One of the main parts in hydraulic system is directional control valve, which is needed in order to operate hydraulic actuator. Practically, a conventional directional control valve has complex construction and moving parts, such as spool. Alternatively, a proposed Magneto-rheological (MR) directional control valve can offer a better solution without any moving parts by means of MR fluid. MR fluid consists of stable suspension of micro-sized magnetic particles dispersed in carrier medium like hydrocarbon oil. The main objectives of this present research are to design a MR directional control valve using MR fluid, to analyse its magnetic circuit using FEMM software, and to study and simulate the performance of this valve. In this research, a comprehensive literature review on the advancement of this technology provides valuable insight on MR valve design by previous researchers. The design of MR directional control valve, the construction of the valve and the principle of work are presented. The design of proposed MR single valve has enabled the development of the MR directional control valve. Design and finite elements analysis using FEMM software of the MR single valve and MR directional control valve were done to obtain the optimal design. The valve was fabricated and the experimental rig for valve test was developed. The experiment presentation for functional working principle of the valve and valve performance were shown. The results of the simulation results show that the valve works in controlling the direction and the speed of hydraulic actuators. The valve can be operated with variable flow rate by varying the electric current. It is found that the electric current is inversely proportional to the flow rate. High current produces low flow rate and vice versa. It does the work of the valves to proportionally control the hydraulic actuators. The valve can be operated as ON-OFF directional control valve as well as proportional directional control valve with meter-in or meter-out by changing electric connection.
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This article aims to provide a bibliometric analysis of intellectual capital research published in the Scopus database from 1956 to 2020 to trace the development of scientific activities that can pave the way for future studies by shedding light on the gaps in the field. The analysis focuses on 638 intellectual capital-related papers published in the Scopus database over 60 years, drawing upon a bibliometric analysis using VOSviewer. This paper highlights the mainstream of the current research in the intellectual capital field, based on the Scopus database, by presenting a detailed bibliometric analysis of the trend and development of intellectual capital research in the past six decades, including journals, authors, countries, inst
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The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
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