How ArianeGroup improves simulation credibility with EikoTwin and DIC

Simulation credibility is a key aspect of the Virtual Testing concept. As the European leader in space launchers, Ariane Group participates with EikoSim in multiple Research and Development projects, including a RAPID R&D project (“MUTATION”) funded by the Direction Générale de l’Armement. This project aims at developing an industrial platform for test-simulation dialogue to meet the challenges of faster and safer development by an improvement of modelling credibility.

simulation credibility

The Galileo dispenser project

Within this R&D project, one of the key use cases was the qualification test of the Galileo Dispenser in the Ariane 6 version. A dispenser is a system placed under the launcher fairing which is designed to release one or several satellites during the launcher mission and to put them into orbit. The test is performed on a flight model which means that only qualification load cases are applied to the structure, but without ever reaching failure.


Digital continuity as a path to simulation credibility

The MUTATION project was the occasion to integrate Digital Image Correlation (DIC) in the data fusion approach to evaluate its benefits for simulation users in a large structural test context. Overall, the dispenser test was the first end-to-end one, that involved every tool that was developed within the project.

simulation credibility
DIC setup

DIC post-processing by EikoSim

Calibration Validation

Hybrid calibration was used to carry out pre-calibration for the more zoomed in ZOIs (ZOI n°1 and n°3). Markers were disposed on the sample surface at precise coordinates so that the link between model coordinate system and measurement frame could be established.


The speckle pattern was sprayed onto the sample surface using paint mat paints, and according to the specification derived from the virtual study. Fig 4c shows that the retained method indeed provided us with enough texture under the reprojected elements.

Calibration was validated with the following observations:

→ Excellent reprojection of the FE on the images, low RMS on reprojection error computed post pre-calibration.

→ What could be validated was normal shape error as derived from the self-calibration procedure in the EikoTwin platform. Strain gauge wires could clearly be identified in the residual field as well as in the shape error, proving the software ability to take them into account.

Results and post-processing of full-field measurements

DIC monitoring was used for all load cases. Images were taken at regular interval during the tests, and extra-images were acquired during loading plateaus. We will focus on experimental results on the plateau corresponding to the maximum load of the first test configuration in the present paper.

For this plateau, it was critical to ensure simulation credibility (ie that the measured strain fields and the FE strain fields matched closely in terms of strain localizations), but also to ensure that local strain values remained admissible with respect to structure integrity.

Side-by-side comparison of simulation and experimental strain fields (Exx component, normalized) at maximum load.

Although local differences could observed between the measured and simulated fields (see fig 5), an generally good agreement was found between experiment and simulation. Strain localizations were correctly anticipated and strain levels remained acceptable.

In fact, since both strain fields are computed using the exact same convention and expressed at the same physical location, a quantitative comparison was also performed (fig 6a). Mainly, the strain field difference shows that differences arise locally, in the vicinity of strain concentrations. Overall though, the difference between the two fields is of the same order of magnitude as measurement uncertainty (ie, no significant difference across most of the FOV for test simulation differences). Simulation credibility can be confirmed from this kind of map.

Test-simulation differences (a) and local analysis over time (b)

Additionally, we could make use of the ‘virtual gauge’ functionality of the DIC software to compare data across three different sources in the area: simulation, DIC (virtual strain gauge), and actual strain gauge data as recorded during the test. Fig 6b presents the normalized local strain evolution during the successive loading steps.

Comparison of virtual gauge measurement and traditional gauge values show that although virtual gauges provided a noisier signal (as expected following preliminary uncertainty analysis given the local element size and the chosen FOV), average strain levels are quite close for each loading step.

Second, comparing simulation previsions with experimental results, it was found that for this pair of gauges the simulation was rather conservative, as higher strain levels were expected in these areas.

Next step is the use of virtual strain gauges in areas where no physical strain gauges were present, especially in peaks in the strain difference fields. In this case we showed that locally some strain localizations reached higher values than expected in the initial simulation, although remaining well below critical levels for structural integrity (see figure 7). Overall credibility was thus judged satisfactory for this simulation effort.

simulation credibility
Use of local virtual strain gauges (computed from DIC results) to perform additional local comparisons in relevant areas with respect to test-simulation discrepancies

How Ariane Group increases the credibility of simulation with DIC

DIC also makes it possible to observe localization of deformation, which is always high-risk for strain gauges. “As is often done in the case of structural tests, we validated DIC measurements with strain gauges on target locations, which allows to ensure DIC credibility and to use all the strain maps for validation purposes”. The predictive aspect of the simulation was validated by full-field comparisons: expected strain localizations are well predicted (for axial, transverse and shear strain fields) and local differences were shown not to be significant with respect to measurement uncertainty in the most critical areas.

Engineers are now able to validate the simulation model from hundreds of measurement points, and to visualize the error map between test and simulation data. “It’s much more comprehensive than trying to interpret strain gauge errors”, confirms Nicolas Swiergiel. Thanks to DIC, error sources are more easily explained, and the model uncertainties are drastically reduced, since CAE engineers have a larger overview on strain gradient areas.

This also opens the way for new validation metrics, that can encompass all measurement sources” explains Florent Mathieu, CEO of EikoSim. “There is some ongoing research work about the ideal criteria to assess the credibility of a simulation effort”.

A virtual test scene as a time-saving effort

Designing specification for large scale applications involving DIC in a complex industrial context can be quite challenging. It is difficult to anticipate appropriate camera positioning to guarantee that the targeted zones of interest (ZOI) will remain visible for the duration of the test. The use of virtual test scenes (also called virtual pre-testing) thanks to the Blender software can help tackle these issues.

