The digital twin definition: bringing the real world into your simulations

• By Florent Mathieu, CEO of EikoSim

In today’s rapidly advancing technological landscape, the concept of the digital twin has emerged as a groundbreaking innovation with the potential to revolutionize various industries.

By seamlessly integrating the virtual and physical worlds, digital twins enable businesses to gain valuable insights, optimize processes, and make informed decisions.

In this article, we will explore the definition of a digital twin and its application in the simulation space.

Digital twins explained

Digital twins enable businesses to bridge the gap between the physical and digital realms by providing a comprehensive understanding of the real-world object or system.

They serve as virtual counterparts that can be analyzed, monitored, and manipulated to optimize performance, troubleshoot issues, and explore what-if scenarios.

Understanding digital twin technology

To understand the definition of a Digital Twin, we suggest you put it in the context of product development.

To estimate a structure’s mechanical behavior, proactive numerical studies have become common practice in mechanical engineering.

Finite element modeling is now involved at all stages of the mechanical design project.

It goes from material behavior estimation (especially for composite materials) to intermediate assemblies all the way up to structural computations.

Digital twin technology defined

The idea of leveraging experiments and model data in a “digital twin” is becoming increasingly popular as it helps companies tackle crucial challenges. 

By creating a virtual replica of a physical system, businesses can gain valuable insights into its behavior and performance, which can be used to optimize operations, reduce costs, and improve overall efficiency. 

With the help of these technologies, companies can analyze vast amounts of data and identify patterns and trends that might have gone unnoticed otherwise. 

This not only helps them make better decisions but also enables them to stay ahead of the competition in today’s fast-paced business and digital environment too.

A digital twin is a virtual representation of the physical version of the observed assembly that incorporates all necessary data to validate the assembly operating behavior by comparing the virtual representation to model-based simulations of responses to real-world information.

It also allows live corrections of the asset behavior if needed by outputting commands directly to the asset.

In predictive maintenance, for example, these types of digital twins can be utilized to control and record the crucial data of an asset (temperature, humidity, rotational speed, etc.) so that the operator can instantly detect discrepancies with his own previsions.

This principle can be generalized to entire factories…

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Digital twins are commonly employed as online simulations, but they have the added capability of being utilized offline to examine actual operational behavior that has been documented.

This feature enables a more comprehensive and in-depth analysis of the data, and asset performance, allowing for a better understanding of the system’s performance and potential areas for improvement.

The application of this concept to mechanical engineering is what CAE engineers need to improve test/simulation communication.

When a mechanical characterization test has been carried out (or is being carried out), it is necessary to ensure that the simulation predicted the experimental behavior satisfactorily.

A simulation challenge

The method of predicting the behavior of virtual structures has evolved significantly with the advent of simulations.

They take into account the technical specifications of the design and help to identify potential issues before the physical prototype is constructed.

However, it is still imperative to perform prototype testing for real-world verification, as the simulations may not always account for all variables and factors.

Nevertheless, the simulation digital models have become an integral part of the design process, enabling designers to understand and optimize both the physical environment and virtual environment and the performance of the structure.

The overarching influence of simulation on mechanical design has two main consequences:

• Budgets allocated to mechanical component development are regularly decreasing because many prototype tests tend to be replaced by simulations.
• Simulation reliability must keep improving. Actual prototype testing is constantly postponed to the later stages of a project, which involves higher risks.

Verification or validation?

Following these trends, technical decision-makers allocate shorter and shorter development periods to design offices.

This is the case even for complex projects involving entire subsystems needing to be redesigned from the ground up or when new materials are introduced.

Nonetheless, an aspect of simulations that is often neglected and that can compromise the reliability of a model. There is a difference between verifying and validating.

Verifying a simulation model means ensuring the assumed constitutive behavior is accurately reproduced by the simulation algorithm.

Such behavior is often expressed by an analytical equation linking strain and stress.

It can be evaluated from simple test cases commonly used to assess the reliability of a finite element code.

Nowadays, all commercial software is carefully verified to ascertain that the equations available to the end-user are correctly ‘transcribed’ in the algorithm.

For a structural calculation engineer, validating a model means asking and answering several questions: What is the applied load? What is the exact shape of the component?

What assumptions should be made to model the interaction of two components? Etc.  

The technical know-how of the engineer consists in giving the best answers based on his/her personal experience and knowledge of the actual mechanical assembly.

The more groundbreaking the product, the two or more components these assumptions can be far removed from the reality of the actual prototype…

How can you effectively validate simulations?

Some conjecture is involved in the previous process.

This conjecture is the reason why despite having the most qualified engineers designing a testing setup, unforeseen experimental setbacks may still occur when the actual test is carried out, to the detriment of the whole project.

Should this happen, last-minute contingency efforts become unavoidable to recalibrate the simulation to correspond more closely to the experimental measurements.

In a worst-case scenario, additional test campaigns must be carried out to determine the cause of discrepancies between simulations and experiments.

Since structural tests usually carry a high financial cost (between 5k€ and 200k€ depending on the assembly complexity), these kinds of setbacks are doubly detrimental: production will be delayed, and additional costs and effort will also be required to complete the project.

What does a structural mechanic’s digital twin look like?

A Digital Twin is a powerful software that effortlessly imports measurement data from multiple sources and accurately represents your component.

It’s a virtual replica of a real-time data and processing system that can be used to simulate, analyze, and optimize the performance enhancements of a real-world system.

A practical use case of a Digital Twin can provide a comprehensive view of your component’s performance, enabling you to make informed decisions and take corrective actions in no time.

In this framework, cameras (EikoTwin DIC will process their images) can just be seen as some experimental data among many. These bring some particular advantages to the digital twin world as they can enable the following:

• It is possible to compare models and experiments across the entire component surface, allowing for the calculation of a generalized model error. This function is similar to MS Word’s compare feature and enables users to identify similarities and differences in their data quickly.
• At any stage, they enable modifications to the model input parameters. For instance, a digital twin can take additional information on experimental boundary conditions into account. An example of this is the study on the train chassis, carried out in collaboration with Alstom and CETIM. 
• Furthermore, sensitivity analysis can also be carried out to determine which parameter should be modified in priority. This reduces the gap between simulation and experiment (material parameters, interfaces, etc.).

Discover our first digital twin technology with this EikoTwin Digital Twin software demo!

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