Tungsten-based heterogeneous multilayer structures via diffusion bonding

Recent work has shown that tungsten (W) and other refractory metals with body-centered cubic (bcc) structures exhibit certain novel behavior when their grain size, d, is refined into the ultrafine (UFG, 100 nm < d < 1000 nm) or nanocrystalline (NC, d < 100 nm) regime. For example, it has been shown that bcc refractory metals with such microstructures show decreased strain rate sensitivity besides their elevated strength and vanishing strain hardening response. Consequently, under both quasi-static and high-strain-rate loading, plastic instability in the form of shear banding becomes the dominant mode of plastic deformation. Such behavior is long sought-after in certain applications. However, due to the technology used to refine the grain size (primarily severe plastic deformation), the inability to scale the dimensions of the material may limit wider use and application of UFG/NC bcc refractory metals. In this work, the feasibility was demonstrated of production of large-scale W parts using a diffusion bonding method. The microstructure, preliminary mechanical properties, and issues and challenges associated with the fabrication procedures were examined and discussed. It is envisioned that diffusion bonding may serve as a promising technology for scaled-up fabrication of UFG bcc refractory metals for the targeted application.     

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods.     

Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach

The proton exchange membrane fuel cell has been widely used for industrial systems; however, its performance gradually degrades during use. Therefore, the study on the performance degradation prediction of fuel cells is helpful to extend its lifespan. In this paper, a novel hybrid approach using a combination of model-based adaptive Kalman filter and data-driven NARX neural network is proposed to predict the degradation of fuel cells. The overall degradation trend (i.e., irreversible degradation process) is captured by an empirical aging model and adaptive Kalman filter. Meanwhile, the detail degradation information (i.e., reversible degradation process) is depicted by the NARX neural network. Moreover, the correlation analysis of the reversible voltage time series is carried out to obtain the number of delays of the NARX neural network based on the autocorrelation function and the partial autocorrelation function. Then, the total degradation prediction is the sum of the overall degradation prediction and the detail degradation prediction. Finally, the prognostic capability of the proposed method is verified by two aging datasets, and the results show the effectiveness and superiority of the proposed method which can provide accurate degradation forecasting and remaining useful life.     

It is in the box! Improving the usability and benefits of surgical safety checklists – A feasibility study

Up to 50% of preventable hospital medical errors occur in or after visiting an operating theater in the United States. To address this issue, hospitals use a Surgical Safety Checklist (SSC), which can reduce complications and mortality rates when used systematically. To execute it, surgeons read aloud from a poster hanging on a wall. However, studies report usability issues with posters and often surgeons execute the checklist from memory, which can reduce completion rates at the expense of patient safety. We present a novel approach to increase the SSC's usability in view of improving patient safety. Our DigitalChecklistBox displays the SSC directly on a patient's surgical drape and enables surgeons to navigate through the checklist using hand gestures. Evaluated with 10 surgeons, it increased the checklist elements' readability and navigation and brought additional functionalities to limit the number of missed elements.     

考虑应力松弛和辐照影响的堆内构件压紧弹簧疲劳可靠性评估方法

针对反应堆堆内构件压紧弹簧疲劳失效模式，在考虑应力松弛和辐照影响的条件下基于仿真方法开展可靠性评估。首先结合疲劳模型和平均应力松弛Landgraf模型，考虑辐照对疲劳参数的影响，构建了压紧弹簧疲劳寿命模型。在压紧弹簧疲劳寿命模型基础上，根据广义应力-强度干涉模型定义压紧弹簧可靠度并开展灵敏度分析。以非能动压水堆AP1000压紧弹簧为例进行案例分析，在95%置信度水平下，分别计算了可靠度为95%和50%时对应的疲劳寿命。结果表明，若不考虑应力松弛，压紧弹簧总疲劳寿命下降88.3%；从经济性角度考虑寿命预测结果较为保守。通过灵敏度分析发现对可靠度影响较大的设计变量是弹性模量和疲劳强度系数，在一定可靠度下可以通过调整设计变量对压紧弹簧的疲劳可靠性评估进行优化。      

Crosstalk noise modeling of multiwall carbon nanotube (MWCNT) interconnects using finite-difference time-domain (FDTD) technique

This paper presents an accurate and efficient model for the transient analysis of multiwall carbon nanotubes (MWCNT) using finite-difference time-domain (FDTD) method. The proposed model can be essentially used to analyze the functional and dynamic crosstalk effects of coupled-two MWCNT interconnect lines. Using the proposed model the voltage and current can be accurately estimated at any point on the interconnect line and furthermore, the model can be extended to coupled-n interconnect lines with a low computational cost. Crosstalk induced propagation delay, peak voltage, and its timing instance are measured using the proposed model and validated by comparing it to the HSPICE simulations. Over a random number of test cases it is observed that the average error in estimating the noise peak voltage on a victim line is less than 1%. The proposed model is extremely useful for accurate estimation of crosstalk induced performance parameters of MWCNT interconnects.     

