Digital twins in the maritime field
A digital twin is a virtual representation of a real object, system or process. It is created using real-time or historical data, mathematical models and algorithms to simulate the behavior and characteristics of the real object.
The digital twin can be used in a variety of fields, including manufacturing, engineering, healthcare, transportation and more. It enables the performance of the real object to be monitored, predicted and optimized, providing precise, real-time information on its status, operation and environment.
Digital twins offer three major advantages: cost reduction, improved safety and reproducibility. They can be used to predict scenarios, but also to train people safely. In the maritime field, digital twins have multiple use cases, which we will discuss throughout this paper.
Digital twins can be used to monitor and manage various aspects of ships, ports and maritime operations. Here are a few examples of digital twins that evolve over time and reflect the changing state of their physical counterparts in the maritime industry:
Ship performance monitoring: Digital twin ships collect real-time data on parameters such as speed, fuel consumption, engine performance and environmental conditions. By comparing performance data from the digital twin with the physical vessel, operators can optimize energy efficiency, predict maintenance requirements and ensure compliance with environmental regulations.
Here’s a practical example: A case study in digital twins for real-time ship routing, taking decarbonization compliance into account.
In this example, digital twin technology is used to facilitate real-time assessment of decarbonization regulatory compliance in ship routing. This approach focuses on real-time monitoring of ships’ carbon emission intensity and identifying potential strategies for mitigating operational risks related to decarbonization targets. By leveraging up-to-date environmental and operational data.
The digital twin approach improves the accuracy of the estimated probability that a specific vessel will comply with regulations throughout its voyage. This example offers a proactive, data-driven approach to supporting the maritime industry’s decarbonization efforts.
Port operations management: Using data from the digital twin, port operations can be monitored, including vessel traffic, container movements and berth utilization. As the port experiences changes in ship arrivals, cargo volumes or infrastructure improvements, the digital twin adapts to reflect the changing conditions. This enables real-time optimization of resources, efficient berth allocation and improved logistics planning within the port.
Here’s a practical example: a digital twin-based approach to optimizing energy consumption in automated container handling operations.
This approach proposes to use digital twin technology to optimise the energy consumption of an automatic stacking crane (ASG) involved in container handling operations. The approach consists of developing a virtual container area that synchronises with the physical container area in the digital twin system in an automated container terminal, for observation and validation purposes. A mathematical model is then established to minimise the overall energy consumption required to complete all the tasks.
Digital twins based on artificial intelligence technologies.
Building a digital twin using artificial intelligence (AI) techniques may be more appropriate in the following scenarios:
High complexity: AI can be beneficial when dealing with highly complex systems involving numerous interactions and interdependencies. By using AI, it becomes possible to model and simulate these complex interactions more accurately.
Heterogeneous data: When data from a variety of sources, with varying formats, structures and resolutions, are needed to build the digital twin, AI can efficiently process and integrate these heterogeneous data, thus creating multimodal models.
Dynamic adaptation: If the real system requires real-time adaptation to changing environmental or operational conditions, AI enables the digital twin to make autonomous decisions and adjust its parameters accordingly.
Here’s a practical example: Digital twins in intelligent transport systems
Traffic management in high-traffic urban and maritime areas remains a major concern. Traditionally, control centers have been used to meet these challenges, but they now require modernization through the incorporation of digital twins and artificial intelligence (AI). The implementation of Intelligent Transport Systems (ITS) offers a solution to the main problems encountered in transport networks while facilitating their development. By using digital twins with the ArchiMate[1] notation model, we can optimize the distribution of traffic flows in the network in time and space.
The Limits of Digital Twins….
Digital twins have gained attention and popularity in various industries, offering the possibility of improving design, simulation and analysis capabilities. These virtual replicas of physical assets enable real-time monitoring, predictive maintenance and performance optimization. However, like any technological advance, digital twins also have their limitations, both in theory and in practice. In this section, we will explore the potential drawbacks and challenges associated with digital twins, highlighting specific examples where these limitations have been observed in real-life scenarios. By understanding these limitations, we can gain a full perspective on the benefits and considerations of using digital twins in the maritime world.
Data accuracy and reliability: The effectiveness of digital twins depends heavily on the accuracy and reliability of the data used to create and update them. Incomplete, obsolete or inaccurate data can lead to discrepancies between the digital twin and the real system, impacting on the reliability of predictions and analyses.
Model complexity and assumptions: Building an accurate digital twin often requires simplifications and assumptions about the real system. However, these assumptions are not always borne out in practice, leading to discrepancies between the predictions of the digital twin and the actual behavior of the system.
