Search Results (127)

With the rapid development of offshore oil and gas fields in China, there is an increasing demand for high-efficiency and high-reliability compression equipment. This study presents the design and localization of a 25 MW gas turbine-driven centrifugal compressor unit specifically developed for offshore platforms. Based on performance calculations, the gas turbine and compressor were selected and structurally optimized. A skid-mounted base frame with vibration isolation was designed to adapt to offshore steel deck structures, and a control system was developed and integrated. Performance verification was conducted through risk-based type tests. The results show that the unit demonstrates excellent operational stability, high efficiency, and reliability, fully meeting the requirements of offshore oil and gas applications. This work provides technical support and engineering experience for promoting the localization of key offshore equipment. Full article

This study introduces a novel and user-centered surface abrasion test designed to assess the injury potential of pavement surfaces, particularly for vulnerable road users such as micromobility users. Traditional pavement evaluation methods focus on skid resistance and texture but do not account for the surface’s mechanical aggressiveness during a fall. To address this gap, the proposed test simulates fall conditions by dragging a paraffin wax specimen—used as a low-cost and reproducible proxy to approximate the abrasive response that could affect human skin—over pavement at a controlled speed and load, quantifying material loss as an indicator of surface abrasiveness. The method was validated on three pavement types (smooth ceramic, bituminous, and concrete), demonstrating its sensitivity and repeatability. Unlike conventional point-based tests, it enables continuous evaluation along a predefined length, offering more representative results. A full-scale case study on a micromobility-dedicated bike lane confirmed the test’s responsiveness to surface changes over time. Results suggest the method is practical, reproducible, and applicable to a wide range of pavements. Beyond micromobility, it can be extended to other vulnerable users, such as motorcyclists. The test represents a new metric for infrastructure safety audits focused on injury mitigation. Full article

The operational risks of equipment in coalbed methane (CBM) gathering stations exhibit dynamic characteristics. To address this, a dynamic risk assessment method based on Dynamic Bayesian Networks (DBNs) is proposed for CBM station equipment. Additionally, a comprehensive safety management evaluation model is established for gathering station equipment. This approach enables accurate risk assessment and effective implementation of safety management in CBM gathering stations. This method primarily consists of three core components: risk factor identification, dynamic risk analysis, and comprehensive safety management evaluation. First, the Bow-tie model is applied to comprehensively identify risk factors associated with station equipment. Next, a DBN is constructed based on the identified risks, and Markov theory is employed to determine the state transition matrix. Finally, a Comprehensive Safety Management (CSM) evaluation model for gathering station equipment is established. The feasibility of the proposed method is validated through case study applications. The results indicate that during the operation of equipment at CBM gathering stations, priority should be given to strengthening maintenance for medium-hole and enhancing prevention and emergency measures for jet fires. Temperature-controlled spiral-wound heat exchangers, skid-mounted circulating pumps, and pipelines have been identified as critical factors affecting accident occurrence at CBM gathering stations. Enhanced daily inspection and maintenance of this equipment should be implemented. Furthermore, compared to other safety evaluation indicators, the Emergency Preparedness and Response indicator has the most significant impact on the operational safety of CBM gathering station equipment. It requires high-priority attention, thorough implementation of relevant measures, and continuous improvement through targeted actions. Full article

