Від СAD і BIM до цифрових двійників
A Digital Twin is a virtual model of a physical object or system that uses real-time data to simulate the behavior of its real counterpart. It can be a product, machine, building, or even an entire city. Digital modeling is a fundamental technology for creating Digital Twins, as it provides a method...
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System research and information technologies| _version_ | 1867334451626770433 |
|---|---|
| author | Petrenko, Anatolii |
| author_facet | Petrenko, Anatolii |
| author_institution_txt_mv | [
{
"author": "Anatolii Petrenko",
"institution": "Educational and Research Institute for Applied System Analysis of the National Technical University of Ukraine \"Igor Sikorsky Kyiv Polytechnic Institute\", Kyiv"
}
] |
| author_sort | Petrenko, Anatolii |
| baseUrl_str | http://journal.iasa.kpi.ua/oai |
| collection | OJS |
| datestamp_date | 2025-07-25T15:56:08Z |
| description | A Digital Twin is a virtual model of a physical object or system that uses real-time data to simulate the behavior of its real counterpart. It can be a product, machine, building, or even an entire city. Digital modeling is a fundamental technology for creating Digital Twins, as it provides a methodology for representing physical objects in the virtual world. CAD (Computer-Aided Design) can be used to create the initial model of a Digital Twin. BIM (Building Information Modeling) is a specialized form of CAD that focuses on building projects incorporating more information than just geometry but also data on time, costs, operations, and maintenance. This paper examines how these technologies are increasingly integrated with each other, utilizing mathematical modeling that, through virtual computational experiments, provides an understanding of the complex functioning of objects and informed decision-making in various fields. The integration of Digital Twins and CAD is transforming the ways products are designed, modeled, and optimized in the industry. BIM models can serve as the basis for creating Digital Twins of buildings, which are then used to optimize energy consumption, maintenance, and repair. With the growth of the Internet of Things (IoT), Digital Twins are receiving more and more real data, making them even more accurate and useful for forecasting and optimization. The use of artificial intelligence to analyze data collected by digital twins allows predicting breakdowns, optimizing processes, and even automating the control of systems. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2025.2.01 |
| first_indexed | 2025-07-27T04:04:07Z |
| format | Article |
| fulltext |
Publisher IASA at the Igor Sikorsky Kyiv Polytechnic Institute, 2025
Системні дослідження та інформаційні технології, 2025, № 2 7
TIДC
ПРОГРЕСИВНІ ІНФОРМАЦІЙНІ ТЕХНОЛОГІЇ,
ВИСОКОПРОДУКТИВНІ КОМП’ЮТЕРНІ
СИСТЕМИ
UDC 004.942: 303.732.4
DOI: 10.20535/SRIT.2308-8893.2025.2.01
FROM CAD AND BIM TO DIGITAL TWINS
A.I. PETRENKO
Abstract. A Digital Twin is a virtual model of a physical object or system that uses
real-time data to simulate the behavior of its real counterpart. It can be a product,
machine, building, or even an entire city. Digital modeling is a fundamental tech-
nology for creating Digital Twins, as it provides a methodology for representing
physical objects in the virtual world. CAD (Computer-Aided Design) can be used to
create the initial model of a Digital Twin. BIM (Building Information Modeling) is a
specialized form of CAD that focuses on building projects incorporating more in-
formation than just geometry but also data on time, costs, operations, and mainte-
nance. This paper examines how these technologies are increasingly integrated with
each other, utilizing mathematical modeling that, through virtual computational ex-
periments, provides an understanding of the complex functioning of objects and in-
formed decision-making in various fields. The integration of Digital Twins and
CAD is transforming the ways products are designed, modeled, and optimized in the
industry. BIM models can serve as the basis for creating Digital Twins of buildings,
which are then used to optimize energy consumption, maintenance, and repair. With
the growth of the Internet of Things (IoT), Digital Twins are receiving more and
more real data, making them even more accurate and useful for forecasting and op-
timization. The use of artificial intelligence to analyze data collected by digital twins
allows predicting breakdowns, optimizing processes, and even automating the con-
trol of systems.
Keywords: mathematical modeling, digital modeling, CAD and BIM, Digital Twins
(DT), Internet of Things (IoT), AI application in DT.
MATHEMATICAL MODELING (SIMULATION)
Among the information technologies that are used in almost all fields of engineer-
ing and science, digital modeling occupies a special place. This is a general term
that encompasses the creation of digital representations of physical objects or sys-
tems. It can include many things: from modeling their functioning to creating
their geometric images. Therefore, mathematical modeling is separately distin-
guished, when under the mathematical model of a physical system, object, or
process is usually understood a set of mathematical relations (formulas, equations,
logical expressions) that determine the characteristics of the state and properties
of the system, object, and process and their functioning depending on the parame-
ters of their components, initial conditions, input disturbances, and time. In gen-
eral, a mathematical model describes the functional relationship between the out-
A.I. Petrenko
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 8
put dependent variables, through which the functioning of the system is reflected,
independent (such as time), and changing variables (such as component parameters,
geometric dimensions, etc.), as well as input disturbances applied to the system.
Mathematical models are determined by the subject area of design:
physical and mathematical description of the laws and conditions of the
object’s functioning;
the environment of functioning and means of interaction of the object
with this environment;
the composition of the object, the element base, the means of organizing
the structure of the object;
parameters that change or are adjusted.
The models distinguish between three types of data: data on the elements of
the modeling object; data on the properties of the object and elements; data on the
relationship between elements and the object. The abstraction of the object is car-
ried out both by the depth of structuring (hierarchical system, system of elements,
indivisible object), and by the degree of abstraction of elements and object prop-
erties (structural, logical, and quantitative levels).
At the structural level, the structure of the object is modeled at the lowest
level of its structuring, when the mathematical model is presented in the form of a
set, the properties and parameters of the elements of which are described through
functional connections, order relations, adjacency, combination. In this case, the
apparatus of set theory and graphs, queuing theory, etc. is used.
At the logical level of modeling, each set, Boolean matrix of binary rela-
tions, or structural graph corresponds to sets of logical relations and variables that
reflect cause-and-effect relationships. In design, the apparatus of mathematical
logic is used.
