CFD Simulation and Experimental Study of a New Elastic Blade Wave Energy Converter

Small moving vehicles represent an important category of marine engineering tools and devices(equipment)typically used for ocean resource detection and maintenance of marine rights and interests.The lack of efficient power supply modes is one of the technical bottlenecks restricting the effective utilisation of this type of equipment.In this work,the performance characteristics of a new type of elastic-blade/wave-energy converter(EBWEC)and its core energy conversion component(named wave energy absorber)are comprehensively studied.In particular,computational fluid dynamics(CFD)simulations and experiments have been used to analyze the hydrodynamics and performance characteristics of the EBWEC.The pressure cloud diagrams relating to the surface of the elastic blade were obtained through two-way fluid-solid coupling simulations.The influence of blade thickness and relative speed on the performance characteristics of EBWEC was analyzed accordingly.A prototype of the EBWEC and its bucket test platform were also developed.The power characteristics of the EBWEC were analyzed and studied by using the blade thickness and motion cycle as control variables.The present research shows that the EBWEC can effectively overcome the performance disadvantages related to the transmission shaft torque load and power curve fluctuations of rigid blade wave energy converters(RBWEC).

The emerging technology of biohybrid micro-robots:a review

In the past few decades,robotics research has witnessed an increasingly high interest in miniaturized,intelligent,and integrated robots.The imperative component of a robot is the actuator that determines its performance.Although traditional rigid drives such as motors and gas engines have shown great prevalence in most macroscale circumstances,the reduction of these drives to the millimeter or even lower scale results in a significant increase in manufacturing difficulty accompanied by a remarkable performance decline.Biohybrid robots driven by living cells can be a potential solution to overcome these drawbacks by benefiting from the intrinsic microscale self-assembly of living tissues and high energy efficiency,which,among other unprecedented properties,also feature flexibility,self-repair,and even multiple degrees of freedom.This paper systematically reviews the development of biohybrid robots.First,the development of biological flexible drivers is introduced while emphasizing on their advantages over traditional drivers.Second,up-to-date works regarding biohybrid robots are reviewed in detail from three aspects:biological driving sources,actuator materials,and structures with associated control methodologies.Finally,the potential future applications and major challenges of biohybrid robots are explored.

Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

Aiming at the difficulty of mining fault prognosis starting points and constructing prognostic models for remaining useful life(RUL)prediction of rolling bearings,a RUL prediction method is proposed based on health indicator(HI)extraction and trajectory-enhanced particle filter(TE-PF).By extracting a HI that can accurately track the trending of bearing degradation and combining it with the early fault enhancement technology,early abnormal sample nodes can be mined to provide more samples with fault information for the construction and training of subsequent prediction models.Aiming at the problem that traditional degradation rate models based on PF are vulnerable to HI mutations,a TE-PF prediction method is proposed based on comprehensive utilization of historical degradation information to timely modify prediction model parameters.Results from a rolling bearing prognostic study show that prediction starting points can be accurately detected and a reasonable prediction model can be conveniently constructed by the RUL prediction method based on HI amplitude abnormal detection and TE-PF.Furthermore,aiming at the RUL prediction problem under the condition of HI mutation,RUL prediction with probability and statistics characteristics under a confidence interval can be obtained based on the method proposed.

Dynamic Investigations on the Wear Behavior of a 3D Revolute Joint Considering Time-Varying Contact Stiffness: Simulation and Experiment

The existence of the relative radial and axial movements of a revolute joint’s journal and bearing is widely known.The three-dimensional(3D)revolute joint model considers relative radial and axial clearances;therefore,the freedoms of motion and contact scenarios are more realistic than those of the two-dimensional model.This paper proposes a wear model that integrates the modeling of a 3D revolute clearance joint and the contact force and wear depth calculations.Time-varying contact stiffness is first considered in the contact force model.Also,a cycle-update wear depth calculation strategy is presented.A digital image correlation(DIC)non-contact measurement and a cylindricity test are conducted.The measurement results are compared with the numerical simulation,and the proposed model’s correctness and the wear depth calculation strategy are verified.The results show that the wear amount distribution on the bearing’s inner surface is uneven in the axial and radial directions due to the journal’s stochastic oscillations.The maximum wear depth locates where at the bearing’s edges the motion direction of the follower shifts.These find-ings help to seek the revolute joints’wear-prone parts and enhance their durability and reliability through improved design.

Design and Implementation of Optical Fiber Test Equipment

In order to meet the needs of the rapid development of optical fiber communication technology, combined with the thinking of the Internet of Things, a new idea of designing an optical fiber test equipment using Raspberry Pi is proposed. At the same time, the design of a multi-parameter measuring device for optical fiber signals based on Flask was completed.

