Modeling life prediction model

Ford, F. P., Modelling and life prediction of stress corrosion cracking in sensitized stainless steel in high temperature water , Proc. of ASME Fall Meeting, 1985... 

The predictive models aim to produce real measurable parameters that can then be monitored. If the measured variable starts to diverge from the predicted value the life prediction can be amended. However, the monitoring may still identify unexpected changes in the operating parameters rather than in the material. Post-exposure testing of extracted specimens can be performed, but, as some level of acceleration is required, the conditions will never be the same as if the component or specimen had continued in service. 

There is, as always, a need for good quality data. Most of this is now available in electronic form and Chapter 11 lists some of the databases available. In spite of proclaimed good intentions, there is little systematic documentation of the successful application of plastics and their lifetimes, only examples of unexpected failure. There is a need for medium-term, lightly accelerated tests under intermediate conditions to validate the predictive models. While inspection of components at end-of-life is more prevalent than expected, there is a need for coupling it to predictive techniques to validate these techniques and to close the loop of life prediction. 

J.C. Newman A review of modeling small-crack behavior and fatigue-life predictions for aluminum alloys. J. Fatigue Fract. Eng. Mat. Struct. 17, 429-439 (1994)... 

What is presented above is a very simplistic approach. Joint geometries, for example, may have a significant effect on the rate of degradation, again depending on the environment. As a result, geometric modeling and finite element analyses have been employed with durability studies to assist in life predictions. 

Proposed computational model seems to be a promising tool as an aid to develop the life-prediction analyses for metallic components and structures subjected to any king hydrogen embrittlement in service. 

Wei, R. P., and Harlow, D. G., Mechanistically Based Probabihty Modelling, Life Prediction and Reliability Assessment, Modelling Simnl. Mater. Sci. Eng. 13 (2005), R33-R51. 

Jet Propulsion Laboratory. "Physlcal/Chemlcal Modeling for Photovoltaic Module Life Prediction" presented at the International Photovoltalcs Conference, Berlin. Jet Propulsion Laboratory Pasadena, CA, 1979. 

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. 

Corrosion of steel in concrete is a very complex phenomenon. Although significant research on modeling in the corrosion processes of steel in concrete has been performed, accurate life prediction for concrete structures is difficult. 

Secondly, accelerated tests can be used to qualify adhesives for specific applications, and the data obtained can sometimes be extrapolated to long-term, real-time performance. Accelerated tests are also valuable as screen tests and as acceptance tests and are specified in material-procurement documents or in hardware-acceptance specifications. Adherence to material and process specifications and their quality-control provisions is an essential element in assuring reliability. Life prediction through modeling is yet another approach, but is outside the scope of this chapter. 

Another model explicidy developed for life prediction under multiaxial loading is that by Fawaz and EUyin [76—78] the actual multiaxial loading conditions and load ratio are considered through modification of a reference fatigue curve. 

For these reasons, we decided to consider these criteria with the aim of assessing their reliability in terms of life estimation, by comparing their predictions with some of the experimental results taken from the extensive database available. This would be of help in obtaining information useful for design purposes, like strengths and weaknesses of each criterion, and, at the same time, in further clarifying the directions and the need for the development of life prediction models of general applicability. 

The criterion is, in general, rather easy to apply, since it requires only two fatigue curves for its calibration this is of great help in overcoming the difficulty in considering, explicitly, variations of the load ratio. A limitation, however, is that the model, in the present form, cannot be applied to life prediction of unidirectional laminates due to their anisotropic response resulting in different limits for the strain energy density in the fiber direction and normal to it. 

The Life-365 software predicts the initiation period assuming ionic diffusion to be the dominant mechanism. This software differs from other diffusion models in that it accounts for the variability of the diffusion coefficient with age and with temperature. It also attempts to model the impact of various additives. For additives such as silica fume and fly ash it reduces the diffusion coefficient to reflect the lower permeability and for corrosion inhibitors it raises the chloride threshold required to initiate corrosion. To include the impact of sealers and membranes it reduces the rate of accumulation of the surface chloride concentration. The rate of accumulation and the maximum accumulation of surface chloride in this program are based on the type of structure, geographic location and exposure. ACI Committee 365 has also published a state-of-the-art report on service life prediction which is in the process of being updated (ACI 365.1R-00 (2000)). 

Computational design and life prediction codes should be developed explicitly for CMCs. Computational models and predictions should be validated by subelement and component tests that include representative thermomechanical loadings. Rigorous analyses of failure modes should then be performed. 

Without proper knowledge of the circumstances in which degradation mechanisms are active and of how they interact, there is no firm base for reliable life prediction models. Products will be over-designed to compensate for the lack of accurate predictions. The models presently available for quantifying the degradation and ageing mechanisms presented in the previous section are reviewed here. 

Hwang, W. and K. Han (1986). Cumulative damage models and multi-stress fatigue life prediction. Composite Materials 20, 125-153. 

Wang, W. and W. Zhang (2008, July). An asset residual life prediction model based on expert judgments. European Journal of Operational Research 188(2), 496-505. 

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