Search Results (17)

In recent years, underground hydrogen storage (UHS) has become a hot topic in the field of deep energy storage. Green hydrogen, produced using surplus electricity during peak production, can be injected and stored in underground reservoirs and extracted during periods of high demand. A profound understanding of the mechanisms of the gas–water two-phase flow at the pore scale is of great significance for evaluating the sealing integrity of UHS reservoirs and optimizing injection, as well as the storage space. The pore structure of rocks, as the storage space and flow channels for fluids, has a significant impact on fluid injection, production, and storage processes. This paper systematically summarizes the methods for characterizing the micro-pore structure of reservoir rocks. The applicability of different techniques was evaluated and compared. A detailed comparative analysis was made of the advantages and disadvantages of various numerical simulation methods in tracking two-phase flow interfaces, along with an assessment of their suitability. Subsequently, the microscopic visualization seepage experimental techniques, including microfluidics, NMR-based, and CT scanning-based methods, were reviewed and discussed in terms of the microscopic dynamic mechanisms of complex fluid transport behaviors. Due to the high resolution, non-contact, and non-destructive, as well as the scalable in situ high-temperature and high-pressure experimental conditions, CT scanning-based visualization technology has received increasing attention. The research presented in this paper can provide theoretical guidance for further understanding the characterization of the micro-pore structure of reservoir rocks and the mechanisms of two-phase flow at the pore scale. Full article

Millions of commuters depend on the London Underground as their primary mode of transportation in the city. Despite its historical significance, the metro’s aging infrastructure contributes to persistent noise pollution. Noise pollution undermines environmental and societal value, which are key pillars of sustainability. This study focuses on the identification and analysis of track noises present on the Northern Line of the London Underground between Camden Town and South Wimbledon. Robust data collection involves onboard noise recordings during multiple train journeys using the MOTIV mobile application. The noise data are meticulously analysed using Fast Fourier Transform (FFT) to break down complex noise recordings into constituent frequencies, allowing for accurate quantification of noise levels. Noise hotspots are graphically represented to highlight areas with disproportionately high noise levels. Correlation analysis of track geometry and noise levels reveals that tighter curves and larger cant heights often coincide with increased noise levels, with a horizontal radius of 353 m and a cant of 79 mm linked to the highest impact noise recorded at 95.98 dB. The findings offer actionable insights for targeted noise mitigation and track maintenance, emphasizing the importance of optimizing track design to reduce noise pollution and support sustainable transport infrastructure. Full article

Precise positioning has become a key technology in the intelligent development of underground mines. To improve the positioning accuracy of mining vehicles, this paper proposes an intelligent underground mining vehicle positioning and tracking method based on the fusion of the YOLOv5 and laser sensor technology. The system utilizes a camera and the YOLOv5 algorithm for real-time identification and precise tracking of mining vehicles, while the laser sensor is used to accurately measure the straight-line distance between the vehicle and the positioning device. By combining the strengths of both vision and laser sensors, the system can efficiently identify mining vehicles in complex environments and accurately calculate their position using geometric principles based on laser distance measurements. Experimental results show that the YOLOv5 algorithm can efficiently identify and track mining vehicles in real time. When integrated with the laser sensor’s distance measurement, the system achieves high-precision positioning, with horizontal and vertical positioning errors of 1.66 cm and 1.96 cm, respectively, achieving centimeter-level accuracy overall. This system significantly improves the accuracy and real-time performance of mining vehicle positioning, effectively reducing operational errors and safety risks, providing essential technical support for the intelligent development of underground mining transportation systems. Full article

In response to the current situation of backward automation levels, heavy labor intensities, and high accident rates in the underground coal mine auxiliary transportation system, the mining trackless auxiliary transportation robot (MTATBOT) is presented in this paper. The MTATBOT is specially designed for long-range, space-constrained, and explosion-proof underground coal mine environments. With an onboard perception and autopilot system, the MTATBOT can perform automated and unmanned subterranean material transportation. This paper proposes an integrated odometry-based method to improve position estimation and mitigate location ambiguities for simultaneous localization and mapping (SLAM) in large-scale, GNSS-denied, and perceptually degraded subterranean transport roadway scenarios. Additionally, this paper analyzes the robot dynamic model and presents a nonlinear control strategy for the robot to autonomously track a planned trajectory based on the path-following error dynamic model. Finally, the proposed algorithm and control strategy are tested and validated both in a virtual transport roadway environment and in an active underground coal mine. The test results indicate that the proposed algorithm can obtain more accurate and robust robot odometry and better large-scale underground roadway mapping results compared with other SLAM solutions. Full article

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger’s wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger’s wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers’ The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers’ route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time > density of information > density of key information > diameter of the pupil. The study analyses the influencing factors affecting passengers’ wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations. Full article