Nonlinear metamaterial enabled aeroelastic vibration reduction of a supersonic cantilever wing plate

The violent vibration of supersonic wings threatens aircraft safety.This paper proposes the strongly nonlinear acoustic metamaterial(NAM)method to mitigate aeroelastic vibration in supersonic wing plates.We employ the cantilever plate to simulate the practical behavior of a wing.An aeroelastic vibration model of the NAM cantilever plate is established based on the mode superposition method and a modified third-order piston theory.The aerodynamic properties are systematically studied using both the timedomain integration and frequency-domain harmonic balance methods.While presenting the flutter and post-flutter behaviors of the NAM wing,we emphasize more on the preflutter broadband vibration that is prevalent in aircraft.The results show that the NAM method can reduce the low-frequency and broadband pre-flutter steady vibration by 50%-90%,while the post-flutter vibration is reduced by over 95%,and the critical flutter velocity is also slightly delayed.As clarified,the significant reduction arises from the bandgap,chaotic band,and nonlinear resonances of the NAM plate.The reduction effect is robust across a broad range of parameters,with optimal performance achieved with only 10%attached mass.This work offers a novel approach for reducing aeroelastic vibration in aircraft,and it expands the study of nonlinear acoustic/elastic metamaterials.

A deep Koopman operator-based modelling approach for long-term prediction of dynamics with pixel-level measurements

Although previous studies have made some clear leap in learning latent dynamics from high-dimensional representations,the performances in terms of accuracy and inference time of long-term model prediction still need to be improved.In this study,a deep convolutional network based on the Koopman operator(CKNet)is proposed to model non-linear systems with pixel-level measurements for long-term prediction.CKNet adopts an autoencoder network architecture,consisting of an encoder to generate latent states and a linear dynamical model(i.e.,the Koopman operator)which evolves in the latent state space spanned by the encoder.The decoder is used to recover images from latent states.According to a multi-step ahead prediction loss function,the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini-batch manner.In this manner,gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self-adaptively tune the latent state space in the training process,and the resulting model is time-invariant in the latent space.Therefore,the proposed CKNet has the advantages of less inference time and high accuracy for long-term prediction.Experiments are per-formed on OpenAI Gym and Mujoco environments,including two and four non-linear forced dynamical systems with continuous action spaces.The experimental results show that CKNet has strong long-term prediction capabilities with sufficient precision.

Local resonance metamaterial-based integrated design for suppressing longitudinal and transverse waves in fluid-conveying pipes

To solve the problem of low broadband multi-directional vibration control of fluid-conveying pipes,a novel metamaterial periodic structure with multi-directional wide bandgaps is proposed.First,an integrated design method is proposed for the longitudinal and transverse wave control of fluid-conveying pipes,and a novel periodic structure unit model is constructed for vibration reduction.Based on the bandgap vibration reduction mechanism of the acoustic metamaterial periodic structure,the material parameters,structural parameters,and the arrangement interval of the periodic structure unit are optimized.The finite element method(FEM)is used to predict the vibration transmission characteristics of the fluid-conveying pipe installed with the vibration reduction periodic structure.Then,the wave/spectrum element method(WSEM)and experimental test are used to verify the calculated results above.Lastly,the vibration attenuation characteristics of the structure under different conditions,such as rubber material parameters,mass ring material,and fluid-structure coupling effect,are analyzed.The results show that the structure can produce a complete bandgap of 46 Hz-75 Hz in the low-frequency band below 100 Hz,which can effectively suppress the low broadband vibration of the fluidconveying pipe.In addition,a high damping rubber material is used in the design of the periodic structure unit,which realizes the effective suppression of each formant peak of the pipe,and improves the vibration reduction effect of the fluid-conveying pipe.Meanwhile,the structure has the effect of suppressing both bending vibration and longitudinal vibration,and effectively inhibits the transmission of transverse waves and longitudinal waves in the pipe.The research results provide a reference for the application of acoustic metamaterials in the multi-directional vibration control of fluid-conveying pipes.

The Course Reform of Mechanical Design Fundamentals to Cultivate Engineering Literacy and Innovation Ability

In view of the shortcomings of traditional teaching in the Mechanical Design Fundamentals course,the teaching resources are integrated,the teaching content,teaching methods,and assessment methods are reformed,scientific research results are introduced into course teaching,and the task-driven teaching practice is applied.These measures have improved classroom activity,stimulated independent learning,and laid the foundation for the cultivation of students’engineering literacy and innovative ability.

