Lifetime prediction parameters

Pressurised pipes for water and gas provide an example of design lives being predicted with confidence on the basis of a large assembly of data, of benchmark quality control tests for existing products, and of two-parameter accelerated testing for new ones. Some types of pipe are particularly sensitive to pressure extremes. Lifetime prediction cannot however take into account poor installation conditions. 

Lifetime predictions of polymeric products can be performed in at least two principally different ways. The preferred method is to reveal the underlying chemical and physical changes of the material in the real-life situation. Expected lifetimes are typically 10-100 years, which imply the use of accelerated testing to reveal the kinetics of the deterioration processes. Furthermore, the kinetics has to be expressed in a convenient mathematical language of physical/chemical relevance to permit extrapolation to the real-life conditions. In some instances, even though the basic mechanisms are known, the data available are not sufficient to express the results in equations with reliably determined physical/chemical parameters. In such cases, a semi-empirical approach may be very useful. The other approach, which may be referred to as empirical, uses data obtained by accelerated testing typically at several elevated temperatures and establishes a temperatures trend of the shift factor. The extrapolation to service conditions is based on the actual parameters in the shift function (e.g. the Arrhenius equation) obtained from the accelerated test data. The validity of such extrapolation needs to be checked by independent measurements. One possible method is to test objects that have been in service for many years and to assess their remaining lifetime. 

It has been shown by Rink et al. [28], that up to a certain number of cycles, interpolation of the experimental data from the example with a power law expression or an exponential expression is equally valid. In a previous study, several alternatives were given for the SN-curve [29], An example of modified lifetime prediction results are depicted in Fig. 9. Here, formulations were used using an SN-curve which is steeper than the SN-curve found from tests at R=-l. Such alternative SN-curves may prove useful if they can be expressed as parametric SN-curves, where e.g. the slope parameter is a function of the material and/or spectral properties. 

It is shown by an example that there are several ways to modify the most commonly used lifetime prediction method, in order to improve its predicting capabilities. These modifications provide an easy and efficient way to perform lifetime predictions. The drawback is, that the modifications have little or no validated physical background. Any correlation between model parameters and macroscopical material properties or damage development characteristics should be established by extensive test programs. This constrains the applicability of these methods to limited situations. 

Lifetime Prediction in System Applications Quantitative methodologies for predicting lifetimes should be developed, coupling advanced models with identification and measurement of critical parameters and with computer-based expert systems. This effort will necessitate generating physicochemical databases to support systems analysis as well as using advances in theory and experimental techniques discussed above. 

The reliability parameters, such as the mean time to failure, have to be determined in experiments under well defined conditions. Failure rates of microsystems for automotive applications are typically in the range of a few ppm (parts per million). This may sound negligible, but due to the large number of sensors sold every year and their increasing numbers in each car, even this failure rate must be decreased further. However, the engineer who tries to investigate failure mechanisms is confronted with the problem of lack of failures in the sense that he finds too few defective samples for a thorough failure analysis. Thus, due to the lack of a statistical basis, the quality of lifetime predictions under normal in-use conditions would be poor. 

Equation (43) can be substituted into Eq. (36) to give a prediction of the pressure dependence of the lifetime of Cr YAG based on a model that considers spin-orbit coupling of the zero phonon E and T2 states. Figure 14 includes a representative lifetime prediction based on this simple spin-orbit coupling model (dot-dash line). As in the pure electronic model, the prediction assumes that fE(P = 0) = 114 s and A (cm ) = 828 cm -i-9.8P (kbar). Since fj fE, we further assume that pressure induced changes in spin-orbit coupling do not significantly affect fx and use f = 6135 s. The final model parameter Vgo was set equal to a typical ambient pressure value, 202 cm [254], and was assumed to be constant with pressure. 

The accuracy of the model in the lifetime prediction was verified even in this case by comparison between model estimations and experimental fatigue lives. The parameters of the calibration fatigue curves together with the elastic properties of the laminates investigated are listed in Table 7.5. In some cases, elastic and static strength properties not available in the original sources were estimated from constituent properties or taken from literature. 

