•Description of the gear profile requirement for continuous-contact helical gear pumps (CCHGP).•The derivation of the kinematic flowrate for CCHGP is derived.•A proof for CCHGP cancellation of ...kinematic flow ripple is provided.•A fluid dynamic simulation model for CCHGP is presented and validated against experiments.•The simulation results provide a justification for pressure ripple generation in CCHGP.
External gear pumps are one of the most commonly used types of positive displacement machines in high pressure hydraulic control systems, fuel-injection and fuel transport systems. Despite many merits of the traditional external gear pump design with involute teeth, the significant flow non-uniformity intrinsic of such design is considered to be a detrimental aspect, since it causes undesired noise emissions and mechanical vibrations. A disruptive concept of continuous-contact helical gear pumps (CCHGP) was proposed and successfully commercialized in the recent past. Such concept was proven to have clear advantages in terms of noise emissions. However, a clear interpretation of the displacing action and the transient features of the delivery flow was never addressed in past literature. This paper addresses this gap by first discussing the family of gear profiles suitable for implementing the CCHGP design. Subsequently, an analysis on the kinematic flow ripple is given, showing how such design concept can reduce or even eliminate the kinematic flow pulsations. The paper also presents a numerical approach for modelling the operation of CCHGPs, starting from the modeling of the geometric features necessary for a fluid dynamic analysis based on a lumped parameter approach. For model validation purposes, a commercial CCHGP was tested at the authors’ research center, and the simulation results were compared against the experiments, to show the level of accuracy of the model, as well as it potentials for future design studies.
•A fully coupled multi-domain multi-physics dynamic system is presented.•Three components of the loadings are explained and analyzed in detail.•The patterns of 3D transient loadings and dynamics are ...discussed with simulation results.•A design routine for the axial balancing mechanism of helical gear pumps is provided.•The pattern of wear seen from experiments can be replicated by the wear model.
This paper presents a multi-domain dynamic simulation model for continuous-contact helical gear pumps, which is an unconventional hydraulic pump design that is capable of canceling its kinematic flowrate variation. In this model, the fluid dynamics (in its lumped-element representation) are fully coupled in a closed feedback loop with other physical domains, including critical tribological interfaces, 3D dynamic loadings on the solid parts, dynamics, and micro-vibrations of the rotors. The model is compared to the analytical derivation and experimental measurements to prove its validity. The model can be used to provide predictive simulations for existing designs, or as a tool to optimize the design to provide better hydrostatic balancing for future iterations of hydraulic pump designs.
The paper investigates the degradation and contamination level of the hydraulic oil used on an endurance stand for testing two different types of gear pumps. Experimental results have been obtained ...using a Brookfield CAP2000+ viscometer, for determining the oil rheological parameters. Also, the paper presents the Parker CM Laser Particle Analyser used for measuring the size and density of the oil wear particles. The results focused on lubricant analysis represent practical ways to apply predictive and proactive maintenance for hydraulic gear pumps, but also for other hydrostatic equipment
Gear pumps represent the majority of the fixed displacement machines used for flow generation in fluid power systems. In this context, the paper presents a review of the different methodologies used ...in the last years for the simulation of the flow rates generated by gerotor, external gear and crescent pumps. As far as the lumped parameter models are concerned, different ways of selecting the control volumes into which the pump is split are analyzed and the main governing equations are presented. The principles and the applications of distributed models from 1D to 3D are reported. A specific section is dedicated to the methods for the evaluation of the necessary geometric quantities: analytic, numerical and Computer-Aided Design (CAD)-based. The more recent studies taking into account the influence on leakages of the interactions between the fluid and the mechanical parts are explained. Finally the models for the simulation of the fluid aeration are described. The review brings to evidence the increasing effort for improving the simulation models used for the design and the optimization of the gear machines.
•Modelling the compressibility of gas is critical for predicting its effects on cavitation.•Effect of increased gas (NCG) content in the fluid is to decrease the cavitation.•More flow fluctuations ...are observed at the outlet with higher NCG content.•Modelling contact between the gears is critical for accurate cavitation prediction.•Higher gear RPM results in higher rate of cavitation.
This paper presents a three-phase fully compressible model applied along with an immersed boundary model for predicting cavitation occurring in a two dimensional gear pump in the presence of non-condensable gas (NCG). Combination of these models is capable of overcoming numerical challenges such as modelling the contact between the gears and simulating the effect of NCG in cavitation. The model accounting for the effect of NCG also has broader applicability, since gas dissolved in liquids can come out of the solution when exposed to low pressures; this plays a significant role in the pump performance and cavitation erosion. Here the simulation results are presented for the gear pump at different operating conditions including the contact between gear, gear RPM and % of NCG; their effects on performance and cavitation is demonstrated. The results suggest that modelling the contact between the gears play a role in the cavitation prediction inside the gear pump. An increase in cavitation is observed when the contact is modelled even for the small pressure difference considered between the inlet and outlet. An increase in the RPM of the gears also results in increased cavitation within the pump, whereas an increase in the percentage of NCG content by a small amount can reduce the cavitation to a greater extent. This reduction is due to the expansion of the gas at a lower pressure which recovers the pressure and prevents or delays the phase-change process of the working fluid. The fluctuations in the outflow rate is also found to increase when the gears are in contact and also with increasing gas content.
