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23 results for “fatigue damage”
Comparison of Two Probabilistic Fatigue Damage Assessment Approaches Using Prognostic Performance Metrics
A general framework for probabilistic prognosis using maximum entropy approach, MRE, is proposed in this paper to include all available information and uncertainties for RUL prediction. Prognosis metrics are used for model comparison and performance evaluation. Several conclusions can be drawn based on the results in the current investigation: The proposed MRE updating approach results in more accurate and precise prediction compared with the classical Bayesian method. The classical Bayesian method is a special case of the proposed MRE approach and MRE approach is more flexible to include additional information for inference, which cannot be handled by the classical Bayesian method. The prognosis metrics can be successfully used for algorithm comparison and can give quantitative values in model (algorithm) performance evaluation. A robustness metric measuring the updating algorithmic sensitivity to prior uncertainty is proposed and applied to both Bayesian and MRE updating approaches. The application examples show that MRE exhibits more robustness against the uncertainty introduced by parameter distribution priors in the sense of prognosis performance. It is important to realize when to apply these metrics to arrive at meaningful interpretations. For instance, use of the convergence metric makes sense only when the algorithm predictions converge (get better) with time.
Change of Fatigue Properties of Steel-S460NL Damaged by Various Levels of Corrosion
<p><span>This study investigates the effect of corrosion on the fatigue performance of S460 NL high-strength steel, which is commonly used in offshore structures exposed to harsh conditions. We conducted fatigue tests and analyzed <em>S</em>-<em>N</em> curves over various exposure periods while simulating corrosion conditions in a laboratory setting. The findings show a direct correlation between prolonged corrosion exposure and increased fatigue damage. </span></p> <p><span>Our findings show that longer corrosion periods result in significant accumulation of corrosion products, thereby increasing the severity of metallic corrosion. Using damage theory, we quantified mechanical property degradation, providing important insights into fracture formation mechanisms. High stress levels were associated with fewer fatigue striations and more cumulative damage.</span></p> <p><span>These findings have significant implications for improving the durability and reliability of S460 NL steel in practical engineering applications.</span></p>
Connecting Microscale And Macroscale Damage Models In A Bayesian Framework for Fatigue Damage Prognostics Of CFRP Composites
Composites offer unique advantages for aerospace structures and are increasingly being adopted into newer designs. However, it is also acknowledged that given current understanding of damage mechanisms in composites there is a significant risk with the extensive use of composites materials in aerospace applications. On one hand the uncertainty in damage evolution along lifetime is extremely large, and on the other hand there is a lack of knowledge about the mechanics of the onset, posterior growth, and interactions between several micro-scale damage modes. All these factors lead to the adoption of high safety margins in the design and costly inspection schedules along the service to mitigate the risks. Structural health monitoring for onboard damage diagnosis and prognosis of structural failures has the potential to reduce maintenance costs and improve the safety of the structure through a condition based maintenance scheduling. In this scheme the current damage state of a specific structural element is estimated and further used as the input for a prognostic algorithm that predicts the propagation of damage through time using updated models and based on some knowledge of the future load conditions. A novel damage prognostics framework for composites FRP under fatigue loadings is proposed in this work. The proposed methodology is grounded on physics-based models for evolution of damage at (1) micro-scale, i.e. micro-cracks and delamination, and (2) macro-scale such as stiffness reduction induced by micro-scale damage modes. Through stochastic embedding, these apriori deterministic models are converted to probabilistic models by introducing a modeling error term. This error term is controlled by a probability density function whose parameters are estimated in addition to the rest of "physical" parameters. The probabilistic damage models are then incorporated in a Bayesian filtering algorithm that sequentially updates both, a damage state variable and the set of model parameters, as fresh damage data become available along the fatigue cycling process. Next, these damage models are used to simulate fault propagation with this updated state information to generate a prognostic estimate of the remaining useful life of the structure in a probabilistic sense. The proposed methodology is demonstrated using experimental NDE damage data for micro-crack density, delamination area, and stiffness reduction from an extensive post-impact tension-tension fatigue test performed over several CFRP [0,90]4s laminates.
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