A more common method for medical devices is to run the life test until failure occurs. Then an exponential model can be used to calculate the percentage survivability. Using a chi-square distribution, limits of confidence on this calculation can be established. These calculations assume that a failure is equally likely to occur at any time. If this assumption is unreasonable (e.g., if there are a number of early failures), it may be necessary to use a Weibull model to calculate the mean time to failure. This statistical model requires the determination of two parameters and is much more difficult to apply to a test that some devices survived. In the heart-valve industry, lifetime prediction based on S-N (stress versus number of cycles) or damage-tolerant approaches is required. These methods require fatigue testing and ability to predict crack growth. " ... 

A correlation between natural and artificial weathering was considered for lifetime prediction in a short exposure time. It was found that the confidence level of predicting time on the basis of artificially accelerated exposure trials is dependent on many parameters which include time, material, equipment, etc. 

Therefore is must be concluded that enhanced weathering tests normally should be cyclic, simulating many practical parameters with their changes in time. Interpretation should be performed with extreme care. Improvements of last testing procedures, e.g., by incorporating electrochemical and dielectric sensors, are of extreme importance in order to arrive at laboratory-based lifetime prediction. 

Research has been carried out on cement matrix mortars and on fibre-reinforced mortars.The materials tested were of very different microstructures. Both standard and fracture mechanics parameters have been measured. The strong relationship between the microstructure and fracture behaviour of the materials has been observed. Lifetime prediction procedure and proof testing have been carried out. 

Material, stress, and environmental parameters relevant to enviroiunentally assisted cracking of stainless steels in BWRs. (From Ford, F.P. et al.. On-line BWR materials monitoring plant component lifetime prediction, in Proc. Nuclear Power Plant Life Extension, Snowbird, UT, June 1988, Published by American Nuclear Society, Vol. 1, pp. 355-366.)... 

Introduction and Commercial Application The reservoir and well behaviour under dynamic conditions are key parameters in determining what fraction of the hydrocarbons initially in place will be produced to surface over the lifetime of the field, at what rates they will be produced, and which unwanted fluids such as water are also produced. This behaviour will therefore dictate the revenue stream which the development will generate through sales of the hydrocarbons. The reservoir and well performance are linked to the surface development plan, and cannot be considered in isolation different subsurface development plans will demand different surface facilities. The prediction of reservoir and well behaviour are therefore crucial components of field development planning, as well as playing a major role in reservoir management during production. 

Accelerated testing depends critically on selecting a parameter whose effect on service life is so well understood that long lifetimes at low values of the parameter can be predicted from shorter lifetimes at higher values. The parameter may be the prime cause of degradation, such as in a stress-rupture test where longer lifetimes at lower loads are predicted by extrapolation from short lifetimes at higher loads. It can also be a secondary parameter, such as when temperature is increased to accelerate chemical attack while the principal factor, chemical concentration, is kept constant. This is because there is more confidence in the relation between rate of reaction and temperature than in the relation of rate of reaction to concentration. It is clearly essential that extrapolation rules from the test conditions to those of service are known and have been verified, such that they can be used with confidence. 

The purpose of the trial also affects the choice of degradation agents and the parameters used to monitor degradation. For comparison and quality control purposes, single agents are most frequently used. For prediction purposes multiple agents are more likely to be representative of service, but at the same time they make extrapolation rules more complicated. The parameters measured in trials to predict lifetime must be those critical to service, but in many instances of comparison or quality checks the choice of parameter can be heavily influenced by experimental convenience. 

It is probable that numerous interfacial parameters are involved (surface tension, spontaneous curvature, Gibbs elasticity, surface forces) and differ from one system to the other, according the nature of the surfactants and of the dispersed phase. Only systematic measurements of > will allow going beyond empirics. Besides the numerous fundamental questions, it is also necessary to measure practical reason, which is predicting the emulsion lifetime. This remains a serious challenge for anyone working in the field of emulsions because of the polydisperse and complex evolution of the droplet size distribution. Finally, it is clear that the mean-field approaches adopted to measure > are acceptable as long as the droplet polydispersity remains quite low (P < 50%) and that more elaborate models are required for very polydisperse systems to account for the spatial fiuctuations in the droplet distribution. 

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