Advances in Fluid Power Systems

The main purpose of this special edition of “Advances in Fluid Power Systems” was to present new scientific work in the field of fluid power systems for the hydraulic and pneumatic control of machines and devices that are used in various industries [...]

Exhaustive Regressor Search (XRS) for Creating Models of Hydraulic Pumps and Motors

Physical models of fluid power systems rely on the validity of the principles used for creating such models. In many cases, pump and motor performance is considered a large contributor to the efficiency of a whole fluid power system and, is used to approximate the behavior of the component and the system coupled to it. Often, estimates of the power losses and efficiency of pumps and motors is limited to manufacturer test data or simplified assumptions based on first principles. However, the use of the limited test data or idealized assumptions reduces the accuracy of the models and limits the validity of the theoretical results. Moreover, the creation of accurate physical models, their numerical implementation using a computer to solve the model and the experimental validation is time consuming and costly. New advances in machine learning, statistical analysis and numerical methods can be used to reduce the time used to develop a model of a pump or motor producing similar or better results. This paper proposes the use of an autonomous and iterative algorithm to obtain linear regression coefficients necessary to characterize the flow response of a pump or motor from existing experimental data. In this study a multivariate linear model for predicting the flow output of a pump or a motor is derived from experimental data by iteratively adding data points and by iteratively and autonomously testing regressor combinations to find the best possible flow model.

Consideration of Air Bubble Dynamics in 1D Hydraulic Pipeline Simulation – Source Term Development and Verification Utilizing Transmission Line Theory

In fluid power systems, the presence of undissolved air greatly influences the properties of the liquid-gas mixture. Even marginal amounts of undissolved air may drastically reduce the apparent bulk modulus of the mixture. In current state-of-the-art 1D simulation tools, the estimation of the apparent bulk modulus of the mixture is based on the assumption that both liquid and gas fractions act as springs. However, the so-called Rayleigh-Plesset equation frequently used for cavitation analysis shows that the gas bubbles should rather be regarded as non-linear mass-spring-damper systems, implicating a frequency-dependent stiffness of the gas phase. In the present paper, these dynamic effects are investigated by considering monodisperse as well as polydisperse mixtures. For the polydisperse case, a log-normal bubble size distribution is used. First, a frequency domain solution for the bubble dynamics is developed by linearizing the Rayleigh-Plesset equation. An expression of the mixture bulk modulus is derived, which is complex-valued and frequency-dependent. Based on the bulk modulus, a theoretical solution for the dynamics of a whole pipeline is developed by utilizing transmission line theory. It is shown that the dynamics of the bubbles leads to a significant shift of the system’s natural frequencies towards lower values — a phenomenon that needs to be accounted for during the design phase of a fluid power system. After the development of this analytical solution, by introducing a bubble dynamics source term, an established numerical scheme for 1D pipe simulation based on the method of characteristics is expanded. Finally, the newly developed numerical approach is compared with the analytical solution in order to determine its accuracy. The findings and simulation approaches in this work will enable fluid power system engineers to predict dynamic system behavior more precisely during early stages of system layout.

Machine Learning Prediction of Journal Bearing Pressure Distributions, Considering Elastic Deformation and Cavitation

This paper presents a machine learning neural network capable of approximating pressure as the distributive result of elastohydrodynamic effects and discusses results for a journal bearing at steady state. Design of efficient, reliable fluid power pumps and motors requires accurate models of lubricating interfaces; however, most existing simulation models are structured around numerical solutions to the Reynolds equation which involve nested iterative loops, leading to long simulation durations and limiting the ability to use such models in optimization studies. This study presents the development of a machine learning model capable of approximating the pressure solution of the Reynolds equation for given distributive geometric boundary conditions and considering cavitation and elastic deformation at steady-state operating conditions. The architecture selected for this study was an 8-layer U-Net convolutional neural network. A case study of a journal bearing was considered, and a 438-sample training set was generated using an in-house multiphysics simulator. After training, the neural network predicted pressure distributions for test samples with great accuracy, and accurately estimated resultant loads on the journal bearing shaft. Additionally, the neural network showed promise in analyzing geometric inputs outside the space of the training data, approximating the pressure in a grooved journal bearing with reasonable accuracy. These results demonstrate the potential to integrate a machine learning model into fluid power pump and motor simulations for faster performance during evaluation and optimization.

Real-Time Simulation of Fluid Power Systems

System-simulations involving fluid-power structures often result in numerically stiff model equations which may require prohibitively small simulation time steps when being tackled with a fixed-step solver. This poses a challenge in situations where real-time performance is required. This paper presents a practical rule-of-thumb to estimate the maximum permissible step-size for a given fluid power system and explains the influence of the relevant physical quantities on the step size requirement in simple terms. A categorization of methods suitable to relax the step-size requirement is proposed. Many research papers have been produced about methods and examples of how to improve real-time performance of fluid power systems, or stiff systems in general. The proposed categorization can be seen as a map for the simulation engineer to understand the basic point-of-attacks for the real-time simulation problem.

A Concept of Risk Prioritization in FMEA Analysis for Fluid Power Systems

FMEA analysis is a tool of quality improvement that has been widely used for decades. Its classical version prioritizes risk of failure by risk priority number (RPN). The RPN is a product of severity (S), occurrence (O), and detection (D), where all of the factors have equal levels of significance. This assumption is one of the most commonly criticized drawbacks, as it has given unreasonable results for real-world applications. The RPN can produce equal values for combinations of risk factors with different risk implications. Another issue is that of the uncertainties and subjectivities of information employed in FMEA analysis that may arise from lack of knowledge, experience, and employed linguistic terms. Many alternatives of risk assessment methods have been proposed to overcome the weaknesses of classical FMEA risk management in which we can distinguish methods of modification of RPN numbers of employing new tools. In this study, we propose a modification of the traditional RPN number. The main difference is that severity and occurrence are valued based on subfactors. The detection number remained unchanged. Additionally, the proposed method prioritizes risk in terms of implied risk to the systems by implementing functional failures (effects of potential failures). A typical fluid power system was used to illustrate the application of this method. The method showed the correct failure classification, which meets the industrial experience and other research results of failures of fluid power systems.