Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність
The results of a review of the digital twin concept development, the areas of their use, and the prospects are highlighted. The history of the emergence and development of the digital twin concept, its definition, and its classification are given. The relevance of the technology under consideration...
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| author | Pankratova, Nataliya Grishyn, Kostiantyn Barilko, Viesta |
| author_facet | Pankratova, Nataliya Grishyn, Kostiantyn Barilko, Viesta |
| author_institution_txt_mv | [
{
"author": "Nataliya Pankratova",
"institution": "Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "Kostiantyn Grishyn",
"institution": "Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
},
{
"author": "Viesta Barilko",
"institution": "Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
}
] |
| author_sort | Pankratova, Nataliya |
| baseUrl_str | http://journal.iasa.kpi.ua/oai |
| collection | OJS |
| datestamp_date | 2023-08-07T15:49:29Z |
| description | The results of a review of the digital twin concept development, the areas of their use, and the prospects are highlighted. The history of the emergence and development of the digital twin concept, its definition, and its classification are given. The relevance of the technology under consideration is reflected. The purpose of this review is to provide the most complete, up-to-date information on the current state of the digital twin technology, its application in various fields of human activity, and further prospects for the development of the industry. An extensive bibliography on the topic is provided, which may be helpful for researchers and representatives of various industries. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2023.2.01 |
| first_indexed | 2025-07-17T10:28:15Z |
| format | Article |
| fulltext |
N.D. Pankratova, K.D. Grishyn, V.E. Barilko, 2023
Системні дослідження та інформаційні технології, 2023, № 2 7
TIДC
ТЕОРЕТИЧНІ ТА ПРИКЛАДНІ ПРОБЛЕМИ І
МЕТОДИ СИСТЕМНОГО АНАЛІЗУ
UDC 004.942: 303.732.4
DOI: 10.20535/SRIT.2308-8893.2023.2.01
DIGITAL TWINS: STAGES OF CONCEPT DEVELOPMENT,
AREAS OF USE, PROSPECTS
N.D. PANKRATOVA, K.D. GRISHYN, V.E. BARILKO
Abstract. The results of a review of the digital twin concept development, the areas
of their use, and the prospects are highlighted. The history of the emergence and de-
velopment of the digital twin concept, its definition, and its classification are given.
The relevance of the technology under consideration is reflected. The purpose of this
review is to provide the most complete, up-to-date information on the current state
of the digital twin technology, its application in various fields of human activity, and
further prospects for the development of the industry. An extensive bibliography on
the topic is provided, which may be helpful for researchers and representatives of
various industries.
Keywords: Industry 4.0, digital twin, classification, areas of application, Internet of
Things, physical and mathematical models.
INTRODUCTION
The increasing complexity of production management task necessitates the adop-
tion of new management methods and systems. Today, production management
requires Industry 4.0 competencies [1], which have emerged relatively recently
and are largely interconnected with IT technologies. The traditional pyramidal
model for designing production management systems is being replaced by mod-
ern approaches to direct interaction between the components of the production
system based on the concept of the Internet of Things (IoT). The digital twin re-
fers to a new innovative toolkit that helps to exploit the advanced scenarios of the
Internet of Things and other technologies. This toolkit is used to create digital
models of the physical environment, and digital analogues will be able to perceive
information from the world around them, interact and exchange data. The result is
a completely new environment where the intelligence embedded in applications
will allow to assess what is happening in the physical world, take into account the
accumulated information and experience to support decision-making. This envi-
ronment creates qualitatively new conditions for business and ensuring environ-
mental safety at work. Thus, the digital and physical worlds are being united, with
applications, services, networking components and edge devices being the Inter-
net of Things. Each element of the Internet of Things in production will have a
digital twin, i.e. its virtual model for rational production management. Based on a
systematic literature review and a thematic analysis of 92 publications on digital
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 8
twins over the past ten years, [2] this work describes this technology, identifies
knowledge gaps and necessary areas for future research. In the characterisation of
a digital twin, the state of the concept, key terminology and related processes are
identified, discussed and combined to produce 13 characteristics (physical en-
tity/twin; virtual entity/twin; physical environment; virtual environment; state;
implementation; metrology; duplication; duplication rate; physical-virtual connec-
tion/twin; virtual-physical connection/twin; physical processes; and virtual proc-
esses) and the complete structure of the digital twin and its operation process. Fol-
lowing this characterisation, seven knowledge gaps and topics for future research
are identified: perceived benefits; digital twin in the product lifecycle; use cases;
technical implementations; confidence levels; data ownership; and integration of
virtual entities, each of which is necessary for digital twin implementation. There
are also other reviews [3]–[6] on the digital twin application in various industries.
The history of the digital twin concept. The first digital twin in history (at
that time, such name and concept did not exist) can be considered a program used
by NASA in the 1960S to design the Apollo 13 mission. It was created to test how
the future object would behave in the physical world. Later, engineers discovered
that the virtual model could be used to control equipment and predict what would
happen to it. The sensor readings during the flight were compared with those pre-
dicted in the digital model in real time [7]. This idea later became the basis for the
creation of modern digital twins.
The emergence of the digital twins concept is associated with the work of
Michael Greaves, a professor at the University of Michigan. In 2002, as part of a
presentation at the University of Michigan for industry representatives, he pro-
posed a model consisting of three components: a real space; a virtual space; and a
mechanism that ensured the exchange of information between them. At the time,
this concept was called “ideal PLM” (Product Lifecycle Management) [8]. A sim-
ilar concept named “Mirror Worlds” [9] was proposed earlier by computer scien-
tist David Gelernter in 1991.
In response to the M. Greaves publication, K. Främling et al. 2003 [10] pro-
posed to modify the PLM concept to solve the problem of organizing the ex-
change of information between a virtual object and a physical one using the Inter-
net of Things. In particular, the authors argued that the standards already existing
at that time were sufficient to organize the computing architecture. In 2003, the
idea was not widely supported due to the imperfection of technologies for proc-
essing a large data flow in real time, with most data stored in paper form.
Later, Michael Greaves developed this idea and presented it in his course on
PLM systems, which he taught at the same university. Despite the fact that the
term “digital twin” appeared a little later, the basic concept of having a physical
object, a virtual object and ensuring the information exchange between them was
developed already then. In the following years, one of the names of this concept
was the “model of mirror spaces” [11], in the period 2006–2010 the term “model
of information mirror” was used [12].
