Mechanisms-Based Analysis of Elastomeric Lag Damper Behavior

Soft-inplane helicopter rotors are susceptible to ground resonance and air resonance and need dampers to augment the stability of these unstable phenomena. Elastomeric dampers have been widely used in these rotors to stabilize the rotor lag mode. Linear mechanical models of the lag damper behavior, based on the complex modulus approach, capture the energy dissipation characteristics correctly, but do not correctly predict the non-linear force versus displacement hysteresis behavior. To correctly describe the rotor dynamics and stability properties, the hysteresis behavior of the damper must be accurately described using non-linear models. Existing models exhibit a pronounced amplitude dependence and are highly dependent on the test data. We propose a mechanisms-based model that can be easily integrated into comprehensive rotorcraft stability analyses without significantly increasing computational effort. We present test results for silicone-rubber based filled elastomer double lap shear specimens. These materials loss factors in the 0.8-1.2 range, which is substantially higher than existing lag dampers with loss factors in the 0.4 to 0.8 range. A mechanisms-based analysis is used to describe the behavior of these lag dampers under single-frequency and dual-frequency sinusoidal loading. The carried using an MTS servo-hydraulic testing machine for the following conditions: 1) single frequency sinusoidal excitation at lag/rev (5 Hz) or 1/rev (7.5 Hz) frequency, 2) dual frequency excitation as a sum of sinusoids at the lag/rev (5 Hz) and 1/rev (7.5 Hz) frequency 3) amplitudes ranging from 10 mils to 200 mils in increments of 10 mils repesenting a maximum shear strain of 50 %, 4) compressive preload of 10 % and no preload. All tests were done at room temperature. The damper model is the linear combination of a linear stiffness element, a viscous damping element, and an elasto-slide element. The hysteresis cycles predicted using the mechanisms-based model correlate well with experimental data.