Preventive maintenance treatments are widely applied to asphalt pavements to mitigate deterioration and extend service life. This study evaluated four common technologies: a high-elasticity ultra-thin overlay, an Stone Mastic Asphalt (SMA)-10 thin overlay, micro-surfacing (MS-III), and a chip seal. Laboratory testing focused on skid resistance, surface texture, and low-temperature cracking resistance. Skid resistance was measured with a tire–pavement dynamic friction analyzer under controlled load and speed, while surface macrotexture was assessed using a laser scanner. Low-temperature cracking resistance was determined through three-point bending beam tests at −10 °C. The results showed that chip seal achieved the highest initial friction and texture depth, immediately enhancing skid resistance but exhibiting rapid texture loss and gradual friction decay. Micro-surfacing also demonstrated good initial skid resistance but experienced a sharp reduction of over 30% due to fine aggregate polishing. By contrast, the high-elastic ultra-thin overlay and SMA thin overlay provided more stable skid resistance, lower long-term friction loss, and excellent crack resistance. The polymer-modified ultra-thin overlay achieved the highest low-temperature bending strain ≈40% higher than untreated pavement, indicating superior crack resistance, followed by the SMA thin overlay. Micro-surfacing with a chip seal layer only slightly improved low-temperature performance. Overall, the high-elastic ultra-thin overlay proved to be the most balanced preventive maintenance option under heavy-load traffic and cold climate conditions, combining durable skid resistance with enhanced crack resistance. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this study investigates their unique compound steering system. Specifically, a compound steering dynamics model is established, and a hierarchical stability control strategy, along with a model predictive control-based trajectory tracking algorithm, are innovatively proposed. First, a compound steering platform dynamics model is established by combining the Ackermann steering and skid yaw moment methods. Then, a trajectory tracking controller is designed using model predictive control algorithm. Finally, the additional yaw moment is calculated based on the lateral velocity error and yaw rate error, with stability control allocation performed using a fuzzy control algorithm. Comparative hardware-in-the-loop experiments are conducted for compound steering, Ackermann steering, and skid steering. The experimental results show that the compound steering technology enables unmanned platforms to achieve trajectory tracking tasks with a lower torque, faster speed, and higher efficiency. Full article

This study aimed to evaluate the effects of varying traffic intensities, the time since harvesting, and the interaction between these two factors on the restoration of herbaceous cover on skid trails in the Hyrcanian forests, Northern Iran. Three compartments were selected from two districts within the pure oriental beech (Fagus orientalis Lipsky) stands of Kheyrud Forest, where ground-based timber extraction had occurred 5, 10, and 15 years prior. In each compartment, three skid trails representing low, medium, and high traffic intensities were identified. Control plots were established 10 m away from the trails. A total of 54 systematically selected 1 m × 1 m sample plots were surveyed: 27 on skid trails (three traffic intensities × three time intervals × three replicates) and 27 control plots (matching the same variables). Within each quadrat, all herbaceous plants were counted, identified, and recorded. Our findings revealed that only traffic intensity had a clear significant impact on plant abundance. High traffic intensity led to a pronounced decline in herbaceous cover, with disturbed skid trails showing reduced species diversity or the complete disappearance of certain species in comparison to the control plots. Time since harvesting and its interaction with traffic intensity did not yield statistically significant effects. Disturbance led to a reduction in the quantities of certain species or even their disappearance on skid trails in comparison to the control plots. Given the pivotal role of machinery traffic intensity in determining mitigation strategies, there is a critical need for research on region-specific harvesting techniques and the development of adaptive management strategies that minimize ecological impacts by aligning practices with varying levels of traffic intensity. Full article

Extreme events such as tropical cyclones frequently occur in coastal areas in China. With high wind speeds and rainfall during such extreme events, the vehicles on sea-crossing bridges may face severe instability problems. In this study, the dynamics of vehicles on a cross-sea bridge under the wind–rain coupling effect were analyzed based on field measurement data using computational fluid dynamics (CFD). Wind field parameters of the coastal area in China were obtained using wind speed data from measurement towers. Based on CFD, the sliding grid method was applied to establish an aerodynamic analysis model of a container truck moving on a bridge under wind and rain conditions. The discrete phase model based on the Euler–Lagrange method was used to investigate the influence of rain and obtain the aerodynamic characteristics of the truck under the coupled wind and rain effects. Based on the computational analysis results, considering the turbulence intensity, the yaw angle peaks of the tractor and trailer increased by 5.2% and 3.8%, respectively, and the lateral displacement of the truck’s center of mass increased by 9.8%. Rainfall may cause the vehicle to have a higher response, resulting in a high risk of skidding. The results show that skidding occurs for the considered container truck when rainfall is at 9.8%. These results can provide parameters for traffic control strategies under such extreme climate events in coastal areas. Full article

