Improved pseudopotential lattice Boltzmann model for liquid water transport inside gas diffusion layers

Liquid water transport inside the gas diffusion layers (GDLs) plays a vital role in water management of proton exchange membrane fuel cells (PEMFCs). In this study, an improved pseudopotential multiphase lattice Boltzmann model is firstly developed to realize the actual density and viscosity ratios in porous media. The proposed model is based on a non-orthogonal multiple-relaxation-time (MRT) LB model and a new improved wettability boundary condition. In terms of the relationship between capillary pressure P_c and saturation s, the proposed model shows a good agreement with the experimental data. Using the validated model, the effects of capillary pressures and contact angles of mixed wettability on the liquid water invasion process for Toray-090 GDLs with two Polytetrafluoroethylene (PTFE) contents (10 wt% and 20 wt%) are studied. It is found that the liquid water shows capillary fingering behaviors and the liquid water saturation profiles along the through-plane direction of the GDLs become more non-uniform with increasing contact angle of PTFE.     

Degradation prediction model for proton exchange membrane fuel cells based on long short-term memory neural network and Savitzky-Golay filter

Proton exchange membrane fuel cell (PEMFC) as a promising green power source, can be applied to vehicles, ships, and buildings. However, the lifetime of the fuel cell needs to be prolonged in order to achieve a wide range of applications. Consequently, the prediction of the health state draws attention lately and is critical to improving the reliability of the fuel cell. Since the degradation mechanism of the fuel cell is not fully understood, the data-driven method is very suitable for designing degradation prediction models. However, the data-driven method usually requires a lot of data, which is difficult to be obtained. To solve the issues, we propose a degradation prediction model for PEMFC based on long short-term memory neural network (LSTM) and Savitzky-Golay filter in this paper. First, we select the monitoring parameters for building the degradation prediction model by analyzing the degradation phenomenon of the fuel cell. Then, Savitzky-Golay filter is utilized to smooth out the selected data, and the sliding time window is used to generate training samples. Finally, the LSTM is applied to establish the degradation prediction model. Moreover, the dropout layer and mini-batch method are adopted to improve the model generalization ability. We use an actual aging data of the fuel cell to verified the proposed degradation prediction model. The results demonstrate that the proposed model can precisely predict the fuel cell degradation. It is worth mentioning that the determination coefficient (R²) of the test set based on the model trained by 25% of data is 0.9065.     

Battery recycling policies for boosting electric vehicle adoption: evidence from a choice experimental survey

Electric vehicles must be widely accepted because of environmental concerns and carbon restrictions. Previous research has looked at consumer policy preferences and their influence on electric vehicle adoption. However, none have investigated the impact of policies linked to battery recycling on electric vehicle adoption. This study used a discrete choice model (the panel-data mixed logit model) to evaluate 552 actual consumer choice data from Southwest China collected via an online questionnaire. Our results indicate that (1) 75% of respondents feel that electric vehicles enhance the environment and are eager to embrace them. However, the lack of strong recycling policies may hinder their adoption of electric vehicles. Specifically, the four battery recycling policies significantly impact electric vehicle adoption. (2) Consumers appreciate producer-oriented incentives more than consumer-oriented incentives to a lesser extent, such as mandated battery recycling policies and electric vehicle battery flow tracing policies. (3) Consumers place a larger willingness to pay on charging station density than vehicle attributes. (4) Regarding consumer heterogeneity, the usual young group in higher-rated cities prefers electric vehicles, while customers who own a car are more inclined to buy electric vehicles. Finally, more management insights and policy recommendations are provided based on these findings to help government and producer policymakers.

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Identifying step-level complexity in procedures: Integration of natural language processing into the Complexity Index for Procedures—Step level (CIPS)

Task complexity plays an important role in performance and procedure adherence. While studies have attempted to assess the contribution of different aspects of task complexity and their relationship to workers’ performance and procedure adherence, only a few have focused on application-specific measurement of task complexity. Further, generalizable methods of operationalizing task complexity that are used to both write and evaluate a wide range of routine or non-routine procedures is largely absent. This paper introduces a novel framework to quantify the step-level complexity of written procedures based on attributes such as decision complexity, need for judgment, interdependency of instructions, multiplicity of instructions, and excess information. This framework was incorporated with natural language processing and artificial intelligence to create a tool for procedure writers for identifying complex elements in procedures steps. The proposed technique has been illustrated through examples as well as an application to a tool for procedure writers. This method can be used both to support writers when constructing procedures as well as to examine the complexity of existing procedures. Further, the complexity index is applicable across several high-risk industries in which written procedures are prevalent, improving the linguistic complexity of the procedures and thus reducing the likelihood of human errors with procedures associated with complexity.     

A Computational Inverse Technique to Determine the Dynamic Constitutive Model Parameters of Concrete

In this paper, a computational inverse technique is presented to determine the constitutive parameters of concrete based on the penetration experiments. In this method, the parameter identification problem is formulated as an inverse problem, in which the parameters of the constitutive model can be characterized through minimizing error functions of the penetration depth measured in experiments and that computed by forward solver LS-DYNA. To reduce the time for forward calculation during the inverse procedure, radial basis function approximate model is used to replace the actual computational model. In order to improve the accuracy of approximation model, a local-densifying method combined with RBF approximation model is adopted. The intergeneration projection genetic algorithm is employed as the inverse solver. Through the application of this method, the parameters of HJC constitutive model are determined. Results show that the identified constitutive parameters' computational penetration depth and projectile deceleration-time curves are closely in accordance with experimental data. The proposed inverse approach is a potentially useful tool to effectively help identify material parameters.     

