PetroVietnam University

Одной из приоритетных задач Комплексной программы инновационного развития холдинга «РЖД» является цифровизация и автоматизация технологических процессов. Реализуемый в компании проект «Цифровая железнодорожная станция» [1–3] (ЦЖС) входит в состав комплексного научно-технического проекта «Цифровая железная дорога», внедряемого в рамках мероприятий по реализации программы «Цифровая экономика Российской Федерации». В ходе реализации проекта ЦЖС разработаны программно-аппаратные комплексы для автоматизации действий дежурного по станции в процессе приготовления маршрутов и контроля ситуации при поездных и маневровых передвижениях. В статье рассказано об Автоматическом комплексе информирования машиниста с интеллектуальным распознаванием речи АКИМ, который предназначен для автоматической передачи информации о приготовленном маршруте машинистам поездных локомотивов. АКИМ планируется применять в составе Цифровой железнодорожной станции для обеспечения выполнения регламента переговоров. One of the priority tasks of the Comprehensive Program innovative development of the holding “Russian Railways” is the digitalization and automation of technological processes. Implemented in the company the project “Digital railway station” (DJS), is part of the complex scientific and technical project “Digital railway”, implemented as part of the implementation of the measures for the implementation of the program “Digital economy of the Russian Federation”. During the implementation of the CJC project, software and hardware complexes were developed to automate the actions of the station attendant in the process of preparing routes and monitoring the situation during train shunting movements. The article describes an Automatic driver information System with intelligent speech recognition AKIM, which is designed to automatically transmit information about the prepared route to train locomotive drivers. AKIM is planned to be used as part of a Digital Railway Station to ensure compliance with the negotiation rules.

Abstract Magnetic flux leakage (MFL) testing is a well-established method for detecting metal loss in ferromagnetic structures, including pipelines, storage tanks, and offshore facilities. The detection performance depends not only on the sensing probe but also on the coupled behavior of permanent magnets (PMs), magnetic bridge, air gap, and specimen properties, as well as the operating point on the material’s B–H curve. This study introduces a design framework that systematically links inspection requirements, such as material type, thickness, target defect, and lift-off, to the development of a compact, high-performance magnetizer. The framework involves: (i) selecting the specimen’s operating point via B–H data; (ii) establishing a magnetic-circuit load line that couples pole geometry, air gap, and specimen field to the PM demagnetization curve; (iii) optimizing PM dimensions via constrained optimization to minimize magnet mass while satisfying field and saturation constraints; and (iv) defining a bridge-thickness criterion to avoid premature saturation. Validation through finite element simulations and a bench test on steel plates with artificial defects demonstrates close agreement with theoretical predictions and confirms the dominant role of bridge thickness in determining signal contrast. The proposed workflow provides a reproducible path from inspection requirements to hardware realization, thereby enhancing MFL sensitivity for safer evaluation of coastal and offshore petroleum infrastructure.

This study aims to develop a robust and computationally efficient forward model for surface defect characterization in Magnetic Flux Leakage (MFL) testing. The proposed approach extends the Magnetic Dipole Model (MDM) into a Numerical Magnetic Dipole Model (NMDM), enabling the simulation of MFL signals for complex defect geometries. A comprehensive evaluation of multiple defect cases demonstrates the model’s effectiveness. First, the NMDM achieves superior accuracy over analytical MDMs when applied to ellipsoidal defect, with the predicted radial component closely matching Finite Element Method (FEM) simulations, yielding a root mean square error (RMSE) of 3.84%. Second, for complex defects, the method maintains high precision, with RMSE values below 6% relative to FEM results. Third, for corrosion-like defects exhibiting gradual depth variations, the model retains high accuracy, achieving a RMSE of 3.94% compared to experimental data. However, for artificial defects with abrupt depth changes, the prediction error increases, reaching a maximum RMSE of 9.01%. Despite this, the method remains computationally efficient, achieving a runtime of under 1 second for predicting MFL signals at 100 field points. These results confirm that NMDM is a robust and practical approach for corrosion defect characterization, particularly when maintaining a sensor lift-off distance below 2 mm to ensure accurate defect reconstruction.