At the quantitative level, each element of the set, Boolean matrix, or logical
variable is assigned an algebraic or other quantitative variable, and logical rela-
tions are transformed into quantitative relations: equations, inequalities, and so
on. Modeling at the quantitative level reflects functional, energy, material, and
spatial connections. These connections are usually described by spatio-temporal
relations and are determined through ordinary differential equations or partial dif-
ferential equations.
If the object consists of a system of elements, the connection between which
is described by only one variable (time), then models with lumped parameters are
used. The elements of the object are quantitatively described by component equa-
tions. Micro models and macro models are distinguished if the internal structure
of the modeled objects or elements is not taken into account.
The main part of the calculations in mathematical modeling (in terms of vol-
ume and costs) is performed at the quantitative level of modeling, where all exist-
ing non-linear relations are taken into account. Modern modeling programs differ
from the simple use of computers in calculations by the fact that they provide
automatic formation of a digital mathematical model of an object based on in-
formation about its structure and the properties of its elements [1]. For example,
for the common case of dynamic non-linear systems, the components of which
can be electronic blocks (logical and analog), mechanical, hydraulic, pneumatic,
electromagnetic components, digital mathematical models of the object in the
domestic complex ALLTED (ALL TEchnology Designer) are described by joint
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 9
systems of algebra-differential equations or only differential equations [2]. At the
same time, all stages of designing non-linear dynamic systems are supported:
constructing a mathematical model of the object, analyzing direct current (DC),
analyzing time dynamics (TR) and frequency properties (AC), statistical analysis
(Monte-Carlo), Fourier analysis (Four), worst-case analysis (WCD), sensitivity
analysis (SA), optimization of parameters and characteristics (OPTIM), optimal
assignment of tolerances (TOLAS), etc. This virtual laboratory is based on client-
server technology and allows serving many clients located in different cities and
countries.
It differs from existing foreign analogues (Pspice (Microsim Corp. USA),
Saber (Analogy Corp. USA)) by:
original algorithms of numerical procedures that allow solving “stiff” and
insufficiently conditioned problems in DC, TR, SA modes;
original algorithms for optimizing variable orders that take into account
not three (as usual), but five terms in the equivalent Taylor series without calcu-
lating derivatives higher than the second order;
powerful procedures for automatically calculating design parameters
(time delays, rise and fall times of pulses, resonance frequency or bandwidth,
power consumption, etc.) and functions of these parameters;
the possibility of solving single- and multi-criteria optimization problems
with parametric and functional constraints, and the optimized variables can be
both primary parameters (voltages, currents, powers) and the above-mentioned
design parameters;
the possibility of using user-defined component models along with the
use of powerful libraries of models and parameters;
the procedure of optimal assignment of tolerances to the parameters of in-
ternal components, based on a given allowable deviation of the values of output
parameters or variables, which allows organizing diagnostics of malfunctions in
the structure of the object.
Such possibilities of modern modeling complexes allow in many cases to
abandon the physical prototyping of designed products, replacing it with mathe-
matical modeling (computational experiment), which is especially important
when physical prototyping is difficult or practically impossible (for example,
modeling a dam break, moving a rover on the surface of Mars, etc.). These inno-
vative toolkits have allowed humanity to reach the current level of development,
in particular, to create modern microchips (as an element base of Industry 4.0),
powerful rocket technology, modernize the capabilities of mechanical engineer-
ing, energy, agriculture, industrial and civil construction, and make space flights
and research.
COMPUTER-AIDED DESIGN (CAD)
CAD (Computer-Aided Design) systems appeared in the early 1960s and are a
form of digital modeling, where the emphasis is on design and technical details.
The term was first introduced in the late 1950s by Dr. Patrick Hanratty. He is
often called the “father of CAD”, and he was responsible for creating PRONTO,
the software that started CAD. But AutoCAD, the first commercially available
drafting software, was released in 1982. CAD is used to create 3D models and 2D
A.I. Petrenko
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 10
models of objects. These models, which contain detailed information about the
geometry, materials, and other characteristics of the object, can be of varying
complexity and detail, from simple parts to complex mechanisms and buildings.
Although CAD and mathematical modeling are closely related, there are
some key differences between them [1]:
Focus:
CAD: the main focus of CAD is on the geometric representation of the
object. It is used to create accurate 2D drawings and 3D models, with an emphasis
on visualization, design, and documentation.
Mathematical modeling: focuses on the mathematical description of the
behavior of a system or process. It uses mathematical equations, formulas, and
algorithms for analysis, forecasting, and optimization.
Tools:
CAD: uses specialized software with tools for creating and editing geo-
metric shapes, dimensions, annotations, etc. Examples: AutoCAD, SolidWorks,
CATIA.
Mathematical modeling: uses a variety of tools, including programming
languages (Python, MATLAB), mathematical packages (Mathematica, Maple),
specialized modeling software (Simulink, AnyLogic).
Results:
CAD: the result is a graphical representation.
Mathematical modelling: the result is a mathematical description of the
system, forecasts, sensitivity analysis, and optimization solutions.
Applications:
CAD: Widely used in engineering, architecture, and design for the design
and development of products, buildings, and mechanisms.
Mathematical modeling: Applied in various fields, including physics,
economics, biology, and finance, for the analysis and prediction of complex sys-
tems and processes.
CAD and mathematical modeling can complement each other. For example,
a CAD model can be used as a basis for mathematical modeling, providing geo-
metric data and parameters. Mathematical modeling, in turn, can help in the
analysis and optimization of a structure created in CAD. In other words: CAD is
like drawing a detailed portrait of an object, and mathematical modeling is like
writing an equation that describes how this object moves or functions.
CAD is an excellent tool for engineers not only at the stage of optimal de-
sign, it also allows preparing high-quality design and technological documenta-
tion for the manufacture of designed objects, as well as diagnosing and testing
manufactured products. Initially developed for two-dimensional design, CAD has
evolved into powerful software for three-dimensional modeling, providing accu-
rate digital modeling for the design and testing of structures and infrastructure.