Blender is a free 3D rendering software. It makes it possible to reproduce the test setup into a virtual scene, around which the user can position cameras. Virtual images corresponding to these cameras FOV can be rendered for initial and deformed states of the mechanical test, based on finite element simulation previsions for the test. To this end, python scripts were designed to deform the mesh node by node in Blender, and to capture virtual camera images at each simulation step. There virtual images can then be processed by a DIC software, so as to give realistic a priori estimate on measurement possibilities for the chosen camera positions (strain and displacement uncertainty, notably).

In the case of the Ariane 6 Galileo dispenser, several challenges needed to be addressed. First, the scale of the component (approximate dimensions = 3mx2mx2m) is quite large for usual DIC setups (usually under 1m^3). Second, there was a need for a DIC monitoring at different scales simultaneously, thus involving several DIC systems that needed to be synchronized along with the rest of the instrumentation. Global DIC results needed to be acquired across the whole component, in order to provide point-wise displacement measurement displacement, and also to check that the experimental boundary conditions do not significantly deviate from the specification. These results are exploited qualitatively (no quantitative comparison to the simulation is sought). Local DIC results, however, are required for more quantitative comparisons with FE previsions. They are needed in three ZOIs of various sizes (upper and lower bracket/frame interactions, see Figure 1) where strain field gradients are expected, and thus strain gauge only instrumentation is not sufficient to qualify the structure.

simulation credibility
Test setup preview in Blender, zones of interest (ZOI)

For ZOIs n°1,2 and 3, the objective of the camera setup will be two-fold.

1 – check that the strain levels remain below a critical level.

2 – use the whole measured field to validate the simulation (were strain localizations accurately predicted, etc)

Thanks to test virtualization, EikoSim was able to propose camera positioning that gave FOVs compatible with validation requirements, and with the test environment and instrumentation, for the 4 camera setups. Camera locations were determined at millimeter precision, and the test rig was designed to accommodate these positions. Adapted lenses were also specified in this step. Finally, the speckle pattern size and method of application was also decided thanks to virtualization. For FE based DIC, ideally each reprojected mesh element on the test images should contain texture that can be analyzed by the DIC algorithm (see Figure 2). Based on this requirement, it was decided that paint should be used for some smaller ZOIs (1,2,3), and painted adhesive film for the global tracking system (ZOI 4).

Figure 2: Mesh over texture previsualization

Additionally, the team was able to ensure that that measurement uncertainty was compatible with the validation of simulation in the most critical area (i.e. measurement uncertainty is significantly lower than the strain levels expected for the close-up shots of the structure). Figure 3 presents a normalized estimate of strain uncertainty fields for ZOIs 1 & 3, where quantitative comparisons will be needed to qualify the structure.

Figure 3: Uncertainty strain fields (global XX direction)

Using the finite element mesh provided by the ArianeGroup simulation team as a basis for our estimate of DIC measurement, it was estimated that the uncertainty strain field (standard deviation of the measured field on virtual images without displacement but with added white noise) approximately 1/5th of the expected maximum strain for the anticipated loads. Thus the camera positioning was deemed satisfactory and the specification was validated.

How Ariane Group saves 50% of DIC preparation and analysis time with EikoSim

Optical measurement methods such as Digital Image Correlation (DIC) are known to yield large quantities of measurement information, and are more and more accepted in the industry as a way to get a better understanding of structural mechanical tests. However, using this technology to its fullest potential, especially with multi-camera setups, requires a long and careful preparation time, plus additional laboratory time on the day of the test.

On such a large structure, there are some zones we just can’t reach without preparation” explains Nicolas Swiergiel. Especially tall structures need the design of a scaffold just for the cameras to be level with the region of interest and correctly oriented. “Doing this kind of operation after the specimen is installed is not compatible with the time frame of such a test, where everything has to be kept as fast and fluid as possible”. In order to allow themselves more freedom in the design of measurement setups, ArianeGroup and EikoSim devided to develop a test preparation tool together, dedicated to the design of DIC setups. The objective was to allow the full virtual preparation of the 8-cameras setup, including scaffolding, that made it possible to fully predict the DIC outcome. “We based this EikoTwin Virtual on the free software Blender, which has all the camera handling and image generation capabilities we needed” says Pierre Baudoin, Research Engineer at EikoSim.

simulation credibility

EikoTwin Virtual helped prepare for the test with commercially unique features:

  • 3D cameras positioning around the test scene;
  • Rendering of virtual images to calculate the measurement uncertainties and validate the regions of interest with DIC;
  • Confirmation of the agreement between FE mesh and measurement objective, and adaptation of the Region Of Interest;
  • Creation of the test implementation map, to help determine the scaffolding exact sizing.   

Now we’re able to fully plan where to put the cameras to optimize the measurement result, and it doesn’t take any facility time” continues Nicolas Swiergiel. “Being effective on the d-day is mandatory, since schedules are extremely tight. We were happy to see the real images look exactly like the virtual ones”. In total, using this new solution helped save 4 days of on-site preparation for this test only, compared to the usual installation of 8 cameras, while maximizing the size of the ROI.

What ambition does this platform allows to have?

This data fusion platform now allows for a more fluid correlation process, which in turns makes it possible to increase the part of development that’s being made based solely on simulation models. “What we’re looking for with this project is to increase our confidence in the credibility of simulation models in the long term,” says Nicolas Swiergiel. “This will allow us to remove the largest tests from the campaigns, which we’re already trying to do now, to develop faster and to save approximately 25% of costs. The Virtual Testing concept is becoming concrete, and this platform is one of the key elements to achieve that goal”.


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