Computing requirements: Implementing and maintaining a digital twin may require significant computing resources, particularly for complex systems or advanced simulation techniques. This requirement could limit scalability and accessibility, particularly in resource-constrained environments. To this we can also add the connectivity of ships at sea, where a satellite system is required to ensure good data frequency.
The three above-mentioned limitations can be illustrated by the example below:
“The use of incomplete or misused data can have serious consequences. A case in point was the crash of two Boeing 737 MAX aircraft. It seems that digital twins were used during the construction process to make modifications to these aircraft. However, it is possible that a discrepancy between the data used in the simulations and the actual data contributed to these accidents.”
Integration challenges: Integrating data from a variety of sources and systems to create a complete digital twin can be challenging. The diversity of data formats, standards and protocols between systems requires complex and time-consuming data integration processes.
Cost and time considerations: Creating and maintaining a digital twin involves significant costs, including data acquisition, sensor deployment, software development and ongoing maintenance. In addition, data collection, processing, modeling and validation are time-consuming.
Privacy and security concerns: The real-time data capture and analysis involved in digital twins raises privacy and security issues. Protection of sensitive data, respect for privacy and protection against cybercrime are all elements that need to be taken into account when designing digital twins.
Human factors and expertise: While digital twins provide valuable information, human interpretation and expertise are essential for drawing meaningful conclusions and making informed decisions. The human element is essential for understanding context, interpreting results and applying domain knowledge to fully exploit the potential of digital twins.
It is important to note that specific limitations may vary depending on the application and implementation of digital twins. Difficulties may arise when integrating data from disparate sources, managing legacy systems lacking standardized interfaces, or managing large volumes of data in real time. In addition, issues relating to data quality, sensor reliability and the need for ongoing calibration and maintenance can affect the accuracy and efficiency of digital twins in practice.
Digital twins can enable operators to monitor and control ships remotely, optimize performance, predict failures and make informed decisions in real time. Automation based on digital twins also offers the possibility of reducing dependence on human labor, increasing operational efficiency and minimizing the risk of error. Smart-ships thus represent a new era in the maritime industry, opening the door to smarter, safer and more sustainable operations.
Author: Mohamed CISSOUMA,
Expert naval engineer,
Responsible for certification of navigation equipment and onboard systems
Capitain
President of ELIT
References
- Diego M. Botín-Sanabria, Adriana-Simona Mihaita, Rodrigo E. Peimbert-García, Mauricio A. Ramírez-Moreno, Ricardo A. Ramírez-Mendoza, Jorge De J. Lozoya-Santos (2022). Digital Twin Technology Challenges and Applications: A Comprehensive Review, Remote sensing, 14(6), 1335. https://doi.org/10.3390/rs14061335
- Dimitrios Kaklis , Iraklis Varlamis , George Giannakopoulos , Takis J. Varelas , Constantine D. Spyropoulos. Enabling digital twins in the maritime sector through the lens of AI and industry 4.0 . ELSEVIER B.V. Volume 3, Issue 2, November 2023, 100178. https://doi.org/10.1016/j.jjimei.2023.100178
- Michaela Ibrion1, Nicola Paltrinieri and Amir R. Nejad (2019). On Risk of Digital Twin Implementation in Marine Industry: Learning from Aviation Industry, Conference Series, Volume 1357. https://iopscience.iop.org/article/10.1088/1742-6596/1357/1/012009
- Mr. Volker Bertram, DNV, 2023-EMM-402 Safe Shipping – Safety and Technology, World Maritime University.
- Qikun Wei , Yan Liu , You Dong , Tianyun Li , Wei Li. A digital twin framework for real-time ship routing considering decarbonization regulatory compliance. ELSEVIER B.V. Volume 278, 15 June 2023, 114407. https://doi.org/10.1016/j.oceaneng.2023.114407
- Rudskoy A, Ilin I, Prokhorov A (2020). Digital Twins in the Intelligent Transport Systems, ELSEVIER B.V. Volume 54, 2021, Pages 927-935. https://doi.org/10.1016/j.trpro.2021.02.152
- Yinping G , Daofang C , Chun-Hsien C (2023). A digital twin-based approach for optimizing operation energy consumption at automated container terminals. ELSEVIER B.V, Volume 385, 135782. https://doi.org/10.1016/j.jclepro.2022.135782
[1] ArchiMate is an architectural modelling language used to represent and communicate enterprise architecture concepts. It provides a graphical notation for describing the various aspects of an architecture, such as actors, applications, services, processes, information flows, etc.