A New Interval Comparison Relation and Application in Interval Number Programming for Uncertain Problems

For optimization or decision-making problems with interval uncertainty, the interval comparison relation plays a very important role, as only based on it a better or best decision can be determined. In this paper, a new kind of interval comparison relation termed as reliability-based possibility degree of interval is proposed to give quantitative evaluations on "how much better" of one interval than another, which is more suitable for engineering reliability analysis and numerical computation than the existing relations. In the new relation, the range of the comparing values is extended into the whole real number field, and the precise comparison is made possible for any pairs of intervals on the real line. Furthermore, the suggested interval comparison relation is applied to the interval number programming, and two kinds of transformation models are developed for both of the linear and nonlinear interval number programming problems, based on which the uncertain optimization problems can be changed into traditional deterministic optimization problems. Two numerical examples are finally investigated to demonstrate the effectiveness of the two transformation models.     

The effect of applied magnetic field on the growth mechanisms of liquid phase electroepitaxy

Binary (GaAs) and ternary (InGaAs) single crystals were grown by the growth process of liquid phase electroepitaxial (LPEE) under an applied static magnetic field. The effect of the applied magnetic field on two main growth mechanisms of the LPEE growth process, namely the “electromigration” and “natural convection” in the liquid zone, were examined numerically and experimentally. Numerical results show that the flow and concentration patterns exhibit three distinct stability characteristics: stable structures up to the magnetic field level of 2.0 kG, transitional structures between 2.0 and 3.0 kG, and unstable structures above 3.0 kG. In the stable region, the applied magnetic field suppresses the flow structures, and the intensities decrease with the increasing magnetic field level. In the transitional region, the flow intensity increases dramatically with the magnetic field strength, and concentrations show very different patterns leading to a wavy growth interface. Under strong magnetic field levels, the flows cells are confined to the vicinity of the vertical wall and exhibit significant non-uniformity near the growth interface.Experiments performed under various magnetic field levels show that the growth process at the 4.5 kG field level yields satisfactory growths. However, the growth experiments at higher field levels were unsatisfactory and unstable. Although the crystals were still grown, large wholes were observed in the grown crystals. This observation was attributed to the strong interaction of the applied electric and magnetic fields, making the convective flow in the solution very strong and unstable. However, lower magnetic field and electric current levels had very beneficial effects, namely flat growth interfaces and prolonged growth due to weak convection in the liquid zone, and a substantial increase in the growth rate (about 5–10 times higher) due to the effect of magnetic field on the mechanisms of “electromigration”. Such positive developments give the LPEE growth process the potential of becoming a commercial technique.     

柴油机 NOx选择性催化还原催化箱出口温度特性及其计算模型

在不增加出口温度传感器的条件下,为准确预测 SCR(selective catalytic reduction)催化箱温度,该文在柴油发动机台摘架上开展 SCR系统催化箱的进出口温度特性测试。并基于 Mtalab/Simulink模块对 SCR催化箱开展仿真计算,提出了出口温度的计算模型。结果表明:稳态工况及过渡工况下,催化箱进口温度与出口温度变化并不相同;过渡工况时,催化箱出口温度改变过程滞后于入口温度改变过程;提出的计算模型在稳态和过渡工况下均对出口排气温度有良好的预测性能,可在不增加出口排温传感器前提下,获得催化箱出口温度,从而提升了 SCR温度控制精度。同时,该文将提出的算法应用到实际整车试验中,在 ESC(European steady state cycle)和 ETC(European transient cycle)标准测试中, NO_x的排放量均低于国IV排放法规限值,试验测试表明该文提出的算法满足了整车的控制需求。     

Thin film atomic layer deposition equipment for semiconductor processing

Atomic layer deposition (ALD) of ultrathin high-K dielectric films has recently penetrated research and development lines of several major memory and logic manufacturers due to the promise of unprecedented control of thickness, uniformity, quality and material properties. LYNX-ALD technology from Genus, currently at beta phase, was designed around the anticipation that future ultrathin materials are likely to be binary, ternary or quaternary alloys or nanolaminate composites. A unique chemical delivery system enables synergy between traditional, production-proven low pressure chemical vapor deposition (LPCVD) technology and atomic layer deposition (ALD) controlled by sequential surface reactions. Source chemicals from gas, liquid or solid precursors are delivered to impinge on reactive surfaces where self-limiting surface reactions yield film growth with layer-by-layer control. Surfaces are made reactive by the self-limiting reactions, by surface species manipulation, or both. The substrate is exposed to one reactant at a time to suppress possible chemical vapor deposition (CVD) contribution to the film. Precisely controlled composite materials with multiple-component dielectric and metal–nitride films can be deposited by ALD techniques. The research community has demonstrated these capabilities during the past decade. Accordingly, ALD equipment for semiconductor processing is unanimously in high demand. However, mainstream device manufacturers still criticize ALD to be non-viable for Semiconductor device processing. This article presents a broad set of data proving feasibility of ALD technology for semiconductor device processing.