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At least 739 records · Page 41

Slow Crack Growth and Fatigue Life Prediction of Ceramic Components Subjected to Variable Load History

Present capabilities of the NASA CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code include probabilistic life prediction of ceramic components subjected to fast fracture, slow crack growth (stress corrosion), and cyclic fatigue failure modes. Currently, this code has the capability to compute the time-dependent reliability of ceramic structures subjected to simple time-dependent loading. For example, in slow crack growth (SCG) type failure conditions CARES/Life can handle the cases of sustained and linearly increasing time-dependent loads, while for cyclic fatigue applications various types of repetitive constant amplitude loads can be accounted for. In real applications applied loads are rarely that simple, but rather vary with time in more complex ways such as, for example, engine start up, shut down, and dynamic and vibrational loads. In addition, when a given component is subjected to transient environmental and or thermal conditions, the material properties also vary with time. The objective of this paper is to demonstrate a methodology capable of predicting the time-dependent reliability of components subjected to transient thermomechanical loads that takes into account the change in material response with time. In this paper, the dominant delayed failure mechanism is assumed to be SCG. This capability has been added to the NASA CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code, which has also been modified to have the ability of interfacing with commercially available FEA codes executed for transient load histories. An example involving a ceramic exhaust valve subjected to combustion cycle loads is presented to demonstrate the viability of this methodology and the CARES/Life program.

Jadaan, Osama↗

Reliability and Confidence Interval Analysis of a CMC Turbine Stator Vane

High temperature ceramic matrix composites (CMC) are being explored as viable candidate materials for hot section gas turbine components. These advanced composites can potentially lead to reduced weight, enable higher operating temperatures requiring less cooling and thus leading to increased engine efficiencies. However, these materials are brittle and show degradation with time at high operating temperatures due to creep as well as cyclic mechanical and thermal loads. In addition, these materials are heterogeneous in their make-up and various factors affect their properties in a specific design environment. Most of these advanced composites involve two- and three-dimensional fiber architectures and require a complex multi-step high temperature processing. Since there are uncertainties associated with each of these in addition to the variability in the constituent material properties, the observed behavior of composite materials exhibits scatter. Traditional material failure analyses employing a deterministic approach, where failure is assumed to occur when some allowable stress level or equivalent stress is exceeded, are not adequate for brittle material component design. Such phenomenological failure theories are reasonably successful when applied to ductile materials such as metals. Analysis of failure in structural components is governed by the observed scatter in strength, stiffness and loading conditions. In such situations, statistical design approaches must be used. Accounting for these phenomena requires a change in philosophy on the design engineer s part that leads to a reduced focus on the use of safety factors in favor of reliability analyses. The reliability approach demands that the design engineer must tolerate a finite risk of unacceptable performance. This risk of unacceptable performance is identified as a component's probability of failure (or alternatively, component reliability). The primary concern of the engineer is minimizing this risk in an economical manner. The methods to accurately determine the service life of an engine component with associated variability have become increasingly difficult. This results, in part, from the complex missions which are now routinely considered during the design process. These missions include large variations of multi-axial stresses and temperatures experienced by critical engine parts. There is a need for a convenient design tool that can accommodate various loading conditions induced by engine operating environments, and material data with their associated uncertainties to estimate the minimum predicted life of a structural component. A probabilistic composite micromechanics technique in combination with woven composite micromechanics, structural analysis and Fast Probability Integration (FPI) techniques has been used to evaluate the maximum stress and its probabilistic distribution in a CMC turbine stator vane. Furthermore, input variables causing scatter are identified and ranked based upon their sensitivity magnitude. Since the measured data for the ceramic matrix composite properties is very limited, obtaining a probabilistic distribution with their corresponding parameters is difficult. In case of limited data, confidence bounds are essential to quantify the uncertainty associated with the distribution. Usually 90 and 95% confidence intervals are computed for material properties. Failure properties are then computed with the confidence bounds. Best estimates and the confidence bounds on the best estimate of the cumulative probability function for R-S (strength - stress) are plotted. The methodologies and the results from these analyses will be discussed in the presentation.