Bilateral teleoperation of skid-steering mobile robots with time-varying delays presents significant challenges in ensuring accurate leader–follower coupling. This article presents a novel controller for a bilateral teleoperation system composed of a robot manipulator and a skid-steering mobile robot. The proposed controller leverages neural networks to compensate for ground–robot interactions, uncertain dynamics, and communication delays. The control strategy integrates a shared scheme between damping injection and two neural networks, enhancing the robustness and adaptability of the delayed system. A rigorous theoretical analysis of the closed-loop teleoperation system is provided, establishing conditions of control parameters to ensure stability and convergence of the coordination errors. The proposed method is validated through numerical testing, demonstrating strong agreement between theoretical outcomes and simulation results. Full article

Accelerating the recovery of compacted soils caused by logging machinery using bioengineering techniques is a key goal of Sustainable Forest Management. This research was conducted on an abandoned skid trail with a uniform 15% slope and a history of heavy traffic, located in the Nav forest compartment of northern Iran. The main objectives were to assess (a) soil physical properties 35 years after skidding by a tracked bulldozer, (b) the impact of natural alder regeneration on soil recovery, and (c) the contribution of alder fine-root development to the restoration of compacted soils in beech stands. Soil physical properties and fine root biomass were analyzed across three depth classes (0–10 cm, 10–20 cm, 20–30 cm) and five locations (left wheel track (LT), between wheel tracks (BT), right wheel track (RT)) all with alder trees, and additionally control points inside the trail without alder trees (CPWA), as well as outside control points with alder trees (CPA). Sampling points near alder trees (RT, LT, BT) were compared to CPWA and CPA. CPA had the lowest soil bulk density, followed by LT, BT, RT, and CPWA. Bulk density was highest (1.35 ± 0.07 g cm⁻³) at the 0–10 cm depth and lowest (1.08 ± 0.4 g cm⁻³) at 20–30 cm. The fine root biomass at 0–10 cm depth (0.23 ± 0.21 g dm⁻³) was significantly higher than at deeper levels. Skid trail sampling points showed higher fine root biomass than CPWA but lower than CPA, by several orders of magnitude. Alder tree growth significantly reduced soil bulk density, aiding soil recovery in the study area. However, achieving optimal conditions will require additional time. Full article

High Friction Surface Treatments (HFSTs) are often utilized as a spot treatment to enhance selected areas with high friction demand rather than extended pavement sections and are helpful in increasing skid resistance and minimizing road accidents. A laboratory design approach was created to assess the fundamental ideas behind the international friction index (IFI) concept and update the present IFI model parameters for HFST applications based on test findings to gain a better understanding of HFST performance. Two aggregate types in three sizes were tested under controlled polishing cycles. Friction and texture were measured using the Dynamic Friction Tester (DFT) and Circular Track Meter (CTM). Three physics-informed empirical models, including logarithmic, power law, and polynomial models, were selected to better represent texture effects, nonlinear scaling, and complex interactions between COF and MPD. Results show that friction performance varies with aggregate type, gradation, and polishing, and that traditional IFI parameters may not fully capture HFST behavior. Model refinements are suggested to better represent HFST surface characteristics with the lowest testing Root Mean Squared Error (RMSE) (0.049) and the highest predictive accuracy R² (0.821); the logarithmic model was found to be the best. Sensitivity analysis revealed that IFI predictions are more sensitive to COF (ΔIFI: 14.3–17.7%) than MPD (ΔIFI: 1.5–6.0%) across all models. These results demonstrate how these models can improve HFST design and performance assessment while providing useful information for enhancing road safety. This process is a useful tool for evaluating HFST friction resistance in a lab setting since it calculates HFST skid resistance using results measured in the lab. Full article