High-quality work with CAD often requires powerful computer systems. Master-
ing CAD software may require significant training and practice. Premium CAD
programs can be expensive, and licenses and updates add to the cost. Although
there are free or cheaper alternatives, they may not offer the same wide function-
ality or compatibility. Therefore, CAD is usually not used for long-term mainte-
nance and servicing of objects during their life cycle.
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 11
BUILDING INFORMATION MODELING (BIM)
In the 1970s, BIM (Building Information Modelling) technology appeared — a
more advanced digital representation of the physical and functional characteristics
of an object. BIM is the process of creating and managing digital information
about a building throughout its life cycle. A BIM model contains detailed infor-
mation about the geometry, materials, structures, systems, and equipment of the
building. With the help of BIM technology, an information model is created that
provides an accurate vision of the project as a whole. The development of Archi-
CAD, one of the most popular BIM software products, began in 1982 under the
leadership of Gábor Bojár.
One of the main advantages of BIM is the ability to visualize the object in
three-dimensional space. This allows designers and customers to more accurately
imagine the future object, as well as make changes and additions in real time.
Visualization in BIM can be represented in various formats — from static images
to interactive 3D models. The BIM model contains information about all compo-
nents of the object — from structural elements to electrical equipment and plumb-
ing. BIM allows integrating all data about the object into a single digital model,
which becomes the basis for all stages of the object’s life cycle — from design
and construction to operation and reconstruction. The use of BIM technology in
the design of houses includes the collection and complex processing of techno-
logical, architectural, structural, and economic information about the building, so
that the building object and everything related to it are considered as a single
whole.
A similar software product from Graphisoft, known in architectural design
circles, is called BIMx and is used as an important addition to their main CAD
program ArchiCAD.
Developing on the basis of the foundations laid by CAD, BIM complements
operations on the object with geometric complex 3D models with a large amount
of data. As with CAD, BIM has several key issues related to the efficiency and
feasibility of its use for the daily management and operation of objects. Detailed,
data-rich BIM models can be overwhelming, especially when only certain subsets
of data are needed. BIM tools and practices can be complex and may require ex-
tensive training for professionals to become proficient. BIM software often re-
quires high-performance computer equipment. Maintaining BIM models up-to-
date, especially for long-term projects, can be resource-intensive.
The building information model is a virtual prototype of a building structure,
so the use of BIM technology in the design of houses allows you to check and
evaluate various solutions before the start of construction work. Today, there are
many different solutions on the BIM software market that allow you to perform
design in a three-dimensional format and use it at all stages of construction. In
Ukraine, the most popular programs for BIM design are AutoCAD Architecture,
Revit and Allplan Architecture [3–5].
The use of BIM technology in construction design makes every action trans-
parent and provides complete control, and in automated mode, which guarantees
high quality of design and construction work. Today, BIM is a standard tool for
working in the construction industry, which allows optimizing the processes of
design, construction and operation of facilities.
A.I. Petrenko
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 12
BIM also provides the ability to create and store digital documentation that
contains all the necessary documents — from drawings and specifications to cal-
culations and statistical data. This allows easy control of the design and construc-
tion process of the object, as well as improve communication between project par-
ticipants. BIM technologies can also be used in the process of building a building.
With their help, it is possible to monitor the execution of works in accordance
with the project and specifications; optimize the construction process and manage
the timing of the project; minimize the risk of errors and conflicts between project
participants and reduce the amount of waste and material costs. BIM technologies
can be used in the process of building operation. With their help, it is possible to
create electronic maps of the building with information about each of its elements
and systems; track changes and maintain the building in real time; optimize the
use of building resources (energy, water, heat, etc.); manage the timing and costs
of maintenance and repair of the building.
BIM can be used for project management, coordination, risk assessment and
compliance with standards. However, there are many other opportunities to ex-
pand the functionality of BIM, including inventory management, quality man-
agement, construction planning, etc. Expanding the functionality of BIM will help
make the construction process more efficient and transparent. The development of
open standards and data exchange protocols can help eliminate this problem and
ensure more flexible integration of BIM with other systems, such as the Internet
of Things (IoT) technology.
The Internet of Things (IoT) is a technology that allows networked objects to
exchange data with each other and with other systems. The integration of BIM
with IoT allows you to receive real-time data on what materials are used, what
conditions are on the construction site, etc. This will help to more effectively
manage the construction process and prevent possible problems.
Virtual and augmented reality (VR and AR) can be integrated with BIM to
create more accurate and realistic visualizations of the project. This will allow
more effectively verify the project for compliance with requirements and interact
with customers and other project participants. In addition, virtual and augmented
reality technologies can help in training personnel and improving safety at the
construction site. For example, virtual simulators can be used to train workers
without risk to their lives and health. In general, the development of virtual and
augmented reality technologies can lead to a large leap in the development of
BIM technologies in the future, increasing their efficiency and accuracy, as well
as simplifying their use.
When preserving architectural heritage, it is important to be able to carry out
historical conservation and restoration of buildings in conditions when the origi-
nal architectural and construction documentation is absent. Laser technology
“Scan to BIM” [6] comes to the rescue, offering accurate 3D scanning of histori-
cal structures and artifacts, which allows creating detailed digital models that re-
liably reflect the intricacies of these objects. This technology allows restorers and
conservation experts to carefully analyze the condition of heritage objects, iden-
tify areas that need attention, and plan restoration work with meticulous accuracy.
In addition, digital preservation using BIM facilitates long-term management and
documentation of historical objects, ensuring that their heritage is preserved for
future generations who can appreciate and learn from it.
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 13
“Scan to BIM” is a revolutionary process that uses laser scanning technology
to create a virtual copy of existing structures, from grand cathedrals to quaint cot-
tages, when laser precision captures dimension, identifies materials, and even re-
veals structural elements hidden behind layers of paint and plaster. This data be-
comes the basis for a smarter and more efficient way to manage, reconstruct, and
even redesign existing buildings. The “Scan to BIM” technology transforms not
only repair work, but also how building management is carried out. Its digital
model tracks performance, predicts problems before they become critical, and
optimizes maintenance schedules. Imagine proactive repairs, extended equipment
life, and reduced operating costs — all thanks to the foresight of BIM’s digital
intelligence.