Murthy, Pappu L. N.↗

Probabilistic Analysis of a Composite Crew Module

An approach for conducting reliability-based analysis (RBA) of a Composite Crew Module (CCM) is presented. The goal is to identify and quantify the benefits of probabilistic design methods for the CCM and future space vehicles. The coarse finite element model from a previous NASA Engineering and Safety Center (NESC) project is used as the baseline deterministic analysis model to evaluate the performance of the CCM using a strength-based failure index. The first step in the probabilistic analysis process is the determination of the uncertainty distributions for key parameters in the model. Analytical data from water landing simulations are used to develop an uncertainty distribution, but such data were unavailable for other load cases. The uncertainty distributions for the other load scale factors and the strength allowables are generated based on assumed coefficients of variation. Probability of first-ply failure is estimated using three methods: the first order reliability method (FORM), Monte Carlo simulation, and conditional sampling. Results for the three methods were consistent. The reliability is shown to be driven by first ply failure in one region of the CCM at the high altitude abort load set. The final predicted probability of failure is on the order of 10-11 due to the conservative nature of the factors of safety on the deterministic loads.

FROM↗

Reproduction drives changes in space use and habitat selection in a highly adaptable invasive mammal

Abstract For ungulates, it is not well understood how the interaction between habitat and reproduction affects movement behavior, space use, and habitat selection. We used known parturition (farrowing) data to validate First Passage Time (FPT) movement analysis to identify wild pig (Sus scrofa) parturition events from data collected with GPS collars. We examined home range and habitat selection during three physiologically distinct time periods in the reproductive cycle: late-gestation; farrowing; and neonate-care period. Sows exhibited a behavioral change 1-2 days prior to farrowing, suggesting FPT analysis can reliably identify parturition events for wide-ranging species that have a focused birthing area. Home range was smallest during the farrowing period, likely reflective of nest building, parturition, and protection of neonates. Home range size during the neonate-care period was intermediate between the late-gestation and farrowing periods, indicative of offspring care that may restrict maternal movement. Across all periods, sows avoided developed areas that have sparse canopy and ground cover and are associated with human activities. During late-gestation, sows avoided pine forests that have an open understory and less vegetative cover. During late-gestation and neonate-care, sows selected bottomland hardwood forests, habitat associated with ample food, cover, and water. During farrowing and neonate-care periods, sows selected upland hardwood forests, habitat with high quality food and cover for neonates. The physiological requirements of reproduction drive female habitat selection and spatial scale of movement patterns associated with parturition. Our study contributes to delineation of the appropriate scale at which to analyze movement data to provide insight about where individuals chose to place their home range, how much space to use, and how they use resources on the landscape to maximize reproductive success and fitness.

Zoology↗

Accurate and reliable thermochemistry by data analysis of complex thermochemical networks using Active Thermochemical Tables: the case of glycine thermochemistry

Active Thermochemical Tables (ATcT) were successfully used to resolve the existing inconsistencies related to the thermochemistry of glycine, based on statistically analyzing and solving a thermochemical network that includes >3350 chemical species interconnected by nearly 35 000 thermochemically-relevant determinations from experiment and high-level theory. Here, the current ATcT results for the 298.15 K enthalpies of formation are −394.70 ± 0.55 kJ mol −1 for gas phase glycine, −528.37 ± 0.20 kJ mol −1 for solid α-glycine, −528.05 ± 0.22 kJ mol −1 for β-glycine, −528.64 ± 0.23 kJ mol −1 for γ-glycine, −514.22 ± 0.20 kJ mol −1 for aqueous undissociated glycine, and −470.09 ± 0.20 kJ mol −1 for fully dissociated aqueous glycine at infinite dilution. In addition, a new set of thermophysical properties of gas phase glycine was obtained from a fully corrected nonrigid rotor anharmonic oscillator (NRRAO) partition function, which includes all conformers. Corresponding sets of thermophysical properties of α-, β-, and γ-glycine are also presented.

Active Thermochemical Tables↗

System-level performance and degradation of 21 GWDC of utility-scale PV plants in the United States

We assess the performance of a fleet of 411 utility-scale (i.e., >5 MWAC and ground-mounted) photovoltaic (PV) projects totaling 21.1 GWDC (16.3 GWAC) of capacity, which achieved commercial operations in the United States from 2007 to 2016. This fleet of projects contributed more than 50% of all solar electricity generated in the United States in 2017. Using detailed information on individual project characteristics, in conjunction with modeled irradiance data, we assess the extent to which actual first-year performance has lived up to both modeled and stated expectations. We then analyze system-level performance degradation in subsequent years by employing a “fixed effects” regression model to statistically isolate the impact of age on system performance. We find that this fleet of utility-scale PV projects has generally lived up to ex ante expectations for first-year performance but that subsequent system-level degradation—found to be −1.3%/year (±0.2%)—has, on average, been worse than both ex ante expectations (commonly −0.5%/year) and results from past studies (ranging from −0.8%/year to −1.0%/year). We emphasize that −1.3%/year is a system-level estimate that captures more than just module degradation (e.g., including soiling, balance of plant degradation, and downtime for maintenance and/or other events). A side analysis of a variety of project characteristics suggests that system-level degradation rates tend to be of lower magnitude among newer projects and larger projects and at sites with lower long-term average temperatures.

