Digital Twins Help Predict Ship’s Structural Performance, Says DNV GL

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According to DNV GL principal specialist Gaute Storhaug, Digital twins can help owners, operators and class societies predict the structural performance of ships, says an article published in Riviera Maritime Media.

Predict structural performance of ships

Digital twins can help owners, operators and class societies predict the structural performance of ships, said DNV GL principal specialist Gaute Storhaug at Riviera Maritime Media’s How digital twins drive vessel efficiency and voyage performance webinar on 13 May.

Importance of using digital twins 

Mr Storhaug described the importance of using digital twins to understand the structural performance of ships and to identify points vulnerable to damage.

Inspection to mitigate risks

He said, structural damage can have severe consequences such as hull breakup and sea pollution. 

“We have inspection regimes to mitigate these risks.” 

Full inspections every five years 

This involves full inspections every five years by class surveyors, but this could be extended to seven years for certain ships.

He added saying DNV GL wants to have an inspection regime that relates to the risk of vessels. 

Design assumptions could differ from real operations

The design assumptions could differ from real operations. Two similar ships, operating in varying environments could have different risks of structural damage, such as fatigue cracks to hull structures.

Ship operating in calm environments

For a ship operating in calm environments, the period between class surveys could be extended. 

Tanker operating in harsher environments

For a tanker operating in harsher environments, this period could be shortened, with inspections focused on the most vulnerable points.

Indicator tool to calculate the risk 

Digital twins of ships could be used to monitor structural performance. DNV GL has an indicator tool to calculate the risk of fatigue cracking and overloading on ships within a fleet.

Mr Storhaug said, Digital twins use machine learning, ship position (AIS) and global wave data, with which ships at risk among a huge fleet can be revealed.

Traffic light system 

A traffic light system is used – of green, orange and red indicating the most at risk – but it has limitations.

Critical structural details 

“You do not know the details of which critical structural details you should inspect,” he said. 

How does it work?

Numerical twin of a ship’s hull structure 

For that DNV GL creates a numerical twin of a ship’s hull structure based on design models and matches ship positions with wave data.

Engineers can virtually slice ship models to reveal critical details such as stiffeners for inspection. 

Where to focus across ship’s hull

Another traffic light system indicates “where you should focus inspections” across the ship’s hull although there are uncertainties such as the wave conditions these structures face in real operations.

Hybrid twin

To account for this, DNV GL created a hybrid twin. “We mix the design model and the numerical twin with sensory information,” said Mr Storhaug. 

“This brings much better accuracy and complete modelling of the structure” including transferring loads across the structure.

Optimising the sensor positions

By optimising the sensor positions, shipowners can gain more detail of stress on hull structures. 

“One of the benefits of the hybrid twin is you can calculate where you should have the sensors,” said Mr Storhaug. 

In his presentation, there were eight sensor positions along the ship’s hull providing stress data.

Reduce operating and compliance costs

Owners can use this information to reduce operating and compliance costs.

“You can use this for maintenance planning or inspection planning, or your due diligence to select the right candidates for conversion,” said Mr Storhaug. 

“It can be useful for lifetime extension and troubleshooting,” he continued. “So, there can be many purposes for these different digital twin concepts.”

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Source: Riviera Maritime Media

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