The leader in the implementation of “Scan to BIM” technology is Harmony
AT, which seamlessly integrates scan data with BIM methodologies, offering
comprehensive solutions tailored to the diverse needs of projects. The company’s
services cover a wide range of applications, including reconstruction, moderniza-
tion, clash detection and facility management. In addition, its commitment to
quality assurance, transparent communication and customer satisfaction makes
the company a reliable partner in the construction industry. Whether it is about
reviving historical monuments or optimizing modern construction projects, Har-
mony AT’s scan-to-BIM services embody efficiency, accuracy and excellence.
DIGITAL TWINS
Digital Twins are a new generation solution based on the foundation laid by CAD
and BIM. While CAD and BIM have made significant contributions to the design
and manufacturing stages, Digital Twins aim to rethink how we interact with,
maintain, and operate the digital environment we create. These are not just static
images, but dynamic models that reflect real objects or systems in real time.
Digital Twins (DT) are virtual models of real objects or processes that reflect
their characteristics and behavior in dynamics [7,8]. They appeared as a result of
the development of information technologies, in particular mathematical model-
ing, CAD and BIM.
The history of Digital Twins’ development has several stages:
The origin of the concept (2002): Michael Grieves first presented the con-
cept of DT at a conference in the USA. He proposed creating virtual copies of
physical objects to manage their life cycle.
First applications (2010s): With the development of technology, the first
real projects for the use of data centres appeared in the aerospace and manufactur-
ing industries. They were used to model, optimise and predict the behaviour of
complex systems.
Active distribution (2015-present): Thanks to the development of the
Internet of Things, artificial intelligence, and cloud technologies, data centres
have become available for a wide range of applications. They are used in various
industries, such as industry, energy, medicine, construction, and others.
A Digital Twin is a virtual representation of a physical object, system or
process that covers its entire life cycle. It is constantly updated with real-time data
and uses modelling, machine learning and artificial intelligence to optimise deci-
sion-making. This technology allows companies to analyse performance, predict
failures, and improve overall efficiency.
A.I. Petrenko
ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 14
The connection between CAD and Digital Twins is that CAD models serve
as a starting point for creating Digital Twins [9]. A CAD model is static, that is, it
reflects the object at a certain point in time. To create a Digital Twin that reflects
the behavior of an object in dynamics, it is necessary to supplement the CAD
model with information about the physical properties of materials, operating con-
ditions, and other parameters. This information can be obtained from various
sources, such as sensors, monitoring systems, and others.
Digital Twins created on the basis of CAD models can be used to solve vari-
ous tasks, such as:
Predicting object behavior: Digital Twins can be used to model the be-
havior of an object in different operating conditions. This allows predicting possi-
ble breakdowns and accidents, as well as optimizing equipment operating modes.
Process optimization: Digital Twins can be used to optimize production
processes, design new products, and other tasks.
Virtual training: Digital Twins can be used to train personnel to work
with complex equipment.
In general, CAD and Digital Twins are interconnected technologies that
complement each other. CAD models are the basis for creating Digital Twins, and
Digital Twins, in turn, expand the capabilities of CAD models, allowing modeling
the behavior of objects in dynamics and solving various tasks.
A Digital Twin of a building, in turn, is an expanded version of a BIM
model, which includes not only static information about the building, but also dy-
namic data about its operation, such as:
Data from sensors (temperature, humidity, energy consumption).
Information about the condition of systems (ventilation, heating, lighting).
Data on the use of the building (number of people, traffic).
The relationship between BIM and Digital Twins:
BIM as a basis: the BIM model is the basis for creating a Digital Twin of
a building. It provides detailed information about the physical characteristics of
the building, which is necessary to create a virtual model.
Dynamic data: the Digital Twin complements the BIM model with dy-
namic data that is collected during the operation of the building. This allows cre-
ating a completer and more accurate picture of the building’s condition.
Analysis and optimization: the Digital Twin, created on the basis of the
BIM model, can be used to analyze and optimize various aspects of building op-
eration, such as energy efficiency, comfort, safety, and others.
Advantages of using Digital Twins based on BIM:
Better understanding: the Digital Twin allows you to get a more complete
understanding of the building and its functioning.
Effective management: the Digital Twin helps to more effectively manage
the building throughout its life cycle.
Optimization: the Digital Twin allows you to optimize various aspects of
building operation, such as energy consumption, comfort, and safety.
Forecasting: the Digital Twin allows you to predict the behavior of the
building in different conditions and scenarios.
In general, BIM and Digital Twins are interconnected technologies that
complement each other. BIM models are the basis for creating Digital Twins, and
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 15
Digital Twins, in turn, expand the capabilities of BIM models, allowing modeling
the behavior of buildings in dynamics and solving various tasks.
A Digital Twin consists of three main components:
Physical object: the real object for which the Digital model is created.
Virtual model: a digital copy of a physical object that contains informa-
tion about its characteristics and behavior.
Connection between them: provides data exchange between the physical
object and its virtual model.
Different components of Digital Twins can be provided by different manu-
facturers and suppliers. Although they should work together, at least in theory, in
practice this is not the case. Moreover, artificial intelligence (AI) and modeling
tools, which are expected to perform the same function, may not support this ca-
pability. It is not uncommon to find an AI tool or modeling application from one
supplier that is not able to replicate the capabilities of another supplier’s product.
This creates an interoperability problem. Different building blocks of Digital
Twins form a large attack surface that cybercriminals can target. And given the
need for hundreds and thousands of sensors connected to the Internet, the attack
surface becomes even larger. Of course, the intentions of the attackers may be due
to the importance of Digital Twins and related data for the activities of organiza-
tions. Having gained access to service data, criminals can demand large sums of
cash from companies so that they do not disclose the illegally obtained find or
compromise the Digital Twin.
Various undesirable consequences of security breaches indicate the need to
give priority to cybersecurity. But effectively managing the cybersecurity of Digi-
tal Twins can be a daunting task for some organizations, given the vast amount of
software, parts, and specialists needed to create a working digital twin.