In 2010, the term “Digital Twin” first appeared in NASA’s technology
roadmap, its authorship could be attributed to J. Vickers. This publication also
used the term “Virtual Digital Fleet Leader” [13]. Since 2011, the concept of digi-
tal twins has been used by the US Air Force Research Laboratory (AFRL) to ef-
fectively resolve the design issues, maintenance and forecasting of the aircraft
service life [13], [14]. A virtual copy of the aircraft accompanied the real object
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 9
throughout its life cycle. In 2013, Germany proposed the concept of Industry 4.0.
Its key idea is the cyber-physical system (CPS). CPS combines people, machines
and things that provide a continuous exchange of information to monitor, collect
data, analyse and optimise production processes. To implement such processes, it
is necessary to use a digital twin [15]. The concept of a “digital twin” is a part of
the fourth industrial revolution and is designed to help businesses detect physical
problems faster, make more accurate predictions, develop better products
[16], [17].
M. Greaves gave a detailed description of the digital twin in 2014 in the so-
called White Paper, intended for corporate society. Some industrial companies,
such as Siemens, almost immediately adopted the terminology and paradigm
outlined in the book [18]. In his book “The Origin of Digital Twins”, M. Greaves
divided any digital twin into three main parts [19]: a physical product; a virtual
product; and data and information that unite the virtual and physical products. In
his opinion, “under ideal conditions, all the information that needs to be obtained
from a product can be provided by a digital twin”.
The design of digital twins is based on simulation modelling methods that
provide the most realistic representation of a physical environment or object in
the virtual world. A mathematical description of digital twins can be obtained via
statistical modelling, machine learning or analytical modelling methods. The
mathematical models of a digital twin for supporting and predicting the
functioning of a physical process of a real objects are given in [20], [21]. The
parametric uncertainty of the mathematical description of the physical process is
taken into account. As an example, the design of a digital twin is given on the
example of an analytical model of an electric heater.
In 2023, the concept of a digital twin is evolving into something more subtle
and incredibly practical: an executable digital twin (xDT). Simply put, xDT is a
digital twin on a chip. xDT uses data from a (relatively) small number of sensors
embedded in a physical product to perform real-time simulations using reduced-
order models. Based on the data from a small number of sensors, it can predict the
physical state anywhere within the object (even in places where sensors cannot be
placed) [22].
The main stages of development of the digital twin concept are shown in
Fig. 1, from the inception of the idea in 1960 to the current state. Source: com-
piled by the authors based on [7]–[22].
Fig. 1. Key stages in the development of the digital twin concept. Source: compiled by
the authors on the basis of [7]–[22]
y. y. y. y. y. y. y. s
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 10
Definition of a digital twin. The review has shown that there is no single
accepted definition of digital twin. The paper “Digital twin paradigm: A system-
atic literature review” counted 30 definitions given by researchers and organiza-
tions [7]. Therefore, we will present the definition proposed by the author of the
concept of digital twin and one of the modern ones, which, in our opinion, is the
most complete among the existing ones.
Definition by M. Greaves. A set of virtual information constructs that fully
describe a potentially or actually produced physical product from microatomic to
macrogeometric level [8].
Definition by Asimov et al. 2018. A virtual copy of a physical object that
can monitor data consistency, perform data mining to identify existing and predict
future problems, and use artificial intelligence to support business decisions [23].
However, the definition of a DT is not finalized. For example, a 2021 meta-
analysis that included 24 articles showed that 83% authors agreed that the concept
was in the early stages of development [24].
The relevance of the topic of digital twins. According to a study conducted
in September 2022, the number of publications in Scopus on the topic of "digital
twin" is growing every year [25]. The popularity of this topic can be seen from the
graph of the number of publications depending on the year. Interest in the creation
of digital twins has been growing significantly since 2016.
More and more countries are adopting development plans based on tech-
nologies that combine physical and information space: “Industry 4.0” in Germany
[15], “Advanced Manufacturing” in the United States [26], “Society 5.0” in Japan
[27], “Made in China 2025” in China [28], and a similar project for the EU, “The
Factory of Future” [29]. Topic relevance is confirmed by numerous highly cited
publications. Authors of these publications have high H-index (Table 1).
Fig. 2. Number of publications on DT depending on the year. Source [20].
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 11
T a b l e 1 . The most cited works on digital twins
Number of
citations
H-index
of authors Title Year
Scopus / Google Scholar
Jay Lee, Behrad Bagheri, Hung-An Kao “A cyber-
physical systems architecture for industry
4.0-based manufacturing systems” [30]
2014 3003/5088
Jay Lee: 58/73
Behrad Bagheri:
12/19
Fei Tao, Jiangfeng Cheng, Qinglin Qi, Meng
Zhang, He Zhang, Fangyuan Sui “Digital twin-
driven product design, manufacturing and service
with big data” [31]
2018 1433/2084
Fei Tao: 69/81
Jiangfeng Cheng:
14/16 Qinglin Qi:
23/24
Dmitry Ivanov “Predicting the impacts of epidemic
outbreaks on global supply chains: A simulation-
based analysis on the coronavirus outbreak
(COVID-19/SARS-CoV-2) case” [32]
2020 1079/1821
Dmitry Ivanov:
62/74
Werner Kritzinger, Matthias Karner, Georg Traar,
Jan Henjes, Wilfried Sihn “Digital twin
in manufacturing: a categorical literature review
and Classification” [33]
2018 1114/1718
Wilfried Sihn:
22/31
Fei Tao, He Zhang, Ang Liu, Andrew Yeh-Ching
Nee “Digital Twin in Industry: State-of-the-Art” [34]
2019 1197/1750 Fei Tao: 69/81
Michael Grieves, John Vickers “Digital Twin:
Mitigating Unpredictable, Undesirable Emergent
Behavior in Complex Systems” [35]
2017 1063/2076
Michael Grieves:
7/16
Abram L. J. Walton, Cynthia L. Tomovic, Michael
W. Grieves, “Product Lifecycle Management:
Measuring What Is Important – Product Lifecycle
Implementation Maturity Model” [36]
2013 6/13
Michael Grieves:
7/16
Tomovic Cyn-
thia: 5/9
F Tao, Q Qi, L Wang, AYC Nee “Digital twins
and cyber-physical systems toward smart
manufacturing and industry 4.0: correlation
and comparison” [37]
2019 487/712
Qinglin Qi: 23/24
Fei Tao: 69/81
Source: compiled by the authors on the basis of [30]–[37]
The study by Semeraro et al. 2021 [7] provides the following facts about the
popularity of the term “digital twin” in the Scopus, Elsevier, and ScienceDirect
databases: 60 journals have published papers on this topic; it was discussed in 47
conferences; 8 chapters in various books have been found.
