DATA BASE AND LIFE PREDICTION

This portion of the project is identified as project element 3 within the work breakdown structure (WBS). It contains five subelements, including (1) Structural Qualification, (2) Time-Dependent Behavior, 

Work on the NDE Development subelement involves conducting NDE development to identify approaches for quantitative determination of conditions in ceramics which affect their structural performance. 

The research content of the Data Base and Life Prediction project element includes (1) experimental life testing and microstructural analysis of SijjN and SiC ceramics, (2) time-temperature strength dependence of SijN ceramics, and (3) static fatigue behavior of PSZ ceramics. 

Major objectives of research in the Data Base and Life Prediction project element are understanding and application of predictive models for structural ceramic mechanical reliability, measurement techniques for longterm mechanical property behavior in structural ceramics, and physical understanding of time-dependent mechanical failure. Success in meeting these objectives will provide U.S. companies with the tools needed for accurately predicting the mechanical reliability of ceramic heat engine components, including the effects of applied stress, time, temperature, and atmosphere on the critical ceramic properties. 

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The focus of this book is on methods and processes designed to predict drug-like properties, exposure and safety during hit and lead discovery. We do not intend to cover specific cultural considerations and marketing aspects [3]. What we will highlight is the need of a risk aware environment for drug discovery, where data-based integrated risk assessment is part of daily life of the team and drives the projects towards molecules with features fit for the description of an efficacious and safe medicine. 

Objective of monitoring. A monitoring system, eventually with computerised data acquisition, should meet specifically defined objectives, such as a) to monitor the durability of the structure and its condition in order to make timely decisions for preventive and/or repair actions, b) to monitor the effect of preventative or repair actions, c) to monitor the condition of stmctures based on new materials and/or new technology (including service-life prediction models), d) to follow the time development in areas where access is difficult. 

To estimate the oxidative stability or susceptibility of a fat to oxidation, the sample is subjected to an accelerated oxidation test under standardized conditions and a suitable end-point is chosen to determine appropriate levels of oxidative deterioration (Figure 7.1). Several parameters such as temperature (60-140°C), metal catalysts (5-100 ppm), oxygen pressure (3-165 psi), or variable shaking to increase reactant contact, are manipulated to accelerate oxidation and development of rancidity in oils and emulsions. The oxidation level used for an end-point varies widely according to the time desired to obtain stability data. For practical purposes, predictions of oxidahve stability in foods and oils based on measurements of induction period should be related to actual product shelf life, and the conditions used should be as close as possible to those under which the food is stored. To translate the induchon period obtained under accelerated conditions to the actual shelf hfe of a product, it is necessary to use an arbitrary factor based on prior experience with the desired product. Much effort has been devoted to more accurately eshmate the shelf hfe of foods... 

Future work must address two areas to provide the foundation for statistically based analyses of high-cycle CF (as well as environmental LCF and FCP). For simple laboratory conditions, the Weibull analysis of mechaniccil HCF failure probability [82] must be extended to include CF. Second, variable load, temperature, and environment chemistry histories are likely to be complex in applications and significantly affect CF Hfe. Such history effects have not been studied. The scaling of Basquin relationship data to predict the Ufe of a structure is qualitative and uncertain. Either the local strain approach to CF crack formation/eeurly growth life or the fracture mechanics analysis of CF propagation provide a better foundation for life prediction and failure analysis. 

However, in this case it is also possible to gain fnrther confidence with regards to the data used for QRA. The use of sensitivity analyses to identify the most critical data items, focused data searching and checking, use of multiple, independent data sources, comparison of model prediction with real life data (base-lining) are all techniques that should be used in support of building confidence in the modelling results. 

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