A cloud model-based method for the analysis of accelerated life test data

Various curve fitting models, including the Arrhenius stress model, inverse power law model, and Eyring model have been used to model the load (stress) – life relationship to aid in planning accelerated life tests; that is, the relationship between the mean of the sample lifetimes and the testing stress level. The load-life relationship is a one-to-one relationship: one mean of the sample lifetimes corresponds to one testing stress level. However, due to the random uncertainties existing in the testing stress, the relationship should be a many-to-many relationship rather than one testing stress corresponding one mean lifetime of the tested product. Based on the one-to-one relationship of the mean of the sample lifetimes to the testing stress level, a many-to-many relationship can be derived using the reasoning method presented in this paper. The reasoning method is constructed as ‘If X, then Y.’ X is termed the rule antecedent, and Y is called the rule consequent. They are constructed with the stress values and the sample lifetimes, respectively, based on the cloud model, which represents random uncertainty and fuzzy uncertainty. The reasoning method presented is called the multi-rule-based cloud reasoner, which can refine the one-to-one relationship established by models such as the Arrhenius stress model to a many-to-many relationship. In the case study, the multi-rule-based cloud reasoner was applied to a thermal stress accelerated life test of ammunition fuses. The results from the multi-rule-based cloud reasoner were compared with the estimation results from a normal cloud generator under a stress level of 20 °C. The results showed that the many-to-many relationship between the uncertain stress level and the means of the sample lifetimes was derived by the multi-rule-based cloud reasoner.     

Pointwise multi-valued fusion

Assessment and improvement of data quality is a major challenge with any modern information source. An aspect of data quality that has gained a lot of interest in the past decades, is the detection and fusion of duplicate data. This paper contributes to the field of duplicate data fusion by investigating a framework of fusion functions. In particular, it is observed that multisets are a data structure for which little is known concerning fusion theory. Therefore, a class of multi-valued functions called pointwise fusion functions, is proposed and investigated. An extensive list of properties is defined in order to compare the behavior of multi-valued fusion functions. Some specific pointwise fusion functions are investigated with respect to the defined properties and they are evaluated in different fusion scenarios. Next, some quality measures are discussed and their usefulness in the different fusion scenarios is discussed.     

Effect of electric field polarity on inter-poly dielectric during cell operation for the retention characteristics

Retention characteristic represents a capability to maintain the storage data and it is related with the reliability of memory device. The retention characteristic is degraded by the leakage of charges from a floating gate to a control gate, and thus the leakage current at low and moderate electric field of inter-poly dielectric (IPD) is one of the important characteristic for floating gate type flash memories. In addition, it is necessary to investigate the effects of the electric field polarity on the electric characteristics of IPD because the electric field polarity is changed as the cell operations such as the programming and erasing. Therefore, in this paper, the variation of the leakage current of IPD at moderate electric field region is measured with varying the previously applied electric field polarity. Based on the result, the effect of sequential change of an applied electric field polarity on the electrical characteristics is analyzed.     

Are electric self-balancing scooters safe in vehicle crash accidents?

With the pressing demand of environmentally friendly personal transportation vehicles, mobility scooters become more and more popular for the short-distance transportation. Similar to pedestrians and bicyclists, scooter riders are vulnerable road users and are expected to receive severe injuries during traffic accidents. In this research, a MADYMO model of vehicle–scooter crash scenarios is numerically set up. The model of the vehicle with the scenario is validated in pedestrian–vehicle accident investigation with previous literatures in terms of throwing distance and HIC₁₅ value. HIC₁₅ values gained at systematic parametric studies. Injury information from various vehicle crashing speeds (i.e. from 10 m/s to 24 m/s), angles (i.e. from 0 to 360°), scooter's speeds (i.e. from 0 m/s to 4 m/s), contact positions (i.e. left, middle and right bumper positions) are extracted, analyzed and then compared with those from widely studied pedestrian–vehicle and bicycle–vehicle accidents. Results show that the ESS provides better impact protection for the riders. Riding ESS would not increase the risk higher than walking at the same impact conditions in terms of head injury. The responsible reasons should be the smaller friction coefficient between the wheel-road than the heel-road interactions, different body gestures leading to different contact positions, forces and timing. Results may shed lights upon the future research of mobility scooter safety analysis and also the safety design guidance for the scooters.