Abstract:Purpose -The purpose of this paper is to investigate experimentally slippers, which have an important role on power dissipation in the swash plate axial piston pumps. Since slippers affect the performance of the system considerably, the effects of surface roughness on lubrication have been studied in slippers with varying hydrostatic bearing areas and surface roughness. Design/methodology/approach -An experimental set-up was designed to determine the performance of slippers, which are capable of increasing the… Show more
“…In the ANN model, however, two layers were used. The neuron numbers in the hidden layers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and the activation functions in (3)-(5) were tried, in all combinations. The model with the lowest MSE (2.6558e-05) had 16 and 13 neurons in the first and second hidden layers, respectively, and incorporated the tansig activation function.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, the thickness of the oil film between the slipper and swash plate adjusts, and the generated compressive force remains in constant balance. As apparent from Figure 1 which is caused by the supply pressure ( ) and acts on the slipper, is balanced by the pressure between the slipper bearing surface and the anticoincident elements [7]. The PD on land under these circumstances can be explained through solution of the Reynolds equation in cylindrical coordinates for flat slipper including average oil film thickness [2]:…”
Section: Theory Of the Systemmentioning
confidence: 99%
“…In an experimental study, Canbulut et al [7] investigated the power loss in APPs and the effect of surface roughness on the slipper. They designed an experiment setup to 2 Mathematical Problems in Engineering determine the slipper performance under different operational conditions.…”
The pressure distribution (PD) and leakage between the slipper and swash plate in an axial piston pump (APP) have a considerable impact on the pump efficiency, affecting aspects such as the load bearing and wear performance of the slipper. Herein, multigene genetic programming (MGGP) and artificial neural network (ANN) machine learning methods (MLMs) are incorporated into a novel approach towards predictive modelling of the PD and leakage on the slipper, which can function hydrostatically/hydrodynamically. Experimentally measured data are used as input for the MGGP and ANN models. The validity of the MGGP and ANN models is verified using test data excluded from the analyses. In addition, the model results are compared with analytic equations (AEs). Both MLMs are found to exhibit strong agreement with the measured data. In particular, the ANN model exhibits superior prediction performance to the MGGP model and AEs.
“…In the ANN model, however, two layers were used. The neuron numbers in the hidden layers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) and the activation functions in (3)-(5) were tried, in all combinations. The model with the lowest MSE (2.6558e-05) had 16 and 13 neurons in the first and second hidden layers, respectively, and incorporated the tansig activation function.…”
Section: Resultsmentioning
confidence: 99%
“…Thus, the thickness of the oil film between the slipper and swash plate adjusts, and the generated compressive force remains in constant balance. As apparent from Figure 1 which is caused by the supply pressure ( ) and acts on the slipper, is balanced by the pressure between the slipper bearing surface and the anticoincident elements [7]. The PD on land under these circumstances can be explained through solution of the Reynolds equation in cylindrical coordinates for flat slipper including average oil film thickness [2]:…”
Section: Theory Of the Systemmentioning
confidence: 99%
“…In an experimental study, Canbulut et al [7] investigated the power loss in APPs and the effect of surface roughness on the slipper. They designed an experiment setup to 2 Mathematical Problems in Engineering determine the slipper performance under different operational conditions.…”
The pressure distribution (PD) and leakage between the slipper and swash plate in an axial piston pump (APP) have a considerable impact on the pump efficiency, affecting aspects such as the load bearing and wear performance of the slipper. Herein, multigene genetic programming (MGGP) and artificial neural network (ANN) machine learning methods (MLMs) are incorporated into a novel approach towards predictive modelling of the PD and leakage on the slipper, which can function hydrostatically/hydrodynamically. Experimentally measured data are used as input for the MGGP and ANN models. The validity of the MGGP and ANN models is verified using test data excluded from the analyses. In addition, the model results are compared with analytic equations (AEs). Both MLMs are found to exhibit strong agreement with the measured data. In particular, the ANN model exhibits superior prediction performance to the MGGP model and AEs.
“…The operating principle of strain transducers lies in changing the electrical resistance of conductors fixed on the elastic member (membrane) depending on their deformation caused by the process pressure [14].…”
Section: Methods Content and Assessment Of Its Efficiencymentioning
Drilling mud circulation is performed by means of positive piston-type mud pumps which cause nonuniform flow in the drill column. Fluctuations of circulation rate and mud pressure result in increasing the dynamics of the drilling tool, early wear and operating trouble of the drill bit, reservoir depressurization, unstable running and breakdowns of downhole drilling motors, operating life reduction of pump components. Delivery line is under the influence of high pressure with a significant value of its fluctuation which causes negative consequences. The paper is devoted to the developed downhole hydromechanical compensator of pressure fluctuation of circulating fluid mounted into the assembly of drilling string bottom. Moreover, it focuses on the description of the laboratory bench for testing the compensator and the obtained results.
“…Only by the test in real condition to verify the accuracy of the theoretical models, can the study on its lubricating performance truly progress. The experimental study on the slipper/swashplate interface also experiences the evolution from static to dynamic [17], from steady to unsteady [18], from indirect to direct measurement [19], from model test to real pump test [20,21]. Chao et al [3] presented a review of the test rigs of the slipper bearing in axial piston pump.…”
The interface between the slipper/swash plate is one of the most important frication pairs in axial piston pumps. The test of this interface in a real pump is very challenging. In this paper, a novel pump prototype is designed and a test rig is set up to study the dynamic lubricating performance of the slipper/swash-plate interface in axial piston machines. Such an experimental setup can simulate the operating condition of a real axial piston pump without changing the relative motion relationship of the interfaces. Considering the lubricant oil film thickness as the main measurement parameter, the attitude of the slipper under the conditions of different load pressure, rotation speed and charge pressure are studied experimentally. After the test, the wear state of the swash plate is observed. According to the friction trace on the surface of the swash plate, the prediction for the attitude of the slipper and the zone easy to wear are verified.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.