Development of a statistical model to classify driving stress levels using galvanic skin responses

Many studies have demonstrated the strong relationships between physiological responses and driving stress, but they have done little to build a model that could be used to identify a driver's stress accurately in real time. The objective of this study is to develop a model that accurately classifies driving stress by monitoring physiological responses—specifically galvanic skin response (GSR). GSR data were collected from nine drivers with licenses obtained in the US in real road driving situations with two stress conditions—rest period (low stress) and highway or city driving (high stress). The validation drive was performed by one driver with licenses obtained in South Korea in real long‐term road driving situations with two stress conditions—rural area (low stress) and highway or highway under construction (high stress). Those two conditions were used to build a binary logistic regression model to classify low stress or high stress based on a driver's measured hand GSR. The overall classification accuracy of the developed model was found to be 85.3%, and the accuracy of cross validation, with a testing dataset, was found to be 83.2%. A simple logit model was developed to identify drivers' stress by incorporating their GSR data. The developed model can be embedded in a wearable device equipped with GSR sensors for drivers to detect their stress level in real time.     

Improved one-dimensional beam model for unbalanced composite single-lap joint

An analytical nonlinear solution was provided for unbalanced composite single-lap joint (CSLJ) using an improved one-dimensional beam model, which incorporated the effect of bending–tension coupling. The bending–tension coupling stiffness was introduced to characterize the coupling bending and tension behavior induced by the un-symmetric stacking sequence of composite laminates. The governing differential equations captured the bending–tension coupling behavior and the geometrically nonlinear features were constructed based on the displacement compatibility conditions of flexible interface. The transverse deformation in overlap region, edge moment factors and adhesive stress distributions for the unbalanced CSLJ with inflexible, intermediate flexibility and flexible adhesive can be determined by the present one-dimensional beam model. The accuracy of the present model was validated by the comparison with nonlinear finite element model. The effect of bending–tension coupling on edge moment factors and peak values of adhesive stresses was shed light on with the present model.     

贾燕1,赵永涛2,张红松2

为确定ZrO2/莫来石热障涂层的单层最佳厚度,采用有限元技术研究单层厚度对ZrO2/莫来石热障涂层热冲击应力的影响。结果表明,莫来石层厚度为0.25mm时,涂层表面及ZrO2/莫来石界面处具有最大热应力,而其它莫来石层厚度时涂层内部热应力则受莫来石层厚度影响不明显。当ZrO2层厚度为0.3mm时,ZrO2/莫来石涂层中的热应力在整个横向距离范围内保持稳定,而其它情况下涂层中的热应力则与ZrO2层厚度成正比。涂层表面处的径向热应力大于ZrO2/莫来石界面处的对应值。     

Inhibition of stress corrosion cracking in 304 stainless steel through titanium ion implantation

The effects of titanium ion implantation on the stress corrosion cracking (SCC) behaviour of 304 austenitic stainless steel were studied. Slow strain rate tests (SSRTs) were conducted on 304 steel in air and in 5?wt-% NaCl solution. The microscopic effects of ion implantation were evaluated by Stopping and Range of Ions in Matter Procedures (SRIM). Fracture morphologies and microstructures were investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The fracture surfaces illustrate that ion implantation significantly inhibits the corrosion pits that initiate SCC. A dense passive film, which inhibits SCC, was formed during the ion implantation process. SCC initiation was restrained due to the dense dislocation nets that were generated by titanium ion implantation.

Highlights

• Ion implantation inhibits SCC susceptibility.

• The lack of Cr at the grain boundary leads to the expansion of SCC along the grain boundary.

• Implantation-induced damage leads to high-density dislocations.

• The surface was amorphised due to high-density dislocations.

