Win-win: Model-Based System Testing

Efficient collaboration between simulation and test

How can you maintain an efficient product development process and easily manage a growing number of variants?

Bring simulation and test together in synergy and overcome modelling and validation challenges with the Simcenter platform.

You can benefit in three ways:

  1. Test for simulation: Validate simulation models with real-life tests
  2. Test with simulation: Combine use of test and simulation with in-the-loop testing and virtual sensing
  3. Simulation for test: Apply simulation for more productive and realistic testing

Validating a product using a combination of FE Model simulation and testing poses significant challenges. At the root, it comes down to how to ensure your FE model represents and corresponds to reality.

How can you:

  • Create and maintain a representative FE model?
  • Define and perform cost-effective tests?
  • Evaluate the accuracy of the FE model?
  • Update the FE model to match reality?
  • Easily perform multi-discipline simulations on a validated FE model?

This post addresses three of these central questions.

1. How can you define and perform a cost-effective test?

Before performing a test, you must define the requirements. These include:

  • Which modes the test will attempt to validate
  • The test configuration and boundary conditions
  • How many sensors and exciters are needed, where they will be located and in which directions they will point

If the sensors and exciters are not well selected, the result may be schedule and cost overruns. Sensors must be defined in a way that the required modes can be clearly identified. Often, this requires that the sensors be geometrically spread out over the structure.

Typically, data must be exchanged several times between the simulation and test groups. It’s important that this data exchange between simulation and test be seamless, and that it include units, local coordinate systems, real and complex mode shapes, as well as plotting elements.

To further increase confidence in the validation, use the Simcenter pre-test simulation, test data acquisition, and modal estimation tools.

2. How accurate is the FE model?

Test–analysis correlation requires coherent simulation and test datasets. But with data coming from different environments in a variety of formats and units, how can these be brought together properly?

For example, FE model data can be provided in one of many solver formats. Tests can be conducted using different frames of reference and different units than those used in simulation. FE correlation manages all this data from various environments, and makes it user-friendly. Simcenter 3D FE Model Correlation supports the native TestLab format and provides the quantitative and qualitative means to compare test and analysis mode shapes.

What if the correlation is poor and far from the limit? One approach is to visually compare test and analysis FRFs and mode shapes. Ideally, you don’t want to spend time reviewing 60 FRFs when you have a discrepancy. FE Model Correlation allows you to use the MAC or Orthogonality metric to compare test and analysis shapes and FRFs.

3. How can you update the FE model to match reality?

Bringing it all together, TestLab makes it possible to easily perform 1D simulation correlation with access to the Amesim data. You can:

  • Correlate any model exported as FMU in Simcenter Testlab
  • Directly access / update the FMU parameters in Testlab Process designer
  • Export correlated FMU model for further use in other environments

This is just a brief glimpse at the benefits and workflow improvements that can be achieved with synergistic simulation-test capabilities. There’s more to explore and understand.

This on-demand webinar offers an in-depth look at common questions and challenges of model-based system testing, providing solutions and insight into how you can get the most out of your product development with cost-effective testing and validation.

Check it out to learn more about:

  • Defining and performing cost effective dynamics tests
  • Reducing iterations between platforms
  • Defining and performing cost effective dynamics tests (fully consistent analytics between test and simulation with easy overlay and correlation of data)
  • Updating the FE model to match reality
  • Seamlessly performing multi-discipline simulations on the validated FE model

Watch the webinar

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