Exploring the Performance and Accuracy of Digital Twin Models

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 517
Exploring the Performance and Accuracy of Digital Twin Models
Srinivas Timmapuram1, Suchitra Gandu2, Shahela3 Vidit Kumar4
1JRF , CAS DRDO, Hyderabad, Telangana, India
4Scientist D , CAS DRDO, Hyderabad, Telangana, India
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Abstract - Digital twin technology has emerged as a
promising approach to model and simulate real-world
systems in various industries, including manufacturing,
energy, and healthcare.However, the accuracy and
performance of digital twin models are critical factors that
determine their effectiveness in practical applications. This
paper reviews the factors that affect the accuracy and
performance of digital twin models, including data quality,
model complexity, model validation, system changes, and
integration with other systems. The paper highlights the
challenges and opportunities associated with digital twin
technology.
Key Words: Digital twins, artificial intelligence, IoT,
predictive analytics, anomaly detection, optimization,
productivity, cost-effectiveness, sustainability,
manufacturing, healthcare, transportation.
1. INTRODUCTION
The creation of a virtual duplicate of a physical system or
asset is known as a "digital twin" and is a cutting-edge
technology. It can be used for a variety of applications,
including monitoring, control, optimisation, and
preventive maintenance. This virtual replica, or digital
twin, is a highly exact and detailed digital version of the
real system. Concept of digital twins has been around for
several years, recent advancements in AI have enabled the
development of AI-powered digital twins, which have even
greater capabilities than their traditional counterparts. AI-
powered digital twins can learn from data, identify
patterns, and make predictions, enabling manufacturers to
predict and prevent issues before they occur.
Fig -1: Representation of Digital Twin
There are several different digital twin types, which can
often run side by side within the same system. While some
digital twins replicate only single parts of an object,
they're all critical in providing a virtual representation.
The most common types of digital twins are the following.
Fig -2: Types of Digital twin
Component twins: A component twin, also known as a
parts twin, is a digital representation of a single system
component. These are necessary components for an
asset's operation, such as a wind turbine's motor.

Asset twins: Assets are described in terms of digital twins
as two or more parts that cooperate to form a larger
system. They simulate how the parts interact and generate
performance data you may use to make wise decisions.
System twins: These represent a greater level of
abstraction than asset twins. A system twin demonstrates
how several assets function as a unit inside a larger
system. You can decide how to improve performance or
increase efficiency thanks to the visibility provided by
system twin technology.
Process twins: The use of process twins can help to
understand how an object's many parts, assets, and units
interact with one another by displaying the digital
environment of the entire entity. The operation of our
complete manufacturing facility, for instance, might be
digitally replicated by creating a "digital process twin" that
combines all of its parts.
2. LITERATURE SURVEY
NASA (National Aeronautics & Space Administration) was
the first organization to propose the idea of utilizing a
digital twin to examine a real object in the 1960s. In order
to emulate the systems in space for exploratory missions,
NASA reproduced its spacecraft on Earth. The Apollo 13
mission served as a notable example of this technology in
action. Mission Control was able to rapidly change the
simulations to fit the conditions of the damaged spacecraft
and troubleshoot methods to return the astronauts safely
to Earth because of the connected twins.
To monitor massive facilities like power plants in the early
1970s, mainframe computers were utilized as systems to
digital twins. In the 1980s, 2D CAD systems like AutoCAD
were developed to create technical drawings, making it
feasible to design anything using a computer. millions of
designers and engineers soon embraced these systems.
By the 2000s, parametric modeling and simulation
enabled by 3D CAD allowed for the intelligent design of
more complex assemblies, such as a database of connected
objects. Time travel to the middle of the 2010s, when all
major 3D CAD suppliers introduced cloud-connected
solutions, primarily for collaboration and project
management, and subsequently for generative design,
even though CAD tools remained desktop-based.
Today it is the beginning of the era of real-time 3D
powered digital twins, which go beyond dashboards and
3D models to unlock data from various sources on any
device or platform for better collaboration, visualization,
and decision-making.
3. APPLICATIONS
Digital twin technology has many potential applications
across a wide range of industries. Some of the most
notable applications of digital twin include:
Manufacturing: Production process optimisation,
downtime reduction, and product quality improvement
are all possible with the usage of digital twins. Businesses
may predict possible issues before they arise and
immediately put remedies into action by simulating the
manufacturing process.
Aerospace and defense: Digital twins can be used to
simulate the performance of aircraft, spacecraft, and other
vehicles. This technology can help improve safety, reduce
maintenance costs, and optimize performance.
Fig -3: Applications of Digital twin
Energy and utilities: Digital twins can be used to monitor
and optimize energy usage, reduce waste, and improve
efficiency. This technology can be used in power plants, oil
and gas refineries, and other energy-intensive industries.
Healthcare: Digital twins can be used to simulate the
human body, allowing doctors to diagnose and treat
patients more effectively. This technology can also be used
to monitor patient health remotely and predict potential
health issues before they occur.
Smart cities: Digital twins can be used to simulate and
optimize city infrastructure, including transportation
systems, utilities, and public services. This technology can
help cities become more sustainable, efficient, and
responsive to the needs of residents.
Overall, digital twin technology has the potential to
revolutionize many industries by providing accurate, real-

time simulations that can be used to optimize
performance, reduce costs, and improve safety. New
concept called AI powered Digital twin can also be used in
above mentioned application where AI powered digital
twin can analyze the available data, identify the patterns,
make predictions, enabling manufacturers to predict and
prevent issues before they occur.
4. ADVANTAGES
Users can profit greatly from digital twins. We'll discuss a
few of these now.
a) A customized response to each need:
The beauty of digital twinning is that it enables businesses
to create virtual, tailored versions of their web
infrastructure to do predictive analysis that is appropriate
for their purposes. It is a customized method of adjusting
security to certain businesses.
b) Higher performance:
We can improve the performance of an equipment, plant,
or facilities using the real-time data and insights supplied
by digital twins. As problems arise, they can be resolved
quickly to maintain system performance and minimize
downtime.
c) Predictive capabilities:
Even if our manufacturing plant, office building, or facility
contains hundreds of different pieces of equipment, digital
twins can nevertheless provide us with a comprehensive
visual and digital image of everything. Every component's
output is monitored by sophisticated sensors, which
immediately alert the user to any problems or errors. As
opposed to waiting until a piece of equipment totally fails,
we can intervene as soon as issues arise.
d) Remote monitoring:
Digital twins virtual nature allows for remote facility
monitoring and management. Additionally, remote
monitoring eliminates the need for personnel to inspect
industrial machinery that might be hazardous.
e) Accelerated production time:
By creating digital duplicates, we can hasten the
manufacturing of goods and infrastructure before they are
actually created. By executing scenarios, we may examine
how our facility or product responds to errors and make
the necessary adjustments prior to beginning actual
production.
AI-powered digital twins will continue to play a key role in
this new phase of manufacturing. Here are some potential
benefits of AI-powered digital twins in manufacturing
industry:
i.Greater collaboration between humans and machines: In
the manufacturing industry, humans and machines will
work together in a highly collaborative and integrated
manner. AI-powered digital twins can enable humans to
interact with machines in a more natural and intuitive
way, improving collaboration and productivity.
ii.Improved customization and personalization: In the
manufacturing industry, there will be a greater focus on
customization and personalization of products. AI-
powered digital twins can be used to simulate and
optimize the production of customized products, resulting
in higher quality and greater customer satisfaction.
iii.Greater flexibility and agility: manufacturing industry
will require greater flexibility and agility in manufacturing
processes, enabling rapid response to changing customer
demands and market conditions. AI-powered digital twins
can be used to simulate and optimize manufacturing
processes in real-time, enabling greater flexibility and
agility.
iv.Enhanced sustainability: manufacturing industry will
require a greater focus on sustainability and
environmental responsibility. AI-powered digital twins
can be used to optimize resource utilization and reduce
waste and energy consumption, resulting in greater
sustainability and environmental benefits.
v.Improved safety: manufacturing industry will require a
greater focus on safety, as humans and machines work
more closely together. AI-powered digital twins can be
used to simulate and test safety scenarios, enabling
manufacturers to identify and mitigate potential safety
hazards before they occur.
Overall, the use of AI-powered digital twins in the
manufacturing industry has the potential to enable greater
collaboration, customization, flexibility, sustainability, and
safety in Manufacturing processes.
5. DIGITAL TWIN CREATION
To generate digital twins, conceptual models are imported
(through Building Information Modeling, Computer aided
design, or Geographic information system) or physical
objects are scanned in the real world for visualization and
analysis with enterprise and Internet of Things (IoT) data.
Real-time 3D, a computer graphics technology that creates
interactive content more quickly than human perception,

can be used to power a digital twin that can filter, arrange,
and present various data sources (both information and
models) as realistic, interactive visualizations.
Digital twins are representations in the virtual realm of
the forces, interactions, and movements that assets may
experience in the real world. Users can interact with
dynamic content that is three-dimensional and responsive
to their actions in real-time. They may efficiently mimic
real-world situations, what-if scenarios, and any situation
imaginable in this virtual environment, and
instantaneously visualize the results on any platform,
including mobile devices, PCs, and augmented, mixed, and
virtual reality (AR/MR/VR) devices.
The deployment of each digital twin is distinct. The
complexity and business effect of deployments often
increase with each stage that is added. With each of its
components dynamically linked to engineering,
construction, operational data, and virtual design, a digital
twin can range from a product configurator of a 3D model
to an exact representation of a network or system as wide
as a metropolis.
Digital twins guide decision-making at every stage of the
life cycle as teams from several disciplines and locations
plan, engineer, build, sell, and eventually run and manage
complex builds.
Although both digital twins and simulations are virtual
model-based simulations, they have some significant
differences. Typically, simulations are used for design and,
in some circumstances, offline optimisation. On the other
hand, digital twins are intricate virtual worlds that can
engage with and change in real time.
Let's take an automobile simulation as an illustration. A
novice driver can engage in an immersive training session,
understand how various automotive components work,
and experience numerous real-world circumstances while
driving virtually. The circumstances aren't related to a real
car, though. A digital replica of the car is connected to the
real thing and is fully informed about it, including
important performance data, previously replaced parts,
prospective problems as detected by the sensors, prior
service history, and more.
6. PERFORMANCE, ACCURACY AND CHALLENGES
OF DIGITAL TWIN
The fidelity and accuracy of digital twin models are critical
factors in determining the effectiveness of digital twin
technology in representing and simulating physical
systems. Fidelity refers to the level of detail and accuracy
with which the digital twin model represents the physical
system. Accuracy refers to the degree to which the digital
twin model can predict the behavior and performance of
the physical system under different conditions. A
simulation-based study can be an effective way to evaluate
the fidelity and accuracy of digital twin models. By
simulating the behavior and performance of the digital
twin model under different conditions, the accuracy of the
model can be verified and any discrepancies can be
addressed.
The accuracy and performance of digital twin models can
be impacted by several factors, some of which include:
Data quality: Digital twins rely on accurate and high-
quality data to model and simulate the real-world system
they represent. If the data used to build the model is
incomplete, outdated, or unreliable, it can lead to
inaccurate predictions and poor performance. Obtaining
real world data of every system is not feasible hence
digital twins for all real world systems is challenging.
Model complexity: Digital twins can be complex models
that incorporate a large number of variables and require
significant computational resources to run. As the
complexity of the model increases, the accuracy and
performance can be impacted, especially if the
computational resources are insufficient. hence digital
twin for complex real world systems is a challenge.
Model validation: Digital twin models must be validated
against real-world data to ensure they are accurate and
reliable. If the model validation process is inadequate or
the validation data is insufficient, it can lead to
inaccuracies and poor performance. Getting Accurate
validation data for real world systems is challenging.
System changes: Digital twins are designed to simulate
real-world systems, but these systems are constantly
changing. If the digital twin model does not account for
these changes or is not updated regularly, it can lead to
inaccuracies and poor performance.
