Trimmed Body Static Stiffness Identification Using Dynamic Measurements: Test Methodology and Correlation with CAE Results 2018-01-1496
A key metric of a car body structure is the body stiffness, which shows significant correlation with different vehicle performance attributes as NVH, comfort and vehicle handling.
Typical approaches to identify static stiffness characteristics are the use of a static stiffness test bench or the ‘static-from-dynamic’ approach in which free-free acquired transfer functions are used to build a modal model from which the static stiffness characteristics are extracted. Both of these approaches have limitations, the static stiffness bench with respect to clamping conditions and reproducing those in CAE, the static-from-dynamic with respect to the modal analysis (EMA) that needs to be performed. EMA is a subjective process, which can limit result robustness. In addition, performing EMA on a trimmed body is difficult due to the high modal density and the high level of damping. Strong benefit however of the static-from-dynamic approach is the ability to characterize the body stiffness without need for clamping of the structure.
In this paper a robust static-from-dynamic approach is described that allows static stiffness identification not only for Body-in-White but also on Trimmed Body structures. High robustness and accuracy is achieved by building a Trimmed Body modal model with a semi-automated EMA approach (Maximum Likelihood Modal Model). Result validation is done by comparison of the Trimmed Body hard-point static stiffness from Test with CAE results. Both global stiffness (torsion, bending) and local stiffness characteristics (hard-points) can be identified with this approach, enabling definition and evaluation of more localized body targets.
Citation: Ottaiano, S., Geluk, T., Teipen, E., and El-Kafafy, M., "Trimmed Body Static Stiffness Identification Using Dynamic Measurements: Test Methodology and Correlation with CAE Results," SAE Technical Paper 2018-01-1496, 2018, https://doi.org/10.4271/2018-01-1496. Download Citation
Simona Anna Ottaiano, Theo Geluk, Elmar Teipen, Mahmoud El-Kafafy
Siemens Industry Software, Volkswagen AG, Vrije Universiteit Brussel (VUB)
10th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference