油柱高度的储盖层组合评价

从储盖层组合角度出发,应用储盖层的两方面特性及水动力活动等参数评价了油藏中的含油饱和度分布。研究表明：储层物性决定了油藏的储集性能,而盖层在很大程度上影响着油气的运移方向。评价实例指出,由于缺乏盖层特性数据可能引入评价误差。     

基于BP神经网络的油气产量历史预测

分析了常规油田开发模型所存在的问题;探讨了应用神经网络技术进行油气产量历史预测的可行性。油田实际资料验证评价结果表明：该法模拟预测精度高,效果好,简单实用,不失为油气产量动态预测的一条新途径。     

企业持续创新的概念与效益定量评价方法探讨

讨论了持续的企业技术创新的重要性并强调指出只有持续的技术创新才是经济效益增长的可靠源泉和企业发展的根本道路．根据熊彼特的创新理论和中国企业的技术创新实践,探索性地引入了企业持续创新概念,进而提出企业持续创新效益定量评价方法．论文最后简介一个中国著名企业持续创新的研究案例．     

多源雪深数据在中国的空间特征评估

积雪的年际和年内变化强烈地影响着区域及全球的水量平衡,同时,积雪反照率反馈也显著地影响着气候变化。目前长时间序列的格网雪深数据主要来自被动微波遥感及再分析资料,但不同数据之间存在着明显差异。基于多源雪深数据的评估,特别是空间特性的评估还很缺乏。因此,本研究选取了AMSR-E、WESTDC、GlobSnow、RA-Interim及MERRA2这5种雪深数据,以站点观测数据为参考真值,对它们进行了中国地区的空间误差对比及基于误差排序的相对表现分析。评估结果初步显示：①WESTDC在我国西北及东北积雪区表现较好,适合用于我国北方的雪深研究;②MERRA2在西北和东北积雪区也有较好的表现,但由于其分辨率较粗,缺乏细节的空间信息,因此认为比较适用于大区域的统计分析;③AMSR-E在我国中部和东南地区表现最好,因此认为适合我国中部及东南部的雪深研究。     

基于层次分析法和指标评分标准的民用飞机客舱舒适性评价方法研究

在保证飞机安全性的基础上,客舱乘坐舒适性是飞机主制造商和航空公司首要考虑的因素。在探讨了客舱舒适性的定义、舒适性过程等相关理论的基础上,深入剖析了影响乘客舒适性的环境、乘客活动和交际三大类因素及其重要度排序,给出了客舱舒适性设计建议;基于层次分析法原理,提出了民用飞机客舱舒适性评价方法框架,构建了完整的客舱舒适性评价指标体系,包括座椅、IFE设备、环境、乘客活动和主观感受五大方面21个具体指标。为便于进行舒适性评价,采用5级评分标准细化制定了主观类和客观类舒适性指标的具体评分标准。最后在某型飞机模型客舱舱段上应用该方法进行了验证。验证结果表明,该方法科学合理、简便易行,较好解决了飞机客舱舒适性评价问题。     

Effects of the aluminum concentration on twin boundary motion in pre-strained magnesium alloys

We investigated the effects of Al concentration on the reciprocated motion of twin boundaries in pre-strained Mg-Al-Zn alloys through a combination of applied compression and tension,in-situ electron-backscattering diffraction observations,and high-angle annular dark-field scanning transmission electron microscopy observations.The twin growth was restricted by increased Al concentration,which resulted in the occurrence of smaller-sized twins.The reverse motion of twin boundaries was also restricted,resulting in the formation of higher fractions of secondary twins and 2-5° boundaries during reverse tension.The secondary twins and 2-5° boundaries mainly contributed to the increased ultimate tensile strength of the pre-strained Mg alloys.This effect is more significant in Mg alloys with larger pre-compression.Moreover,the increased amount of the Al solute atoms,rather than the precipitates,mainly contributed to the increased strengthening effect on the twin boundary motion.Our research contributes to development of high-strength Mg alloys by stabilizing twin boundaries.     

Influence of alloy element partitioning on strength of primary α phase in Ti-6Al-4V alloy

The partitioning effect of Al(α-phase stabilizer) and V elements(β-phase stabilizer) on strength of the primary α phases in the α/β Ti-6 Al-4 V alloy with the bimodal microstructure was investigated.It was found that partitioning of Al and V elements took place in the Ti-6 Al-4 V alloy during the recrystallization process,leading to the variation of the content of Al and V elements in the primary α phases with changing the volume fraction of the primary α phase.Nanoindentation tests reveal a general trend that the strength of the primary α phases increases with decreasing the volume fraction of the primary α phases,and such trend is independent on the loading direction relative to the c-axis of the α phase.The enhanced strength is attributed to the increase of the content of Al element in the primary α phase,but it is not dominated evidently by the change of the V content.The solid solution strengthening contributed from both the elastic strain introduced by the solute atoms and the variation of the density of states was estimated theoretically.     

Investigations of hypocycloidal shaft and hub connections

This paper presents current research results for polygonal connections with hypocycloidal profiles (H‐profiles). It proposes a design concept including permissible stress with respect to limits of plasticity. Moreover, notch effect figures are specified for use in the nominal stress concept. A comparison with conventional shaft‐hub connections reveals the benefits of new polygonal connections.     

Fatigue-induced in-situ strength deterioration of micro-polyvinyl alcohol (PVA) fiber in cement matrix

For soft fiber and brittle matrix system such as polymeric fiber-reinforced cementitious composites, the fiber strength deterioration dominates the performance of composites subject to fatigue loading. The fatigue-induced in-situ fiber strength deterioration in brittle matrix, however, has rarely been studied. In this paper, fatigue-induced in-situ strength deterioration of micro-polyvinyl alcohol (PVA) fiber in cement matrix was experimentally investigated. The effects of fiber embedment, fiber inclination, and fiber surface treatment on the in-situ strength of micro-PVA fibers are reported. The results show that fiber embedment into cement matrix not only reduces the in-situ strength of fiber but also changes the fatigue stress-cycle (S-N) curve and failure mode of fiber. Fiber inclination further decreases the in-situ strength of embedded fiber due to local stress concentration of bent fibers. Oil-treatment on fiber surface can effectively delay fatigue-induced in-situ strength deterioration of micro-PVA fiber.     

A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling

Kansei evaluation is crucial to the process of Kansei engineering. However, traditional methods are subjective and random. In order to eliminate the differences of individual evaluation criteria in product Kansei attributes evaluation, and further improve the evaluation efficiency, a novel automatic evaluation and labeling architecture for product Kansei attributes was proposed in this paper based on Convolutional Neural Networks (CNNs). The architecture consists of two modules: (1) Target detection module (Faster R-CNN was taken as an example), (2) Fine-Grained classification module (DFL-CNN was taken as an example). A case study was provided to validate the proposed architecture. The proposed architecture transformed design evaluation tasks into the recognition and classification tasks. The experiments achieved 98.837%, 96.899%, 86.047%, and 81.008% accuracy in the binary, triple, and two five-classification tasks, respectively. Our results proved the feasibility of using computer vision to mimic human vision for the automatic evaluation of Kansei attributes.