Bulk Modulus Of Hydraulic Oil Research Articles

Integrated design of a novel force tracking electro-hydraulic actuator

Conventional hydraulic actuators in which the output force is regulated through servo-valves or variable pumps usually suffer from great noise and low efficiency. To overcome this hurdle, a novel force tracking electro-hydraulic actuator based on Pascal's principle is developed. The novel device uses an electric cylinder to change the volume of enclosed fluid and generate the desired loading pressure. In order to further improve the performance of the novel actuator, an integrated design procedure in which the mechanisms and control structure are designed simultaneously is studied. Firstly, the complete mathematical model of the novel actuator is established. The nonlinear system is translated into a linear time-varying model by replacing the effective bulk modulus of hydraulic oil with a time-varying coefficient. The feasibility of the semi-closed control structure with pressure feedback is analyzed on frequency domain and a compound controller is given. Secondly, performance indexes such as stability criteria, bandwidth, control errors and continuous operation capability of the novel actuator are presented in detail. Interactions of the design variables are analyzed through numerical simulations. Afterwards, a nonlinear constrained multi-objective optimization problem including both mechanical and control parameters is formulated. To prevent the optimization progress from falling into local minimum point, a particle swarm algorithm is employed to carry out the optimization design. Experiment results demonstrate the effectiveness of the proposed integrated design method. And simulations are executed to verify the higher efficiency of the proposed novel electro-hydraulic actuator which is compared with the valve-controlled counterpart.

Research on Theoretical Model of Dynamic Bulk Modulus of Gas-Containing Hydraulic Oil

The bulk modulus of the gas-containing hydraulic oil is an important indicator reflecting its compressibility, directly affecting the stability, accuracy, rapidity, and reliability of the fluid transmission system. In the time-varying system with high speed and pressure, since the gas release rate differs from the gas dissolution rate, there exists a large difference between the static and dynamic values of the bulk modulus of hydraulic oil. Therefore, it is necessary to research the dynamic bulk modulus of the gas-containing hydraulic oil. Based on the improvement of Henry's law and gas-polytropic-process equation, a modified model of dynamic bulk modulus of the gas-containing hydraulic oil was proposed. Through the analysis of parameter influence, the influence of initial gas content and pressure on the dynamic bulk modulus of the gas-containing hydraulic oil during compression and expansion was discussed. Comparing the modified model and the Zhou model with the experimental results, the goodness of fit between the Zhou model and the experimental results during compression and expansion was 0.9936 and 0.9864, respectively. And the goodness of fit between the modified model and the experimental results during compression and expansion was 0.9937 and 0.9936, respectively. The prediction precision of the dynamic bulk modulus was improved while verifying the accuracy of the modified model. This research can provide a reference for optimizing the theoretical model of the dynamic bulk modulus of the gas-containing hydraulic oil.

Nonlinear model of a servo-hydraulic shaking table with dynamic model of effective bulk modulus

Abstract In this paper, based on fluid mechanical expressions and a new modified effective bulk modulus model of hydraulic oil built upon IFAS model (developed at the Institute fur Fluidtechnische Antriebe und Steuerungen, RWTH Aachen university), an empirical nonlinear model for a servo-hydraulic uni-axial shaking table is developed. This new model can precisely simulate the acceleration, velocity and position outputs of the system with respect to different kinds of inputs such as pulse and sinusoidal signals for a wide range of frequencies and different weights of the specimen. Therefore, it can be helpful for designing and optimizing the parameters of a model-based controller for tracking reference force or acceleration signals, which is the goal of the shaking table with only position sensor. In the new modified IFAS model, the effective bulk modulus of hydraulic oil on both sides of the piston has been considered as two nonlinear springs, which are connected serially. The minimum stiffness of the spring effect of the hydraulic oil in a symmetric double-acting hydraulic cylinder occurs, when the piston is in the center of its travel, which can be characterized with differential pressure on its both sides. When the differential pressure is less than a specific threshold pressure, these springs have the minimum stiffness and reserve energy in themselves. Based on the experimental observations, this effect has been modeled with a function, which multiplies the IFAS model. The experimental acceleration output of the system demonstrates the dynamic behaviors of the effective bulk modulus of hydraulic oil, which occurs in the center of the piston travel. The parameters of the simulated model are estimated with the nonlinear least square method in MATLAB. Finally, the accuracy of the proposed model for simulating the motion states of the shaking table, by comparing the experimental and simulated results in different ranges of amplitudes and frequencies with respect to the new and the previous model of the hydraulic servo-systems have been shown.

Mathematical model for bulk modulus of hydraulic oil containing air bubbles

Mathematical model for bulk modulus of hydraulic oil containing air bubbles

Modeling and Experimental Validation of the Effective Bulk Modulus of a Mixture of Hydraulic Oil and Air

It is well known that the presence of entrained air bubbles in hydraulic oil can significantly reduce the effective bulk modulus of hydraulic oil. The effective bulk modulus of a mixture of oil and air as pressure changes is considerably different than when the oil and air are not mixed. Theoretical models have been proposed in the literature to simulate the pressure sensitivity of the effective bulk modulus of this mixture. However, limited amounts of experimental data are available to prove the validity of the models under various operating conditions. The major factors that affect pressure sensitivity of the effective bulk modulus of the mixture are the amount of air bubbles, their size and the distribution, and rate of compression of the mixture. An experimental apparatus was designed to investigate the effect of these variables on the effective bulk modulus of the mixture. The experimental results were compared with existing theoretical models, and it was found that the theoretical models only matched the experimental data under specific conditions. The purpose of this paper is to specify the conditions in which the current theoretical models can be used to represent the real behavior of the pressure sensitivity of the effective bulk modulus of the mixture. Additionally, a new theoretical model is proposed for situations where the current models fail to truly represent the experimental data.

Measurement of Effective Bulk Modulus for Hydraulic Oil at Low Pressure

Oil properties are very important input parameters for the simulation of hydraulic components. Precise values of effective bulk modulus at low pressures are especially required to improve the simulation accuracy of the pumps suction side or of cavitation in pumps or valves. So far, theoretical equations to compute the effective bulk modulus of hydraulic oil have not been experimentally verified, and only poor measured data are available to calculate the effective bulk modulus at low pressure. Therefore in this paper, the theoretical equation was verified for effective bulk moduli based on measurements of pressure change as a function of volume change at low pressures, varying temperature, entrained air content, and type of state change. Furthermore, the comparison of effective bulk moduli calculated with three different methods (mass-change, volume-change, and sound-speed method) shows that the effective bulk modulus can be calculated well from the measurement results of all three methods. The calculated effective bulk moduli values show little variation among the methods. Additionally, the release pressure of dissolved air in oil and the change of the polytropic gas constant depending on the speed of volume change rate were identified in this study.

Research and Online Measurement of Bulk Modulus of Hydraulic Oil

In view of requirements for better position accuracy, response time and stability of hydraulic system, it is more important than ever to pay attention to bulk modulus of hydraulic oil. The effects of the bulk modulus of hydraulic oil on system performance are analyzed. Based on the theoretical model, studies on the effects of air content, pressure and temperature on bulk modulus are done though simulation. An online measuring device of bulk modulus of hydraulic oil is designed according to the definition of bulk modulus. The simulation and experiment results show that the method of analysis and measurement of bulk modulus provided is very useful to confirming the oil bulk modulus in real hydraulic system.

Noise identification for hydraulic axial piston pump based on artificial neural networks

The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.