Digital Twins are an important tool for increasing efficiency and optimizing
various processes. They allow [10]:
Predict the behavior of objects and systems.
Model different scenarios and make informed decisions
Optimize production processes and reduce costs.
Improve the quality of products and services.
Create new products and services.
In general, a Digital Twin acts as a single source of information about a pro-
ject, which helps to improve collaboration. In addition, it provides all stake-
holders with a deeper understanding of the products, processes, environments, and
personnel involved in the project. It is also worth noting that several Digital
Twins can be integrated, providing a deeper understanding of the interdependen-
cies and the ecosystem in which they exist.
Digital Twins are classified according to different criteria, depending on the
purpose and scope of application. Here are some of the most common types:
By level of detail:
Digital Twins of objects: reflect individual physical objects, such as an
engine, a machine tool, or a building.
Digital Twins of processes: model technological or business processes,
for example, production of products or logistics.
Digital Twins of systems: describe complex systems consisting of many
components, such as the energy system of a city or a transport network.
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ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 16
By purpose:
Digital Twins for design: used to develop and design new products or systems.
Digital Twins for production: used to manage production processes, op-
timize equipment operation, and control product quality.
Digital Twins for operation: serve to monitor the condition of equipment,
predict breakdowns and maintenance.
By the nature of the connection with the real object:
Digital Twins that reflect the past: contain information about the object
for a certain period of time in the past.
Digital Twins that reflect the present: reflect the current state of the object
in real time.
Digital Twins that predict the future: used to predict the behavior of an
object in the future based on data analysis and modeling.
By complexity:
Simple Digital Twins: contain a limited amount of information about the
object and its behavior.
Complex Digital Twins: include detailed information about the object, its
interaction with the environment, and complex behavior models.
One Digital Twin can combine several types, for example, be both a Digital
Twin of an object that reflects the present and is used to predict the future. The
choice of the type of Digital Twin depends on the specific task and requirements
for the accuracy of the model. It is important to note that the development of
technologies, such as artificial intelligence and machine learning, contributes to
the creation of increasingly complex and functional Digital Twins, which find
applications in various industries.
APPLICATIONS OF DIGITAL TWINS IN CAD
Digital Twins are used in many areas, helping to solve various problems and op-
timize processes. Here are some specific examples [7,10]:
Industry
Manufacturing: Digital Twins are used to model production lines, opti-
mize equipment operation, predict breakdowns, and manage product quality. For
example, General Electric uses Digital Twins of aircraft engines to monitor their
condition in real time and predict the need for maintenance.
Energy: Digital Twins help in managing energy systems, forecasting con-
sumption, optimizing equipment operation, and integrating renewable energy
sources.
Automotive: Digital Twins are used to design cars, simulate their behavior
on the road, optimize production, and develop driver assistance systems.
Cities and infrastructure
Smart cities: Digital Twins of cities are used to manage traffic flows, op-
timize the operation of utilities, monitor the state of infrastructure, and respond to
emergencies.
Construction: Digital Twins of buildings help in the design, construction,
and operation of buildings, allowing to optimize the use of resources, control the
quality of work, and predict the condition of structures.
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 17
Healthcare
Personalized medicine: Digital Twins of patients can be used to simulate
the body’s response to various treatments and predict the development of diseases.
Drug development: Digital Twins help in the development of new drugs
by simulating their effect on the body and predicting effectiveness.
Other areas
Logistics: Digital Twins help in optimizing delivery routes, managing
warehouse stocks, and forecasting demand.
Agriculture: Digital Twins of fields are used to monitor the condition of
crops, predict yields, and optimize the use of resources.
These are just some examples of the use of Digital Twins. With the devel-
opment of technology, the scope of their use is constantly expanding, opening up
new opportunities for optimizing and managing various processes. Usually, CAD
is a fundamental step towards creating a Digital Twin; it is the foundation [11].
Therefore, it can be said that without CAD there is no Digital Twin. In fact, Digi-
tal Twin platforms integrate with CAD and BIM solutions. For example, Auto-
desk Tandem, a Digital Twin platform, is designed to integrate CAD geometry
with Revit, geospatial data, facility management data, IoT data.
It is possible to develop a “fault dictionary” for the future Digital Twin using
mathematical modelling and CAD tools during the design of objects and proc-
esses. To do this, it is necessary to identify in advance a set of influential parame-
ters or model variables to measure which control ports should be provided in the
structures and processes to be designed. Then, using powerful techniques for mul-
tivariate analysis, sensitivity analysis, optimization, worst-case evaluation of the
deviation of component parameters from nominal values (WCD), and the inverse
of this assigning optimal tolerances, statistics can be collected for DC, TR, and
AC modes on the deviation of the output parameters of the object (process) from
the effects of destabilizing factors (temperature, radiation, humidity, etc.) and, for
example, component “aging” over time. All this can be amplified by statistical
analysis, which introduces certain distribution laws for the above-mentioned de-
viations of component parameters. The availability of such a pre-modelled “fault
dictionary” will greatly facilitate the interpretation of measurement data taken on
a real installation manufactured by the design documentation. If the mathematical
and CAD models are kept together with the modelled “fault dictionary”, the tran-
sition to the Digital Twin will be reduced to additional investigations of these
models, taking into account the measured data from the real object, if the “fault
dictionary” information is not sufficient.
THE PROCESS OF CREATING A DIGITAL TWIN
The process of creating a Digital Twin can be imagined as a sequence of steps,
each of which has its own characteristics and requires certain resources.
1. Defining the goal and scope:
At this stage, it is important to clearly define why a Digital Twin is being
created (monitoring, analysis, simulation, optimization, etc.), what tasks it should
solve, what parameters of the object or process need to be modeled.
It is also important to determine what level of detail of the model is
needed to achieve the goals (machine, production process, building, city). Today,
A.I. Petrenko
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it is common to refer to objects with a deep interconnection between their physi-
cal and computational elements as “cyber-physical systems” [12,13].
2. Data collection:
To create a Digital Twin, data about the object or process being modeled
is required.