В статье рассказано о модуле автоматической подготовки поездных и маневровых маршрутов, который является одним из важнейших элементов Цифровой железнодорожной станции. Он предназначен для реализации режима автоматического исполнения операций по установке маршрутов (определения трассы маршрута), обеспечения движения поездов и маневровых составов, а также специального самоходного подвижного состава. Модуль осуществляет управление не только поездными, но и маневровыми передвижениями. Он динамически корректирует трассы маршрутов для исключения создания враждебности и обеспечивает максимально эффективную производительность станции. Модуль автоматической подготовки поездных и маневровых маршрутов в автономном режиме с августа текущего года введен в опытную эксплуатацию на станции Челябинск-Главный Южно-Уральской дороги. The article describes the Module for automatic calculation of train routes, which is one of the most important elements of the Digital Railway Station. It is designed to automatically calculation the route, ensure the movement of trains and shunting trains, as well as special self-propelled rolling stock. The module controls not only train movements, but also shunting movements. It dynamically calculation routes to prevent the creation of hostility and ensures the most efficient performance of the station. The Module for automatic calculation of train routes in autonomous mode has been put into experimental operation at the Chelyabinsk-Glavny station of the South Ural Railway since August of this year.

This paper presents modelling the mechanical behaviours of polypropylene sheet (PP) at room temperature. The standard linear solid model (SLS) is used to establish the constitutive equation for FEM simulation of incremental forming process for PP. The tensile, relaxation and creep tests are performed to calibrate the constituting model. This model is integrated into Abaqus environment by using a user-material subroutine in Fortran language. The cone model is simulated in this FEM environment and is verified through laboratory experiments with model formed by the incremental forming process. The results show that the established constitutive model has responded well to the mechanical behaviour of PP. There is significant agreement between the simulated model and empirical experiments in terms of the thickness distribution and geometric accuracy.

In recent years, the issue of oil and organic spillage caused by human population growth has become increasingly urgent, not only in Vietnam but also worldwide. Researchers are showing great interest in the research and development of materials capable of selectively absorbing oils and organic solvents while repelling water. In this project, an oil-absorbing material was developed using reduced graphene oxide particles incorporated into a polyurethane (PU) foam base. Utilizing PU sponge as the base material enhances the oil absorption capacity of the material. Graphene oxide was initially synthesized using the Hummers method and then reduced with ascorbic acid to form reduced graphene oxide (RGO). RGO was applied to the sponge with varying loading amounts, ranging from 0 to 254%. Subsequently, the porous material was coated with high-density polyethylene (HDPE) to assess its hydrophobicity and its ability to adsorb oil and organic solvents. The results indicate that the oil and organic solvent absorption capacity of RGO and HDPE coating materials is highest at RGO loading percentages exceeding 64%, yielding absorption rates ranging from 35 to 63 times the weight of the material. Additionally, the contact angle of RGO and HDPE coating materials is approximately 150°, demonstrating the high hydrophobicity of the material.

Two ionic liquids named triethyl ammonium oleate ([TEA][Oleate] and triethyl ammonium stearate ([TEA][Stearate]) were synthesized from triethyl amine and oleic/stearic acids in water and in ethanol solvents. The synthesized ionic liquids were characterized by FT-IR and NMR spectra to confirm their structure. The influence of some parameters such as structure of ionic liquid anion, solvent, reaction time, reaction temperature, and acid/amine molar ratio on the synthesis processes of triethyl ammonium oleate and triethyl ammonium stearate was investigated. Ethanol was found suitable as a solvent for these processes because it dissolves formed ionic liquids but does not dissolve co-product KCl. In order to shift the equilibrium to increase the ionic liquid yields, an access amount of acids was used. Stearate-based ionic liquid should be synthesized at higher a temperature and longer time than oleic-based ionic liquids.

Currently, the oil and gas industry continue to rely on manual inspection methods, despite their labor-intensive, time-consuming nature and potential safety hazards. To address these challenges, this paper presents the development of a Tank Inspection Robot (TIRbot) aimed at improving inspection, operational efficiency, and worker safety. Notably, the paper solely focuses on the adhesion moving mechanism, a critical component of TIRbot that ensures its stability while inspecting ferromagnetic vertical surfaces. In practice, the utilization of Permanent Magnetic Wheels (PMWs) is deeply considered as their balance of various factors. Specifically, determining the appropriate PMW for a TIRbot requires careful consideration of factors, which comprise the weight, operational speed, and weld seam height. Hence, this study proposes a mechanical design and deterministic method for PMWs using MATLAB and SolidWorks to meet overall weight and motor torque requirements for TIRbot. As a result, the paper carried out the experiment in two scenarios, including the static and dynamic state of TIRbot. The first scenario experiment verifies the adhesion capabilities in the static state. The other scenario experiment reveals the change of torque in the TIRbot movement on the 10 mm steel plates and when the TIRbot overcomes the weld height of 3 mm.