Advances in Engineering Advances in Engineering features breaking research judged by Advances in Engineering advisory team to be of key importance in the Engineering field. Papers are selected from over 10,000 published each week from most peer reviewed journals.
Significance
The rapid development leap in digitalization increased the need for speed in order to unleash the full potential of digital twin technology by integrating structural health monitoring systems and real time fatigue-life prediction models on a full scale. That is to say, a high-fidelity method for efficient discrete fatigue crack growth simulation in large structures constitutes one of the cornerstones of a state-of-the-art digital twin.
In this context, discrete fatigue crack growth pertains to an insidious phenomenon during which a crack continues to inexorably propagate driven by the repeatedly applied loading cycles the structure experiences. Such fatigue cracks can remain undetected for a long period of time until structural failure occurs suddenly and without warning, making it reside among the most prominent root causes of catastrophic failure in wind turbines. This emphasizes the need for (1) a health monitoring system to detect the crack and with it (2) a fast fatigue lifetime prediction model to rapidly assess the severity of the situation. Whilst ‘fast and furious’ predictions are relatively easily achieved in the realm of discrete fatigue analysis, ‘fast and faithful’ is an entirely different story – an oxymoron of some sorts: In order to gain accuracy requires models with a high spatial discretization level – particularly in the vicinity of the crack – making them computationally heavy. Simultaneously, these models need to be re-executed several thousand times in order to update the crack position as it grows with sufficient temporal resolution. This inevitably compromises the computation speed inasmuch every attempt to increase the fidelity decreases the computation speed – and vice versa.
Against this backdrop, it can be agreed that the renaissance of digital twin technology heralded by recent advancements in machine learning raises the demand for structural analysis tools for real time predictions of the structural performance and ultimately the remaining lifetime. As such, there is a need to develop a technique to suffice this demand. In this view, researchers from the Department of Wind Energy at Technical University of Denmark: Dr. Martin Alexander Eder and Professor Xiao Chen, proposed a novel approach, termed FASTIGUE, for computationally super-efficient discrete fatigue crack growth analysis of large structures particularly for bondlines with high aspect ratios. Their goal was to considerably increase the computational efficiency by essentially decoupling the finite element analysis module from the crack growth analysis module. The research is now published in the Digital Twin Special Issue in the journal of Engineering Fracture Mechanics. https://doi.org/10.1016/j.engfracmech.2020.107075
In their approach, the computationally expansive analysis was performed prior to the fatigue analysis as a single pre-processing step for a comparatively small number of different fatigue crack propagation stages under the imposition of a set of qualified assumptions. The actual fatigue crack propagation analysis was then performed subsequently using an explicit crack growth simulation routine for a given set of growth parameters and initial crack situations in conjunction with the SERR functions previously obtained. Lastly, a practical demonstration of the proposed approach was applied to the fatigue growth analysis of a crack located in the trailing edge bondline of a 14.3 m long composite wind turbine blade model subject to flapwise cyclic loading.
In a statement to Advances in Engineering, Professor Xiao Chen pointed out by outsourcing the computationally expensive fracture analysis in a pre-processing step and by performing the crack growth prediction in a subsequent post-processing step, FASTIGUE outperforms conventional update-and-rerun schemes and offers unparalleled computation speed, making the digital twin concept of large scale structure close to reality.
The research is supported by DARWIN: Drone Application for pioneering Reporting in Wind turbine blade Inspections, Innovation Fund Denmark (6151-00020B) and RELIABLADE: Improving Blade Reliability through Application of Digital Twins over Entire Life Cycle, EUDP (64018-0068).
Reference
Martin Alexander Eder, Xiao Chen. FASTIGUE: A Computationally Efficient Approach for Simulating Discrete Fatigue Crack Growth in Large-scale Structures. Engineering Fracture Mechanics Volume 233, 2020, 107075.
