Improving Accuracy in Fatigue Life Assessments and Predictions

One of the biggest challenges for integrity management in the oil and gas industry is the ability to accurately predict fatigue life of critical mechanical components. Understanding fatigue as a failure mode is straightforward at first glance — components subjected to cyclic loading become vulnerable to fatigue damage and potential failure. Unfortunately, translating a cyclic loading problem into a tangible fatigue analysis process for predicting when and how failure will occur is rarely simple. Without the right input and assumptions, fatigue analysis can quickly turn into a random number-generating exercise! Might as well ask a fortune teller!

Fatigue analysis can feel like fortune telling

Figure 1: Much like asking Zoltar, the fortune teller, at a carnival for your fortune, fatigue analysis can feel similar to using a random number generator if you don’t have the proper knowledge and experience to guide the analysis.

For accurate fatigue-life predictions, integrity engineers must be able to view cyclic loading issues on a systemic level. An ideal fatigue analysis process involves careful consideration of cyclic loads (think demand) and fatigue curves (think resistance or capacity). Furthermore, complex mechanical systems will sometimes need to be evaluated on two different levels – a global level for general system response to loading, and a local level for very localized, concentrated response in a component due to unique design features. The calculations then need to be presented in a manner that can be readily used by an integrity team to manage operations and/or modify designs to resolve any undesirable fatigue performance issues.

Fatigue analyses routinely require substantial domain knowledge and operational familiarity, along with strong analytical capabilities to allow successful navigation through uncertainties in load and resistance predictions and ambiguous statistical judgments on the outcomes. Uncertainties in analytical fatigue performance prediction also elevate the need for validation testing. Without a proper understanding of the system, comprehensive planning, and statistical sampling considerations, validation testing can quickly become extremely resource-intensive.

Fatigue analysis chart comparing stress range and life, indicating the wide range of possible outcomes

Figure 2: In fatigue assessments, the range of possible outcomes makes interpretation of results especially challenging. That’s why an advance knowledge of statistical models and validation through testing is important.

Successful fatigue assessment requires a systemic approach that utilizes a wide range of tools from numerical analysis, such as finite element analysis (FEA) and damage modeling, to sub-scale and full-scale testing. The ability to view cyclic loading problems from multiple perspectives is critical in formulating effective solutions.

ADV Integrity is uniquely equipped with expertise in advanced numerical modeling, and full-scale and sub-scale testing for comprehensive fatigue assessments. The breadth and depth of ADV’s expertise enables a holistic approach for advancing the industry’s understanding of fatigue assessments for integrity management.

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Atul Ganpatye
Atul GanpatyeSenior Associate