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The deformation behavior of automotive double-cell thin-walled tubes under oblique loading is inherently complex. To address this crashworthiness design challenge, this study employs a decision-tree-based data-mining method to guide the structural design of thin-walled tubes. In contrast to conventional black-box optimization approaches, the proposed approach not only accomplishes structural optimization but, more importantly, derives design rules for the tubes, enabling critical parameter identification and design domain reduction. By analyzing the crashworthiness responses of double-cell tubes under oblique loading, the method establishes an interpretable linkage between design variables and crashworthiness performance, thereby providing engineers with design guidance. The results show that the optimization design efficiency based on data mining improved by nearly 10 times relative to traditional methods, highlighting its potential to significantly reduce design time and development cost.

To optimize the efficiency and noise performance of high-frequency vibrating screens, this study investigates the airflow behavior of lightweight materials during free fall and their impact dynamics on the screening surface. Based on the energy conservation theorem and the characteristics of tobacco screening, a computational model for the air resistance coefficient was derived. KT board specimens were employed as experimental substitutes for tobacco leaves to examine the effects of porosity and geometric dimensions on descent velocity and air resistance. The results indicated that materials with higher porosity exhibited greater descent velocities and lower air resistance, whereas larger geometric dimensions lead to increased aerodynamic drag and higher resistance coefficients. Furthermore, field operational modal analysis revealed that the sieve plate exhibited subharmonic resonances within the 9.9-10 Hz and 20-30 Hz frequency bands under nonlinear excitation. These findings could provide theoretical and data support for structural optimization aimed at noise reduction and screening efficiency enhancement.

One of the main issues in the pipes’ structure that could be affected by wind that led to the failure of the cylinder pipe structure is vortex-induced vibration (VIV). Therefore, there is a need to control the wind that is going through the pipe to avoid vibration. This phenomenon leads to the failure of the structure due to the resonance phenomenon when the natural frequency of the structure is equal to the vortex frequency. The main contributions of this work are mathematical modeling of the system using NARX and suppressing the vibration caused by wind currents through simulation and experiment ways. PI-PSO control employed to reduce the unwanted vibration as a simulation work. Then, an experimental study implemented on the structure as an open and closed loop control techniques to decrease the vibration with disturbance vibrations of 5 and 10 m/s. Open loop active vibration control (OLAVC) is proposed in this work using dual control rods made from hollow stainless steel and driven by dual DC motors in two positions at 6, 8, 10, and 12 DCV. The control rods are located beside the main cylinder pipe (CRBCP) and from the upper and lower of the hollow cylinder pipe. The effectiveness of the passive control strategy was confirmed before supplying electricity to the two DC motors on both sides. The PI controller tuned by the PSO method was developed to control unwanted model vibration. Based on the control results, the best values of KP and KI were 35.78 and 50 respectively at the lowest MSE of 1.3557×10-4, and the frequency magnitude was reduced by 81.17 %. The findings also showed that the cylinder pipe vibration could not be sufficiently suppressed by the passive control method. While OLAVC succeeded in reducing the vibration when the motor voltage was at 12 V. Finally, the closed-loop control technique decreased the vibration up to 61.24 % and 58.65% for disturbance wind speeds of 5 and 10 m/s, respectively.

Correctly assessing seepage amount of the regulated reservoir is vital to water resources efficlent utilization and engineering safety. A regulated reservoir in mudstone area without seepage control measures was used as the engineering prototype. Based on the mutual verification of field monitoring and numerical simulation, the estimation formula of the seepage amount of the regulated reservoir was determined. In addition, the sensitivity analysis of the factors affecting the seepage amount of the regulated reservoir was carried out, and the seepage prediction models were established based on the different optimization algorithms. The research results indicate that the regulated reservoir without lying geomembranes exists in a certain amount of seepage every day, accounting for about 7 to 10 % of the total reservoir capacity. The sensitivity ranking of influencing factors in descending order is as follows: reservoir bottom width, water depth, hydraulic conductivity, and saturated volumetric water content. The prediction accuracy of Bayesian regression is significantly better than that of traditional regression models under small training samples. This research approach provides a highly accurate and strongly robust solution to the small sample engineering prediction problem.

Accurate attitude tracking of six degree of freedom electrohydraulic shaking tables (EHSTs) is restricted by parameter uncertainty, nonlinearity, strong coupling, and external disturbances. Existing sliding mode control schemes are limited by fixed switching gains and the absence of integral compensation, which restrict steady-state accuracy and dynamic adaptability under nonlinear hydraulic effects and multi-axis coupling. An Adaptive Integral Sliding Mode Control (AISMC) scheme is developed to address these factors through two coordinated elements: an integral sliding surface that removes steady state deviation caused by static disturbances such as servo valve dead zones and hydraulic leakage, and an adaptive switching gain that regulates the reaching dynamics online without reliance on conservative bounds; a decay term in the gain update restrains parameter drift and keeps the adaptation bounded. Lyapunov analysis establishes closed loop stability and finite time convergence of the tracking error under bounded uncertainties and excitations. Simulation studies on a six degree of freedom EHST with a broadband random reference (0.1-10 Hz, 10 mm) compare AISMC with Sliding Mode Control (SMC) along X, Y, and Z. Pose tracking shows consistent gains, with the maximum value reduced by about 11.5-11.9 % and the root mean square (RMS) reduced by about 34.9-35.1 %. Pose error decreases from 0.392-0.396 mm to 0.035-0.036 mm in maximum value and from 0.175-0.177 mm to 0.015-0.016 mm in RMS. Acceleration tracking under AISMC approaches the reference in X and Z and improves in Y, while acceleration error decreases by about 83.5 % in X and Y and about 88 % in Z. The results indicate higher control precision, smoother transients with reduced chattering, and robust multi axis coordination suitable for practical vibration testing applications.