This paper presents a numerical modeling approach for investigating crescent‐type internal gear pumps (CIGPs) considering radial micromotions of both gears. The modeling approach couples a lumped ...parameter pressure solver on the bulk fluid domain, with a transient computational fluid dynamics film simulator based on transient gap geometries solved from the gear loading forces. Simulation results on a commercial unit shows that for a wide range of operating conditions, the model gives volumetric efficiency predictions with errors less than 2 % comparing to measurements. To highlight the importance of considering radial micromotions, the paper also provides the results achieved assuming nominal position for both gears.
A numerical approach for investigating crescent‐type internal gear pumps, coupling a lumped parameter pressure solver with a CFD film simulator based on transient gap geometries solved from gear loading forces was proposed. Volumetric efficiency prediction was validated by measurements. The ring gear film leakage was identified as the major volumetric loss source in the unit by the model.
The design of gear pumps and motors is focused on more efficient units which are possible to achieve using advanced numerical simulation techniques. The flow that appears inside the gear pump is very ...complex, despite the simple design of the pump itself. The identification of fluid flow phenomena in areas inside the pump, considering the entire range of operating parameters, is a major challenge. This paper presents the results of simulation studies of leakages in axial and radial gaps in an external gear pump carried out for different gap shapes and sizes, as well as various operating parameters. To investigate the processes that affect pump efficiency and visualize the fluid flow phenomena during the pump’s operation, a CFD model was built. It allows for a detailed analysis of the impact of the gears’ eccentricity on leakages and pressure build-up on the circumference. Performed simulations made it possible to indicate the relationship between leakages resulting from the axial and radial gap, which has not been presented so far. To verify the CFD model, experimental investigations on the volumetric efficiency of the external gear pump were carried out. Good convergence of results was obtained; therefore, the presented CFD model is a universal tool in the study of flow inside external gear pumps.
The article analyzes the cavitation behaviour of gear pumps. If the liquid is more viscous and the liquid volume is underpressure less time, the dissolved air escapes in a significantly less amount ...than under a given pressure according to Henry's law.
The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various ...operating periods is not distributed evenly. Accurately diagnosing faults in internal gear pumps is significantly complicated by these factors. In light of these issues, accelerated life testing was performed in order to collect signals from an internal gear pump during various operating periods. Based on the architecture of a convolutional auto-encoder network, preprocessing of the signals in the various operating periods was performed to suppress noise and enhance operating period-representing features. Thereafter, variational mode decomposition was utilized to decompose the preprocessed signal into multiple intrinsic mode functions, and the multi-scale permutation entropy value was extracted for each intrinsic mode function to form a feature set. The feature set was subsequently divided into a training set and a test set, with the training set being trained to utilize a particle swarm optimization-least squares support vector machine network. For pattern recognition, the test set samples were fed into the trained model. The results demonstrated a 99.2% diagnostic accuracy. Compared to other methods of fault diagnosis, the proposed method is more effective and accurate.
The internal gear pump is simple in structure, small in size and light in weight. It is an important basic component that supports the development of hydraulic system with low noise. However, its ...working environment is harsh and complex, and there are hidden risks related to reliability and exposure of acoustic characteristics over the long term. In order to meet the requirements of reliability and low noise, it is very necessary to make models with strong theoretical value and practical significant to accurately monitor health and predict the remaininglife of the internal gear pump. This paper proposed a multi-channel internal gear pump health status management model based on Robust-ResNet. Robust-ResNet is an optimized ResNet model based on a step factor h in the Eulerian approach to enhance the robustness of the ResNet model. This model was a two-stage deep learning model that classified the current health status of internal gear pumps, and also predicted the remaining useful life (RUL) of internal gear pumps. The model was tested in an internal gear pump dataset collected by the authors. The model was also proven to be useful in the rolling bearing data from Case Western Reserve University (CWRU). The accuracy results of health status classification model were 99.96% and 99.94% in the two datasets. The accuracy of RUL prediction stage in the self-collected dataset was 99.53%. The results demonstrated that the proposed model achieved the best performance compared to other deep learning models and previous studies. The proposed method was also proven to have high inference speed; it could also achieve real-time monitoring of gear health management. This paper provides an extremely effective deep learning model for internal gear pump health management with great application value.
PDF
