle influence test of the original design aft sjirt and the STA-3 test of the redesigned aft skirt. it is suggested that the sensitivity of the failure area material strength and stress/strain state to material properties and therefore to small manufacturing or processing variables is the most likely cause of failure below the expected material ultimate properties.

Berry, David M.↗

Characterization of C/Enhanced SiC Composite During Creep-Rupture Tests Using an Ultrasonic Guided Wave Scan System

An ultrasonic guided wave scan system was used to nondestructively monitor damage over time and position in a C/enhanced SiC sample that was creep tested to failure at 1200 C in air at a stress of 69 MPa (10 ksi). The use of the guided wave scan system for mapping evolving oxidation profiles (via porosity gradients resulting from oxidation) along the sample length and predicting failure location was explored. The creep-rupture tests were interrupted for ultrasonic evaluation every two hours until failure at approx. 17.5 cumulative hours.

Roth, Don J.↗

Worldwide Physics-Based Lifetime Prediction of c-Si Modules Due to Solder-Bond Failure

Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as the performance ratio method, Statistical clear sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the nonlinear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this article, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the variations of the temperature. Integrating the worldwide weather information from NASA/NSRDB databases, the model predicts the location-specific output-power degradation and the lifetime of a module due to solder bond failure. The model parameters are calibrated against qualification tests involving thermal cycling of specific batches of modules from a specific technology/manufacturer. The results may be summarized as: 1) Modules installed at higher latitudes show a longer lifetime due to reduced damage accumulation. 2) The reduction of temperature fluctuation close to large bodies of water, such as seashores, increases solder bond lifetime significantly. 3) Relatively speaking, modules installed close to the Tropic of Cancer/Capricorn (23.5 degrees North/South) suffer from a higher solder bond damage and have a shorter lifetime, suggesting a conservative design. This model should serve as a building block of a comprehensive reliability framework that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradation.

14 SOLAR ENERGY↗

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗

Health Assessment and Performance Monitoring of Large Machine Diagnostics

Presentation to be given at the CCS-6 Statistical Seminar Series. Regularly maintained and operated diagnostic machines are a backbone of data collection and are a vital component of the Stockpile Stewardship Program’s efforts to better understand nuclear physics. The Cygnus X-ray machine, stationed at the Nevada National Security Site’s U1a underground facility, is one such diagnostic that provides a radiographic capability for the subcritical experiment program. Component failures within Cygnus can result in catastrophic downtime for the diagnostic, affecting performance, schedules, and cost. However, over the years various measurements have been collected on the two Cygnus axes, including voltage and current measurements at different locations, which we believe have predictive power to indicate machine health. We will share preliminary insight into this data and the machine learning approaches we are taking to assess Cygnus’ health, observe declining performance, and predict failures.

97 MATHEMATICS AND COMPUTING↗

Failure risk assessment by analysis and testing

The sources of information on which to base an evaluation of reliability or failure risk of an aerospace flight system are (1) experience from tests and flights and (2) engineering analysis. It is rarely feasible to establish high reliability at high confidence by testing aerospace systems or components. Moreover, failure prediction by conventional, deterministic methods of engineering analysis can become arbitrary and subject to serious misinterpretation when uncertain or approximate information is used to establish analysis parameter values and to calibrate the accuracy of engineering models. The limitations of testing to evaluate failure risk are discussed, and a statistical approach which incorporates both engineering analysis and testing is presented.

Moore, N.↗

Application of Weibull analysis to SSME hardware

Generally, it has been documented that the wearing of engine parts forms a failure distribution which can be approximated by a function developed by Weibull. The purpose here is to examine to what extent the Weibull distribution approximates failure data for designated engine parts of the Space Shuttle Main Engine (SSME). The current testing certification requirements will be examined in order to establish confidence levels. An examination of the failure history of SSME parts/assemblies (turbine blades, main combustion chamber, or high pressure fuel pump first stage impellers) which are limited in usage by time or starts will be done by using updated Weibull techniques. Efforts will be made by the investigator to predict failure trends by using Weibull techniques for SSME parts (turbine temperature sensors, chamber pressure transducers, actuators, and controllers) which are not severely limited by time or starts.

Gray, L. A. B.↗

ARGES: an Expert System for Fault Diagnosis Within Space-Based ECLS Systems

ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) System. ARGES monitors and reduces data in real time from either the SAWD controller or a simulation of the SAWD assembly. It can detect gradual degradations or predict failures. This allows graceful shutdown and scheduled maintenance, which reduces crew maintenance overhead. Status and fault information is presented in a user interface that simulates what would be seen by a crewperson. The user interface employs animated color graphics and an object oriented approach to provide detailed status information, fault identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual faults. ARGES is seen as a forerunner of AI-based fault management systems for manned space systems.

Pachura, David W.↗

Use of Statistical Analysis of Acoustic Emission Data on Carbon-Epoxy COPV Materials-of-Construction for Enhanced Felicity Ratio Onset Determination

Broadband modal acoustic emission (AE) data were acquired during intermittent load hold tensile test profiles on Toray T1000G carbon fiber-reinforced epoxy (C/Ep) single tow specimens. A novel trend seeking statistical method to determine the onset of significant AE was developed, resulting in more linear decreases in the Felicity ratio (FR) with load, potentially leading to more accurate failure prediction. The method developed uses an exponentially weighted moving average (EWMA) control chart. Comparison of the EWMA with previously used FR onset methods, namely the discrete (n), mean (n (raised bar)), normalized (n%) and normalized mean (n(raised bar)%) methods, revealed the EWMA method yields more consistently linear FR versus load relationships between specimens. Other findings include a correlation between AE data richness and FR linearity based on the FR methods discussed in this paper, and evidence of premature failure at lower than expected loads. Application of the EWMA method should be extended to other composite materials and, eventually, composite components such as composite overwrapped pressure vessels. Furthermore, future experiments should attempt to uncover the factors responsible for infant mortality in C/Ep strands.

