Abstract
This research presents a lumped parameter numerical model aimed at designing and optimizing an axial piston pump. For the first time, it has been shown that a lumped parameter model can accurately model axial piston pump dynamics based on a comparison with CFD models and experimental results. Since the method is much more efficient than CFD, it can optimize the design. Both steady-state and dynamic behaviors have been analyzed. The model results have been compared with experimental data, showing a good capacity in predicting the pump performance, including pressure ripple. The swashplate dynamics have been investigated experimentally, measuring the dynamic pressure which controls the pump displacement; a comparison with the numerical model results confirmed the high accuracy. An optimization process has been conducted on the valve plate geometry to control fluid-born noise by flow ripple reduction. The NLPQL algorithm is used since it is suitable for this study. The objective function to minimize is the well-known function, the Non-Uniformity Grade, a parameter directly correlated with flow ripple. A prototype of the best design has been realized and tested, confirming a reduction in the pressure ripple. An endurance test was also conducted. As predicted from the numerical model, a significant reduction of cavitation erosion was observed.
Noise Reduction of an Axial Piston Pump by Valve Plate Optimization