Many DT-related technologies have been patented. As part of the review, we
searched for patents with the term “digital twin” using the query TAC=”digital
twin”. At the time of writing, Google Patents returned 6181 results for this query
dated 2003-01-01. The largest patent holders are large corporations: Siemens AG
(13.4% of all patents); General Electric (9.8%); Beijing University of Aeronautics
and Astronautics (3.4%). A study that conducted a cluster analysis of DT patents
from the Webpat and Derwent databases found 140 records by 2018 [38].
The classification of DT. The most complete list of classifications is given
in the paper [39]: by application, by level of integration, by hierarchy, by level of
maturity/complexity, including the classification introduced by M. Greaves [40].
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 12
Reading literature, one may also encounter terms “digital model” and “digital
shadow”. They should be distinguished from a digital twin. In particular, the
difference between these terms was explained by Kritzinger et al [33]:
A digital model is a virtual representation of an entity in which there is no
automatic data exchange between a physical and a virtual object. All information
is transferred manually.
A digital shadow is a virtual representation of an entity where data is
transferred from a physical object to a virtual one, but not vice versa. Thus, a digi-
tal shadow is a kind of logbook where all important changes are recorded.
A digital twin is a virtual representation in which data flows in both direc-
tions, from a digital entity to a physical one and vice versa.
AREAS OF APPLICATION OF DIGITAL TWINS
1. Aerospace industry. As noted in the IEEE publication [40], leading aerospace
industry agencies such as NASA, EASA, the US Air Force, and the Royal Cana-
dian Air Force (RCAF) consider DT as a key element in production. This tech-
nology enables the implementation of the Operation & Maintenance paradigm,
which prioritises the prevention of accidents and breakdowns, and fixing them
before they occur.
Digital twins are indispensable in the design of critical components such as
engines, which are expensive and technologically complex to test. Computer sim-
ulations allow to test different flight modes, altitudes, climatic conditions, emer-
gency situations, and system failures before physical testing. Simulations make
possible to study engine behaviour under extreme conditions without physical
experiments [40].
In-service DT reduces the need to rely on probabilistic models to determine
when a part may need maintenance. Various machine learning algorithms are
used in the aerospace industry: Support Vector Machines, autoencoders, convolu-
tional neural networks. A full list of algorithm applications is available in the
IEEE publication [40]. More details can be found in a textbook dedicated to the
problem of using DT in aircraft [41].
Fig. 3. Data flow in a digital model, shadow and twin. Compiled on the basis of the
publication [33]
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 13
The DTs are used by Rolls-Royce in the design and operation of its aircrafts.
Once released, the virtual model accompanies the aircraft during each flight,
comparing predicted performance with real-world performance, and building
What If scenarios [42].
2. Automotive industry. Digital twins are used to speed up the design of
new car models. First, a virtual model is developed and tested, then a physical
prototype is built and improved in parallel with the digital prototype. A virtual
twin makes it possible to review different design options and run computer simu-
lations of various situations (for example, how a car would behave on a slippery
road or in icy conditions). Siemens, for instance, offers software solutions for cre-
ating digital prototypes of cars with the function of developing a 3D model and
conducting simulations [43].
Maserati has replaced the costly and time-consuming wind tunnel streamlin-
ing procedure with a digital twin. Also, the engine sound (which is a symbol of
the brand) is tuned in a computer simulation, whereas previously a physical proto-
type was built and the acoustics were tested on mannequins with microphones. In
general, the use of this technology reduces the new car development time by 30%
[44]. Renault uses the DT to conduct virtual crash tests, study body aerodynamics,
engine efficiency, even check how well air conditioner cools the interior [45].
Digital twins are used not only for design. Cars with a high degree of
computerisation form the so-called Internet of Vehicles [46], which provides the
city with many advantages: increased safety, traffic optimisation, information
about available parking spaces, and the possibility of remote maintenance [47].
A striking example of the use of DT during operation is Tesla, which creates a DT
for each car sold. During the trip, sensor readings are continuously taken and sent
to servers, artificial intelligence detects problems and forms an individual
approach to their solution (such as changing the settings to make the doors close
more quietly) [48].
3. Oil and gas industry. The digital twin plays an important role in modern
drilling operations throughout the entire life cycle of a borehole, starting with the
exploration stage. The DT of borehole is a complex system: the drilling model is
made up of four unique elements, each of which is a digital twin itself: wellbore,
drill string, drilling mud, and formation. Computer simulation allows to find the
best types of tools and rig settings. Real-time monitoring of all processes and
forecasting via machine learning methods helps to identify equipment problems
that can lead to unplanned downtime or correct process deviations before they
lead to a deterioration in the quality of the workflow [49]. Ericsson uses systems
to monitor the condition of workers through smartwatches and bracelets to
prevent mistakes due to fatigue [50]. The most comprehensive bibliography of the
use of digital twins in the oil industry is provided by Cameron et al. 2018 [51].
4. Chemical and pharmaceutical industry. In the chemical and pharma-
ceutical industries, each batch of products is created with a DT that combines all
the necessary information about the batch. Based on these data, it is possible to
run simulations and predict the optimal technological parameters for the next pro-
duction stages to ensure the planned product quality.
Information about changes in product properties depending on the parame-
ters can be stored in databases, which allows to “design” new substances on the
computer. This functionality makes DT a key element of the quality management
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 14
process [49]. For example, such software was developed by Atos and Sie-
mens [52].
5. Digital Twins of Cities. There are terms like “Smart City” and “Digital
City” that do not mean that a city has a digital twin. A smart city is one that fol-
lows a strategy of saving resources through innovative solutions. A digital city is
a city that has implemented IoT technologies [53].