This can be data on the physical properties of the object (geometry, mate-
rials, physical characteristics), sensor data on the state of the object (temperature,
vibration, pressure, etc.), previous data on the operation of the object, accidents,
maintenance.
It is important to ensure the quality and reliability of the data, since the
accuracy and adequacy of the Digital Twin depends on this.
3. Building a model through modeling and simulation:
3D Modeling: creating a three-dimensional model of an object using
CAD (software).
Physical models: development of models that describe physical processes
(mechanical, thermodynamic, electromagnetic models).
Behavioral models: models that predict the behavior of the system under
various conditions.
4. Data integration:
Platform: selection or development of a platform that can integrate all
data streams and ensure interaction between physical and digital objects.
API and Interfaces: development or use of existing APIs for data ex-
change between different systems.
5. Analytics and Data Processing
Analytical tools: using machine learning, artificial intelligence to analyze
data, predict wear and tear, optimize work.
Visualization: creating interfaces for visualizing the status and results of
the analysis.
6. Validation and verification:
Testing: checking the accuracy of the Digital Twin by comparing its be-
havior with a real object.
Adjustment: Making changes to the model based on real data to improve
accuracy.
7. Deployment and use:
Integration into business processes: using a Digital Twin in daily opera-
tions, maintenance planning, equipment modernization.
Personnel training: training users to work with a Digital Twin.
8. Update and support:
Monitoring: continuous monitoring of both the physical object and the
Digital Twin to detect deviations.
Updating: regularly updating the model with new data to maintain rele-
vance.
Creating a Digital Twin is a complex and multi-stage process that requires
significant resources and competencies. However, a properly created Digital Twin
can be a powerful tool for increasing efficiency, optimizing processes, and mak-
ing informed decisions.
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 19
Recall that digital modeling is the basis for the functioning of Digital Twins,
so the choice of modeling algorithms plays a dominant role in their implementa-
tion due to the significant difference in the nature of objects and the tasks they
perform [11]. This can be demonstrated by examples of specifications of Digital
Twins for a rocket flight control unit, for a bridge structure and a process micro-
controller (PCM), presented in Fig. 1, Fig. 2 and Fig. 3, respectively.
DIGITAL TWIN OF A ROCKET FLIGHT CONTROL UNIT
Main tasks:
Specifications of the Digital Twin of the rocket flight control unit.
Modeling the dynamics of rocket flight in real time.
Predicting the trajectory and possible deviations.
Controlling the guidance system and trajectory correction.
Diagnostics and troubleshooting.
Algorithms and methods:
Mathematical modeling:
Differential equations of motion (Newton’s, Euler’s laws) taking
into account interaction with
the atmosphere and space environment.
Models of aerodynamics and jet propulsion.
Trajectory calculation algorithms and ballistic models.
Signal and data processing:
Kalman filters for processing data from sensors.
Methods of statistical analysis to estimate flight parameters.
Pattern recognition algorithms to detect deviations.
Artificial intelligence and machine learning:
Neural networks for forecasting and control.
Reinforcement learning algorithms for control optimization.
Fig. 1. Specifications of the Digital Twin of the rocket flight control unit
DIGITAL TWIN OF A BRIDGE STRUCTURE
Main tasks:
Modeling the behavior of the bridge under load.
Analysis of the strength and stability of the structure.
Predicting wear and possible damage.
Monitoring the condition of the bridge in real time.
Mathematical modeling:
Finite element method (FEM):
Dividing the structure into many elements.
Calculation of stresses and strains in each element.
Analysis of the general behavior of the structure under load.
Material modeling:
Models of elasticity, plasticity and fracture of materials.
Taking into account the influence of temperature, humidity and other factors.
Statistical analysis:
Risk assessment and probability of damage.
Predicting the residual life of the structure.
Artificial intelligence and Machine learning:
Classification algorithms for detecting defects based on data.
Neural networks for predicting durability.
Fig. 2. Specifications of the digital twin of a bridge structure
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DIGITAL TWIN FOR A PROCESS MICROCONTROLLER (PCM)
Main tasks:
Real-time modelling: the Digital Twin should reflect the state of the PCM
with high accuracy and synchronization.
Interaction with the external environment: the digital twin should take
into account the impact of external factors on the operation of the PCM
Debugging and testing capability: the Digital Twin allows developers to test
the operation of the IPCM without using additional equipment.
Modelling the logic of work:
Boolean algebra: to describe logical operations, input/output states, switching
conditions.
State machine: to simulate the sequence of actions, transitions between
operating modes.
Controller programming languages (Ladder Logic, ST, FBD):
to describe process control algorithms.
Modelling process dynamics:
Differential equations: to describe the dynamics of physical quantities
(temperature, pressure, level, etc.).
Difference equations: for discrete modeling of processes in time.
Transfer functions: to describe the dynamic properties of control objects.
Signal processing algorithms:
Filtering: to clean signals from noise and distortion (Kalman filter,
moving average).
Conversion: to convert signals from analog to digital form and vice versa
(ADC, DAC).
Scaling: to bring signals to the desired range of values.
Communication algorithms:
Data exchange protocols (Modbus, Profibus, Ethernet/IP): to ensure
communication between the PCM and other systems.
Synchronization algorithms: to coordinate the operation of various components
of the digital twin.
Error handling mechanisms: to ensure communication reliability.
Visualization and interface algorithms:
Graphic libraries: to create a visual representation of the state of the PCM and
the technological process.
Data visualization algorithms: to display trends, graphs, charts.
User interfaces: to provide convenient access to information and control.
Fig. 3. Specifications of the Digital Twin of a process microcontroller
Differences and features
Different physical models: for a rocket models of flight dynamics and
aerodynamics are used, for a bridge — models of structural mechanics and resis-
tance of materials, for a process microcontroller — models of functioning under
the influence of external factors.
Different time scales: rocket flight and microcontroller reactions occur
quickly, so real-time modeling is required. The bridge is operated for a long time,
so long-term forecasting is important.
Different types of data: for a rocket, data from sensors and control sys-
tems are used, for a bridge — data on loads, the state of materials and structures,
for a microcontroller — data from sensors about the state of the technological
process.