14 SOLAR ENERGY↗

Advanced reliability method for fatigue analysis

When design factors are considered as random variables and the failure condition cannot be expressed by a closed form algebraic inequality, computations of risk (or probability of failure) may become extremely difficult or very inefficient. This study suggests using a simple and easily constructed second degree polynomial to approximate the complicated limit state in the neighborhood of the design point; a computer analysis relates the design variables at selected points. Then a fast probability integration technique (i.e., the Rackwitz-Fiessler algorithm) can be used to estimate risk. The capability of the proposed method is demonstrated in an example of a low cycle fatigue problem for which a computer analysis is required to perform local strain analysis to relate the design variables. A comparison of the performance of this method is made with a far more costly Monte Carlo solution. Agreement of the proposed method with Monte Carlo is considered to be good.

Wu, Y.-T.↗

Design and Analysis of a Flexible, Reliable Deep Space Life Support System

This report describes a flexible, reliable, deep space life support system design approach that uses either storage or recycling or both together. The design goal is to provide the needed life support performance with the required ultra reliability for the minimum Equivalent System Mass (ESM). Recycling life support systems used with multiple redundancy can have sufficient reliability for deep space missions but they usually do not save mass compared to mixed storage and recycling systems. The best deep space life support system design uses water recycling with sufficient water storage to prevent loss of crew if recycling fails. Since the amount of water needed for crew survival is a small part of the total water requirement, the required amount of stored water is significantly less than the total to be consumed. Water recycling with water, oxygen, and carbon dioxide removal material storage can achieve the high reliability of full storage systems with only half the mass of full storage and with less mass than the highly redundant recycling systems needed to achieve acceptable reliability. Improved recycling systems with lower mass and higher reliability could perform better than systems using storage.

reliability↗

FEAMAC/CARES Stochastic-Strength-Based Damage Simulation Tool for Ceramic Matrix Composites

Reported here is a coupling of two NASA developed codes: CARES (Ceramics Analysis and Reliability Evaluation of Structures) with the MAC/GMC (Micromechanics Analysis Code/ Generalized Method of Cells) composite material analysis code. The resulting code is called FEAMAC/CARES and is constructed as an Abaqus finite element analysis UMAT (user defined material). Here we describe the FEAMAC/CARES code and an example problem (taken from the open literature) of a laminated CMC in off-axis loading is shown. FEAMAC/CARES performs stochastic-strength-based damage simulation response of a CMC under multiaxial loading using elastic stiffness reduction of the failed elements.

composite structures↗

Stochastic-Strength-Based Damage Simulation Tool for Ceramic Matrix Composite

Reported here is a coupling of two NASA developed codes: CARES (Ceramics Analysis and Reliability Evaluation of Structures) with the MAC/GMC (Micromechanics Analysis Code/ Generalized Method of Cells) composite material analysis code. The resulting code is called FEAMAC/CARES and is constructed as an Abaqus finite element analysis UMAT (user defined material). Here we describe the FEAMAC/CARES code and an example problem (taken from the open literature) of a laminated CMC in off-axis loading is shown. FEAMAC/CARES performs stochastic-strength-based damage simulation response of a CMC under multiaxial loading using elastic stiffness reduction of the failed elements.

ceramic matrix composites↗

FPGA Reliability and Failure Rate Analysis for Launch and Space Vehicles Environments

Field Programmable Gate Arrays (FPGAs) integrated circuits (IC) are one of the key electronic components in today's sophisticated launch and space vehicle complex avionic systems, largely due to their superb reprogrammable and reconfigurable capabilities combined with relatively low non-recurring engineering costs (NRE) and short design cycle. Consequently, FPGAs are prevalent ICs in communication protocols and control signal commands. This paper will demonstrate guidelines to estimate FPGA failure rates for ascent and in space operations. The guidelines will account for hardware and radiation-induced failures, as well as Bayesian updates to failure rates. The hardware contribution of the approach accounts for physical failures of the FPGA IC. The radiation portion will expand on FPGA susceptibility to different space radiation environments.

Al Hassan, Mohammad↗