The path tracking control of electric mining articulated vehicles (EMAVs), critical equipment commonly used for mining and transportation in underground mines, is a research topic that has received much attention. The path tracking control of EMAVs is subject to several system constraints, including articulation angle and articulation angular velocity. In light of this, many researchers have initiated studies based on model predictive control (MPC). The principal design schemes for existing MPC methods encompass linear MPC (LMPC) utilizing a single reference point, so named the single reference point LMPC (SRP-LMPC), and nonlinear MPC (NMPC). However, NMPC exhibits suboptimal real-time performance, while SRP-LMPC demonstrates inferior accuracy. To simultaneously improve the accuracy and real-time performance of the path tracking control of EMAV, based on the SRP-LMPC, a path tracking control method for EMAV based on nonlinear compensated multiple reference points LMPC (MRP-LMPC) is proposed. The simulation results demonstrate that MRP-LMPC simultaneously exhibits a commendable degree of accuracy and real-time performance. In all simulation results, the displacement error amplitude and heading error amplitude of MRP-LMPC do not exceed 0.2675 m and 0.1108 rad, respectively. Additionally, the maximum solution time in each control period is 5.9580 ms. The accuracy of MRP-LMPC is comparable to that of NMPC. However, the maximum solution time of MRP-LMPC can be reduced by over 27.81% relative to that of NMPC. Furthermore, the accuracy of MRP-LMPC is significantly superior to that of SRP-LMPC. The maximum displacement and heading error amplitude can be reduced by 0.3075 m and 0.1003 rad, respectively, representing a reduction of 65.51% and 73.59% in the middle speed and above scenario. Full article

Aiming at the problem of large deformations and difficult maintenance of cross-mining floor roadways, taking the track transportation roadway of the cross-mining east wing floor in Qinan Coal Mine as the engineering background, the stress field distribution of mining stress in floor strata and surrounding rock of floor roadway during the cross-mining process of the working face is studied by combining theoretical analysis with numerical simulation. The results show that the influence of mining stress on the vertical stress of floor strata is reflected in the stress-increasing area in front of the coal wall and the stress-decreasing area in the rear of the coal wall. With the increase in the depth of the floor strata, the peak value of the vertical stress gradually decreases, and the distance from the peak value of the vertical stress to the coal wall and the influence range of the vertical stress gradually increases. When the width of the coal pillar is greater than the influence range of advance abutment pressure of the working face, the development speed of the plastic zone is slow. When the roadway is located in the influence range of advance abutment pressure, the plastic zone of the roadway’s surrounding rock develops rapidly. When the working face crosses the floor roadway more than 10 m, the depth of the plastic zone of the surrounding rock of the roadway is no longer increased; the siltstone above the roadway is the key layer of fracturing, and the deformation of the roadway has been effectively improved after hydraulic fracturing. Through the analysis of numerical simulation results, the fracturing scheme has a significant effect on the stability control of the surrounding rock of the cross-mining floor roadway. This study has certain guiding significance for the maintenance and sustainable utilization of floor roadways in the cross-mining process, which is conducive to ensuring the sustainable mining of underground coal and the safety of personnel and equipment and is of great significance to the sustainable development of the coal mining industry. Full article

It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of ${-2.5 \mathrm{m}/\mathrm{s}}^2$ within a deviation of $\pm 0.1 \mathrm{m}/{\mathrm{s}}^2$, compared with the deviation of $0.11 \mathrm{m}/{\mathrm{s}}^2$ of fuzzy PID and the deviation of $0.13 \mathrm{m}/{\mathrm{s}}^2$ of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery. Full article

Efficient sign layouts play a crucial role in guiding driving in underground construction caverns and enhancing transportation safety. Previous studies have primarily focused on evaluating drivers’ gaze behavior in tunnels to optimize individual traffic sign layouts. However, the lack of a theoretical framework for visual perception of visual capture and information conveyed by sign groups hinders the measurement of drivers’ comprehensive visual perception and the layout optimization of sign groups. To address this gap, this study introduces a calculation method for sign group information volume and a visual cognition model, establishing a comprehensive evaluation approach for sign group visual cognition. Eye movement data, collected using eye-tracking technology, were utilized to evaluate the comprehensive visual perception and optimize the layout of sign groups. The findings indicate that a low information volume fails to enhance recognition ability and alleviate the psychological burden. Conversely, excessive information may result in overlooking signs positioned on the left and top. Furthermore, drivers are unable to improve cognitive efficiency and driving safety even with self-regulation when faced with an information volume exceeding 120 bits within a 100 m span. Overall, this study demonstrates the effectiveness of the proposed method in promoting the long-term safety effect of temporary signage layouts in underground construction areas. Full article

Underground coal mines belong to the GNSS-denied environment, and the Strapdown Inertial Navigation System (SINS) has a significant advantage in the precise positioning of equipment in this environment because of its operation without requiring interaction with external information and strong anti-interference capabilities. Nonetheless, the vibrations of the installation platform adversely affect the positioning accuracy of SINS. This article focuses on the monorail crane in coal mines as the subject of research, developing a dynamic model for the motion unit consisting of the “track + drive unit + driver’s cab”, while analyzing the relationship between track roughness conditions and the vibration excitation of this unit. Subsequently, utilizing the dynamic model, the study calculated the angular and linear vibration characteristics and formulated models to address coning error and sculling error specific to the SINS in this vibration condition. Lastly, by employing a multi-sample compensation algorithm, this article compensated for positioning errors in the SINS caused by track roughness-induced vibrations during uniform straight-line motion of the motion unit, thus achieving optimal positioning information for the monorail crane. The simulation results demonstrated that employing a four-sample compensation algorithm reduces the coning error in SINS positioning calculations by a minimum of 50% and decreases the sculling error by at least 31%, satisfying the positioning accuracy requirements for precise parking of the monorail crane during the transportation phase, while establishing the foundation for autonomous precise positioning and integrated navigation of underground track transport equipment in coal mines. Full article

