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 computational fluid dynamic (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 nonlinear programming by quadratic Lagrangian (NLPQL) algorithm is used since it is suitable for this study. The objective function to minimize is the well-known function, the nonuniformity grade (NUG), 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.
|Original language||English (US)|
|Journal||Journal of Fluids Engineering, Transactions of the ASME|
|State||Published - Sep 1 2021|
Bibliographical noteFunding Information:
A prototype pump’s mathematical model has been developed and then validated with data supplied by Duplomatic MS, the pump manufacturer. We appreciate the support from Michele Pavanetto and Alessandro Corrente of Duplomatic MS, from Raf-faele Invrea, Bryan Edlund, and Travis Piehl of Continental Hydraulics, Inc., from Ilaria Garofalo of BSIM s.r.l., and from Gennaro Stingo and Giuseppe Iovino of the Department of Industrial Engineering—University of Naples “Federico II.” This research has been part of a Ph.D. Program supported by the MIUR (Ministero dell’Istruzione dell’Universitàe della Ricerca) of the Italian Government; it involves two Universities (the University of Naples “Federico II” and the University of Minnesota) and the international company Duplomatic MS.
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