The smart city concept emerged in response to the Kyoto Protocol, which
obliged countries to reduce greenhouse gas emissions [53]. Intensive urbanisation
(in particular, according to UN forecasts, 68% of the world’s population will live
in cities in 2050 [54]) has led to cities being the main source of pollution. Digital
transformation has been proposed as a way to reduce emissions. With the devel-
opment of 3D modelling technologies, it became possible to visualise infrastruc-
ture, but the models were static and used only for infrastructure planning, they
could not display real-time processes, nor could they be remotely controlled –
they were not digital twins [55]. It was only with the appearance of IoT technolo-
gies, cloud computing, the implementation of artificial intelligence and big data
that it became possible to set up data exchange between the physical and digital
essence of the city. This moment can be considered the emergence of digital twins
of cities [53].
The world’s first digital twin city was created for Singapore in 2014 at a cost
of US $73 million. In 2022, the system was replaced with an advanced version
that includes data from sensors, drones, government agencies, etc. [56]. The city
of Zurich has its own DT with a detailed 3D map of roads, underground and
above-ground facilities [57]. According to the World Economic Forum 2022, by
2030, the technology of DT will have saved $289 billion on city planning and
construction; in 2020, investments in DT of Chinese cities exceeded $380 bil-
lion [58].
The most comprehensive bibliography on digital twin cities is provided in
the review, which lists the main applications: planning and forecasting, visualisa-
tion, engagement, monitoring, data management [59].
6. Digital twins of urban water supply systems. The problem of drinking
water is becoming more urgent in the world, as such, there is a need for its
rational use. The International Water Association (IWA) has published a paper on
digital twins of urban water supply systems as a way to reduce water
consumption [60].
An example of a successfully implemented project is the digital twin of
water supply system of Valencia (Spain) and its suburbs, developed by Global
Omnium. It feeds approximately 1.6 million inhabitants, and the water supply
system provides water to 325,000 nodes. The programme contains a detailed
model to monitor the system and predict emergencies [61]. The digital twin can
predict possible problems 24 hours in advance. Simulations and records of
previous accidents are used to train personnel [62].
No less successful is the H2PORTO project, a digital twin of the water
supply system in Porto (Portugal). It provides real-time monitoring of the system,
uses weather and tidal data to model water levels and warn of flood risk.
H2PORTO can run virtual simulations of emergency situations: pipe bursts, valve
closures, pumping station shutdowns [63].
7. Energy. Power grids are among the most complex engineering systems in
the world. With the appearance of solar panels and wind turbines, power supply
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 15
networks are becoming more complex, requiring effective management. For
example, Siemens has set up a research centre to develop a digital twin of
Australia’s power grid. In 2023, €3.4 million was invested in the project. It is
planned to be developed via the Siemens programme PSS software [64].
The lack of a single standard for digital twins remains a problem in the en-
ergy sector. Management is complicated if several power grids are connected but
have different digital twins. In a publication issued by the IEEE, a single standard
for digital twins was proposed. The authors note that the architecture is flexible,
which will spare rebuilding the system in the future and enable connecting exist-
ing digital twins [65]. The most comprehensive bibliography on digital twin pow-
er grid structures is provided in the article [66].
8. Construction. Several reviews have already been conducted on this topic
[67, 68]. Found publications are classified by the stages of the life cycle of the
object where the digital twin is used: design, construction, and operation. In the
construction process, complete digital twins are used as part of the implementa-
tion of Building information modelling (BIM), a strategy to improve the effi-
ciency of the construction process [69]. Papers have been published that propose
the design of the digital twin for the operation of the building. The topics raised
include building maintenance, fault detection, scheduling and personnel manage-
ment, etc. [70, 71].
9. Retail trade. In the book “Advances in computers Vol. 130”, there is a
separate chapter devoted to DT in retail trade as a tool for effective planning. In
its simplest form, a DT can be a virtual copy of a physical supply chain with in-
formation on weather, fuel prices, etc. This allows to simulate how, for example,
a transport delay due to bad weather can affect the flow of products in the supply
chain [72].
According to Forbes, retailers and companies such as Lowes, Kroger, and
Tyson Foods are already exploring or implementing digital twins of their supply
chains. French supermarket chain Intermarché has already created digital twins of
most of its stores. [73].
10. Preservation of cultural heritage. Museums use digital twins to moni-
tor the preservation of exhibits. Some institutions create digital twins of entire
buildings of cultural value. Such technologies are developed by Weiss AG. For
example, the Forbidden City Museum (Beijing, China) conducts periodic 3D
scans of ancient buildings to monitor whether restoration is needed [74]. Digital
twins are also used to organise virtual tours. More details about the application
can be found in the review dedicated to the use of digital twins in museums [75].
THE PROSPECTS OF DIGITAL TWINS. THE LARGEST PROJECTS OF
DIGITAL TWINS.
A digital twin of the port of Rotterdam. Currently, IBM is creating a digital
twin of the port of Rotterdam [76], the largest port in the world. The project com-
plexity is comparable to projects that develop digital twin of large city. For
example, in the annual report of 2022, it was stated that revenue was 825,700,000
euros [77]. It is planned to organise the port territory into a single digital space
through IBM Cloud and IBM Internet of Things systems. The digital twin of the
port will provide the following opportunities [76]:
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 16
collection of meteorological and hydrological data to determine the most
favourable time for getting to the port;
autonomous ship control and data exchange between vessels;
fuel economy, optimisation of approach and mooring speed;
A digital twin of the planet Earth [78]. The European Union plans to be-
come a climate-neutral region by 2050. The key point of this plan is digital trans-
formation, and for this purpose, in particular, a digital twin of the planet Earth is
being developed, in which 1 trillion euros have been invested. It will perform the
following tasks:
combining data from satellites, scientific stations, sensors, etc. into a sin-
gle high-resolution information platform;
analysis of human impact on nature, such as carbon emissions, water pol-
lution, littering;
more accurate forecasting of weather and climate in the long-term perspective;
building what-if scenarios to rationally implement environmental projects.
Today, some companies have developed software products for creating digi-
tal twins. Digital twin software is an advanced type of modelling software that
generates a digital simulation of a physical object. Here are examples of software
for creating digital twins:
1. Siemens Digital Twin allows to create digital twins for equipment and in-
dustrial processes. It provides real-time monitoring and control of equipment, as
well as the ability to predict possible failures and diagnose problems.
2. ANSYS Twin Builder – allows to create digital twins for various indus-
trial systems, including automotive, electronics, and power generation. It allows
to conduct virtual testing and analyse various system scenarios, which helps to
reduce costs and improve productivity.
