04 Sep
  • What a digital twin?
  • Common applications and benefits 
  • How to create a digital twin for predictive maintenance
  • Primary modeling methods
  • How to implement a digital twin with MATLAB® and Simulink®

  A digital twin is an up-to-date representation of an actual current asset in operation that includes the asset’s condition and relevant historical data. Read this ebook to learn the basics of digital twins, including:  

Shipping companies, classification societies and offshore operators are beginning to invest in digital twins as they recognise the benefits.

During Riviera’s Extending intelligent monitoring of onboard machinery webinar, in September, World Maritime University (WMU) associate professor (safety and security) Dimitrios Dalaklis highlighted importance of digital twins along with IoT and cloud computing. 

When combined, these solutions can contribute towards improved safety, logistics, reduced fuel costs and lower emissions.

“Take the concept of a digital twin for example,” Mr Dalaklis explained. “We can now create a theoretical model and manipulate it in real time to make changes that have an almost instantaneous result in the real world.”

This optimises the decision-making process by “using highly accurate data, saving costs and having a huge impact on efficiency, both during the development stage and when the model becomes a reality,” Mr Dalaklis said. NYK Bulkship (Asia) operations director Capt K K Mukherjee also highlighted its importance. 

He said the future for condition monitoring and cognitive maintenance will involve virtual reality and digital twins. These will help in repair and maintenance over a ship’s lifecycle and enable owners to identify areas that need action and improvement, he said. 

Classification society DNV GL has introduced methods of class verification involving digital twins. As part of its smart vessel notation, it has introduced ways to verify vessel condition using digital technology. 

DNV GL has added a chapter to its ship classification rules with three new notations covering digital features, including data-driven verification (DDV). This sets the requirements for gathering, treating and delivering collected data to ensure the quality of the data for use in a class assessment. 

For specified systems, the verified data can be used in the certification and classification of those systems in maritime and offshore vessels. The notation covers several different verification methods, including self-verifying systems and digital twins. 

In offshore, Shell is working with Akselos on digital twin technology for structural integrity assessments of offshore assets. 

An early example is a structural digital twin generated of the Bonga production storage and offloading (FPSO) ship operating offshore Nigeria.

 Akselos is working with Shell to develop similar digital models of other offshore assets

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