Empirical Models for the Viscoelastic Complex Modulus with an Application to Rubber Friction
| Autor: | Kyriakos Grigoriadis, Marco Furlan Tassara, Georgios Mavros |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
viscoelastic modulus QH301-705.5 QC1-999 Quantitative Biology::Tissues and Organs friction rubber Modulus 02 engineering and technology Viscoelasticity Physics::Fluid Dynamics Condensed Matter::Materials Science 0203 mechanical engineering Natural rubber Dynamic modulus General Materials Science Biology (General) Instrumentation QD1-999 Mathematics Fluid Flow and Transfer Processes Polynomial (hyperelastic model) empirical modeling Process Chemistry and Technology Physics Mathematical analysis General Engineering Dynamic mechanical analysis 021001 nanoscience & nanotechnology Engineering (General). Civil engineering (General) Physics::Classical Physics Computer Science Applications Condensed Matter::Soft Condensed Matter Chemistry 020303 mechanical engineering & transports visual_art Piecewise visual_art.visual_art_medium TA1-2040 0210 nano-technology Dynamic testing |
| Zdroj: | Applied Sciences Volume 11 Issue 11 Applied Sciences, Vol 11, Iss 4831, p 4831 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11114831 |
| Popis: | Up-to-date predictive rubber friction models require viscoelastic modulus information thus, the accurate representation of storage and loss modulus components is fundamental. This study presents two separate empirical formulations for the complex moduli of viscoelastic materials such as rubber. The majority of complex modulus models found in the literature are based on tabulated dynamic testing data. A wide range of experimentally obtained rubber moduli are used in this study, such as SBR (styrene-butadiene rubber), reinforced SBR with filler particles and typical passenger car tyre rubber. The proposed formulations offer significantly faster computation times compared to tabulated/interpolated data and an accurate reconstruction of the viscoelastic frequency response. They also link the model coefficients with critical sections of the data, such as the gradient of the slope in the storage modulus, or the peak values in loss tangent and loss modulus. One of the models is based on piecewise polynomial fitting and offers versatility by increasing the number of polynomial functions used to achieve better fitting, but with additional pre-processing time. The other model uses a pair of logistic-bell functions and provides a robust fitting capability and the fastest identification, as it requires a reduced number of parameters. Both models offer good correlations with measured data, and their computational efficiency was demonstrated via implementation in Persson’s friction model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|