Integration with other systems: Digital twins are often
integrated with other systems such as sensors, data
analytics platforms, and control systems. If the integration
is not seamless or the data exchanged between systems is
not accurate, it can impact the accuracy and performance
of the digital twin model.
Cost: Developing and implementing digital twin
technology can be costly, especially for complex systems.
The cost of obtaining and managing data, building and
validating the model, and integrating the model with other
systems can all add up.

Security: Digital twins can contain sensitive data about the
real-world system they represent, making them a potential
target for cyber-attacks. Ensuring the security of the
digital twin system can be a significant challenge.
Overall, ensuring the accuracy and performance of digital
twin models requires careful consideration of these and
other factors, challenges as well as ongoing validation and
updating of the model as the real-world system evolves.
7. RESULT
The accuracy and performance of digital twin models
depend on several factors such as the quality of the data
used, the complexity of the model, the validation process,
system changes, and integration with other systems. To
ensure the accuracy and performance of digital twin
models, it is essential to carefully manage the data used in
the model and to ensure that the model is validated
against real-world data. The complexity of the model must
also be carefully managed to ensure that it is accurate and
reliable while being computationally feasible.
The digital twin model must also be updated regularly to
account for system changes and to integrate seamlessly
with other systems. Addressing these challenges requires
careful consideration of the specific system being
modeled, the data and computational resources available,
and the organizational context in which the digital twin is
being developed and implemented. Ultimately, ensuring
the accuracy and performance of digital twin models is
crucial to their successful adoption and use in various
industries and applications.
8. CONCLUSION
In conclusion, Digital twin uses several advanced
technologies to provide a digital model of an asset. These
technologies are Internet of Things (IoT), big data
analytics, and artificial intelligence (AI) to enhance
productivity, quality, and efficiency across various sectors,
including manufacturing, healthcare, and transportation.
To ensure the accuracy and performance of digital twin
models, it is essential to carefully manage the data used in
the model and to ensure that the model is validated
against real-world data. The digital twin model must also
be updated regularly to account for system changes and to
integrate seamlessly with other systems.
9. FUTURE WORK
The future scope of "Exploring the Performance and
Accuracy of Digital Twin Models" is vast, as the use of
digital twin models is expected to grow rapidly in various
industries. Future work will include exploration of
following:
a) Development of techniques for data collection and
analysis, such as the use of Internet of Things (IoT)
sensors and edge computing.
b) Exploration of open-source digital twin platforms
for developing digital twin model and application and
validation of it.
Overall, the future of "Exploring the Performance and
Accuracy of Digital Twin Models" is promising, with
continued research and development expected to lead to
significant advancements in the field.
ACKNOWLEDGEMENT
For his continued cooperation with the research for
drafting this study at CAS, Sri BV Papa Rao, Outstanding
Scientist and Director, is gratefully acknowledged by the
authors. The authors thank Associate Director Sri Praveen
Tandon Sc G for his unwavering support. The authors are
extremely grateful to all CAS staff members for their
selfless contributions of time and knowledge to this study
in a variety of ways.
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BIOGRAPHIES
[1] Shri. Vidit Kumar, is presently working as a Scientist-
D at CAS DRDO Hyderabad. he has more than 10 years
experience in the field of communication, computer
networking & software development.
[2] Sri Timmapuram Srinivas is presently working as
JRF in DRDO, CAS, Hyderabad. He has more than 6 years of
experience in the field of embedded systems, IT
networking. He has published a research paper in
International Journal of Research on IoT based embedded
smart lock control systems.
[3] Smt G.Suchitra is presently working at DRDO, CAS,
Hyderabad as a JRF. She completed her M.Tech in
Computer Science & Engineering from Osmania
University. She has 2 years of work experience in the field
of computer science. She is certified as CISSP-Certified
Information Systems Security Professional.
[4] Smt Shahela is presently working as a JRF at
DRDO,CAS,Hyderabad.She completed her M.Tech in
VLSI.She has 1.5 years of work experience in the field of IT
& Cybersecurity. She has published a paper on Quantum
Circuitry in International Journal & Magazine of
Engineering, Technology, Management and Research. She
is certified as a ISO 27001 lead auditor.

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