Numerical modeling of the hydraulic GEROLER motor using the artificial neural network
AbstractThis paper deals with the numerical modeling of the hydraulic GEROLER motor. GEROLER motors are known for their cost effectiveness and balance between simplicity, robustness, compactness, versatility, and noise. The analysis was carried out using a black-box approach taking into account the nonlinear dynamic behavior of the GEROLER motor. The artificial neural networks method was used to define the black-box model. Two models of neural networks were used: the first one comprising multi-layer feed-forward neural networks and the second one the NARX dynamic neural networks. The results obtained by the numerical simulations of the GEROLER motor model were compared with the experimental measurements performed on a laboratory hydraulic system. The derived model has provided results that allow a high degree of a generalized approach to the motor design.
Copyright 2022 by Faculty of Engineering University of Rijeka, Faculty of Civil Engineering University of Rijeka. All rights reserved. This material may not be reproduced or copied, in whole or in part, in any printed, mechanical, electronic, film, or other distribution and storage media without the written consent of the publisher.
The journal Engineering Review’s publishing procedure is performed in accordance with the publishing ethics statements, defined within the Publishing Ethics Resource Kit. The Ethics statement is available in the document Ethics Policies.