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=== Example 2: Particle Collider ===
=== Example 2: Particle Collider ===
The second example is a partile collider, such as the [https://home.cern/science/accelerators/large-hadron-collider Large Hadron Collider at CERN].
The particle collider is using a 3D grid of ruggedized radio activity sensors in a cavern of the collider to capture radio activity after the collision. This data is fed into a hugely complex tier of compute nodes, which are applying advanced analytics concepts in order to create a digital reconstruction of the particle collision. This Digital Twin is then the foundation of the analyis of the physical phenomens that could be observed.
[[File:0.3.1-LHCExample.png|800px|frameless|center|AIoT & Digital Twin: Particle Collider Example]]
[[File:0.3.1-LHCExample.png|800px|frameless|center|AIoT & Digital Twin: Particle Collider Example]]



Revision as of 21:38, 5 June 2021

More...More...More...More...More...More...More...More...More...More...More...More...More...DigitalTwin 101

Digital Twins can be used in order to create a digital representation of the physical entities. They can help with managing complexity and establishing a semantic layer on top of the more technical layers. This in turn can make it easier to realize business goals and implement AI/ML solutions using machine data. The following provides an overview, some concrete examples, as well as a discussion in which situations the Digital Twin approach should be considered in an AIoT initiative.

Overview

Digital Twin - Overview

Example

A good example for a Digial Twin is a system which makes route recommendations to drivers of electric vehicles, including stop points at available charging stations. For these recommendations, the system will need a representation of the vehicle itself (including charging status), as well as the charging stations along the chosen route. If this information is logically aggregated as a Digital Twin, and AI in the backend can then use this DT in order to make the route calculation, without having to worry about technical integration with the vehicle and the charging stations in the field.

Similarly, the feature responsible for reserving a charging stations after a stop has been selected can benefit if the charging station is made available in the form of a Digital Twin - allowing to make the reservation without having to deal with the underlying complexity of the remote interaction.

The Digital Twin in this case is providing a higher level of abstraction than would be made available, for example, via a basic API architecture. Especially if the Digital Twin is taking care of data synchronization issues.

DigitalTwin Example

Digital Twin and AIoT

In an AIoT initiative, the Digital Twin concept can play an important role in providing a semantic abstraction layer. IoT plays the role of providing connectivity services. AI, on the other hand, can play two roles:

  • Reconstruction: AI can be an important tool for the reconstruction process, i.e. the process which is reconstructing the virtual representation based on the raw data from the sensors.
  • Application: Once the Digital Twin is reconstructed, AI and be applied to the semantically rich representation of the Digital Twin to support the business goals
Digital Twin and AIoT

Example 1: Electric Vehicle

The first example to demonstrate this concept is building on the EV scenario from earlier on. In addition, the DT concept is now also applied to the Highly Automated Driving Function of the vehicle, which includes short term trajectory and long term path planning.

For the short term planning, a digital twin of the vehicle surroundings is created (here, the AI is supporting the reconstruction of the DT). Next, an AI is using the semantically rich interfaces of the digital twin of the vehicle surroounding to perform the short term trajectory planning. This AI will also take the long-term path into consideration, e.g. to determin necessary turns on a highway.

Digital Twin and AIoT - Example

Example 2: Particle Collider

The second example is a partile collider, such as the Large Hadron Collider at CERN.

The particle collider is using a 3D grid of ruggedized radio activity sensors in a cavern of the collider to capture radio activity after the collision. This data is fed into a hugely complex tier of compute nodes, which are applying advanced analytics concepts in order to create a digital reconstruction of the particle collision. This Digital Twin is then the foundation of the analyis of the physical phenomens that could be observed.

AIoT & Digital Twin: Particle Collider Example

DT Resolution and Update Frequency

Digital Twin: Soccer Example
Digital Twin: Soccer Example (Details)

Conclusion

Conclusions - Digital Twin and AIoT