In response to the coexisting problems of mining vibrating separators, such as strength redundancy, insufficient stiffness, and susceptibility to fatigue wear, an integrated analytical scheme encompassing dynamic characteristics and multi-objective structural optimization was developed. Based on the Lagrange equation, the dynamic model of the vibrating system was established. Subsequently, the multi-rigid body simplified model was constructed using ADAMS to determine the motion law of the centroid. The modal, harmonic response, and fatigue response characteristics of the vibrating separator box body were analyzed via the finite element method, and the simulation model was validated through hammer impact tests. The research findings indicate that the minimum fatigue life of the box body exceeds 107 cycles, satisfying the operational requirements. The regions of high deformation are mainly located on the side of the box body, junctions of crossbeams and stiffening plates, while the high-stress areas are found at geometric discontinuities such as the edges of holes and weld seams. The first order natural frequency of the box body is close to the operating frequency, thus presenting a resonance risk. With the thickness of the side plate, the diameter of the circular beam, the height, and the width of the box body defined as design variables, the objectives were set to minimize the peak stress and maximize the first-order natural frequency, subject to the constraint that the structural mass does not increase. Through Latin hypercube sampling, the Kriging model was established to represent the response surface, and the particle swarm optimization algorithm was employed for solution. As a result, two sets of optimal solutions were obtained. The results demonstrate that, without increasing the mass, these two optimization solutions can respectively reduce the peak stress by 13.6 % and 12.9 %, and increase the first-order natural frequency by 19.7 % and 23.2 %. The research can solve the problem of the disconnection between dynamic design and static design, thereby ensuring the vibration coordination and comprehensively improving its mechanical performance, providing theoretical and engineering references for the optimization of mining vibrating separators.

In response to the challenges posed by the substantial volume of monitoring data from rotating machinery, the considerable effort required for manual interpretation, and the scarcity of labeled fault samples, this study proposes a vibration-based anomaly-detection method that applies active learning to unlabeled vibration signals. The key novelty is a redundancy-aware batch active learning scheme, in which predictive-entropy from a committee is combined with a long short-term memory fully convolutional network (LSTM–FCN) deep-clustering module. One most representative sample is selected from each cluster to increase diversity and reduce labeling cost. The method comprises two stages: first, predictive entropy is computed for all unlabeled samples to rank uncertainty and perform an initial screening; second, a deep-clustering procedure mitigates redundancy among high-uncertainty candidates, after which the highest-entropy instance in each cluster is selected for expert labeling. Evaluations on vibration datasets from a rolling-bearing accelerated-life rig and a centrifugal-compressor rig show consistent improvements in accuracy and recognition stability over conventional clustering-based anomaly-detection methods, alleviating the dependence on scarce labeled anomalies in real-world rotating machinery monitoring.

To systematically investigate the protective effects of helmets against human head injuries under various shock wave conditions, a finite element head-helmet coupling model was developed. This model analyzed how helmets influence biomechanical response parameters, such as intracranial and cranial pressure, when subjected to a single blast wave and its accompanying shock wave. While extensive research exists on single blast scenarios, studies on the more complex and militarily relevant accompanying shock waves, which pose a greater threat due to prolonged loading and multiple reflections, remain scarce. Several impact scenarios were considered, including single frontal impact, positive continuous impacts, successive sidewall impacts, and simultaneous frontal and lateral impacts. The study examined the dynamic changes in brain tissue within a blast environment to assess the efficacy of helmets in protecting the human head. In single frontal impact scenarios, helmets effectively reduced intracranial pressures in the frontal, occipital, and parietal lobes by 32 %, 38 %, and 19 %, respectively, while significantly decreasing the stress peak at the back of the skull. During positive continuous impacts, helmets decreased intracranial pressure in the parietal and occipital lobes by 36 % and 21 %, respectively, although their effectiveness in reducing frontal lobe pressure was limited due to inadequate facial protection. For successive sidewall impacts, helmet protection delayed the blast wave, reducing intracranial pressure in the frontal lobe by 60 kPa but increasing pressure in the parietal lobe by 80 kPa. This alleviated stress on the skull’s rear while increasing stress on the opposite side. In scenarios involving simultaneous frontal and lateral impacts, lateral blasts increased parietal intracranial pressure by 20 kPa, with the right hemisphere experiencing more pressure than the left due to the mitigating effect of reflective side blasts on skull stress. The study found that, compared to single blast waves, accompanying shock waves present a greater risk of cranial injuries due to their prolonged impact. These findings address a critical gap in blast neurotrauma research and provide valuable insights into the biomechanics of head injuries under realistic multi-blast conditions, which can directly inform the design of improved helmets with enhanced protection in complex blast environments. However, because shock waves may originate from multiple directions and elevations, the protective capability of conventional helmets for the facial region remains limited.