Inconel 690传热管GTAW对接焊接头组织及力学性能研究

对Inconel 690传热管材进行钨极气体保护焊(GTAW)对接焊,采用拉伸试验机、压扁试验机和光学显微镜测试和分析传热管焊接接头,同时利用ANSYS软件开展焊接接头在设计工况失压时的一次应力强度校核。研究结果表明:焊缝中心为树枝胞状晶,熔合线附近为粗大柱状晶。室温时接头的平均抗拉强度为619 MPa,平均屈服强度为292 MPa,350℃时接头平均抗拉强度为475 MPa,平均屈服强度为206 MPa,拉伸接头断裂从熔合区开始贯穿整个焊缝组织,呈塑性断裂。压扁试验和反向压扁试验结果表明管接头完好。通过ANSYS分析可知,设计工况下传热管接头350℃许用应力强度150 MPa限值可满足其一次应力强度要求,且裕量较大。     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

高氯酸铵含量对高铝富燃料推进剂中重要组分高氯酸铵/铝 一次燃烧性能和铝粉团聚的影响

采用CO2激光点火装置联合高速摄影系统及扫描电子显微镜等凝聚相燃烧产物分析技术，研究了高氯酸铵(AP)含量对高Al富燃料推进剂中重要组分AP/Al一次燃烧过程中燃烧现象、引燃时间、燃烧扩散时间、燃尽时间、燃烧效率、颗粒团聚及凝聚相燃烧产物的表面形貌、粒径及其分布的影响。结果表明，各AP/Al混合粉体的燃烧过程均可分为表面引燃、燃烧扩散和火焰熄灭3个阶段，但各样品在不同燃烧阶段的燃烧现象存在明显差异。AP含量由10wt%增至30wt%，样品燃烧剧烈程度增强，燃烧过程中固相颗粒的溅射现象越加明显；在火焰熄灭阶段，各样品燃烧由以停留在样品燃面处的燃烧为主逐渐变为以溅射颗粒的燃烧为主，且随反应进行，燃面已燃固相颗粒最先熄灭，各样品表面引燃时间、燃烧扩散时间、燃烧持续时间均缩短，即燃烧反应速率逐渐加快。在AP/Al混合物中，铝粉的燃烧效率、凝聚相燃烧产物粒度及其团聚程度随AP含量增加而增加。     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

电喷雾质谱法示踪钨回收过程离子的转化路径

将环隙式离心萃取器(ACCs)与电喷雾飞行时间质谱(ESI-TOF-MS)相结合，在线监测了回收过程中的钨萃取行为(宏观)和钨形态的转化路径(微观)，发现宏观萃取反应和微观离子形态转化同时发生并相互补充。伯胺N1923萃取钨在144 s内即可达到萃取平衡，萃取率高达98%以上，同时，酸钨比n(H)/n(W)是一个关键变量，当酸钨比n(H)/n(W)=2.4时，全流程钨回收率超过93%。最后，得到了基于钨形态监测的萃取机理，同时，减少原料液中W1含量，增加W10含量，可有效提高钨回收效率。     

食物成瘾的研究进展及启示

食物成瘾是一种复杂的慢性疾病，导致人们在日常生活中对某种食物产生过度依赖与过度进食等相关行为特征，近年来已被认为是影响欧美发达国家肥胖症、暴食症、糖尿病等发病率持续升高的关键原因，引起学术界的极大关注，成为研究热点。本文主要介绍食物成瘾的概念、诊断标准，综述了食物成瘾的病理机制、影响因素和应对措施等方面的研究进展，并探讨食物成瘾对我国食品界的启示，为预防食物成瘾，保障公众健康提供有益借鉴。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.