Impact of the anode operating conditions on the liquid water distribution in the cathode gas diffusion layer

Previous experimental results indicate that the humidification conditions at the anode have an impact on the liquid water distribution in the cathode gas diffusion layer. Numerical simulations are developed to reproduce and analyze this effect. Results consistent with the experimental results are first obtained by playing with the partition coefficients of an advanced pore network model computing the liquid water formation and transfer in the cathode gas diffusion layer (GDL) for a large range of operating conditions. Then, a model for the full anode – cathode assembly is developed by combining the pore network model of the cathode GDL and a 1D model describing the heat and water transfer in the various components of the anode-cathode assembly. This enables one to generalize the dry – wet regime diagram introduced in a previous work by incorporating the effect of the humidity condition at the anode.     

APP软件性能效率研究

性能效率是APP软件的重要质量属性，但目前缺乏APP软件性能效率的通用模型。分析了APP软件的性能特征，基于ISO/IEC 25010标准提出了APP软件的性能效率模型，定义了APP软件性能效率的子特性和度量指标。基于提出的APP软件性能效率模型，通过实验对APP软件的性能效率进行了度量及相关分析。     

Damage evolution in polymer due to exposure to high-pressure hydrogen gas

The use of hydrogen as a fuel is increasing exponentially, and the most economical way to store and transport hydrogen for fuel use is as a high-pressure gas. Polymers are widely used for hydrogen distribution and storage systems because they are chemically inert towards hydrogen. However, when exposed to high-pressure hydrogen, some hydrogen diffuses through polymers and occupies the preexisting cavities inside the material. Upon depressurization, the hydrogen trapped inside polymer cavities can cause blistering or cracking by expanding these cavities. A continuum mechanics–based deformation model was deployed to predict the stress distribution and damage propagation while the polymer undergoes depressurization after high-pressure hydrogen exposure. The effects of cavity size, cavity location, and pressure inside the cavity on damage initiation and evolution inside the polymer were studied. The stress and damage evolution in the presence of multiple cavities was also studied, because interaction among cavities alters the damage and stress field. It was found that all these factors significantly change the stress state in the polymer, resulting in different paths for damage propagation. The effect of adding carbon black filler particles and plasticizer on the damage was also studied. It was found that damage tolerance of the polymer increases drastically with the addition of carbon black fillers, but decreases with the addition of the plasticizer.     

Impact of high-intensity ultrasound on physical properties and degree of oxidation of lipase modified menhaden oil with caprylic acid and/or stearic acid

The purpose of this research was to determine the effect of high-intensity ultrasound (HIU) on physical properties, degree of oxidation, and oxidative stability of structured lipids (SLs). Caprylic acid (C) and stearic acid (S) were incorporated into menhaden oil using Lipozyme® 435 lipase to obtain five samples: (1) LC 20 (menhaden oil with 20% of C), (2) LC 30 (menhaden oil with 30% C), (3) LS 20 (menhaden oil with 20% S), (4) LS 30 (menhaden oil with 30% S), and (5) Blend C (menhaden oil with 16.24% C and 13.04% S). Samples were crystallized for 90 min at the following temperatures: (1) LC 20 at 15.5°C, (2) LC 30 at 17.5°C, (3) LS 20 at 24°C, (4) LS 30 at 30°C, and (5) Blend C at 18.0°C, and HIU was applied at the onset of crystallization. Physical properties, degree of oxidation, and oxidative stability were evaluated in sonicated and nonsonicated samples. All SLs had statistically higher G′ after sonication. Sonicated LS 30, LC 30, and Blend C had a higher melting enthalpy than the nonsonicated ones, while enthalpy values in sonicated LS 20 and LC 20 samples were not statistically different than the nonsonicated ones. No significant difference between sonicated and nonsonicated samples was observed in peroxide values (1.2 ± 0.1 meq/kg, p > 0.05) and in the oxidative stability index (6.3 ± 0.2 h, p > 0.05). These results showed that HIU was effective at changing physical properties without affecting the oxidation of the samples.     

Practical Application of the Probabilistic‐Statistical Model of the Suspension Separation in Hydrocyclones

An effective practical approach that allows not only a significant reduction in the scope of practical experiments in the course of studying suspension separation processes in hydrocyclones, but also makes it possible to assess the intensity of random components of the processes and define the interrelation between such components and hydrodynamics of flows in a hydrocyclone is presented. Within the frames of the developed probabilistic‐statistical model of suspension separation in hydrocyclones on the basis of statistical self‐similarity properties, a relationship was found between determined and random components of the processes. This allowed transitioning from three‐parameter probability density functions for suspension particles in hydrocyclones to two‐parameter functions; thus significantly improving the efficiency of practical application of the developed model.     

冷链条件下气调包装酱卤鸭肉制品货架期预测模型的建立

目的 以气调包装酱卤鸭肉制品为研究对象,在冷链温度范围内建立一套准确、高效的货架期预测模型。方法 利用选择性培养基测定不同温度下产品各微生物数量,确定4~25℃条件下产品优势腐败菌。对乳酸菌数量与感官评定值进行了回归分析确定最小腐败量Ns。分别采用修正的Gompertz方程和平方根方程建立一、二级模型,并通过预测值与实测值对比验证模型的可靠性。结果 确定了4~25℃条件下产品优势腐败菌为乳酸菌,最小腐败量Ns=6.14(lg(cfu /g))。一、二级模型拟合度均良好,三种温度下模型预测值与实际值间的差异均在30％左右,波动幅度在10%以内。结论 实现了对4~25℃内任何时间点产品剩余货架期的预测,为冷链条件下气调包装酱卤鸭肉制品品质的变化提供了理论指导。     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

[Zr0.73(Cu0.59Ni0.41)0.27]87Al13合金过冷液相区热塑性成形研究

为探究[Zr_(0.73)(Cu_(0.59)Ni_(0.41))_(0.27)]_(87)Al_(13)非晶合金的热塑性成形性能以及绘制其对应的热加工图谱,用Gleeble3500型热模拟压缩实验机对该非晶合金进行不同参数下的热模拟压缩实验。结果表明,合金在压缩过程中变形行为由牛顿流变演变为非牛顿流变;同时,过高或过低的热加工温度均能导致合金晶化;进一步对数据分析得到该合金在不同热塑性成形参数下的功率耗散图与流变失稳图,并绘制出相应的热加工谱图,谱图分析结果表明,该合金在温度为420与430℃、应变速率为10~(-3) s~(-1)时具有较高功率耗散系数且没有失稳区域,因此,合金可选的热塑性加工参数为温度420～430℃,应变速率10~(-3) s~(-1)。     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.