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 21
That is, the creation of digital twins for different objects requires the use of
different algorithms and modeling methods, due to the specifics of the tasks and
physical principles underlying the functioning of each of them.
CONCLUSIONS
Digital Twins are becoming increasingly commonplace in many industries, shap-
ing the future. The growing adoption of Digital Twin technology is driven by the
convergence and development of technologies such as the Internet of Things, sen-
sors, artificial intelligence, machine learning, cloud computing, simulations, and
more. This technology uses data to bring to life once static CAD models. This
gives many advantages.
Digital Twins can describe physical objects, diagnose problems, analyze re-
sults, and predict future events. Due to these advantages, companies and organiza-
tions in the manufacturing, medical, mining, infrastructure and planning indus-
tries, agriculture and logistics are implementing Digital Twins. However, their
implementation has not been without certain problems. From low data quality and
lack of data standardization to complex change management, cybersecurity
threats, inaccurate representation, and the need to protect intellectual property.
Fortunately, there are ways to get around these problems.
Let’s present a comparison of the basic properties of CAD, BIM and Digital
Twin technologies in the form of a table.
Comprehensive comparison between CAD, BIM and Digital Twins
Feature CAD (Computer-
Aided Design)
BIM (Building
Information Modeling)
Digital Twins
Main function
Creating 2D and
3D models
of for objects
Creating and managing
building information
throughout its lifecycle
Virtual representation
of a physical object or
process, reflecting its
behavior in real time
Model type Geometric model Information model Dynamic model
Data
Geometry,
dimensions,
materials
Geometry, dimensions,
materials, structures,
systems, equipment,
lifecycle data
Data from sensors, status
information, historical
data, forecasts
Connection with
the real object
Static Static with dynamic
elements
Dynamic, updated
in real time
Applications Design, visualization,
documentation
Design, construction,
operation of buildings
Monitoring, analysis,
optimization,
forecasting, training
Examples
Drawings of parts,
models of machines,
diagrams
3D model of a building
with information about
all its elements
Digital Twin of an aircraft
engine, virtual copy
of a city, patient model
Limitations
Limited information
about the object,
static model
Limited information
about the dynamics
of the object
Complexity of creation
and maintenance, requires
a large amount of data
Key differences:
CAD focuses on the geometry and shape of the object, while BIM in-
cludes a wider range of information about the building, including its functionality
and characteristics.
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Digital Twins go even further, providing a dynamic representation of an
object or process that is updated in real time.
CAD and BIM are mainly used for design and construction, while Digital
Twins have a wider range of applications, including monitoring, analysis, and
optimization.
Artificial intelligence (AI) is significantly impacting the development of
Digital Twins, enhancing their capabilities and expanding their scope of applica-
tion. Here are some key aspects of this impact [14]:
Real-time data analysis
Prediction: AI can analyze data streams coming from IoT sensors to predict
the future state or behavior of a physical object. For example, AI can predict
when a machine will need maintenance before a breakdown occurs.
Optimization: Using machine learning, Digital Twins can optimize the
operation of systems, such as the energy consumption of buildings or the perform-
ance of production lines.
Process automation
Automatic model updates: AI can automatically update digital models based
on new data, ensuring that the Digital Twin is up-to-date with its physical coun-
terpart.
Automation of response: AI can not only detect anomalies or problems, but
also automatically initiate appropriate actions, such as ordering spare parts or
adjusting system parameters.
Improving simulations
Complex scenarios: AI allows modeling and testing much more complex
and diverse scenarios, making it possible to explore “what if” situations without
risk to the real object.
Adaptive learning: AI models can learn from historical data and real-world
interactions, improving the accuracy of simulations over time.
Personalization and adaptation
Individual solutions: AI can adapt Digital Twins for individual needs, such
as customizing the user interface in cars or adapting medical equipment for a spe-
cific patient.
Improving human-machine interaction
Natural language and interfaces: AI, especially NLP (natural language proc-
essing), allows more natural interaction with Digital Twins through voice
commands or text queries.
Virtual assistants: Using AI to create virtual assistants that can help manage
Digital Twins by providing recommendations or performing tasks.
Security and cybersecurity
Threat detection: AI can be used to detect anomalies that may indicate
cyberattacks or other security problems in systems associated with Digital Twins.
Future development
Cognitive Digital Twins: In the future, the development of cognitive Digital
Twins is expected, which not only reflect the state of the object, but can also
“think” and “make decisions” similar to their physical counterpart, using ad-
vanced AI algorithms.
From CAD and BIM to Digital Twins
Системні дослідження та інформаційні технології, 2025, № 2 23
Integration with blockchain: To ensure data security and trust in Digital
Twins, blockchain technology can be used to ensure data immutability and
transparency.
Thus, AI not only improves the capabilities of Digital Twins, but also ex-
pands their application, making them more intelligent, adaptive, and integrated
into various aspects of human life and industry.
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Received 20.02.2025
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ISSN 1681–6048 System Research & Information Technologies, 2025, № 2 24
INFORMATION ON THE ARTICLE
Anatolii I. Petrenko, ORCID: 0000-0001-6712-7792, Educational and Research Institute
for Applied System Analysis of the National Technical University of Ukraine “Igor
Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: tolja.petrenko@gmail.com
ВІД СAD і BIM ДО ЦИФРОВИХ ДВІЙНИКІВ / А.І. Петренко
Анотація. Цифровий двійник — це віртуальна модель фізичного об’єкта або
системи, яка використовує дані в реальному часі для моделювання поведінки
свого реального аналога. Це може бути продукт, машина, будівля або навіть
ціле місто. Цифрове моделювання є фундаментальною технологією для ство-
рення цифрових двійників, оскільки воно забезпечує методологію для подання
фізичних об’єктів у віртуальному світі. Computer-Aided Design (CAD) може
бути використано для створення початкової моделі цифрового двійника.