Threat Assessment Method Considering Target Instantaneous and Historical States

The sea surface escort formation faces various threats in reality. For example, suicide boats may carry explosives or other dangerous items, aiming to cause maximum damage by colliding or detonating escort targets. Since suicide boats have a certain degree of concealment, it is necessary to establish a threat assessment algorithm to timely identify and respond to such fast and concealed threats. This paper establishes a threat assessment model that considers the instantaneous and historical states of the target. The instantaneous state of the target takes into account six evaluation indicators, including target category, target distance, target heading, target speed, collision risk, and ship automatic identification system(AIS) recognition status;in terms of historical state information mining, a target typical intention recognition method based on graph neural network is proposed to achieve end-to-end target typical intention recognition. Furthermore, this paper introduces a multi-attribute decision analysis method to weight the evaluation indicators, improves the relative closeness calculation method between different evaluation schemes and positive and negative ideal schemes, and determines the target threat ranking based on relative closeness. Based on Unity3D, a set of unmanned boat confrontation simulation system is designed and developed, and typical intention recognition data sets and threat assessment scenario simulation data are generated through real-life confrontation. Comparative analysis shows that the threat assessment model in this paper can accurately and timely detect raid target threats and give scientific and reasonable target threat ranking results.

Target-driven visual navigation in indoor scenes using reinforcement learning and imitation learning

Here,the challenges of sample efficiency and navigation performance in deep rein-forcement learning for visual navigation are focused and a deep imitation reinforcement learning approach is proposed.Our contributions are mainly three folds:first,a frame-work combining imitation learning with deep reinforcement learning is presented,which enables a robot to learn a stable navigation policy faster in the target-driven navigation task.Second,the surrounding images is taken as the observation instead of sequential images,which can improve the navigation performance for more information.Moreover,a simple yet efficient template matching method is adopted to determine the stop action,making the system more practical.Simulation experiments in the AI-THOR environment show that the proposed approach outperforms previous end-to-end deep reinforcement learning approaches,which demonstrate the effectiveness and efficiency of our approach.

Vector transformation and reduction to the pole for regional magnetic data with variable field direction

The vector transformation and pole reduction from the total-field anomaly are signifi cant for the interpretation.We examined these industry-standard processing procedures in the Fourier domain.We propose a novel iteration algorithm for regional magnetic anomalies transformations to derive the vertical-component data from the total-field measurements with the variation in the core-fi eld direction over the region.Additionally,we use the same algorithm to convert the calculated vertical-component data into the corresponding data at the pole and realize the processing of diff erential reduction to the pole(DRTP).Unlike Arkani-Hamed’s DRTP method,the two types of iterative algorithms have the same forms,and DRTP is realized by implementing this algorithm twice.The synthetic model’s calculation results show that the method has high accuracy,and the fi eld data processing confi rms its practicality.

Development of conductive hydrogels: from design mechanisms to frontier applications

Owing to their excellent mechanical flexibility, electrical conductivity, and biocompatibility, conductive hydrogels(CHs) are widely used in the fields of energy and power, and biomedical technology. To arrive at a better understanding of the design methods and development trends of CHs, this paper summarizes and analyzes related research published in recent years. First,we describe the properties and characteristics of CHs. Using Scopus, the world’s largest abstract and citation database, we conducted a quantitative analysis of the related literature from the past 15 years and summarized development trends in the field of CHs. Second, we describe the types of CH network crosslinking and basic functional design methods and summarize the three-dimensional(3D) structure-forming methods and conductive performance tests of CHs. In addition, we introduce applications of CHs in the fields of energy and power, biomedical technology, and others. Lastly, we discuss several problems in current CH research and introduce some prospects for the future development of CHs.

Effect of magnetic nanoparticles on magnetic field homogeneity

The mechanism of magnetic nanoparticles(MNPs)affecting magnetic field uniformity is studied in this work.The spatial distribution of MNPs in liquid is simulated based on Monte Carlo method.The induced field of the single MNP is combined with the magnetic field distribution of magnetofluid.In the simulation,magnetic field uniformity is described by a statistical distribution.As the chemical shift(CS)and full width at half maximum(FWHM)of magnetic resonance(MR)spectrum can reflect the uniformity of magnetic field,the simulation is verified by spectrum experiment.Simulation and measurement results prove that the CS and FWHM of the MR spectrum are basically positively correlated with the concentration of MNPs and negatively correlated with the temperature.The research results can explain how MNPs play a role in MR by affecting the uniform magnetic field,which is of great significance in improving the temperature measurement accuracy of magnetic nanothermometers and the spatial resolution of magnetic particle imaging.

Position Errors and Interference Prediction-Based Trajectory Tracking for Snake Robots

This work presents a trajectory tracking control method for snake robots.This method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined trajectory.The optimized line-of-sight(LOS)guidance strategy drives the robot’s steering angle to maintain its anti-sideslip ability by predicting position errors and interferences.Then,the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control input.The compensation is adopted to enhance the compatibility of a robot within ever-changing environments.Simulation and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors,reduce adjustment time,and improve accuracy.

