Low-Order Modeling of Freely Vibrating Flexible Cables Public
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A low-order, dynamical systems approach is applied to the modeling of flow induced vibrations of flexible cables. By combining a coupled map lattice wake model with a linear wave equation cable model, both the free response of the cable as well as the resulting wake structures are examined. This represents an extension of earlier coupled map lattice models that only modeled the wake of forced cable vibration. The validity of the model is assessed through comparisons with both Computational Fluid Dynamics models (NEKTAR spectral element code) and wake experiments. The experimental wake data was collected through the use of hot-film anemometry techniques. Eight hot-film probes were placed along the span of a flexible cable mounted in the test section of a water tunnel. Through the use of frequency domain correlation algorithms, the phase of vortex shedding was calculated along the cable span from the hot-film velocity data. Results for an elastically mounted rigid cylinder showed that the freely vibrating CML model predicted behavior characteristic of a self-induced oscillator; the maximum amplitude of vibration was found to occur at a cylinder natural frequency that did not coincide identically with the natural shedding frequency of the cylinder. Furthermore, the variation of the frequency of cylinder vibration with its natural frequency was seen to be linear. For standing wave cable responses, the freely vibrating CML model predicted lace-like wake structures. This result is qualitatively consistent with both the NEKTAR simulations and experimental results. Little difference was found between the wakes of forced and freely vibrating cables at the Reynolds number of the study $Re=100$. Finally, it was found that the freely vibrating CML could match numerical predictions of cross-flow amplitude as the cable mass-damping parameter was varied over an order of magnitude (once the CML was tuned to match results at a specific mass-damping level). In addition to providing wake patterns for comparisons with the freely vibrating CML, experimental data was supplied to a self-learning CML scheme. This self-learning CML was able to estimate the experimental wake data with good accuracy. The self-learning CML is seen as the next extension of the freely-vibrating CML model, capable of estimating unmodeled wake dynamics through the use of experimental data.
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