International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
www.irjet.net
Exploring the Performance and Accuracy of Digital Twin Models Srinivas Timmapuram1, Suchitra Gandu2, Shahela3 Vidit Kumar4 1JRF , CAS DRDO, Hyderabad, Telangana, India 2JRF , CAS DRDO, Hyderabad, Telangana, India 3JRF , 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.
Fig -1: Representation of Digital Twin
Key Words: Digital twins, artificial intelligence, IoT, predictive analytics, anomaly detection, optimization, productivity, cost-effectiveness, sustainability, manufacturing, healthcare, transportation.
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.
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. AIpowered digital twins can learn from data, identify patterns, and make predictions, enabling manufacturers to predict and prevent issues before they occur.
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.
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