Jian Chen

Efficient tractor coverage planning requires suitable swath orientations and kinematically feasible connections. In two-pass diagonal harrowing, sequential boustrophedon planning treats the two swath families independently and may introduce avoidable connection travel. This study proposes a Field-Geometry-Aware Bidirectional Coverage and Connection Path Co-Optimization algorithm (FG-BCO) for a single tractor operating in convex, obstacle-free polygonal fields. FG-BCO clips two parallel swath families to the field boundary, constructs feasible cross-pass links between oriented endpoints, assembles alternating route paths, and applies subset dynamic programming to optimize their visiting order and traversal orientation. The resulting route is converted into a forward-only trajectory and checked against boundary-containment and minimum-turning-radius constraints. FG-BCO was compared with Sequential Boustrophedon Coverage Path Planning (SB-CPP) in rectangular, regular pentagonal, regular hexagonal, and irregular fields using 648 matched angle pairs per field. When the minimum path length of each algorithm was selected independently from the tested angle pairs, FG-BCO achieved reductions of 18.65%, 9.65%, 15.20%, and 17.11% in the four fields, respectively. However, FG-BCO produced shorter routes in only 57.87%, 41.82%, 40.43%, and 40.59% of the matched cases. The corresponding mean paired saving rates were 0.646%, −0.484%, −0.144%, and −0.833%, with medians of 0.939%, −1.060%, −0.923%, and −1.094%. These results distinguish the potential reduction obtainable through angle selection from performance under an arbitrary prescribed angle pair. FG-BCO is advantageous when the two swath families produce favorable endpoint proximity, heading compatibility, and turning space, but its improvement is not universal. Preliminary evaluation of the working-angle pair in relation to the target field geometry is therefore recommended before application.

Active seat suspensions are a potential approach for reducing vertical vibration exposure in vehicle and construction-machinery seats. In most existing studies on active seat-suspension control, acceleration signals are rarely used as direct feedback because of their high noise sensitivity. However, acceleration can be measured at low cost and directly reflects ride comfort, which makes it attractive for prototype-level vibration control. This paper proposes an acceleration-feedback-based adaptive weighted control strategy for a motor-driven active seat-suspension prototype with input delay. A 2-DOF driver-seat model is employed to describe the dominant vertical dynamics. An auxiliary virtual state variable is introduced to embed a deformation-dependent weighting mechanism into the control objective, allowing the controller to coordinate ride-comfort improvement and suspension-stroke safety according to real-time suspension deformation. Based on the Linear Matrix Inequality (LMI) method, a state-feedback H-infinity controller is synthesized while considering actuation delay and input saturation. The stability of the controlled system is proved under the stated model assumptions, and the controller performance is examined through numerical simulation and laboratory prototype experiments. The acceleration transmissibility from the vibration-platform floor to the driver is evaluated experimentally in the frequency domain, and random-excitation responses are investigated through both simulation and experimentation. The results show that the proposed strategy can reduce the dominant vibration responses and satisfy the imposed stroke and actuation constraints on the laboratory test rig.

To enhance riding comfort for drivers of heavy-duty vehicles, this paper introduces a novel adaptive prescribed performance control (APPC) for active seat suspension systems. The model incorporates dynamic friction and hysteresis damping effects to capture the complex behavior of the seat suspension. The accuracy of the proposed model is validated through experimental data. The controller utilizes a prescribed performance function (PPF) to regulate the dynamic response of the system, combined with an adaptive backstepping control (ABC) method to account for system uncertainties, such as variations in driver weight, friction, suspension stiffness, and damping coefficients. A set of parameter estimators, governed by innovative adaptive laws, compensates for estimation errors. Furthermore, the stability of the controlled system is rigorously demonstrated. Both simulation and experimental tests, including bump and random excitation tests, are conducted to assess the controller performance in both time and frequency domains. The results confirm that the proposed controller effectively mitigates vibrations in the driver–seat system and demonstrates robustness against system uncertainties.

An oil filter is a necessary and significant part of many manufacturing processes and equipment. Unlike the structural design and filter material selection, the particle movement in the filter during filtration is the fundamental factor influencing the filter’s performance, but this has not attracted enough attention. Due to the small size and large number of particles in the filter, it is difficult to monitor every particle’s movement. Therefore, this work used a hydraulic oil filter as a case study. Computational Fluid Dynamics (CFD) was coupled with the Discrete Phase Model (DPM) to investigate the particle motion in the filter. A filter boundary function was programmed to simulate the filter cartridge zone. The effects of inlet velocity and oil temperature/viscosity on the particle movement and filtration performance were studied. The results showed that a low-velocity zone existed and trapped some contaminant particles, particularly for particles with large Stokes numbers. The results also demonstrated that increased temperature induced an apparent reduction in filtering efficiency within the first 1.8 s from 0.61 to 0.49 when the temperature increased from 15 °C to 70 °C for 25 μm particles.

