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Multi-Objective Optimization of Gerotor Port Design by Genetic Algorithm with Considerations on Kinematic vs. Actual Flow Ripple
Technical Paper
2019-01-0827
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The kinematic flow ripple for gerotor pumps is often used as a metric for comparison among different gearsets. However, compressibility, internal leakages, and throttling effects have an impact on the performance of the pump and cause the real flow ripple to deviate from the kinematic flow ripple. To counter this phenomenon, the ports can be designed to account for fluid effects to reduce the outlet flow ripple, internal pressure peaks, and localized cavitation due to throttling while simultaneously improving the volumetric efficiency. The design of the ports is typically heuristic, but a more advanced approach can be to use a numerical fluid model for virtual prototyping. In this work, a multi-objective optimization by genetic algorithm using an experimentally validated, lumped parameter, fluid-dynamic model is used to design the port geometry. This optimization is repeated for five pumps with different kinematic flow ripples, and the simulated performance of the pumps with optimized port geometries is compared to the kinematic flow in each case. The performance of the pumps with the optimized ports in each case was a significant improvement over the pumps with kinematically-timed ports. The kinematic flow ripple did not predict the exact shape of the simulated flow ripple with great accuracy, but it did predict the trend of the signal power of the simulated flow ripples very well. In addition to demonstrating a multi-objective optimization strategy for port geometry, this work also demonstrates that the kinematic flow ripple is a suitable design metric for comparing gerotor gearsets apart from a full fluid simulation.
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Robison, A. and Vacca, A., "Multi-Objective Optimization of Gerotor Port Design by Genetic Algorithm with Considerations on Kinematic vs. Actual Flow Ripple," SAE Technical Paper 2019-01-0827, 2019, https://doi.org/10.4271/2019-01-0827.Data Sets - Support Documents
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