The mathematical modeling of journal bearings has advanced significantly since the Reynolds equation was first proposed. Advances in the processing capacity of computers and numerical techniques led to multi-physical models that are able to describe the behavior of hydrodynamic bearings. However, many researchers prefer to apply simple models of these components in rotor-bearing analyses due to the computational effort that complex models require. Surrogate modeling techniques are statistical procedures that can be applied to represent complex models. In the present work, Kriging models are formulated to substitute the thermohydrodynamic (THD) models of three different bearings found in a Francis hydropower unit, namely a cylindrical journal (CJ) bearing, a tilting-pad journal bearing (TPJ) bearing, and a tilting-pad thrust (TPT) bearing. The results determined by using the proposed approach reveal that Kriging models can be satisfactorily used as surrogate THD-models of hydrodynamic bearings.
Vibration control in supercritical rotors is a challenging task due to the underlying complex dynamics that high-speed machinery undergoes. When taking into account both structural (e.g., structural integrity and material properties) and operational (e.g., rotational speed variation and environmental effects) uncertainties that can plague such systems, the task of designing and implementing a robust and reliable control system becomes increasingly daunting. In this contribution, a Kriging modeling approach is used to derive a representative surrogate-based controller applied to a flexible rotor supported by active magnetic bearings. The surrogate controller is formulated by considering the relationship between the shaft position and actuator current. This means that after proper training, the surrogate control takes the rotor position (proximity sensor readings) as input, and the output corresponds to the current levels for the actuators to control the system vibration amplitudes. Experimental tests considering a test rig composed of a flexible rotor supported by two active magnetic bearings were used to validate the proposed approach for different operational scenarios, namely, steady-state, run-up, and transient-state. In this case, the surrogate model was trained considering the shaft positions and the corresponding control currents measured with the rotor operating close to its first bending critical speed. The obtained results illustrate how the Kriging-based surrogate controller can reliably maintain acceptable vibration levels, even when exposed to unexpected constant rotation speeds and other operating conditions not manifested in previous sample sets.
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