Method of simulating hybrid STT-MTJ/CMOS circuits based on MATLAB/Simulink

The spin-transfer-torque(STT)magnetic tunneling junction(MTJ)device is one of the prominent candidates for spintronic logic circuit and neuromorphic computing.Therefore,building a simulation framework of hybrid STT-MTJ/CMOS(complementary metal-oxide-semiconductor)circuits is of great value for designing a new kind of computing paradigm based on the spintronic devices.In this work,we develop a simulation framework of hybrid STT-MTJ/CMOS circuits based on MATLAB/Simulink,which is mainly composed of a physics-based STT-MTJ model,a controlled resistor,and a current sensor.In the proposed framework,the STT-MTJ model,based on the Landau-Lifshitz-Gilbert-Slonczewsk(LLGS)equation,is implemented using the MATLAB script.The proposed simulation framework is modularized design,with the advantage of simple-to-use and easy-to-expand.To prove the effectiveness of the proposed framework,the STT-MTJ model is benchmarked with experimental results.Furthermore,the pre-charge sense amplifier(PCSA)circuit consisting of two STT-MTJ devices is validated and the electrical coupling of two spin-torque oscillators is simulated.The results demonstrate the effectiveness of our simulation framework.

Effect of Mean Flow on Acoustic Wave Propagation in a Duct with a Periodic Array of Helmholtz Resonators

Sound propagation properties of a duct system with Helmholtz resonators(HRs)are affected by mean flow.Previous studies have tended to focus on the effects of mean flows on acoustic response of a duct system with a finite number of HRs.Employing an empirical impedance model,we present a modified transfer matrix method for studying the effect of mean flow on the complex band structure of an air duct system with an infinite periodic array of HRs.The efficiency of the modified transfer matrix is demonstrated by comparison between an example of transmission response calculation for a finite single HR loaded duct and the finite element simulation result calculated using the COMSOL software.Numerical results are presented to analyze the effect of mean flow on the band structure and transmission loss of the sound wave in the duct system.It is hoped that this study will provide theoretical guidance for acoustic wave propagation of HR silencer in the presence of mean flow.

Bio-inspired Design and Inverse Kinematics Solution of an Omnidirectional Humanoid Robotic Arm with Geometric and Load Capacity Constraints

Inspired by the driving muscles of the human arm,a 4-Degree of Freedom(DOF)concentrated driving humanoid robotic arm is proposed based on a spatial double parallel four-bar mechanism.The four-bar mechanism design reduces the inertia of the elbow-driving unit and the torque by 76.65%and 57.81%,respectively.Mimicking the human pose regulation strategy that the human arm picks up a heavy object by adjusting its posture naturally without complicated control,the robotic arm features an integrated position-level closed-form inverse solution method considering both geometric and load capacity limitations.This method consists of a geometric constraint model incorporating the arm angle(φ)and the Global Configuration(GC)to avoid joint limits and singularities,and a load capacity model to constrain the feasible domain of the arm angle.Further,trajectory tracking simulations and experiments are conducted to validate the feasibility of the proposed inverse solution method.The simulated maximum output torque,maximum output power and total energy consumption of the robotic arm are reduced by up to 2.0%,13.3%,and 33.3%,respectively.The experimental results demonstrate that the robotic arm can bear heavy loads in a human-like posture,effectively reducing the maximum output torque and energy consumption of the robotic arm by 1.83%and 5.03%,respectively,while avoiding joints beyond geometric and load capacity limitations.The proposed design provides a high payload–weight ratio and an efficient pose control solution for robotic arms,which can potentially broaden the application spectrum of humanoid robots.

Mission-oriented capability evaluation for combat network based on operation loops

With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop.Firstly,a combat network model is given that takes into account the capability properties of combat nodes.Then,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search.Given the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance measure.Through a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is verified.The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.

Deep reinforcement learning using least-squares truncated temporal-difference

Policy evaluation(PE)is a critical sub-problem in reinforcement learning,which estimates the value function for a given policy and can be used for policy improvement.However,there still exist some limitations in current PE methods,such as low sample efficiency and local convergence,especially on complex tasks.In this study,a novel PE algorithm called Least-Squares Truncated Temporal-Difference learning(LST2D)is proposed.In LST2D,an adaptive truncation mechanism is designed,which effectively takes advantage of the fast convergence property of Least-Squares Temporal Difference learning and the asymptotic convergence property of Temporal Difference learning(TD).Then,two feature pre-training methods are utilised to improve the approximation ability of LST2D.Furthermore,an Actor-Critic algorithm based on LST2D and pre-trained feature representations(ACLPF)is proposed,where LST2D is integrated into the critic network to improve learning-prediction efficiency.Comprehensive simulation studies were conducted on four robotic tasks,and the corresponding results illustrate the effectiveness of LST2D.The proposed ACLPF algorithm outperformed DQN,ACER and PPO in terms of sample efficiency and stability,which demonstrated that LST2D can be applied to online learning control problems by incorporating it into the actor-critic architecture.