Science China Technological Sciences最新文献

Screening current is recognized as one of the critical elements limiting the progression of superconducting magnets toward achieving higher magnetic fields. Currently, most non-insulated (NI) superconducting magnets consider the magnet as insulated when addressing the issue of screening current. However, the bypass current in the NI magnet can modify the actual history of magnetization, so the screening current in NI magnet will be different from that in the insulated magnet. This paper presents a novel method based on the homogenized T-A formulation (T is the current vector potential, and A is the magnetic vector potential), which enables real-time simulation of both the bypass current behavior and the implications of screening current in NI superconducting magnets, even when these magnets contain tens of thousands of turns. We have developed a 32 T NI hybrid superconducting magnet and validated the effectiveness of this method through experiments. Employing this efficacious method, we conducted a comprehensive calculation of screening current in NI magnets, comparing them with insulated magnets in terms of screening current-induced stress (SCIS), screening current-induced field (SCIF), and losses. The results indicate that in the NI insert coils, the sequential excitation of background coils and insert coils induces a reverse screening current, resulting in slightly lower SCIF and SCIS compared to those in the insulated magnets. The method and results can contribute to the enhancement of magnet design and provide valuable insights for the development of ultra-high fields (UHF) NI magnets.

Gesture recognition has diverse application prospects in the field of human-computer interaction. Recently, gesture recognition devices based on strain sensors have achieved remarkable results, among which liquid metal materials have considerable advantages due to their high tensile strength and conductivity. To improve the detection sensitivity of liquid metal strain sensors, a sawtooth-enhanced bending sensor is proposed in this study. Compared with the results from previous studies, the bending sensor shows enhanced resistance variation. In addition, combined with machine learning algorithms, a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study, and various gestures are accurately identified. In the fields of human-computer interaction, wearable sensing, and medical health, the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

In this study, we comprehensively investigated the scaling law for elastic properties of three-dimensional honeycomb-like graphenes (3D graphenes) using hybrid neural network potential-based molecular dynamics simulations and theoretical analyses. The elastic constants were obtained as functions of honeycomb hole size, denoted by the graphene wall length L. All five independent elastic constants in the large-L limit are proportional to L⁻¹. The associated coefficients are combinations of elastic constants of two-dimensional graphene. High-order terms including L⁻² and L⁻³ emerge for finite L values. They have three origins, the distorted areas close to the joint lines of 3D graphenes, the variation in solid angles between graphene plates, and the bending distortion of graphene plates. Significantly, the chirality becomes essential with decreasing L because the joint line structures are different between the armchair and zigzag-type 3D graphenes. Our findings provide insights into the elastic properties of graphene-based superstructures and can be used for further studies on graphene-based materials.

Metal surfaces play a crucial role in numerous applications, from self-cleaning and anti-icing to anti-fogging and oil-water separation. The regulation of their wettability is essential to enhance their performance in these areas. This paper proposes a multi-state regulation method for metal surface wettability, leveraging nanosecond laser ablation. By creating non-uniform microstructures on a metal surface, the contact area between the solid and liquid phases can be increased, resulting in the attainment of superhydrophilic properties (contact angle (CA), ranging from 4.6° to 8.5°). Conversely, the construction of uniform microstructures leads to a decreased solid-liquid contact area, thereby rendering the metal surface hydrophilic (CA = 12.2°–53°). Furthermore, through heat treatment on a surface with uniform microstructures, organic matter adsorption can be promoted while simultaneously reducing surface energy. This process results in the metal surface acquiring hydrophobic properties (CA = 92.1°–133.5°), facilitated by the “air cushion effect.” Building on the hydrophobic surface, stearic acid modification can further reduce surface energy, ultimately bestowing the metal surface with superhydrophobic properties (CA = 150.1°–152.7°, and sliding angle = 3.8°). Performance testing has validated the durability and self-cleaning effectiveness of the fabricated superhydrophobic surface while also highlighting the excellent anti-fog performance of the superhydrophilic surface. These findings strongly indicate the immense potential of these surfaces in various engineering applications.

Tactile signals play a crucial role in enabling robots to successfully manipulate unfamiliar objects. For robots to grasp unknown objects securely and without causing damage, it is essential that they can analyze grasping stability in real time through tactile signals and respond promptly. This study introduces a novel method for analyzing the stability of robotic hand grasping, utilizing the Wilcoxon signed rank test. The efficacy of this method is demonstrated through its static and dynamic performance, and evaluated across a series of experiments. The findings of this research highlight the method’s ability to accurately detect when an object begins to slip from the robot’s grasp. Employing this method allows the gripper to maintain a secure hold on objects by applying the minimal necessary force. It also enables the gripper to dynamically adjust the force it applies in real time, thus preventing the object from slipping during the movement of the robotic arm. Moreover, the gripper demonstrates the ability to stably grasp objects of varied weights and with unknown characteristics, showcasing the versatility and effectiveness of the proposed method.

