微絮凝接触过滤处理低浊度含藻水的研究

通过现场试验，对微絮凝过滤处理生活用水所带来的一系列问题进行了详细、科学的分析，在大量试验数据基础上对产生问题的原因进行了研究并取得了初步结论。在此基础上，提出了改进措施，取得了实效。     

莫莫格湿地生态演变及水源解决途径探析

湿地的生态演变是区域性生态演变的前奏，而水是生态环境的控制性因素。     

环境因素对碳钢点蚀临界电势的影响

采用动电势扫描法测定了不同体系中碳钢发生点蚀的临界电势Eb。结果表明 :当Cl- 浓度 <0 .0 1mol/L时 ,随Cl- 浓度增加 ,临界电势负移并与Cl- 浓度成线性关系 ;pH值在 7～ 11之间 ,随pH值增加 ,临界电势正移并与pH值成线性关系 ;随温度增加 ,临界电势负移并与温度成线性关系。     

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解决注水泥作业的防气窜问题，是提高固井质量，保护油气层的关键技术。现有预测和防止水泥浆气管理的方法虽然较多，但应用于现场却有一定困难，主要表现在所考虑的因素比较单一，有些高温、高压下的水泥浆性能参数难以测定。文章在分析国内外预测和防止气窜方法的基础上，应用水泥浆浆体在环空的组成，GFP系数、水泥浆稠度阻力系数（A）及多凝水泥和环空加压等措施，拟定出一套提高水泥浆顶替效率，保证水泥浆动态和静态过程压力平衡关系的具体设计方法。     

依靠弯道控导河势流路

在天然河道中，河势演变虽具有一定的随机性，但仍有一定的规律可循，在一个河段内，一般可归纳出2－3条基本流路，为控制河势变化，需要选择出一条基本流路进行河道整治，当连续发生枯水期或洪水期时，部分河段的河势可能出现大的变化，为防止不利河势向下游传播，可修建较长的龙头弯道工程，利用龙头弯道工程分段调整，控导河势，保持河势稳定。     

轴对称问题线元上分布奇点的诱导速度势和诱导速度

本文给出轴对称问题线元上高次分布源和偶极在无界域中的诱导速度势和诱导速度的计算公式,可供用分布奇点法求解轴对称势流问题时参考.     

基于神经网络的河道浅滩演变预测模型

河道浅滩演变是一个复杂的非线性动力学过程, 作者借助神经网络处理非线性问题的优势, 在分析影响河道浅滩演变因素的基础上, 建立了预测河道浅淮演变的BP网络模型, 并对模型中的输入因子和样本的提取进行了探讨. 以闽江竹岐至侯官河段为实例,用“试控法”给出了BP网络模型的建模方案, 用正交设计原理选取相应的训练样本集, 利用该样本集对网络进行学习和训练, 并用训练好的BP网络模型预测浅滩上年内最小水深和年平均淤积厚度. 计算结果表明: 模型预测结果与实际值吻合良好. 这为河道浅滩演变预测研究提供了新方法.     

Adsorption and inhibitive properties of benzimidazole derivatives in acid mild steel corrosion

Eight diazoles were investigated as corrosion inhibitors of mild steel in 1 M hydrochloric acid using gravimetric and polarization techniques. It was found that the inhibition efficiency increased with the increase of organic substrate concentration, while the adsorption followed the Frumkin isotherm. A correlation between the electronic parameters (induction and mesomeric effects of the substituents, ionization potential values) as well as those of the chemical structure (molecular area) of diazoles molecules and their inhibiting properties was looked for. The results obtained show that a definite interdependence exists but it is not as simple and straightforward as might be expected.     

A Method of Combined SHPB Technique and BP Neural Network to Study Impact Response of Materials

Abstract:  A new method combining the split Hopkinson pressure bar (SHPB) technique with the back-propagation (BP) neural network program is proposed. By this method, the treated strain wave signals become smooth with less noises induced by the transverse inertia. Moreover, the dynamic rate-dependent constitutive behaviour of materials can be identified, without any pre-assumption of a constitutive model. It is found that by taking the experimentally measured data of strain, strain rate and time as 'input' and the corresponding data of stress as 'output' of the BP neural network, the dynamic constitutive behaviour with internal damage or phase transformation evolution is easy to be identified, where the time could represent either the internal damage evolution or phase transformation process accompanied with the deformation process. It is emphasised that the data learnt by the BP neural network must include both loading and unloading processes, if the whole loading and unloading response is to be correctly predicted. The comparisons between the predictions and experimental results are in good agreement for both polyamide (PA) polymer (as an example of nonlinear viscoelastic materials) and Ti–Ni alloy (as an example of superelastic materials with stress-induced phase transformation).     

水冷热模法离心铸造大口径球铁管用管模涂料

阐述水冷热模法管模涂料的组成。说明了耐火基料，悬浮剂、粘结剂，减水剂的各自作用机理，提出了合理的涂料配方、合适的喷涂方式。