3. Microsoft Azure Digital Twins is a cloud-based solution that allows to
create digital twins for different types of buildings and urban environments. It
allows to visualise the structure of a building and its various systems, as well as
predict and manage energy consumption.
4. Predix Digital Twin is a solution from General Electric that allows to cre-
ate digital twins for equipment and infrastructure in the energy and transport in-
dustries. The software product provides equipment condition monitoring and
forecasting of possible failures, which helps to reduce maintenance and repair
costs.
5. The computer game Factorio is a game about building and managing fac-
tories to produce various goods. One of the key aspects is that players have to col-
lect resources, process them and build new factories and machines to improve
production and get more products.
As for the digital twin aspect, Factorio players create a virtual model of the
production process. The game allows to create complex mechanisms from a vari-
ety of elements, such as conveyors, workers, sorting machines, resource mines,
etc. Each element performs a specific function in the production process, and
players must properly configure their work to maximise production.
In Factorio, one can create and save templates of his own production proc-
esses. This allows players to create digital twins of their factories and mechanisms
that can be saved and transferred between different game worlds. In this way,
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 17
players can use digital twins to quickly restore the production process in the case
of a failure or destruction.
6. “AJAX Systems” is a Ukrainian company specialising in the development
and production of wireless security alarm systems and intercoms. AJAX Systems
main products are wireless security alarm systems that use advanced technologies
and are highly reliable and efficient. AJAX systems are easy to install and config-
ure, as well as the ability to control their operation using a mobile application. In
addition to wireless security alarm systems, the company also produces wireless
intercom systems that allow to control access to the building and communicate
with visitors using a mobile application.
CONCLUSIONS
The article provides a detailed overview of the history of the emergence and de-
velopment of the digital twin concept, provides definitions, explains the differ-
ence between this term and related concepts, and substantiates its relevance and
application in various fields of activity.
The topic relevance is substantiated by calculating number of publications in
Scopus, Elsevier, analysing the number of patents in Google Patents, and
analysing the industrial plans of developed countries such as Germany, the USA,
Japan, and China. In particular, it can be concluded that industrial giants such as
Siemens and General Electric have a large number of patents for this technology.
A detailed review of digital twin applications in various industries was carried
out: aerospace, automotive, oil, chemical, energy, urban planning, water supply,
construction, retail, and cultural heritage preservation. Links to specifically
focused reviews are provided in case of need to learn more about the use of digital
twins in a particular industry.
The global digital twin industry was estimated to be worth USD 6.5 billion
in 2021 and is predicted to reach USD 125.7 billion by 2030. The rise of IoT and
cloud technologies, as well as the motivation to reduce costs and shorten product
development time, are key factors contributing to this growth.
REFERENCES
1. H. Lasi, P. Fettke, HG. Kemper et al., “Industry 4.0,” Business & Information Sys-
tems Engineering, vol. 6, pp. 239–242, Aug 2014. doi: 10.1007/s12599-014 0334-4.
2. D. Jones, C. Snider, A. Nassehi, J. Yon, and B. Hicks, “Characterising the Digital
Twin: A systematic literature review,” CIRP Journal of Manufacturing Science and
Technology, vol. 29, pp. 36–52, 2020. doi: 10.1016/j.cirpj.2020.02.002.
3. M. Liu, S. Fang, H. Dong, and C. Xu, “Review of digital twin about concepts, tech-
nologies, and industrial applications,” Journal of Manufacturing Systems, 2020. doi:
10.1016/j.jmsy. 2020.06.017.
4. M. Enders and N. Hoßbach, Dimensions of Digital Twin Applications - A Literature
Review. 2019.
5. Q. Liu, B. Liu, G. Wang, and C. Zhang, “A comparative study on digital twin mod-
els,” AIP Conference Proceedings, vol. 2073, no. 1, 2019. doi: 10.1063/1.5090745.
6. F. Tao, H. Zhang, A. Liu, and A.Y.C. Nee, “Digital Twin in Industry: State-of-the-
Art,” IEEE Transactions on Industrial Informatics, vol. 15, pp. 2405–2415, 2019.
doi: 10.1109/TII.2018.2873186.
7. C. Semeraro, M. Lezoche, H. Panetto, and M. Dassisti, “Digital twin paradigm: A
systematic literature review,” Computers in Industry, vol. 130, p. 103–469, 2021.
doi: 10.1016/j.compind.2021.103469.
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 18
8. M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable
Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on
Complex Systems: New Findings and Approaches, F.-J. Kahlen, S. Flumerfelt, and
A. Alves, Eds. Cham: Springer International Publishing, 2017, pp. 85–113. doi:
10.1007/978-3-319-38756-7_4.
9. David Gelernter, Mirror Worlds: Or: The Day Software Puts the Universe in a
Shoebox...How It Will Happen and What It Will Mean. Oxford University Press,
‘233Index’ 11, 1991.
10. K. Främling, T. Ala-Risku, M. Kärkkäinen, and J. Holmström, “Agent-based model
for managing composite product information,” Computers in Industry, vol. 57, no. 1,
pp. 72–81, 2006. doi: 1016/j.compind.2005.04.004.
11. M. Grieves, “Product lifecycle management: the new paradigm for enterprises,” Interna-
tional Journal of Product Development, vol. 2, 2005. doi: 10.1504/IJPD. 2005.006669.
12. “DRAFT Modeling, Simulation, Information Technology & Processing Roadmap
Technology Area 11,” NASA Report, November, 2010.
13. E.J. Tuegel, A.R. Ingraffea, T.G. Eason, and S.M. Spottswood, “Reengineering Air-
craft Structural Life Prediction Using a Digital Twin,” International Journal of Aer-
ospace Engineering, vol. 2011, pp. 154–198, Oct. 2011. doi: 10.1155/2011/154798.
14. E. Glaessgen and D. Stargel, The digital twin paradigm for future NASA and U.S. air
force vehicles. 2012. doi: 10.2514/6.2012-1818.
15. H. Kagermann, J. Helbig, A. Hellinger, and W. Wahlster, Recommendations for Im-
plementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German
Manufacturing Industry; Final Report of the Industrie 4.0 Working Group. 2013.
