The digital twin is a digital (virtual) model to predict the behavior of a physical system. For example, a digital twin can be employed to predict the dynamic characteristics of an entire aircraft. Alternatively, it can also be used to evaluate the fatigue life of a critical aircraft component based on the complex flight spectrum. A complex system like an aircraft or its engine will have many digital twins to describe the behavior.
Building a digital twin:
To build a digital twin model, the data from the physical system is the first requirement. This data may be obtained based on prior experience of a similar system. Alternately, using sensors (temperature, pressure, flow, voltage, noise, acceleration, etc.,) in a simulated environment, the data may be gathered from the physical system.
From these data, the mathematical models are developed that cover the entire range of operations of the physical system. The developed models connect the input operational parameters to the output parameter(s). These models may be derived based on physics or experimental data. The physics-based models may be based on various methods – from empirical relations to very detailed numerical methods, such as finite element methods. On the other hand, the input data from experimental observations can be used to build models using statistics and data science. The models, thus developed, form the core of the digital twins.
Once these models are formulated, the operational input data from a specific system is input to them. The models predict the output that are very specific to that system.
These models can be continuously improvised based on the continuous feed of the operational data of the physical system. The digital twin has found many applications due to the incredible progress made in the computing and communication technologies. As these technologies grow more, the digital twin is becoming more accurate and reliable.
Digital twins are finding their way in each and every industry. They form the crux of the IOT technology.
The digital twin provides significant savings and supports safe operation of the physical system, as they can quickly predict any abnormal behavior.
The development of the models is a key research area. For example, developing algorithms using efficient data science methods that can handle big data quickly and provide a faster response is the need of the hour.