June 2024

News for the SDTools community
June 2024 Visit SDTools website Connect on Youtube Connect on ReasearchGate Connect on LinkedIn Connect on GitHub Contact us

[Conference] Hybrid FEM/test twin building, an electric engine case history

SDTools was present at the CSMA 2024 conference https://csma2024.sciencesconf.org/

The talk has addressed challenges associated to the generation of a Hybrid FEM/test twin model of an automotive electric car engine in partnership with Stellantis:

  • Describing test outputs
  • Choosing model parameters
  • Building a reduced parametric model
  • Building a Hybrid FEM/test twin model

Read post… 📖 Read conference paper…

[Release] SDT 7.5 has been released

Last year’s orientations focused on

  • GUI implementation and customization capabilities to answer customer requests on making numerical and test processes accessible through GUI.
  • Parametric superelement/reduced model handling, that is a key capability of SDT and is under continuous development. It enables industrial scalability (external software interaction, post-treatments, data volume management…).
  • Test handling for experimental vibration applications progress. Now integrates parametric tests and improvements for Siemens TestLab compatibility.
  • Custom solvers various optimization.
  • Piezo capabilities for active control and SHM applications.

Read post… Read release notes…

[Expertise] Solutions for flange contact modelling in structural dynamics

Flange support around fasteners is a commonly overlooked topic in industrial structural dynamics applications. It is due to implementation complexity in already large models, and the difficulty of finding relevant values for input parameters. It is however a very sensitive aspect that can prevent from validating a model when ignored.

Check out our take on the subject for vibration applications, and how a zero thickness (ZT) element based implementation can be ported to other codes for simulation process integration

Read post…

[Project] Identification of material property dependency to temperature (Source project)

🤝 In partnership with the “SOURCE” ANR project, SDT has been used by Nassim Benbara, Marc Rébillat and Nazih Mechbal from the DISCoH team in Laboratoire PIMM to identify a polypropylene plate material property temperature dependency, with some surprises on the Poisson coefficient evolution.

A summary of this work is the object of a news post in our website
Read post…

More detailed explanations (among other results) have been published in the Journal of Sound and Vibration
Connect on ReasearchGate Read paper…
and the “SOURCE” ANR project presentation page is available at
📚 Read project presentation…


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Identification of material property temperature dependency

In partnership with the “SOURCE” ANR project, SDT has been used by the DISCoH researcher’s team from PIMM laboratory (Nassim Benbara, Marc Rebillat and Nazih Mechbal) to identify a polypropylene plate material property temperature dependency. A summary of this work is provided below and more detailed explanations (among other results) have been published in [1].

The plate is fitted with two piezoelectric patches, one used as an actuator, the other as a sensor. A sweep signal (100 – 1500 Hz) is sent to the actuator and the transfer function between this signal and the voltage measured by the sensor is built.

To assess the influence of temperature, the plate is placed in an oven equipped with a temperature sensor. It is drilled at two corners and suspended in the oven using two nylon wires, which effectively decouple the vibratory behavior of the plate itself from its environment (grid, vibration of the oven’s internal walls, etc.).

The measurement protocol is defined as follows:

  • A temperature hold is applied to homogenize the plate temperature.
  • A temperature ramp from 0 to 60°C is defined, slow enough to avoid significant temperature gradient across the plate (approx. 1°C/min).
  • An automatic measurement is triggered every two minutes to obtain a series of transfers at different temperatures.

To assess temperature influence, the strategy is to identify the system’s modes (frequency and damping) and analyze their evolution with temperature. A dedicated strategy has been implemented in the SDT toolbox to avoid manually identifying all the transfers, and to enable automated post-processing of the results:

  • Initialize modes on transfers corresponding to the first temperature
  • Automatic, sequential optimization of poles from one temperature to another
  • Construction of a database containing the evolution of all frequencies and modal damping associated with each measurement temperature.
  • Generate curves to analyze results.

Analysis of the evolution curves shows that the modes are highly sensitive to temperature, with maximum damping of around 4% around 20°C. Modal frequencies evolve by +/- 25% compared to the reference temperature of 25.4°C.

The evolution with temperature of each mode is very similar, which was expected given that the material can be considered isotropic: the evolution of the Young’s modulus E(T) impacts all modes equally. The small differences can be explained by

  • a possible degree of anisotropy
  • a temperature gradient in the plate
  • identification bias

In addition to analyzing the influence of temperature on modes, the second objective was to identify the relation between the Young’s modulus and temperature. To achieve this, the system has been modelled in SDT, including the piezoelectric patches whose geometry and properties are available in SDT libraries.

Classical model updating would have tried to minimize the difference between the model and test mode frequencies. The PIMM’s researchers preferred to work on the comparison of transfers directly. The advantage of this strategy is that modal identification is no longer necessary, provided that the test is of good quality, and that the modes haven been first identified. The simulated transfer is obtained in SDT by entering the actuator voltage as input and the receiver voltage as output. The aim is then to use an optimization loop to find the material parameters that minimize the difference between synthesized and measured transfers, for all temperatures.

To limit the number of unknown parameters in this work, the loss factor was fixed to twice the average identified damping at each temperature. First updating attempts showed that it was difficult to obtain a good superposition by only varying the Young’s modulus. Poisson’s ratio was thus considered as an additional parameter. This greatly improved trtansfers superposition and led to the identification of the following material dependency to temperature:

The study’s strategy implemented in SDT to identify model parameters is finally summarized in the figure below.

To be completely independent from modal identification (and perhaps to further improve the quality of recalibration), material loss factor should also be a model parameter, in the same way as Young’s modulus and Poisson’s ratio. This was kept as an improvement perspective of this work.

It is worth mentioning that the influence of Poisson’s ratio on model updating has been little studied and that the variation, at least for this specific test case, is not negligible.


[1] Bending waves focusing in arbitrary shaped plate-like structures: Study of temperature effects, development of a digital twin and of an associated neural-network based compensation procedure. 
N. Bernbara, G. Martin, M. Rébillat, N. Mechbal. Journal of Sound and Vibration,Volume 526, 2022.

CSMA 2024 : Hybrid FEM/test twin building, an electric engine case history

SDTools will be present at the CSMA 2024 conference https://csma2024.sciencesconf.org/

The talk [1] to be given will address challenges associated to the generation of a Hybrid FEM/test twin model of an automotive electric car engine in partnership with Stellantis:

  • Describing test outputs
    The electric engine case study being detailed combines strong dominance of harmonic responses and un-measured inputs. The harmonic balance vector signal model chosen gives a space/time/frequency approximation of the response.
  • Choosing model parameters
    Geometry, contacts in bolted joints and laminated stacks, non-linear viscoelastic bushings have here a notable impact.
  • Building a reduced parametric model
    This provides a 2 to 3 orders of magnitude speedup that is necessary for any practical application.
  • Building a Hybrid FEM/test twin model
    Test and FEM are combined using an expansion-based state/parameter estimation process.

[1] https://csma2024.sciencesconf.org/499609/document