Digital Twins vs. Simulations

The concept of digital twins and their relationship to simulations is exciting but often hard to grasp. This short article leverages relevant industry examples and detailed “double clicks” to help you make better decisions and understand how and why these technologies will become increasingly relevant for most industries.

What is a digital twin?

Wikipedia?defines a digital twin as a “virtual representation?that serves as the?real-time digital counterpart?of a?physical object or process.”

In theory, a digital twin will gather input from connected sensors, machinery, and people to store and display them in a cloud-hosted application. However, besides being able to look back in time into what happened when and perform some post-mortem analysis, a digital twin limited to a backward-looking view won’t have much business interest.

Therefore, digital twins often integrate a simulation component. This simulation can be at the device, process, or even plant level. It will allow users to leverage this combination of real-time data and simulator-based system-level behavior modeling for multiple use cases.

For instance, a digital twin can be used to:?

• Replay a system behavior based on historical data?
• Do an advanced “what-if” analysis before deciding which path to choose?
• Train new operators on virtual processes before letting them work on the actual real-life process?
• Simulate a process to train an AI Agent using Deep Reinforcement Learning (DRL), for example, by integrating it into the Microsoft Project Bonsai platform?
• And more?depending on the industry?that digital twins are used in?

Digital twin vs. simulator?

A simulator’s scope is often limited to a particular piece of equipment or process, although not always. Once programmed or trained, the simulator will run separately from the real-life process.

Conversely, a digital twin will often encompass a broader process comprised of multiple pieces of equipment, and it will remain connected to the live system to represent it faithfully.

Therefore, a simplified way to think about the difference between digital twins and simulations is to consider that a digital twin is?a simulation whose states?(inputs, outputs)?are updated to reflect their real-life value accurately. A simulator could end up drifting from real-life or even provide wrong data, a digital twin won’t if it remains connected.

Conversely, simulators operate separately from a real-life process and can even be developed without an existing process to test hypotheses.

Enabling technologies for digital twins and simulations

To build and run a digital twin, several technology blocks are required.

1. A simulation engine
2. Real-time process data collection technologies
3. Cloud and data services to collect, store and analyze the process and simulation data

How to build a simulation?

There are multiple ways to build a simulator, and the three most used are:

• Physics-based simulators, such as the?plastic extruder?one that Neal developed in Python for the Microsoft Project Bonsai in-box demo?
• Software package-based?simulators using products such as?AnyLogic?or?Simulink?
• AI or data-based?simulators that?train AI models, most often deep neural networks-based ones?

For the latter approach, data-logging only digital twins can be used to create the dataset necessary to train this AI simulator. The historical process data (both inputs, states, and outputs) that the digital twin recorded can provide the breadth and quantity of labeled data required for those types of AI simulators supervised learning. This is one of the areas where a partner with extensive data science experience can significantly help with the speed and quality of the simulation development.

Additional?enabling technologies?

To collect real-time process data, smart sensors using technologies such as Azure IoT are going to be required. Adding intelligence at the edge to existing sensors or deploying new smart sensors such as?vision AI?ones, we will be able to instrument all the relevant process inputs and outputs.

This real-time data and the simulator(s) will be hosted on an appropriate cloud platform to enable the above-mentioned use cases. Solutions such as Azure Digital Twin will enable easy integration of those elements and access to a device, process, line (or building), or plant (or campus) digital twin.

Although this article somewhat oversimplifies both what digital twins are and what is required to build and run them, it provides a base for more in-depth research if a more comprehensive understanding of the subject is required. To help with this additional research, I have listed a few links below to get you started.??

• AnyLogic simulation demo: Optimizing distribution centers
• Azure: Digital Twins
• The promise of?Digital Twin: a Microsoft whitepaper??
• Siemens: Defining the Digital Twin
• IBM: What is a Digital Twin?
• Why Digital Twin technology is so important – Forbes

(This article was originally published on Neal Analytics blog)