     

上保护层开采底板破坏深度和应力分析研究

上保护层开采导致的底板变形、应力释放会使被保护层内高瓦斯沿着上覆岩层内形成的空隙裂隙结构向上运移，很容易造成所采工作面采空区内瓦斯增加，造成工作面瓦斯超限。以峰峰集团以黄沙矿为研究实例，针对其主采的2号煤层利用FLAC^3D模拟软件进行上保护层开采底板应力研究，揭示了上保护层开采底板应力的规律特性，对于保护层开采工作瓦斯治理具有一定的指导意义。     

槽式太阳能集热管内相变微胶囊悬浮液的热力性能分析

针对传统使用水基和油基的太阳能集热器换热效果低和管壁热应力大的问题，以相变微胶囊悬浮液为工作流体，对抛物型槽式太阳能集热器进行了三维建模。采用蒙特卡罗射线追踪法结合有限容积法和有限元法的方法求解了太阳能集热管的光?热?力耦合问题，采用欧拉?欧拉多相流模型研究了相变微胶囊悬浮液在集热管内的流动换热特性。结果表明，相变微胶囊悬浮液强化了集热管内的对流换热，不仅降低了集热管的沿程壁温，且减少了集热管的周向温差，均化了集热管温度分布。集热管周向等效热应力呈花瓣型分布，对应的5个高温度梯度的位置附近(圆周角θ=5°, 90°, 175°, 225°和315°)出现等效应力局部峰值。吸热管内壁面θ=90°处轴向热应力为压应力，作用于整个管程，而径向热应力和切向热应力为拉应力，主要作用在进出口端。相变微胶囊悬浮液浓度越高，强化换热效果越好，集热管热应力越小，但产生的压降也随之增大。     

起重机自重应力对安全评估中应力测试实验结果的影响

应力测试是起重机安全评估中必须进行的一项检测。目前在安全评估工作中常采用应变电测法来采集起重机工作过程中的应力时程数据,由于该方法只能采集到结构由于载荷变化产生的应力,而无法采集到结构因自重产生的应力,导致应力测试无法获取结构的完整应力。利用起重机金属结构自重应力在总应力中占有的权重,分析其对应力测试实验结果的影响大小,试验结果表明在危险截面自重应力大小占总应力大小可达60%,此时不考虑自重应力进行结构安全性的判定是不全面的。可考虑利用仿真技术获取自重应力[1,2],从而使得安全评估应力测试实验结论更加准确。     

Up-pulling force analysis for ESC hollow cylindrical casting

Electroslag casting(ESC)is an important method to produce high quality castings.In this study,the ESC up-pulling inner mold method(EUPIM)was used to produce hollow cylindrical castings with the multiple consumable electrodes.The radial deformation,the axial and radial internal stress of the inner mold,and the axial internal stress of the slag shell were analyzed using the finite element method(FEM)with the aid of ANSYS software.The ProCAST software was used to calculate the specific heat,heat conductivity and density curve of Cu.Simulation results show that the radial deformation,the axial and radial internal stress of the inner mold,and the axial internal stress of the slag shell near the slag-metal interface of hollow cylndrical casting gradually increase from 0 s to 360 s after the ESC starting(slagging)process but before applying the up-pulling force.The suitable initial up-pulling moment of the inner mold is at around 180-198 s after the starting process.     

基于双应力加速寿命试验的关节轴承寿命预测与可靠性分析

自润滑关节轴承寿命主要是由衬垫的磨损性能决定的,但衬垫的磨损一般是非线性的,使得寿命难以预测。采用同时改变载荷和摆频2种应力的方法进行加速寿命试验,建立以pv值、磨损量退化数据为输入参数,寿命值为输出参数的灰色神经网络预测模型。经验证该预测模型对关节轴承寿命预测的最大误差仅为7.33%,平均误差仅为3.892%。对不同加速应力下自润滑关节轴承可靠性进行评估,结果表明,关节轴承的可靠性在L_(10)(可靠度为90%时的寿命)之前下降趋势缓慢,然后迅速下降,pv值越大可靠性下降越迅速;随着pv值的增大,关节轴承寿命近似呈指数下降,经验证可用逆幂率加速模型反映二者关系。