16. A. Parrott and L. Warshaw, “Industry 4.0 and the digital twin Manufacturing meets
its match,” Deloitte, pp. 1–17, May 2017. Available: https://www2.deloitte.com/us/
en/insights/focus/industry-4-0/digital-twin-technology-smart-factory.html
17. C. Monsone, E. Mercier-Laurent, and J.Dr. Jósvai, “The Overview of Digital Twins
in Industry 4.0: Managing the Whole Ecosystem,” in Proceedings of the 11th Inter-
national Joint Conference on Knowledge Discovery, Knowledge Engineering
and Knowledge Management, vol. 0IC3K, pp. 271–276, 2019. doi:
10.5220/0008348202710276.
18. M. Grieves, Digital Twin: Manufacturing Excellence through Virtual Factory Repli-
cation. 2015.
19. M. Grieves, Origins of the Digital Twin Concept. 2016. doi: 10.13140/RG.2.2.26367.61609
20. N. Pankratova and I. Golinko, “Digital Twin Simulation for Cyber-Physical Systems
on Electric Heater Example,” 2022 IEEE 3rd International Conference on System
Analysis & Intelligent Computing (SAIC), Kyiv, Ukraine, 2022, pp. 1–6. doi:
10.1109/SAIC57818.2022.9922971.
21. N.D. Pankratova and I.M. Golinko, “Development of digital twin based on a model
with fractional-rational uncertainty,” Computer Modeling and Intelligent Systems
2023. Proceedings of The Sixth International Workshop on Computer Modeling and
Intelligent Systems (CMIS 2023) Zaporizhzhia, Ukraine, May 3, 2023, pp. 11–22.
Available: https://doi.org/10.327 82/cmis/3392-2
22. S. Ferguson, “Five reasons why Executable Digital Twins are set to dominate engi-
neering in 2023”, Siemens, January 2023. Available: https://blogs.sw.siemens.
com/simcenter/the-executable-digital-twin/
23. R. Asimov, S. Chernoshey, I. Kruse, and V. Asipovich, Digital Twin in the Analysis
of a Big Data. 2018.
24. K. Kuehner, R. Scheer, and S. Straßburger, “Digital Twin: Finding Common Ground
– A Meta-Review,” Procedia CIRP, vol. 104, pp. 1227–1232, 2021. doi:
10.1016/j.procir.2021.11.206.
25. K. Kukushkin, Y. Ryabov, and A. Borovkov, “Digital Twins: A Systematic Litera-
ture Review Based on Data Analysis and Topic Modeling,” Data, vol. 7, no. 12,
2022. doi: 10.3390/data7120173.
26. White House. 2022. Available: https://www.whitehouse.gov/wp-content/ up-
loads/2022/10/ National-Strategy-for-Advanced-Manufacturing-10072022.pdf
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 19
27. A. Deguchi et al., What Is Society 5.0?. 2020. doi: 10.1007/978-981-15-2989-4_1.
28. J. Wang, H. Wu, and Y. Chen, “Made in China 2025 and manufacturing strategy de-
cisions with reverse QFD,” International Journal of Production Economics,
vol. 224, pp. 107539, 2020, doi: 10.1016/j.ijpe.2019.107539
29. Factories of the future. Multi-annual roadmap for the contractual PPP under Hori-
zon 2020. European Factories of the Future Research Association, Report, 2020.
30. J. Lee, B. Bagheri, and H.-A. Kao, “A Cyber-Physical Systems architecture for In-
dustry 4.0-based manufacturing systems,” SME Manufacturing Letters, vol. 3, 2014.
doi: 10.1016/j.mfglet.2014.12.001.
31. F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven prod-
uct design, manufacturing and service with big data,” The International Journal of
Advanced Manufacturing Technology, vol. 94, no. 9, pp. 3563–3576, Feb. 2018. doi:
10.1007/s00170-017-0233-1.
32. D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A
simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2)
case,” Transp. Res. E. Logist. Transp. Rev., vol. 136, p. 101922, Mar. 2020. doi:
10.1016/j.tre.2020.101922.
33. W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in manu-
facturing: A categorical literature review and classification,” IFAC-PapersOnLine,
vol. 51, pp. 1016–1022, 2018. doi: 10.1016/j.ifacol.2018.08.474.
34. F. Tao, H. Zhang, A. Liu and A.Y.C. Nee, “Digital Twin in Industry: State-of-the-
Art,” in IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415,
April 2019. doi: 10.1109/TII.2018.2873186.
35. M. Grieves and J. Vickers, Digital Twin: Mitigating Unpredictable, Undesirable
Emergent Behavior in Complex Systems. 2017, pp. 85–113. doi: 10.1007/978-3-319-
38756-7_4.
36. A.L.J. Walton, C.L. Tomovic, and M.W. Grieves, “Product Lifecycle Management:
Measuring What Is Important – Product Lifecycle Implementation Maturity Model,”
in Product Lifecycle Management for Society, pp. 406–421, 2013.
37. F. Tao, Q. Qi, L. Wang, and A. Nee, “Digital Twins and Cyber–Physical Systems
toward Smart Manufacturing and Industry 4.0: Correlation and Comparison,” Engi-
neering, vol. 5, pp. 653–661, 2019. doi: 10.1016/j.eng.2019.01.014.
38. K.-J. Wang, T.-L. Lee and Y. Hsu, “Revolution on digital twin technology—a patent
research approach,” The International Journal of Advanced Manufacturing Technol-
ogy, vol. 107, pp. 4687–4704, 2020. doi: 10.1007/s00170-020-05314-w.
39. M. Singh, E. Fuenmayor, E. Hinchy, Y. Qiao, N. Murray, and D. Devine, “Digital
Twin: Origin to Future,” Applied System Innovation, vol. 4(2), p. 36, 2021. doi:
10.3390/asi4020036.
40. L. Li, S. Aslam, A. Wileman and S. Perinpanayagam, “Digital Twin in Aerospace
Industry: A Gentle Introduction,” in IEEE Access, vol. 10, pp. 9543–9562, 2022. doi:
10.1109/ACCESS.2021.3136458.
41. T. Wang and Z. Liu, “Digital Twin and Its Application for the Maintenance of Air-
craft,” in Handbook of Nondestructive Evaluation 4.0, N. Meyendorf, N. Ida,
R. Singh, and J. Vrana, Eds. Cham: Springer International Publishing, 2022,
pp. 1035–1052. doi: 10.1007/978-3-030-73206-6_7.
