IEEE Robotics & Automation Magazine - September 2023 - 9

RELATED WORK
The digital agenda is one of the pillars of the Europe's
growth strategy, which lists ship intelligence as one of the
main areas through which to achieve growth. In marine system
and transport, digitalization can significantly improve
design, operation, and management through accurate information
on operational and infrastructure conditions, and on
the location of vehicles and/or system behavior models. Better
access to and sharing of digital data (traffic, travel, vehicle,
cargo, and so forth.) for both public and private
stakeholders along the supply chain can foster seamless
information flows and open up a wide range of new business
opportunities.
The last few years have witnessed a strong, renewed interest
in digital twin technologies. Their applications include
manufacturing, agriculture, energy management,
and the automotive industry
[3]. Digital twins integrate artificial
intelligence, machine learning (ML),
and data analytics with data to create
living digital models that update and
change as their physical counterparts
change. A digital twin is a model of a
physical asset, implemented, for example,
as a mathematical, an information,
or a visual model. ML offers a way to
create and update models of the physical
system based on sampled data [6]. In
various industrial sectors, digital twins
are being used to optimize the operation
and maintenance of physical assets, systems,
and manufacturing processes. It
is a formative technology for the Industrial
Internet of Things, where physical objects can live and
interact with other machines and people virtually. A digital
twin can be used for monitoring, diagnostics, and prognostics
to optimize asset performance and utilization. Therefore,
complex prognostics and intelligent maintenance system can
leverage the use of digital twins in finding the root cause of
issues and improve system productivity.
The main difference between digital twins and generic
models is that the former are specific to, and reflect their
physical counterparts with, dynamic data flow interaction.
Normally, modeling and simulation is used to build a system
and subsystems in a single, solitary software environment for
design, control, and optimization. When the complexity of the
whole system increases to a certain level, such as a surface vessel
with multidomain components, modeling everything in one
software environment is almost impossible. The digital twin
follows its corresponding real-life twin through its lifecycle by
collecting sensor measurements, simulation model updates,
and software upgrades.
Models are key issues in a digital twin. The last decades
have seen increasing interest in developing computer-based
design and analysis tools for different applications, including
the marine industry. First, some general-purpose simulation
environments are well known in research and education, including
MATLAB/Simulink [7], Modelica [8], and so on. In parallel,
a great number of specialized analysis software programs
for structures, hydrodynamics, computational fluid dynamics,
and power systems and control systems are currently used in the
design process to assess special subsystem performance. Integrating
these two approaches in maritime design processes is
important due to differences in the emphasis on system modularity
and model accuracy in the software.
The next key issue is data sharing and model integration.
"
THE MAIN DIFFERENCE
BETWEEN DIGITAL TWINS
AND GENERIC MODELS
IS THAT THE FORMER ARE
A Functional Mock-Up Interface (FMI) [9] offers model
exchange and a cosimulation mechanism to couple different
models. The essential difference is that the units compliant
with an FMI for model exchange expect to be solved by a
given master algorithm, while those for cosimulation, on the
other hand, contain a solver so that the
master algorithm is required only for coordination
of data exchange. A cosimulation
mechanism provides a solution
for how to specify the parameters and
variables to exchange and how to make
it happen.
In essence, for the maritime industry,
SPECIFIC TO, AND REFLECT
THEIR PHYSICAL COUNTERPARTS
WITH, DYNAMIC
DATA FLOW INTERACTION.
„
the digital twin should be able to take
advantage of all available digital information
for an asset: the system and data
information model, 3D visualization models,
mathematical models, dependability
models, condition and performance indicators,
and data analytics. An increasing
number of systems and processes on
board a modern vessel are dependent on
computers and networks for monitoring
and control. Feedback loops from system measurements are
included in the computations, and affect the controllers.
More importantly, in the current maritime industry, a balance
between the number of defined domain models and the
scale of the whole system will be met to make the digital twin
system flexible and fast enough. Maritime systems communicate
through a number of formats and protocols. The variety
in protocols and in installed instruments and systems makes it
difficult to integrate with new systems, especially with those
that require information from many different subsystems. In
addition, each of these protocols must be set up with explicit
knowledge of where and how to access each piece of information.
Open source is a key factor to establish a cloud-enabled
cosimulation platform and model ecosystem for collaborative
and managed model sharing and time-domain system simulation.
The core components should be open source and governance
by the research consortium. The submodels, apps, tools,
and service will be offered in the digital twins at various IP
terms. In this way, industrial partners could get firsthand help
and speed up the design and operation.
As mentioned previously, digital twins are promising but
challenging in the maritime domain. In the next section, we
introduce how we built our digital twin of the RV Gunnerus.
SEPTEMBER 2023 IEEE ROBOTICS & AUTOMATION MAGAZINE
9
IEEE Robotics & Automation Magazine - September 2023

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