Autonomous navigation in mining environments is challenged by complex wheel–terrain interaction, traction losses caused by slip dynamics, and sensor limitations. This paper investigates the effectiveness of Deep Reinforcement Learning (DRL) techniques for the trajectory tracking control of skid-steer mobile robots operating under terra-mechanical constraints. Four state-of-the-art DRL algorithms, i.e., Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor–Critic (SAC), are selected to evaluate their ability to generate stable and adaptive control policies under varying environmental conditions. To address the inherent partial observability in real-world navigation, this study presents an original approach that integrates Long Short-Term Memory (LSTM) networks into DRL-based controllers. This allows control agents to retain and leverage temporal dependencies to infer unobservable system states. The developed agents were trained and tested in simulations and then assessed in field experiments under uneven terrain and dynamic model parameter changes that lead to traction losses in mining environments, targeting various trajectory tracking tasks, including lemniscate and squared-type reference trajectories. This contribution strengthens the robustness and adaptability of DRL agents by enabling better generalization of learned policies compared with their baseline counterparts, while also significantly improving trajectory tracking performance. In particular, LSTM-based controllers achieved reductions in tracking errors of 10%, 74%, 21%, and 37% for DDPG-LSTM, PPO-LSTM, TD3-LSTM, and SAC-LSTM, respectively, compared with their non-recurrent counterparts. Furthermore, DDPG-LSTM and TD3-LSTM reduced their control effort through the total variation in control input by 15% and 20% compared with their respective baseline controllers, respectively. Findings from this work provide valuable insights into the role of memory-augmented reinforcement learning for robust motion control in unstructured and high-uncertainty environments. Full article

To enhance the precision and real-time performance of trajectory tracking control in differential-steering intelligent sweeping robots and to improve the adaptability of the control algorithm to errors caused by sensor noise, tire slip, and skid, an MPC-PID (Model Predictive Control–Proportional-Integral-Derivative) dual closed-loop control strategy was proposed. This strategy integrates a Kalman filter-based state estimator and a sliding compensation module. Based on the kinematic model of the intelligent sweeping robot, a model predictive controller (MPC) was designed to regulate the vehicle’s pose, while a PID controller was used to adjust the longitudinal speed, forming a dual closed-loop control algorithm. A Kalman filter was employed for state estimation, and a sliding compensation module was introduced to mitigate wheel slip and lateral drift, thereby improving the stability of the control system. Simulation results demonstrated that, compared to traditional MPC control, the maximum lateral deviation, maximum heading angle deviation, and speed response time were reduced by 50.83%, 53.65%, and 7.10%, respectively, during sweeping operations. In normal driving conditions, these parameters were improved by 41.58%, 45.54%, and 24.17%, respectively. Experimental validation on an intelligent sweeper platform demonstrates that the proposed algorithm achieves a 16.48% reduction in maximum lateral deviation and 9.52% faster speed response time compared to traditional MPC, effectively validating its enhanced tracking effectiveness in intelligent cleaning operations. Full article

Winch-assisted harvesting has expanded considerably in recent years as it enables ground-based machines to work safely on steep slopes. To analyze operator exposure to whole-body and hand–arm vibration (WBV, HAV) and noise exposure (LA_eq, LC_peak) during winch-assisted harvesting (TW) and harvesting without winch assistance (NTW), a field study using a Ponsse Scorpion King harvester and an Ecoforst T-winch traction winch was conducted. Vibrations were measured at three locations inside the cabin (seat, seat base/floor, control lever), while noise exposure was recorded both inside and outside the cabin. WBV exposure during work time operations was highest in the Y-direction, both on the seat (0.49–0.87 m/s²) and on the floor (0.41–0.84 m/s²). The WBV and HAV exposure levels were highest while driving on the forest and skid road. Exposure during the main productive time was significantly influenced by the harvesting system, diameter at breast height (DBH), and tree species. Noise exposure was higher, while WBV and HAV exposures on the seat, floor and control lever were lower during non-work time than during work time. The daily vibration exposure on the seat exceeded the EU action value, while LC_peak noise exposure surpassed the limit value of 140 dB(C) on all measured days. Noise and vibration exposure were constantly higher during TW than NTW harvesting but differences were small. Compared to other studies, the results show that harvesting on steep terrain increases noise and vibration exposure, while non-work time has the opposite effect on vibration and noise exposure. Full article