A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

Carbon fiber composites,characterized by their high specific strength and low weight,are becoming increasingly crucial in automotive lightweighting.However,current research primarily emphasizes layer count and orientation,often neglecting the potential of microstructural design,constraints in the layup process,and performance reliability.This study,therefore,introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic(CFRP)drive shafts.Initially,parametric modeling of the microscale cell was performed,and its elastic performance parameters were predicted using two homogenization methods,examining the impact of fluctuations in microscale cell parameters on composite material performance.A finite element model of the CFRP drive shaft was then constructed,achieving parameter transfer between microscale and macroscale through Python programming.This enabled an investigation into the influence of both micro and macro design parameters on the CFRP drive shaft’s performance.The Multi-Objective Particle Swarm Optimization(MOPSO)algorithm was enhanced for particle generation and updating strategies,facilitating the resolution of multi-objective reliability optimization problems,including composite material layup process constraints.Case studies demonstrated that this approach leads to over 30%weight reduction in CFRP drive shafts compared to metallic counterparts while satisfying reliability requirements and offering insights for the lightweight design of other vehicle components.

Research on cognitive load evaluation with subjective method in manual assembly

Subjective scales have different kinds of applicability in diverse fields.This study intends to implement a quantitative approach to determine the applicability of subjective scales in manual as-sembly work and evaluate the cognitive load of assembly workers.A multi-scale research paradigm based on subjective evaluation method is proposed.Three typical task stages are extracted from the process of assembly work.The National Aeronautics and Space Administration Task Load Index(NASA-TLX)scale,PAAS scale and Workload Profile Index Ratings(WP)scale are selected for the design of 3×3 multi-factor mixed experiment.The power spectrum density(PSD)characteris-tics of electroencephalogram(EEG)are utilized to identify the difficulty levels of the three task sta-ges.The relevant indicators of scale applicability are assessed.The results show that in terms of sensitivity,NASA-TLX scale reaches the highest sensitivity(F=999.137,P=0<0.05).In terms of validity,NASA-TLX scale possesses the best concurrent validity(P=0.0255<0.05).In terms of diagnosticity,NASA-TLX scale based on 6 dimensions takes on the best diagnostic performance.In terms of subject acceptability,WP scale performs the worst.According to the analytic hierarchy process(AHP)model,the applicability scores of NASA-TLX scale,PAAS scale and WP scale are determined as 3,2.55 and 1.6714,respectively.Therefore,NASA-TLX scale is regarded as the most suitable subjective evaluation questionnaire for assembly workers,which is also an effective quantitative evaluation method for the cognitive load of assembly workers.

Engineering a flexible and mechanically strong composite electrolyte for solid-state lithium batteries

Lithium-ion batteries(LIBs)have greatly facilitated our daily lives since 1990s[1,2].To meet the ever-increasing demand on energy density,Li metal is seen as the ultimate anode because of its ultra-high specific capacity(3860 m Ah/g)and the lowest electrochemical potential(-3.04 V vs.the standard hydrogen electrode)[3–6].However,issues of Li metal anode,such as Li dendrite formation and large volume change during plating/stripping。

Using Event-Based Method to Estimate Cybersecurity Equilibrium

Estimating the global state of a networked system is an important problem in many application domains.The classical approach to tackling this problem is the periodic(observation)method,which is inefficient because it often observes states at a very high frequency.This inefficiency has motivated the idea of event-based method,which leverages the evolution dynamics in question and makes observations only when some rules are triggered(i.e.,only when certain conditions hold).This paper initiates the investigation of using the event-based method to estimate the equilibrium in the new application domain of cybersecurity,where equilibrium is an important metric that has no closed-form solutions.More specifically,the paper presents an event-based method for estimating cybersecurity equilibrium in the preventive and reactive cyber defense dynamics,which has been proven globally convergent.The presented study proves that the estimated equilibrium from our trigger rule i)indeed converges to the equilibrium of the dynamics and ii)is Zeno-free,which assures the usefulness of the event-based method.Numerical examples show that the event-based method can reduce 98%of the observation cost incurred by the periodic method.In order to use the event-based method in practice,this paper investigates how to bridge the gap between i)the continuous state in the dynamics model,which is dubbed probability-state because it measures the probability that a node is in the secure or compromised state,and ii)the discrete state that is often encountered in practice,dubbed sample-state because it is sampled from some nodes.This bridge may be of independent value because probability-state models have been widely used to approximate exponentially-many discrete state systems.

Unexpected Kirkendall effect in twinned icosahedral nanocrystals driven by strain gradient

Nanoscale Kirkendall effect has been widely used for rationally fabricating high-quality hollow nanocrystals, but often requires the intrinsic diffusion coefficient of out-diffusion materials higher than that of in-diffusion components. Here we demonstrate an unexpected Kirkendall effect that occurs in diffusing intrinsically faster Cu atoms into Pd icosahedra, leading to the formation of PdCu alloyed hollow nanocrystals. The control experiment with Pd octahedra replacing icosahedra indicates the critical role of twin boundaries in facilitating such unexpected Kirkendall effect. In addition, geometric phase analysis and density functional theory calculation show that out-diffusion of Pd atoms in the icosahedra is faster than in-diffusion of Cu atoms, particularly through the twin boundaries, upon the strain gradient with an inward distribution from tensile to compressive strains. The unexpected Kirkendall effect is also found in the interdiffusion of Ag and Pd atoms in Pd icosahedra. Our finds break the limitation of the intrinsic diffusion coefficient for the synthesis of hollow nanocrystals through Kirkendall effect and are expected to enormously enrich the family of hollow nanocrystals which have shown great potential in broad areas, such as fine chemical production, energy storage and conversion, and environmental protection. This work also provides a deep understanding in the diffusion behavior of atoms upon the strain gradient.

Semantic Segmentation-Based Road Marking Detection Using Around View Monitoring System

Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic information.Nevertheless,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level data.Besides,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly obtained.In order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact analysis.The proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking fitting.Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.

The research of traffic density extraction method under vehicular ad hoc network environment

Purpose–Traffic density is one of the most important parameters to consider in the traffic operationfield.Owing to limited data sources,traditional methods cannot extract traffic density directly.In the vehicular ad hoc network(VANET)environment,the vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)interaction technologies create better conditions for collecting the whole time-space and refined traffic data,which provides a new approach to solving this problem.Design/methodology/approach–On that basis,a real-time traffic density extraction method has been proposed,including lane density,segment density and network density.Meanwhile,using SUMO and OMNet11 as traffic simulator and network simulator,respectively,the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed.Findings–Based on the simulation platform,a simulated intersection in Shanghai was developed to investigate the adaptability of the model.Originality/value–Most research studies use separate simulation methods,importing trace data obtained by using from the simulation software to the communication simulation software.In this paper,the tight coupling simulation method is applied.Using real-time data and history data,the research focuses on the establishment and validation of the traffic density extraction model.

Evaluating the moderating effect of in-vehicle warning information on mental workload and collision avoidance performance

Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.