42. Rolls-Royce Official Website. 2019. Available: https://www.rolls-royce.com/media/
our-stories/discover/2019/how-digital-twin-technology-can-enhance-aviation.aspx.
43. Siemens Official Website. Available: https://www.siemens.com/global/en/markets/
automotive-manufacturing/digital-twin-product.html
44. Siemens Official Website. Available: https://new.siemens.com/global/en/ com-
pany/stories/industry/getting-to-market-quickly.html
45. Renault Official Website. Available: https://www.renaultgroup.com/en/news-on-
air/news/vehicle-digital-twin-when-physical-and-digital-models-unite/
46. M. Sadiku, M. Tembely, and S. Musa, “Internet of Vehicles: an Introduction,” Inter-
national Journal of Advanced Research in Computer Science and Software Engineering,
vol. 8, p. 11, 2018. doi: 10.23956/ijarcsse.v8i1.512.
47. B. Poudel, “Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging
Trends, and Open Issues,” Energies, vol. 15, 2022. doi: 10.3390/en15051908.
N.D. Pankratova, K.D. Grishyn, V.E. Barilko
ISSN 1681–6048 System Research & Information Technologies, 2023, № 2 20
48. B. Marr, “The best examples of digital twins everyone should know about,” Forbes
Online. Available: https://www.forbes.com/sites/bernardmarr/2022/06/20/the-best-
examples-of-digital-twins-everyone-should-know-about/?sh=520cdb3d225f.
49. S. Malakuti et al., Digital Twins for Industrial Applications. Definition, Business
Values, Design Aspects, Standards and Use Cases. 2020
50. Ericsson Official Website. Available: https://www.ericsson.com/en/blog/2021/11/
how-digital-twins-in-the-oil-and-gas-industry-can-modernize-your-business.
51. D. Cameron, A. Waaler, and T. Komulainen, “Oil and Gas digital twins after twenty
years. How can they be made sustainable, maintainable and useful?” in Conf. Exergy
Analysis for Combined Heat and Power (CHP) Plants, 2018. doi:
10.3384/ecp181539.
52. Siemens Official Press. Available: https://press.siemens.com/global/en/ pressre-
lease/atos-and-siemens-introduce-digital-twin-solution-within-global-
pharmaceutical-industry
53. A. Cocchia, “Smart and Digital City: A Systematic Literature Review,” in Smart
City: How to Create Public and Economic Value with High Technology in Urban
Space, R. P. Dameri and C. Rosenthal-Sabroux, Eds. Cham: Springer International
Publishing, 2014, pp. 13–43. doi: 10.1007/978-3-319-06160-3_2.
54. United Nations (DESA): The 2018 Revision of World Urbanization Prospects.
Available: https://esa.un.org/unpd/wup
55. J. Yan, S. Zlatanova, M. Aleksandrov, A. Diakité, and C. Pettit, Integration of 3D
objects and terrain for 3D modelling supporting the digital twin. 2019.
56. Singapore government programme “Virtual Singapore”. Available:
https://www.nrf.gov.sg/programmes/virtual-singapore
57. G. Schrotter and C. Hürzeler, “The Digital Twin of the City of Zurich for Urban
Planning,” PFG – Journal of Photogrammetry Remote Sensing and Geoinformation
Science, vol. 88, 2020. doi: 10.1007/s41064-020-00092-2.
58. “Digital Twin Cities: Framework and Global Practices”, World Economic Forum.
Report April 2022. Available: https://www3.weforum.org/docs/WEF_Global_ Digi-
tal_Twin_ Cities_Framework_and_Practice_2022.pdf
59. E. Shahat, C.T. Hyun, and C. Yeom, “City Digital Twin Potentials: A Review and
Research Agenda,” Sustainability, vol. 13, no. 6, 2021. doi: 10.3390/su13063386.
60. “Digital Water: Operational digital twins in the urban water sector”, International
Water Association, 2021. Available: https://iwa-network.org/wp-content/uploads/
2021/03/ Digital-Twins.pdf
61. P. Conejos, F. Martínez Alzamora, M. Hervás Carot, and J. Alonso Campos, Devel-
opment and Use of a Digital Twin for the Water Supply and Distribution Network of
Valencia (Spain). 2019.
62. P. Conejos, F. Martínez Alzamora, M. Hervás Carot, and J. Alonso Campos, “Build-
ing and exploiting a Digital Twin for the management of drinking water distribution
networks”, Urban Water Journal, vol. 17, pp. 1–10, 2020. doi: 10.1080/1573062X.
2020.1771382.
63. “Águas do Porto: Douro River Basin,” International Water Association. Available:
https://iwa-network.org/aguas-do-porto/
64. Siemens official website. Available: https://assets.new.siemens.com/siemens/assets
/api/uuid: 4398972e-3e6f-46f5-8e38-077a07ea4af8/HQSIPR202302146651EN.pdf
65. F. Arraño-Vargas and G. Konstantinou, “Modular Design and Real-Time Simulators
Toward Power System Digital Twins Implementation,” IEEE Transactions on Indus-
trial Informatics, vol. 19, no. 1, pp. 52–61, 2022. doi: 10.1109/TII.2022.3178713.
66. M. Zhou, J. Yan and D. Feng, “Digital twin framework and its application to power
grid online analysis,” in CSEE Journal of Power and Energy Systems, vol. 5, no. 3,
pp. 391–398, 2019. doi: 10.17775/CSEEJPES.2018.01460.
67. D.-G.J. Opoku, S. Perera, R. Osei-Kyei, and M. Rashidi, “Digital twin application in
the construction industry: A literature review,” Journal of Building Engineering, vol.
40, p. 102726, 2021. doi: 10.1016/j.jobe.2021.102726.
68. M. El Jazzar, M. Piskernik, and H. Nassereddine, Digital Twin in Construction: an
Empirical Analysis. 2020.
Digital twins: stages of concept development, areas of use, prospects
Системні дослідження та інформаційні технології, 2023, № 2 21
69. Y. Tchana, G. Ducellier, and S. Remy, “Designing a unique Digital Twin for linear
infrastructures lifecycle management,” Procedia CIRP, vol. 84, pp. 545–549, 2019.
doi: 10.1016/j.procir.2019.04.176.
