Virtual test set-up

Hardware connection

Model-based image

processing

Eikotwin Digtal Twin X2

Reality-inspired simulations

EikoTwin Digital Twin

EikoTwin Digital Twin allows the engineer to improve their simulation by making the most of their test data.

It implements "augmented simulations", which use the measured data as boundary conditions, but also allows the identification of material parameters to bring the simulation closer to the expected result.

Principle

  • Integration of test results for the validation of simulations.
  • Import of sensor measurements and direct comparison with the simulation result.

Features

  • Use of measurements to enrich the simulation model.
  • Creation of measured boundary conditions to release idealized conditions.
  • Export of a simulation model enhanced with measurement information.

Results

  • Quantification of errors due to boundary conditions.
  • Automatic identification of the simulation parameters required (materials, etc.).
  • Determination of the residual model error.

Testimonials

IRT Saint Exupery

Using EikoTwin allowed us, through augmented simulation, to correct overly idealized boundary conditions, or to isolate regions of interest. The inverse identification proposed by Digital Twin also gave very promising results for reviewing the calibration and validation processes of the models by reducing the number of tests.

Ludovic Barrière

Project manager

Applications and tutorials

Safran Ceramics trusts EikoSim with the “Thermal” plug-in

Thermal plug-in

Benjamin Lacombe is a Mechanical Design Engineer at Safran Ceramics. He tells us about his experience with EikoTwin DIC and the development of the “Thermal” plug-in. « I work at Safran Ceramics, Safran group’s center of excellence for high-temperature composites. We are in charge of developing high-temperature solutions for tomorrow’s aircraft. We conduct a large number…

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Technical functionality: selection of the measurement mesh

Capture

On EikoTwin DIC, the measurements are directly expressed on the simulation mesh. In order to choose which areas of interest for the test are visible to the camera(s), the so-called measurement mesh must first be selected. Indeed, this mesh will be the one where the results will be calculated. It is therefore essential to select…

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Preparing your Digital Image Correlation test with Blender

Image3

This article invites you to care about an unexpected topic: the preparation of digital image correlation tests with Blender. While research articles do not always mention this aspect of digital image correlation measurements, test preparation is a crucial step in the procedure. The aim is to ensure that the images that will be taken by cameras…

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Simulation validation with EikoTwin DIC rely heavily on image processing. Image analysis methods provide full-field test data, that can be used to validate and improve your simulation. Using the unique method of “Model-Based testing”, EikoTwin DIC allows to measure displacements and strains of the observed parts directly on the simulation mesh. Thus, you will be…

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EikoTwin DIC 1.3 is available!

Image (5)

On the menu of the main improvements of this first update of the year 2021 : Reduced computation times and lighter results. Emphasis has been placed on computation performance and storage in EKT files. New “Batch” mode. It will allow you to perform a series of calculations directly with EikoTwin DIC. Do not hesitate to…

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Measurement errors and digital image correlation

Article Incertitudes En

When conducting a test campaign instrumented by digital image correlation, it must be possible to determine the characteristic measurement error for the quantities of interest studied (measured displacement or strain). This analysis is essential to know whether the measurement made is indeed the desired “signal” and not the measurement “noise”. All measurements must be related…

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