Because robotic milling has become an important means for machining significant large parts, obtaining the structural frequency response function (FRF) of a milling robot is an important basis for machining process optimization. However, because of its articulated serial structure, a milling robot has an enormous number of operating postures, and its dynamics are affected by the motion state. To accurately obtain the FRF in the operating state of a milling robot, this paper proposes a method based on the structural modification concept. Unlike the traditional excitation method, the proposed method uses robot joint motion excitation instead of hammering excitation to realize automation. To address the problem of the lack of information brought by motion excitation, which leads to inaccurate FRF amplitudes, this paper derives the milling robot regularization theory based on the sensitivity of structural modification, establishes the modal regularization factor, and calibrates the FRF amplitude. Compared to the commonly used manual hammering experiments, the proposed method has high accuracy and reliability when the milling robot is in different postures. Because the measurement can be performed directly and automatically in the operation state, and the problem of inaccurate amplitudes is solved, the proposed method provides a basis for optimizing the machining posture of a milling robot and improving machining efficiency.

Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas. Landslide forecasting and early warning based on surface displacements have been widely investigated. However, the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration. In this paper, we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir (TGR) region, China, spanning a whole hydrologic year since February 2021. The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers, indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective. Considering the time lag effect, we reexamined and quantified potential controls of accelerated movements using a data-driven approach, which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift. To identify critical hydrometeorological rules in accelerated movements, accounting for the dual effect of rainfall and reservoir water level variations, we thus construct a landslide prediction model that relies upon the boosting decision tree (BDT) algorithm using a dataset comprising daily rainfall, rainfall intensity, reservoir water level, water level fluctuations, and slip zone strain time series. The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels (i.e., < 169.700 m) and large-amount and high-intensity rainfalls (i.e., daily rainfall > 57.9 mm and rainfall intensity > 24.4 mm/h). Moreover, this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset. Standing on the shoulder of this landslide case, our study informs a practical and reliable pathway for georisk early warning based on subsurface observations, particularly in the context of enhanced extreme weather events.

We propose a large combined moving component composed of carbon fiber reinforced polymer (CFRP) laminates for making lightweight machine tools with high dynamic performance. The accurate dynamic prediction of composite machine tools is essential for the new generation machine tool. This paper aims to address two challenges in numerical dynamic modeling and the design of composite machine tools to enhance development efficiency. (1) Anisotropic composite laminates, which form the composite machine tool, exhibit coupling in various directions. We propose the generalized continuity condition of the boundary to tackle this dynamic modeling challenge. (2) Composite machine tools feature numerous composite-metal coupled structures. The mechanical model correction of isotropic metals is performed to address their dynamics. We take the example of a five-axis gantry machine tool with composite moving parts, establish a dynamic model for efficient prediction, and verify it through simulation and experimentation. The proposed method yields remarkable results, with an average relative error of only 3.85% in modal frequency prediction and a staggering 99.7% reduction in solution time compared to finite element analysis. We further discuss the dynamic performance of the machine tool under varied stacking angles and layer numbers of the composite machine tool. We propose general design criteria for composite machine tools to consider the modal frequency and manufacturing cost of machine tools.

We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles. The longitudinal nonlinear model is first established and transformed into the control-oriented error equations, and the control scheme is organized by a steady-compensation combination. To overcome and eliminate the impact of model uncertainties and external disturbances, an adaptive radial basis function neural network (RBFNN) is designed by a q-gradient approach. Taking the height-velocity error system with estimated uncertainties into account, the adaptive learning-based optimal tracking control (ALOTC) scheme is proposed by combining the critic-only adaptive dynamic programming (ADP) framework and parameter optimization of system settling time. Furthermore, a novel weight update law is proposed to satisfy the online iteration requirements, and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory. Finally, four simulation cases are provided to prove the effectiveness, accuracy, and robustness of the proposed scheme for the hypersonic longitudinal control system.

Membrane fouling inevitably occurs during nanofluidic reverse electrodialysis. Herein, the impact of multi-fouling on the energy conversion performance of negatively charged conical nanochannels under asymmetrical configurations is systematically investigated. The results reveal that in Configuration I, where a high-concentration solution is applied at the tip side, at small concentration ratios, multiple foulings reduce the electric power. In Configuration II, where a low-concentration solution is applied at the tip side, multiple foulings near the base side contribute to the electric power. Any fouling that formed near the low-concentration entrance diminished the electric power and energy conversion efficiency. Multi-fouling lowered the electrical power consumption by 69.27% and 99.94% in Configurations I and II, respectively. In Configuration I, the electric power first increased with increasing fouling surface charge density, reached its maximum value, and thereafter decreased. In Configuration II, the electric power first decreased with increasing fouling surface charge density, reached its minimum value, and thereafter increased. Large negative or positive charge densities of fouling contribute to the electric power and energy conversion efficiency.