70. F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven prod-
uct design, manufacturing and service with big data,” The International Journal of
Advanced Manufacturing Technology, vol. 94, no. 9, pp. 3563–3576, 2018. doi:
10.1007/s00170-017-0233-1.
71. Z.-Z. Hu, P.-L. Tian, S.-W. Li, and J.-P. Zhang, “BIM-based integrated delivery
technologies for intelligent MEP management in the operation and maintenance
phase,” Advances in Engineering Software, vol. 115, pp. 1–16, 2018. Available:
https://doi.org/10.1016/j.advengsoft.2017.08.007
72. D.S. Vijayakumar, “Chapter Eleven - Digital twin in consumer choice modeling,” in
The Digital Twin Paradigm for Smarter Systems and Environments: The Industry
Use Cases, vol. 117. Elsevier, 2020, pp. 265–284. doi: 10.1016/bs.adcom.2019.09.010.
73. R. Shivatsava, “How Digital Twin Technology Can Help Retail CIOs,” Forbes
Online. 2022. Available: https://www.forbes.com/sites/forbestechcouncil/2022
/11/11/how-digital-twin-technology-can-help-retail-cios/?sh=2449caf32836
74. Weiss AG company official website. Available: https://weiss-ag.com/museumgallery/
75. W. Luther, N. Baloian, D. Biella, and D. Sacher, “Digital Twins and Enabling Tech-
nologies in Museums and Cultural Heritage: An Overview,” Sensors, vol. 23, p.
1583, 2023. doi: 10.3390/s23031583.
76. “How the Port of Rotterdam is using IBM digital twin technology to transform itself
from the biggest to the smartest,” Official IBM website. Available:
https://www.ibm.com/blog/iot-digital-twin-rotterdam/
77. “Highlights annual report 2022 – Port of Rotterdam Authority,” Port of Rotterdam
Official Report, 2022.
78. P. Bauer, B. Stevens, and W. Hazeleger, “A digital twin of Earth for the green transition”
Nature Climate Change, vol. 11, pp. 1–4, 2021. doi: 10.1038/s41558-021-00986-y.
Received 01.05.2023
INFORMATION ON THE ARTICLE
Nataliya D. Pankratova, ORCID: 0000-0002-6372-5813, Educational and Research In-
stitute for Applied System Analysis of the National Technical University of Ukraine “Igor
Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: natalidmp@gmail.com
Kostiantyn D. Grishyn, ORCID: 0009-0006-5950-3739, Educational and Research Insti-
tute for Applied System Analysis of the National Technical University of Ukraine “Igor
Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: constantine1223h@gmail.com
Viesta E. Barilko, ORCID: 0009-0002-3903-899X, Educational and Research Institute
for Applied System Analysis of the National Technical University of Ukraine “Igor Sikor-
sky Kyiv Polytechnic Institute”, Ukraine, e-mail: vesta8904@gmail.com
ЦИФРОВІ ДВІЙНИКИ: ЕТАПИ РОЗВИТКУ КОНЦЕПЦІЇ, ГАЛУЗІ
ВИКОРИСТАННЯ, ПЕРСПЕКТИВНІСТЬ / Н.Д. Панкратова, К.Д. Грішин,
В.Є. Барілко
Анотація. Висвітлено результати огляду розвитку концепції цифрових двій-
ників, галузі їх використання, перспективність. Наведено історію виникнення
та розвитку концепції цифрового двійника, означення, класифікацію. Відобра-
жено актуальність технології, що розглядається. Метою роботи є надання най-
більш повної сучасної інформації про поточний стан технології «цифровий
двійник», її застосування в різних сферах діяльності людини та подальші пер-
спективи розвитку індустрії. Наведено широку бібліографію з теми, яка може
бути корисна дослідникам і представникам різних галузей.
Ключові слова: Індустрія 4.0, цифровий двійник, класифікація, галузі викори-
стання, Інтернет речей, фізичні та математичні моделі.
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| spelling | journaliasakpiua-article-2853772023-08-07T15:49:29Z Digital twins: stages of concept development, areas of use, prospects Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність Pankratova, Nataliya Grishyn, Kostiantyn Barilko, Viesta Індустрія 4.0 цифровий двійник класифікація галузі використання Інтернет речей фізичні та математичні моделі Industry 4.0 digital twin classification areas of application Internet of Things physical and mathematical models The results of a review of the digital twin concept development, the areas of their use, and the prospects are highlighted. The history of the emergence and development of the digital twin concept, its definition, and its classification are given. The relevance of the technology under consideration is reflected. The purpose of this review is to provide the most complete, up-to-date information on the current state of the digital twin technology, its application in various fields of human activity, and further prospects for the development of the industry. An extensive bibliography on the topic is provided, which may be helpful for researchers and representatives of various industries. Висвітлено результати огляду розвитку концепції цифрових двійників, галузі їх використання, перспективність. Наведено історію виникнення та розвитку концепції цифрового двійника, означення, класифікацію. Відображено актуальність технології, що розглядається. Метою роботи є надання найбільш повної сучасної інформації про поточний стан технології «цифровий двійник», її застосування в різних сферах діяльності людини та подальші перспективи розвитку індустрії. Наведено широку бібліографію з теми, яка може бути корисна дослідникам і представникам різних галузей. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2023-06-30 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/285377 10.20535/SRIT.2308-8893.2023.2.01 System research and information technologies; No. 2 (2023); 7-21 Системные исследования и информационные технологии; № 2 (2023); 7-21 Системні дослідження та інформаційні технології; № 2 (2023); 7-21 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/285377/279796 |
| spellingShingle | Індустрія 4.0 цифровий двійник класифікація галузі використання Інтернет речей фізичні та математичні моделі Pankratova, Nataliya Grishyn, Kostiantyn Barilko, Viesta Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| title | Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| title_alt | Digital twins: stages of concept development, areas of use, prospects |
| title_full | Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| title_fullStr | Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| title_full_unstemmed | Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| title_short | Цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| title_sort | цифрові двійники: етапи розвитку концепції, галузі використання, перспективність |
| topic | Індустрія 4.0 цифровий двійник класифікація галузі використання Інтернет речей фізичні та математичні моделі |
| topic_facet | Індустрія 4.0 цифровий двійник класифікація галузі використання Інтернет речей фізичні та математичні моделі Industry 4.0 digital twin classification areas of application Internet of Things physical and mathematical models |
| url | https://journal.iasa.kpi.ua/article/view/285377 |
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