Virtual Twins and Applications

- Overview

Virtual twins and digital twins have different definitions. 

A digital twin is a virtual representation of an object or system that spans its lifecycle, updated from real-time data, and uses simulation, machine learning (ML), and reasoning to aid decision-making. 

A virtual twin is a three-dimensional analytical model used to represent the original physical size, shape and behavior, constructed and manipulated using data typically from sensors and connected devices. Once established, the model allows for trial-and-error experiments to determine how to achieve precisely the desired results. 

Virtual twins are often used for physical objects, such as airplanes and cars, where operating conditions can be easily measured. Before building these machines, or even welding a screw or piece of metal, manufacturers first use software to build a virtual twin. 

These digital copies allow them to quickly see how the design will perform in different real-world scenarios and make changes as needed. 

Most importantly, these twins are ideal for solving problems that are impossible, immoral or too expensive to explore in the real world, such as space travel, or the inside of the human body as we now see it. 

If a virtual twin of a car can perform an unlimited number of virtual crash tests and produce results that replicate its behavior in a real-world crash, could a virtual human occupant replace crash test dummies to further improve safety?

- Digital Twins vs Virtual Twins

A digital twin is a real-time, live, virtual model of a physical asset, process, or system that spans its lifecycle and is continuously updated with data from sensors, using machine learning and simulation to aid decision-making and optimization. 

A virtual twin is a related concept that emphasizes a 3D analytical model for trial-and-error experiments, especially during the design and engineering phases, to precisely achieve desired results before physical construction. 

While a digital twin provides real-time interaction with the physical world, a virtual twin focuses more on engineering insights and design optimization. 

A. Digital Twin: 

1. Definition:

• A live, data-rich virtual replica of a physical object, system, or process.

2. Function:

• Spans the entire lifecycle of an asset, using real-time data to monitor and control its physical counterpart.

3. Data Flow:

• Data flows from sensors and connected devices on the physical asset to the digital twin and back.

4. Key Features:

• Real-time Data: Continuously updated with live information from the physical world.
• Simulation & AI: Employs machine learning and simulations to analyze performance and predict outcomes.
• Lifecycle Support: Used for monitoring, analysis, maintenance, and continuous improvement of an asset throughout its life.

5. Examples:

• A factory process, a wind turbine, or even an entire city.

B. Virtual Twin:
1. Definition:

• A 3D analytical model built using data, primarily for design and engineering to simulate behavior and performance.

2. Function:

• Allows for trial-and-error experimentation in a virtual environment to understand and optimize design outcomes.

3. Data & Modeling:

• Constructed and manipulated using data from sensors to represent the original physical characteristics.

4. Key Features:

• Design Focus: Primarily used in the early stages of design and development to test various scenarios.
• Experimentation: Enables engineers to conduct unlimited virtual tests (e.g., crash tests) to see how designs will perform in different conditions.
• Problem Solving: Ideal for exploring situations that are impossible, immoral, or too expensive to investigate in the real world, such as space travel or the human body.

5. Examples:

• Simulating airplane or car performance during the design phase before physical construction.