This study investigates the ground reaction force of lever-spring landing gear (LSLG) equipped with compressible elastomer shock absorbers (ESA) during the landing process. First, a numerical dynamic model of the LSLG was developed in MATLAB/Simulink, revealing that runway roughness exerts a negligible influence on the ground reaction force during landing. The load characteristics established fundamental references for subsequent FEA-based structural design. Furthermore, an FEA model integrating the LSLG and the aircraft was developed with parameters calibrated for elastic units. The multibody dynamics simulation (MBDS) quantified the vertical ground reaction force and the structural stresses of LSLG, demonstrating two critical relationships: (1) the overload coefficient correlated with the sinking velocity yet exhibits no correlation with aircraft mass and (2) the peak of oscillating force attenuated faster with heavier landing weight at higher sinking velocities. A nonlinear multi-variables function was fitted to predict the maximum vertical ground reaction force. Subsequently, experimental validation via a landing gear drop test (LGDT) showed a maximum error of 8.39% between the results of the LGDT and the MBDS, confirming the accuracy of simulation and the fitting surface function for force prediction. The study further validates the feasibility and reliability of using the MBDS to model and study the LSLG with ESAs.

Oil monitoring for wind turbine gearboxes can reflect wear and lubrication conditions, and better identify pits on the tooth surface, fatigue wear, and other early faults. However, oil monitoring with one or several single predicting models brings inaccuracy due to the intrinsic merits and demerits of the models. In this work, oil monitoring and fault pre-warning of wind turbine gearboxes were studied based on oil inspection data of three wind turbines that have been working continuously for 3.5 years. The Grey Model (GM) and the Double Exponential Smoothing (DES) were combined by a modified inverse-variance weighting method proposed in this work, which used relative errors to calculate weight coefficients, reducing the errors and improving the accuracy as a whole. The predicted data were compared with the measured data to verify the predicting accuracy. Subsequently, a statistical method and linear regression method were adopted to jointly develop a pre-warning threshold for the oil inspection data. Comparing the predicted data with the threshold, the results showed that one of the wind turbines was in a warning state. The prediction was validated by an endoscope inspection of the gearbox, which found that some parts were slightly worn.

The functionalized applications of nanoporous metals place clear requirements on their basic mechanical properties, yet there is a lack of research on the mechanical response under multiaxial loading conditions. In this work, the mechanical behaviors of nanoporous gold under multiaxial tension are investigated via molecular dynamics simulations. The mechanical properties under different loading conditions are compared and the microstructure evolution is analyzed to clarify the deformation mechanisms of nanoporous gold in biaxial and triaxial tension. It is found that the modulus of nanoporous gold in multiaxial tension is strengthened and the strength is softened compared to uniaxial tension. The failure of nanoporous gold in multiaxial tension is dominated by the progressive yielding, necking, and rupture of ligaments along the multiple uniaxial loading directions. The dislocation activity under multiaxial loads is more intense and more prone to plastic deformation, ultimately resulting in lower strength and smaller failure strain. The findings provide more insight into the understanding of the deformation mechanisms of nanoporous metals under complex stress states.

Introducing CO₂ electrochemical conversion technology to the iron-making blast furnace not only reduces CO₂ emissions, but also produces H₂ as a byproduct that can be used as an auxiliary reductant to further decrease carbon consumption and emissions. With adequate H₂ supply to the blast furnace, the injection of H₂ is limited because of the disadvantageous thermodynamic characteristics of the H₂ reduction reaction in the blast furnace. This paper presents thermodynamic analysis of H₂ behaviour at different stages with the thermal requirement consideration of an iron-making blast furnace. The effect of injecting CO₂ lean top gas and CO₂ conversion products H₂–CO gas through the raceway and/or shaft tuyeres are investigated under different operating conditions. H₂ utilisation efficiency and corresponding injection volume are studied by considering different reduction stages. The relationship between H₂ injection and coke rate is established. Injecting 7.9–10.9 m³/tHM of H₂ saved 1 kg/tHM coke rate, depending on injection position. Compared with the traditional blast furnace, injecting 80 m³/tHM of H₂ with a medium oxygen enrichment rate (9%) and integrating CO₂ capture and conversion reduces CO₂ emissions from 534 to 278 m³/tHM. However, increasing the hydrogen injection amount causes this iron-making process to consume more energy than a traditional blast furnace does.