Digital twinning and CFD simulation take the strain out of testing and prototyping for maritime applications
The Marine technology industry has moved on considerably from what is sometimes referred to as the “old normal”, to something that encompasses the use of artificial intelligence (AI), digital twinning and simulation technologies to embrace a more modern approach to both marine technology and operations planning.
Dejan Radosavljevic shows how Siemens Digital Industries are playing a part in the modernisation of the industry although, as he explains, there is still a tendency to cling on to older ways. For example, there is a continued insistence on confirming each design’s suitability via towing tank testing of a physical scaled model. The limitations of towing tanks include the problem of translating model-scale measurements to full-scale vessels, which is open to many uncertainties and requires the use of empirical scaling formulae. Also, it is difficult to study interactions between key components accurately such as propellers, appendages and hulls.
Now, explains Radosavlijevic, in a time when it is increasingly critical for the industry to meet stringent environmental regulations, there is an acceptance that things need to change, as it has done in other industries. Aviation uses CFD (Computational Fluid Dynamics) simulation alongside wind tunnel tests, speeding up certification programmes and reducing the overall cost.
Radosavlijevic believes it is time to move to the marine “new normal” with designs that are analysed using CFD simulation, at full scale. The additional benefit of this approach is that multiple designs can be tested under realistic operating conditions, such as wind, waves and self-manoeuvring, all of which are not possible in a towing tank. This is a well-established, well-validated tool with many proven results and successes in other industries as well as marine. The 2017 Lloyd’s Register workshop on full-scale CFD simulation proved that when used properly, CFD can accurately match sea trial data.
Many experienced users in the maritime sector are already routinely and successfully applying CFD simulations under full-scale conditions. For those who are still hesitating, it is time to start gathering experience since the trend is clear.
“Using Siemens’ Simcenter STAR-CCM+ makes it possible to meet the goal of conducting full-scale analysis of complete systems under realistic operating conditions by creating a digital twin of the real system,” he says.
He believes that the marine new normal in design will use an agile digital twin of a vessel that includes full-scale CAD and simulation data including sea trial data. This digital twin covers all aspects of vessel performance and is accessible in real-time to all stakeholders. With this in place, multiple ‘what if’ scenarios can be quickly tested. What is the impact on safety and performance of damage or adverse weather conditions? What if we redesign part of the hull? What if we repurpose the vessel to alternative routes? What if we change to a new engine?. All these questions can be answered using digital twins.
AI Enhanced Marine Operations
As well as vessel design, the world of operational vessel planning is also benefitting from digital technologies. Yara Marine Technologies, Artificial Intelligence (AI) application developers Molflow, and Chalmers University of Technology and social science specialists from Halmstad University and Gothenburg University have been collaborating for over 3 years to develop and trial an AI-based semi-autonomous voyage planning system. Initiated in August 2020, the Via Kaizen project explores how AI and machine learning can enable more energy-efficient voyage planning for ship operators.
Funded by the Swedish Transport Administration Trafikverket, the project used pre-existing tools to enable a higher degree of digitisation and automation in vessel operations. These included Yara Marine’s propulsion optimisation system FuelOpt and performance management and vessel data reporting tool Fleet Analytics, as well as Molflow’s vessel modelling system Slipstream. Existing work practices onboard and user needs were analysed during the design process to ensure the technology facilitated processes and decisions with the greatest impact on energy efficiency.
The resulting system was trialled onboard two vessels, a PCTC car carrier operated by UECC and a Rederiet Stenersen product tanker. The wide-ranging results indicated successful energy efficiency optimisation based on estimated time of arrival (ETA), with one of the two trial vessels opting to continue using the system.
According to Mikael Laurin, Head of Vessel Optimisation at Yara Marine Technologies, the Via Kaizen project is particularly relevant to current practices in the industry where the intersections of digitisation, decarbonisation and crewing determine any successes in addressing climate change.
The use of AI and machine learning to plan and predict energy-efficient voyages has significance for an industry looking to lower emissions while addressing rising fuel costs. Similarly, new technologies can streamline operations but require collaboration and buy-in from stakeholders across the board, necessitating crew familiarisation and training, proactive design, and new corporate strategies.
“As a result, the insights and information gained from the project carry broader significance for our industry’s future,” he says.
The Via Kaizen project demonstrated that incorporating machine-learning algorithms for improved predictive modelling of ship propulsion power can result in more accurate performance forecasting and optimisation. It also provided evidence of the necessity of constructive collaboration between technology developers and users, as well as between ship operators and their customers.
Joakim Möller, CEO at Moflow, says, “The project afforded an invaluable opportunity to explore and advance industry understandings of the role big data, data handling and model development can play in supporting lower emission strategies and maximised fuel efficiencies. Recent advances in vessel data tracking and analysis, weather information, and more can be used to gauge where operations have the potential to be streamlined. As the maritime industry seeks to use good data to help decision-making, AI and machine learning can play a key role in processing and simplifying available data for clear, actionable outcomes.”
Throughout the trials, crew played a key role in determining the success of energy efficient voyages. This shows the necessity giving ship crews and management every opportunity to engage with, understand and embrace the value of AI-powered ship operation support technology in assisting daily operations onboard and ashore.
Drawing conclusions from the project, Simon Larsson from Gothenburg University observed that the Via Kaizen project documented potential challenges to implementing energy efficient voyages, notably the impact of crew training and corporate processes that either facilitated or hindered the effective use of AI tools to improve efficiency. These findings are not specific to the project and have wider ramifications for an industry seeking advanced solutions to rapidly reduce emissions.
“While crew training will afford a much-needed bridge to build understanding and accelerate support for AI-powered voyage efficiency solutions among seafarers, it is just as important that we ensure effective channels of communication with management and corporate processes,” he concludes