Just an Opinion..,
“What is the difference between a digital twin and a simulation?”This question implies that the two concepts are similar, if not the same. Digital twins and simulations, however, are two very different things.
The origin of this confusion and resulting “twinning” of the concepts is that while digital twins and simulation concepts are different, they are also extremely complementary. A digital twin uses simulation to not only produce information about how a product will perform in the physical world under a wide variety of conditions, but also how that performance will change throughout its lifetime.
Simulations can be performed on digital twins of products that already exist. However, simulations can be performed on products under development to validate that the new product will meet its requirements once it is physically manufactured. While there can be digital twins of intangible processes, such as supply chains, logistics, and financial systems, the focus here will be on tangible products that have a physical form through manufacturing.
As defined in “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” which I wrote with J. Vickers and published in 2017, a digital twin is:
. . . a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level. At its optimum, any information that could be obtained from inspecting a physical manufactured product can be obtained from its Digital Twin.
However, what is more useful than a definition of digital twin is a visual model. The model I developed in 2002 as the underlying premise for product lifecycle management, which has been simplified a bit over the years, is shown below. It consists of three main components:
The digital twin, as a repository of product information over the entire lifecycle of the product, has three types: the digital twin prototype (DTP), which is all the information needed to create physical products; the digital twin instance (DTI), which is all the information about individual products that have been manufactured; and the digital twin aggregate (DTA), which is the composite information on all the DTIs in existence. We can use the repository information of these digital twins to reduce waste of physical resources and be much more effective and efficient.
While digital twins are a relatively new concept, simulations are not. Webster’s dictionary defines a simulation as “the imitative representation of the functioning of one system or process by means of the functioning of another.”
What this means is that we can replicate a system from the physical world in the virtual world of computers. In a simulation, we attempt to take the inputs and initial conditions of a system and apply the rules that pertain to the system in its defined environment. We can then produce the theoretical external outputs and internal state changes to the system.
This is illustrated in the diagram below. A simulation that attempts to re-create real life is successful if it produces the same outputs and state changes that the physical system would produce in the same time periods.
More broadly, a simulation can be based on reality, or it can be completely artificial. The purpose of a simulation is simply to investigate the changes on an object or system and its outputs over time, given a set of inputs. Simulations are on a spectrum from highly realistic flight simulators used to train military and commercial pilots, to complete fantasy, such as the board game Dungeons and Dragons.
Even first-person shooter video games like Quake, where a rocket can help you jump higher rather than blowing you to bits, are simulations—highly complex, but by no means representative of reality.
This is where digital twins and simulations come together. We want to simulate the forces that the digital twin will produce, and the forces that act upon that digital twin to see how it will behave over time. Since both digital twins and simulations operate in an increasingly less expensive digital space, they create immense value compared with the traditional physical-based ways of creating, testing, and operating products.
The combination of digital twins and simulation is necessarily at the highly realistic end of the simulation spectrum. A simulation takes the system of a product or process with its internal operations and rules and combines it with the operations and rules of the external environment. The simulation then processes a variety of defined inputs to determine the system states, outputs, and behavior of the system defined by the digital twin, and checks that the results match the outputs and behaviors that are required.
At the beginning of the product’s lifecycle, when there is no physical product in existence yet, the digital twin prototype uses simulation to verify and validate the product’s predicted behavior and performance. Simulations can take the structure, operations, and rules of the proposed product’s DTP, apply user-defined inputs, and cycle in time through the state changes and outputs of the product. If the product produces the performance and behavior that meet the requirements of the product, then the physical version of it can be produced with confidence that the product will perform as the developers intended.
When products have been manufactured and are in operation, each of the physical products will have its own digital twin instance. We can use simulation with a DTI and all the information from other products in the DTA to predict individual future performance. We can capture real-time data from IoT sensors. We can then input that data as initial conditions into our product simulation and predict future performance on a continual basis.
Called front running simulation (FRS), these continual simulations using the latest data from the DTI and DTA enable us to predict future product malfunctions and repair or replace the component predicted to malfunction before it fails.
The combination of digital twins and simulations can create immense value compared with traditional physical-based prototyping and iteration during product design and can yield massive benefits during the product’s lifetime in the form of accurate predictive maintenance and performance behavior.
While the digital twin concept is still developing, DTIs already exist for high-end products like Tesla automobiles and F-35 Lightning II jet fighters.
As technology continues to improve and computing resources become less expensive, we will see digital twin usage trickle down to more and more products at all phases of the product lifecycle.
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