Abraham, Arick Reed A.↗

Progressive Failure And Life Prediction of Ceramic and Textile Composites

An engineering approach to predict the fatigue life and progressive failure of multilayered composite and textile laminates is presented. Analytical models which account for matrix cracking, statistical fiber failures and nonlinear stress-strain behavior have been developed for both composites and textiles. The analysis method is based on a combined micromechanics, fracture mechanics and failure statistics analysis. Experimentally derived empirical coefficients are used to account for the interface of fiber and matrix, fiber strength, and fiber-matrix stiffness reductions. Similar approaches were applied to textiles using Repeating Unit Cells. In composite fatigue analysis, Walker's equation is applied for matrix fatigue cracking and Heywood's formulation is used for fiber strength fatigue degradation. The analysis has been compared with experiment with good agreement. Comparisons were made with Graphite-Epoxy, C/SiC and Nicalon/CAS composite materials. For textile materials, comparisons were made with triaxial braided and plain weave materials under biaxial or uniaxial tension. Fatigue predictions were compared with test data obtained from plain weave C/SiC materials tested at AS&M. Computer codes were developed to perform the analysis. Composite Progressive Failure Analysis for Laminates is contained in the code CPFail. Micromechanics Analysis for Textile Composites is contained in the code MicroTex. Both codes were adapted to run as subroutines for the finite element code ABAQUS and CPFail-ABAQUS and MicroTex-ABAQUS. Graphic user interface (GUI) was developed to connect CPFail and MicroTex with ABAQUS.

Xue, David Y.↗

Plane-stress fracture of compact and notch-bend specimens

Thin-gaged or high toughness materials containing cracks usually fail in a ductile manner with nominal failure stresses approaching the ultimate strength of the material. For such materials, a two-parameter fracture criterion was developed. An equation which related the linear elastic stress-intensity factor, elastic nominal stress, and two material parameters was previously derived and has been used as a fracture criterion for surface- and through-cracked specimens under tensile loading. This two-parameter fracture criterion was rederived in a more general form and was extended to compact and notch-bend fracture specimens. A close correlation was found between experimental and predicted failure stresses.

Newman, J. C., Jr.↗

Analysis of the Space Shuttle Orbiter skin panels under simulated hydrodynamic loads

The Space Shuttle orbiter skin panels were analyzed under pressure loads simulating hydrodynamic loads to determine their capability to sustain a potential ditching and to determine pressures that typically would produce failures. Two Dynamic Crash Analysis of Structures (DYCAST) finite element models were used. One model was used to represent skin panels (bays) in the center body, while a second model was used to analyze a fuselage bay in the wing region of the orbiter. From an assessment of the DYCAST nonlinear computer results, it is concluded that the probability is extremely high that most, if not all, of the lower skin panels would rupture under ditching conditions. Extremely high pressure loads which are produced under hydrodynamic planning conditions far exceed the very low predicted failure pressures for the skin panels. Consequently, a ditching of the orbiter is not considered to have a high probability of success and should not be considered a means of emergency landing unless no other option exists.

Carden, Huey D.↗

A real-time diagnostic and performance monitor for UNIX

There are now over one million UNIX sites and the pace at which new installations are added is steadily increasing. Along with this increase, comes a need to develop simple efficient, effective and adaptable ways of simultaneously collecting real-time diagnostic and performance data. This need exists because distributed systems can give rise to complex failure situations that are often un-identifiable with single-machine diagnostic software. The simultaneous collection of error and performance data is also important for research in failure prediction and error/performance studies. This paper introduces a portable method to concurrently collect real-time diagnostic and performance data on a distributed UNIX system. The combined diagnostic/performance data collection is implemented on a distributed multi-computer system using SUN4's as servers. The approach uses existing UNIX system facilities to gather system dependability information such as error and crash reports. In addition, performance data such as CPU utilization, disk usage, I/O transfer rate and network contention is also collected. In the future, the collected data will be used to identify dependability bottlenecks and to analyze the impact of failures on system performance.

Dong, Hongchao↗

A critical verification of beam and shell models of wind turbine blades

Ever-increasing wind turbine size has challenged predictive capabilities on several fronts. Here, to address part of the blade structural modeling uncertainty, a systematic model fidelity comparison study was conducted on commonly used finite elements. pyNuMAD was utilized to create beam, shell, and solid models of a 100 m long blade undergoing large static deflections. The solid model avoided the use of layered-solid elements by resolving core and facesheet layers. An unprecedented model with 73.7 million elements revealed insights that have never been possible from prior experimental and numerical studies. As compared to the solid element model, the tip deflection from the shell and beam model was found to be about 2% and 4.3% too low, respectively. The twist from the beam model was found to be about 5.6% too high, while the twist from shell model was 24% too low, though improvement was demonstrated with mesh refinement. The beam model adhesive stresses were more accurate than the shell model. Out-of-plane stresses were of great significance near geometric and material discontinuities, and neither the shell nor beam model captured these effects well. Failure predictions from beam, shell, or layered-solid models are unlikely to be reliable at trailing edges, adhesives, ply-drops, spar-cap boundaries.

17 WIND ENERGY↗