基于心理图式的知识挖掘与创新设计——以国庆彩车设计为例

为了提高设计创新效率,本研究基于心理图式提出了国庆彩车的设计程序与方法。研究按照浅层、中层和深层的次序递进,设计心理图式划分为“造型图式”“叙事与动作图式”以及“人物和情感图式”的基本类别,并提出了综合性、时态性、差异性和集体性四类基本属性。而后结合新中国成立70周年彩车设计,对不同心理图式的应用方法和属性进行了案例分析。本文所提出的方法在设计实践中得到了实证,具有一定的实用价值。     

气瓶内部淬火过程的流动换热特性

 大直径厚壁气瓶内部淬火时的流动换热过程极其复杂，受到多种因素的影响，而研究气瓶内部压强和温度的变化规律对改善流动换热效果、提高产品组织性能具有重要的理论指导意义。以914 mm厚壁气瓶和瓶内流体为研究对象，建立了二维等效流 固耦合模型；采用多喷嘴系统对气瓶内外进行喷水淬火，研究了气瓶总长、喷水流速及淬火时间对瓶内压强及内壁温度的影响，通过间歇淬火试验验证了数学模型的正确性。结果发现，气瓶长度对瓶内压强和瓶壁温度的影响显著，喷水流速次之，当喷水流速大于8 m/s后，水量对瓶壁的冷却效果大大降低；气瓶内壁长度方向的温度梯度分别随气瓶总长的增加和淬火时间的延长而减小，但基本不受喷水量的影响。     

Data sharing mechanism of various mineral resources based on blockchain

Basins with various mineral resources coexisting and enriching often occupy an important strategic position. The exploration of various mineral resources is repetitive at present due to unshared data and imperfect management mechanism. This situation greatly increases the cost of energy exploitation in the country. Traditional data-sharing mode has several disadvantages, such as high cost, difficulty in confirming the right of data, and lack of incentive mechanism, which make achieving real data sharing difficult. In this paper, we propose a data-sharing mechanism based on blockchain and provide implementation suggestions and technical key points. Compared with traditional data-sharing methods, the proposed data-sharing mechanism can realize data sharing, ensure data quality, and protect intellectual property. Moreover, key points in the construction are stated in the case study section to verify the feasibility of the data-sharing system based on blockchain proposed in this paper.     

超分子作用阻尼材料的动态力学模型修正

为提高飞机起落架载荷实测精度，以支柱式起落架结构为研究对象，探讨处理其载荷标定数据的工程方法。首先，对实际受载情况和单向加载工况标定数据的分析，挖掘出三向载荷、缓冲支柱压缩行程与应变码值间的数学关系，将预测的数学关系代入到多向工况的标定数据中，验证了它们的准确性;其次，根据两种标定数据回归方法，提出了对应使用的支柱式起落架载荷-应变标定方程的数学模型;最后，将两种标定方程代回到标定数据中，计算起落架三向载荷，所有反算载荷的误差均在可控范围内，表明标定方程满足精度要求。将标定方程代入实测数据中，实测曲线符合变化规律。该处理方法的应用能有力提高支柱式起落架的起飞-着陆载荷实测和载荷谱编制的准确度。     

A novel bio‐based phthalonitrile resin derived from catechin: synthesis and comparison of curing behavior with petroleum‐based counterpart

The development of bio‐based thermosetting resins with good thermal stability can potentially afford sustainable polymers as replacements for petroleum‐based polymers. We report a practical route to a novel catechin‐based phthalonitrile resin precursor (CA‐Ph), which contains free phenolic hydroxyl groups that result in ‘self‐curing’ at elevated temperatures to afford a thermostable polymer. Comparison of the performance of this CA‐Ph resin with that of a conventional petroleum‐based bisphenol A phthalonitrile resin (BPA‐Ph; containing 5 wt% of the curing agent 4,4′‐diaminodiphenylsulfone) revealed that CA‐Ph exhibits a lower melting point and curing temperature. Cured CA‐Ph resin retains 95% of its weight at 520 °C under a nitrogen atmosphere, which compares favorably with results obtained for BPA‐Ph resin that retains 95% of its weight at a lower temperature of 484 °C. Kinetic results indicated that the curing reactions of both CA‐Ph and BPA‐Ph systems follow an autocatalytic mechanism. These results suggest that catechin is a useful bio‐based feedstock for the preparation of self‐curing and thermally stable phthalonitrile resins for advanced technological applications. © 2017 Society of Chemical Industry     

生物质源多孔碳制备及其对废水中药物吸附研究进展

城市现代化与工业化的快速发展给环境造成的污染日趋严重，尤其以水体污染最为明显。近年来，工业废水中药物的含量逐年上升，污染程度已经不容忽视。因此，开发新型多孔材料用于废水中药物分子的吸附分离，已经成为了当前的研究热点。综述了近年来生物质源多孔碳（生物炭）在废水中污染物吸附分离方面的研究，首先简单介绍了废水中污染物的治理方法，在此基础上重点探讨了生物炭的制备与修饰，并结合碳材料表面化学性质与孔道结构，总结并展望了生物炭对药物的吸附性能。     

基于SVM的煤与瓦斯突出预测模型及应用

为有效预测矿井内煤与瓦斯突出的危险程度,对其影响因素做了分析与探讨,分别构建了基于粒子群优化算法以及遗传算法支持向量机的煤与瓦斯突出预测模型,并且通过实例对两种模型预测的准确性进行了验证。分别利用单项以及综合指标、BP神经网络以及PSO-SVM模型、GA-SVM模型,对寺河煤矿二号井的突出区域进行预测比较。结果表明,PSO-SVM的预测模型不仅可以在小样本数据中预测出煤与瓦斯突出程度的大小,而且综合预测结果更加精确,其在解决矿井内煤与瓦斯突出的小样本数据中显示出更加强大、通用的性能。     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.