Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis

Jorgensen, Jack; Hodkiewicz, Melinda; Cripps, Edward; Hassan, Ghulam Mubashar

doi:10.1016/j.compind.2022.103806

Cited by 9 publications

(2 citation statements)

References 81 publications

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“…Additionally, DT technology is widely recognised for its ability to effectively support the maintenance of complex systems. Studies show that DTs can be used for real-time monitoring, maintenance prediction, and decision support for maintenance activities [6]. Furthermore, DTs can be used for anomaly detection, as seen in studies like that of Calvo-Bascones et al [7].…”

Section: Introductionmentioning

confidence: 99%

Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs

D’Amico

Addepalli

Erkoyuncu

2023

BDCC

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The digital twin (DT) research field is experiencing rapid expansion; yet, the research on industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are involved in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. This study’s findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the breakeven point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. This study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.

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Section: Introductionmentioning

confidence: 99%

Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs

D’Amico

Addepalli

Erkoyuncu

2023

BDCC

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“…Numerical models represent the underlying physics of a system and calculate the propagation of the physical states, data-only models find structure in the observed data that may be used to predict future states, and hybrid models incorporate both numerical and data-only techniques. With the increasing volume of data that are being measured, and the development of technologies that allow rapid propagation of uncertainty through computationally demanding maritime models such as statistical emulation (Astfalck et al, 2019b) and digital twins (Ward et al, 2021; Jorgensen et al, 2023), the value of probabilistic forecasting in engineering operations is being recognized (see, for instance, Pinson, 2013 and Anderson Loake et al, 2022). In practice, there is often a plurality of competing forecasts, often at the expense of paying third-party contractors or maintaining measurement equipment to record data required by statistical or machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluating probabilistic forecasts for maritime engineering operations

Astfalck

Bertolacci

Cripps

2023

DCE

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Maritime engineering relies on model forecasts for many different processes, including meteorological and oceanographic forcings, structural responses, and energy demands. Understanding the performance and evaluation of such forecasting models is crucial in instilling reliability in maritime operations. Evaluation metrics that assess the point accuracy of the forecast (such as root-mean-squared error) are commonplace, but with the increased uptake of probabilistic forecasting methods such evaluation metrics may not consider the full forecasting distribution. The statistical theory of proper scoring rules provides a framework in which to score and compare competing probabilistic forecasts, but it is seldom appealed to in applications. This translational paper presents the underlying theory and principles of proper scoring rules, develops a simple panel of rules that may be used to robustly evaluate the performance of competing probabilistic forecasts, and demonstrates this with an application to forecasting surface winds at an asset on Australia’s North–West Shelf. Where appropriate, we relate the statistical theory to common requirements by maritime engineering industry. The case study is from a body of work that was undertaken to quantify the value resulting from an operational forecasting product and is a clear demonstration of the downstream impacts that statistical and data science methods can have in maritime engineering operations.

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Condition Monitoring of Spud in Cutter Suction Dredger using Physics based Machine Learning

Barik,

Vijayan

2024

J. Vib. Eng. Technol.

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Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis

Cited by 9 publications

References 81 publications

Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs

Industrial Insights on Digital Twins in Manufacturing: Application Landscape, Current Practices, and Future Needs

Evaluating probabilistic forecasts for maritime engineering operations

Condition Monitoring of Spud in Cutter Suction Dredger using Physics based Machine Learning

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