MOBILIZATION OF SLUDGE IN HORIZONTAL CYLINDRICAL TANKS WITH SUBMERGED NOZZLES

Over 378,000 m³ (100 million gal) of radioactive waste is being stored in hundreds of tanks at several U.S. Department of Energy (DOE) sites. The environment within the tanks is highly radioactive and chemically harsh. The waste typically consists of a heterogeneous sludge layer covered by a layer of supernatant. A few of the tanks have leaked to the environment, while others are corroding. Removing the waste from the tanks and processing it to a stable final form are desired to prevent any additional contamination of the environment. The tanks vary in size and geometric shape and do not have systems for removing the sludge waste.

Mobilization and mixing studies were conducted with a surrogate sludge (e.g., kaolin clay) using submerged jets in two sizes of horizontal tanks with nominal capacities of 0.87 m³ (230 gal) and 95 m³ (25,000 gal). Mobilization efficiencies and mixing times were determined for single and bidirectional jets in both tanks with the discharge nozzles positioned at various locations in the tanks. Approximately 80% of the surrogate sludge was mobilized in the 95-m³ tank using a fixed bidirectional jet (inside diameter = 0.035 m) and a jet velocity of 6.4m/s (21 ft/s). The effective cleaning length, which is defined as the distance between the jet and the edge of the remaining sludge bank, was approximately 3.2 m (10 ft) on each side of the bidirectional jet.     

Gas hold-up in non-newtonian solutions in a bubble column with internals

Bubble characteristics and gas hold-up were studied in a two phase (air-aqueous CMC solution) bubble column provided with helical coils and straight tubes as internals. The effects of superficial gas velocity, rheological properties, and volume fraction covered by the internals, on gas hold-up were studied. Hold-up values determined directly and by simultaneous pressure drop measurements matched well. Enhancement of gas hold-up values up to 55 per cent was achieved in systems using internals. The gas hold-up results were also compared with the values obtained from correlations reported in the literature.     

基于FLUENT振荡浮子式波浪能转换装置浮子的数值仿真

基于VOF模型,运用三种不同的造波方法建立了二维数值波浪水槽,并对其进行了比较,选取了在特定波况下较为合适的数值水槽。基于该数值波浪水槽,考察了波浪作用下的运动状态以及受力状态,为波能转换器的设计提供指导。     

存储器相关概念教学分析

为了改变存储器知识点“学生难学,教师难教”的局面,在教学过程中通过理论联系实际的方法介绍存储器相关概念,期望学生能够加深理解,为后续课程的学习及往后在工程实践中运用相关知识解决实际问题打下坚实的基础。     

3-D hydrodynamics generated in a stirred vessel by a multiple-propeller system

The aim of this study is to optimize the position and the number of propellers in a non-standard tall vessel. Laser sheet flow visualization experiments were carried out for selected geometrical arrangements which produced stable flow patterns and good transport between the propellers. Four double-propeller arrangements corresponding to frequent industrial cases and a three-propeller system have been chosen. Comparison of LDA measurements in the r-z plane, dimensionless global parameters N_Qp, Nt_m, N_p, E_p and spatial distribution of local energy dissipation rate ? shows that a three-propeller system is the most efficient.     

液氨贮存设计分析

氨是生产含氮肥料及尿素的基本原料，为确保原料供应，必须设置其贮存设施。介绍了常温中压、降温低压和低温常压三种不同的液氨贮存工艺及其特点，并以某大型氨站设计为例，分析了液氨贮存工艺流程、主要设备、材料的选型和工艺参数的确定。     

外加电流阴极保护在海水冷却槽中的应用

论述了金属的电化学腐蚀和外加电流阴极保护原理。说明对以海水为介质的冷却、冷凝设备,采用外加电流阴极保护是防止设备腐蚀的有效方法。举例说明了应用效果。     

基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

为了利用分数阶累加算子在灰色短期预测中的高效性能,首次将分数阶累加算子引入变异时序回归模型以期取得更高的预测精度。主要方法如下：首先取湖北省链子崖某监测点1978—1987年的十年数据作为训练集并使用引力搜索算法确定最佳分数阶累加阶数,而1988—1993年的六年数据作为验证集验证提出的模型;其次对比了经典灰色模型GM(1,1)、分数阶累加灰色模型、变异时序回归模型TSGM(1,1)三种灰色模型。结果如下：首先修正了陈西江等人变异时序回归模型仿真时出现的错误,其次表明了相比于其他的模型,基于引力搜索算法的分数阶累加时序回归模型在进行灰色长期预测中具有较高的预测精度。因此,通过分数阶累加算子提高了灰色理论中长期预测模型的精度,为灰色长期预测提供了指导。     

连接管对调压室涌波水位和水锤压力的影响

在传统大波动过渡过程计算中,调压室底部连接管的影响往往是作为阻抗孔来考虑,在连接管较长的情况下,采用阻抗孔处理时而忽略了连接管的水体弹性,因而容易造成较大误差。通过建立考虑了连接管及不考虑连接管时的调压室节点数学模型,采用Gardel公式来处理调压室的阻抗系数,将调压室的阻抗系数变为负荷实际情况的动态阻抗系数,并结合具体工程实例来分析连接管的长度及管径的变化对调压室涌浪、蜗壳进口压力以及机组转速所产生的影响。研究结果表明,对于连接管较长的情况,应考虑到连接管的作用,为了能充分反射水锤,连接管的面积不宜过小。     

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.