Building Information Modeling (BIM) є спеціалізованою формою CAD, яка зо-
середжена на будівельних проектах і містить більше інформації, ніж просто
геометрія. BIM включає в себе не тільки геометрію, але й дані про час, витра-
ти, експлуатацію та обслуговування. Досліджено, як ці технології дедалі біль-
ше інтегруються одна в одну, базуючись на застосуванні математичного моде-
лювання, яке шляхом проведення віртуальних обчислювальних експериментів
забезпечує розуміння складного функціонування об’єктів та прийняття обґру-
нтованих рішень у різних сферах. Інтеграція цифрових двійників і CAD
трансформує способи проектування, моделювання та оптимізації продукції в
промисловості. BIM моделі можуть стати основою для створення цифрових
двійників будівель, які потім використовуються для оптимізації енергоспожи-
вання, обслуговування та ремонту. З ростом інтернету речей (IoT), цифрові
двійники отримують дедалі більше реальних даних, що робить їх ще більш то-
чними і корисними для прогнозування та оптимізації. Використання штучного
інтелекту для аналізу даних, зібраних цифровими двійниками, дозволяє про-
гнозувати поломки, оптимізувати процеси і навіть автоматизувати управління
системами.
Ключові слова: математичне моделювання, цифрове моделювання, CAD і
BIM, цифрові двійники (ЦД), інтернет речей (IoT), застосування штучного ін-
телекту в ЦД.
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| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
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| spelling | journaliasakpiua-article-3358042025-07-25T15:56:08Z From CAD and BIM to digital twins Від СAD і BIM до цифрових двійників Petrenko, Anatolii математичне моделювання цифрове моделювання CAD і BIM цифрові двійники (ЦД) інтернет речей (IoT) застосування штучного інтелекту в ЦД mathematical modeling digital modeling CAD and BIM Digital Twins (DT) Internet of Things (IoT) AI application in DT A Digital Twin is a virtual model of a physical object or system that uses real-time data to simulate the behavior of its real counterpart. It can be a product, machine, building, or even an entire city. Digital modeling is a fundamental technology for creating Digital Twins, as it provides a methodology for representing physical objects in the virtual world. CAD (Computer-Aided Design) can be used to create the initial model of a Digital Twin. BIM (Building Information Modeling) is a specialized form of CAD that focuses on building projects incorporating more information than just geometry but also data on time, costs, operations, and maintenance. This paper examines how these technologies are increasingly integrated with each other, utilizing mathematical modeling that, through virtual computational experiments, provides an understanding of the complex functioning of objects and informed decision-making in various fields. The integration of Digital Twins and CAD is transforming the ways products are designed, modeled, and optimized in the industry. BIM models can serve as the basis for creating Digital Twins of buildings, which are then used to optimize energy consumption, maintenance, and repair. With the growth of the Internet of Things (IoT), Digital Twins are receiving more and more real data, making them even more accurate and useful for forecasting and optimization. The use of artificial intelligence to analyze data collected by digital twins allows predicting breakdowns, optimizing processes, and even automating the control of systems. Цифровий двійник — це віртуальна модель фізичного об’єкта або системи, яка використовує дані в реальному часі для моделювання поведінки свого реального аналога. Це може бути продукт, машина, будівля або навіть ціле місто. Цифрове моделювання є фундаментальною технологією для створення цифрових двійників, оскільки воно забезпечує методологію для подання фізичних об’єктів у віртуальному світі. Computer-Aided Design (CAD) може бути використано для створення початкової моделі цифрового двійника. Building Information Modeling (BIM) є спеціалізованою формою CAD, яка зосереджена на будівельних проектах і містить більше інформації, ніж просто геометрія. BIM включає в себе не тільки геометрію, але й дані про час, витрати, експлуатацію та обслуговування. Досліджено, як ці технології дедалі більше інтегруються одна в одну, базуючись на застосуванні математичного моделювання, яке шляхом проведення віртуальних обчислювальних експериментів забезпечує розуміння складного функціонування об’єктів та прийняття обґрунтованих рішень у різних сферах. Інтеграція цифрових двійників і CAD трансформує способи проектування, моделювання та оптимізації продукції в промисловості. BIM моделі можуть стати основою для створення цифрових двійників будівель, які потім використовуються для оптимізації енергоспоживання, обслуговування та ремонту. З ростом інтернету речей (IoT), цифрові двійники отримують дедалі більше реальних даних, що робить їх ще більш точними і корисними для прогнозування та оптимізації. Використання штучного інтелекту для аналізу даних, зібраних цифровими двійниками, дозволяє прогнозувати поломки, оптимізувати процеси і навіть автоматизувати управління системами. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2025-06-28 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/335804 10.20535/SRIT.2308-8893.2025.2.01 System research and information technologies; No. 2 (2025); 7-24 Системные исследования и информационные технологии; № 2 (2025); 7-24 Системні дослідження та інформаційні технології; № 2 (2025); 7-24 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/335804/324669 |
| spellingShingle | математичне моделювання цифрове моделювання CAD і BIM цифрові двійники (ЦД) інтернет речей (IoT) застосування штучного інтелекту в ЦД Petrenko, Anatolii Від СAD і BIM до цифрових двійників |
| title | Від СAD і BIM до цифрових двійників |
| title_alt | From CAD and BIM to digital twins |
| title_full | Від СAD і BIM до цифрових двійників |
| title_fullStr | Від СAD і BIM до цифрових двійників |
| title_full_unstemmed | Від СAD і BIM до цифрових двійників |
| title_short | Від СAD і BIM до цифрових двійників |
| title_sort | від сad і bim до цифрових двійників |
| topic | математичне моделювання цифрове моделювання CAD і BIM цифрові двійники (ЦД) інтернет речей (IoT) застосування штучного інтелекту в ЦД |
| topic_facet | математичне моделювання цифрове моделювання CAD і BIM цифрові двійники (ЦД) інтернет речей (IoT) застосування штучного інтелекту в ЦД mathematical modeling digital modeling CAD and BIM Digital Twins (DT) Internet of Things (IoT) AI application in DT |
| url | https://journal.iasa.kpi.ua/article/view/335804 |
| work_keys_str_mv | AT petrenkoanatolii fromcadandbimtodigitaltwins AT petrenkoanatolii vídsadíbimdocifrovihdvíjnikív |