Prolonged driving is necessary in underground construction caverns to transport materials, muck, and personnel, exposing drivers to high-risk and complex environments. Despite previous studies on attention and gaze prediction at tunnel exit-inlet areas, a significant gap remains due to the neglect of dual influences of long-distance driving and complex cues. To address this gap, this study establishes an experimental scenario in a construction environment, utilizing eye-tracking and simulated driving to collect drivers’ eye movement data. An analysis method is proposed to explore the visual change trend by examining the evolution of attention and calculating the possibility of visual cues being perceived at different driving stages to identify the attentional selection mechanism. The findings reveal that as driving time increases, fixation time decreases, saccade amplitude increases, and some fixations transform into unconscious saccades. Moreover, a phenomenon of “visual adaptation” occurs over time, reducing visual sensitivity to environmental information. At the start of driving, colorful stimuli and safety-related information compete for visual resources, while safety-related signs, particularly warning signs, always attract drivers’ attention. However, signs around intense light are often ignored. This study provides a scientific basis for transport safety in the construction environment of underground caverns. Full article

With the depletion of surface resources, mining will develop toward the deep surface in the future, the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient mining of deep space is inseparable from movable and flexible production and transportation equipment such as scrapers. In the new era, intelligence is leading to the development trend of scraper (LHD), path tracking control is the key to the intelligent scraper (LHD), and it is also an urgent problem to be solved for unmanned driving. This paper describes the realization of the automatic operation of articulating the scraper (LHD) from two aspects, a mathematical model and trajectory tracking control method, and it focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, an LQR controller. On this basis, combined with different intelligent clustering algorithms, the parameters of the LQR controller are optimized to find the optimal solution of the LQR controller. Then, the path tracking control of an intelligent LHD unmanned driving technology is studied, focusing on the optimization of linear quadratic optimal control (LQR) and the intelligent cluster algorithms AGA, QPSO, and ACA; this research has great significance for the development of the intelligent scraper (LHD). As mining engineers, we not only need to conduct research for practical engineering projects but also need to produce theoretical designs for advanced mining technology; therefore, the area of intelligent mining is the one we need to explore at present and in the future. Finally, this paper serves as a guide to starting a conversation, and it has implications for the development and the future of underground transportation. Full article

In this study, the factors influencing the choice of the type of urban railroad transportation in the metropolitan areas of Korea were analyzed. As the populations of metropolitan areas are expanding, the importance of rail transportation, which has a high travel reliability in terms of travel time, has increased, and various types of railroad systems have emerged accordingly. This study was focused on the choice behavior of travelers on local and express trains that use the same track and differ only in the number of stations and operating times. To compare the choice behavior of travelers between local and express trains, factors such as the waiting time on the platform and the in-car travel time were considered. We also investigated the system choice behavior for an existing express subway and high-speed rail trains in tunnels at a great depth in terms of horizontal access time (walking), vertical access time, in-vehicle travel time, and travel fare. For a high-speed rail built underground at a great depth of 50 m, the stated preference survey was designed, and data were collected in consideration of the Great Train Express being promoted in the Seoul metropolitan area by the Korean government. The results of this study are expected to be considered important data for improving the rail system design from the user’s perspective to increase the demand for urban rail transportation in metropolitan areas. Full article

The world’s oil and gas is transported using a network of steel pipelines most of which lie underground. The length of this network in the US/Canada alone is 3.5 million kilometers. Keeping track of pipes in such a network for pipeline-health monitoring, maintenance, and logistics is an acute problem faced by pipeline-operators. Recently, radio-frequency-identification tags (RFIDs) have been proposed for tracking pipelines and even for monitoring pipeline health with additional built-in sensors. Low-cost RFID tags are wirelessly powered and battery-less. However, RFIDs do not function optimally in the presence of magnetic carbon steel pipes that are prevalent in the industry. High-frequency wireless signals also attenuate rapidly through wet soils. In this research, the use of passive RFID sensor platforms for interrogating buried pipes up to 1.25 m deep in the LF bands is proposed. Using magnetic-induction-based communication, a test-comparison between conventional full/half duplex (FDX/HDX) and sequential (SEQ) RFID schemes is detailed. Wireless measurements in the presence of an industry-standard ASTM A-53 carbon-steel pipe show a SEQ RFID offering better immunity against magnetic proximity effects of the pipe’s wall with an 8.3 dB (x6.8) improvement over a FDX/HDX RFID operating under similar conditions over a distance of 80–125 cm at which pipes are typically buried. Full article

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes’ movements on each line. Given the passenger’s accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers’ positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines. Full article