Fatigue crack growth prediction method for offshore platform based on digital twin

Fang, Xin; Wang, Honghui; Li, Wenjing; Liu, Guijie; Cai, Baoping

doi:10.1016/j.oceaneng.2021.110320

Cited by 43 publications

(18 citation statements)

References 34 publications

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“…The previous subsection covered the case of a digital twin whose model is based on the laws of physics. Another approach is to define the model by defining statistical properties of sensor data coming from the physical system of interest such as in (Coraddu et al, 2019) and in (Fang et al, 2022), where a neural network and a Gaussian process are used, respectively. In this review we assume that with statistics-based approaches a model update occurs whenever there is a switch between models based on statistical properties corresponding to different real conditions in the physical system.…”

Section: Model Update In Statistics-based Modelingmentioning

confidence: 99%

Digital Twins for Marine Operations: A Brief Review on Their Implementation

Zocco¹,

Wang²,

Van³

2023

Preprint

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While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions.

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Section: Model Update In Statistics-based Modelingmentioning

confidence: 99%

Digital Twins for Marine Operations: A Brief Review on Their Implementation

Zocco¹,

Wang²,

Van³

2023

Preprint

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“…Step 4: Resampling. The effective particle capacity is estimated using equation (9). The results of Step 3 are compared with the given threshold N th , and if N eff \ N th , the particle set is resampled.…”

Section: Gaussian Particle Filtering Algorithmmentioning

confidence: 99%

“…5 Many researchers have carried out a lot of research on the fusion of a dynamic Bayesian network and physical model for probabilistic prediction of structural damage. [6][7][8][9] The particle filtering algorithm, as an inference algorithm of the Bayesian network, is a powerful tool to deal with prediction problems affected by uncertainties. In recent years, it has been widely used in remaining life prediction, crack propagation prediction and other fields.…”

Section: Introductionmentioning

confidence: 99%

Prediction of fatigue crack propagation based on dynamic Bayesian network

Wang

Yang

et al. 2022

Advances in Mechanical Engineering

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To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper. The Paris Law of crack propagation and the extended finite element method (XFEM) are combined to establish the state equation of crack propagation. The uncertain factors of crack propagation are analyzed, and the prediction model of fatigue crack propagation based on the dynamic Bayesian network is constructed. A Bayesian inference algorithm based on the combination of Gaussian particle filter and firefly algorithm is proposed. The fatigue experiment of the specimen with the pre-cracks is carried out to test the correlation between the fatigue load cycles and the crack propagation depth. The experimental results show that the crack propagation prediction method proposed in this paper can effectively improve the prediction accuracy of crack propagation depth.

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“…As more and more industries adopt digital twin to improve performance, marine and offshore industry is compelled to do the same. Digital twin of ship hull structures has attracted interests of researchers in recent years ( [6], [7], [8], [9]), and some concepts/products have been implemented to real hull structures, such as DNV's 'Nerves of Steel" and MRAIN's AHMS system.…”

Section: Concept Design Of a Digital Twin Architecturementioning

confidence: 99%

Concept design of a digital twin architecture for ship structural health management

Zeng

Tan

Cai

et al. 2022

J. Phys.: Conf. Ser.

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Ocean-going vessels frequently experience severe weather conditions. Occasionally, the actual environmental loads are much larger than design values, resulting in deviations from design conditions that may cause serious structural damage. A structural digital twin can enhance predictability and reliability of marine structures, and thus provide vessel operators, owners, and stakeholders with a more accurate overview of a vessel’s condition. It can also help reduce lifecycle costs through proposing rational and optimal inspection, maintenance, repair programs, and assist on maximizing service life and mission availability. This paper uses a benchmark vessel to investigate structural behaviors of the vessel through dynamic loading approach. The correlation between structure members and critical responses are established, and high stress concentration areas have been identified. Subsequently, this paper develops a conceptual digital twin architecture. The proposed digital twin predicts responses of the entire structure to measured or predicted environmental conditions with a hybrid model combining numerical simulations with measurements. While the traditional structural monitoring system focuses on the health condition of typical hot spots, the proposed digital twin may improve structural health awareness of the entire hull structure based on a limited number of sensors.

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Fatigue crack growth prediction method for offshore platform based on digital twin

Cited by 43 publications

References 34 publications

Digital Twins for Marine Operations: A Brief Review on Their Implementation

Digital Twins for Marine Operations: A Brief Review on Their Implementation

Prediction of fatigue crack propagation based on dynamic Bayesian network

Concept design of a digital twin architecture for ship structural health management

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