Enhancing sulphur self-retention by building-in CaO in straw–bitumen pellets

Combined straw–bitumen pellets have been proposed as an alternative fuel. An interesting finding is the potentiality of straw ash constituents to retain sulphur as bitumen that has relatively high sulphur content. The aim of the present work is to enhance sulphur self-retention to directly meet the environmental regulations by building-in CaO in the pellet instead of feeding sorbent separately. CaO powder has been mixed with the pellet constituents during production processes.     

无溢流管多层流化床换热器的热态模型研究

本文对无溢流管多层流化床换热器进行了热态模型实验研究，得出了气－固两相间的界膜换热系数曲线，所得结论可供参考。     

泥沙起动条件分析及推移质输沙率公式

基于床面均匀泥沙颗粒相对位置关系,确定了瞬时作用流速及有效作用高度,建立了平坡泥沙起动条件与床面泥沙暴露度的关系。从理论上分析得到,泥沙跃移起动要求水流条件最高,滚动起动及滑动起动取决于泥沙颗粒的暴露角,且暴露角θ不随拖曳力系数C_D和上举力系数C_L的变化而变化。当暴露角θ小于42.8°时,泥沙更易于滚动起动;当暴露角θ大于42.8°时,泥沙更易于滑动起动。在此基础上,推导出了考虑两种起动模式的起动全概率公式,建立了床面推移质单宽输沙率公式。通过经典的推移质输沙率实测资料验证及误差计算表明,公式结构合理,计算精度较高,对于高、中、低输沙率都具有较好的适用性。     

一种固结桩式薄板丁坝及其桩体的抗倾覆能力

介绍一种新型且经济的、带固结桩的预制插板丁坝,其固结桩是该丁坝的主要抗倾覆构件。通过室内试验和三维扫描技术,在细砂基床上开展了固结桩的抗倾覆性能研究。结果表明:固化剂与土体反应后在桩周形成"脚靴"状固结体,显著提高了桩的抗倾覆力;固结桩的倾覆过程分为微偏阶段、倾斜阶段、倾覆阶段和拔移阶段;固结桩抗倾覆力主要受桩体几何形状、埋深、土体性质、固化剂注浆剂量和注入流量等因素的影响。通过量纲分析和试验数据拟合,提出了固结桩临界水平抗倾覆力及临界倾斜角的计算公式。     

Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating with various lighting conditions. Classifying networks were given a single image or multiple images of various lighting conditions for training and testing. CNNs demonstrated significantly better performance than NNs across all tasks. Furthermore, CNNs demonstrated improved generalizability, i.e., the ability to generalize to more diverse data than either the training or validation data sets. Specifically, CNNs trained on high-resolution layerwise images from one build showed minimal loss in performance when applied to data from an independent build, whereas the performance of the NNs degraded significantly. CNN accuracy was also demonstrated to be a function of flaw size, suggesting that smaller flaws may be produced by mechanisms that do not alter the surface morphology of the build plate. CNNs demonstrated accuracies of 93.5 % on large (>200 μm) flaws when testing and training on components from the same build and accuracies of 87.3 % when testing on a previously unseen build. Finally, evidence linking the formation of large lack-of-fusion defects to the presence of process ejecta is presented.     

三相电压不平衡情况下电机车整流技术的研究

针对煤矿井下直流电机车变流系统中存在三相电压不平衡而使传统的PWM整流器的交流侧出现负序电流分量的问题,提出了一种在三相电压不平衡情况下抑制PWM整流器交流侧负序电流分量的控制策略;详细介绍了该控制策略的具体实现。仿真及现场实验结果表明,采用该控制策略后,PWM整流器的交流侧电流波形基本接近正弦波形,实现了单位功率因数运行。     

基于CAN总线的嵌入式智能防褥疮气垫床的设计

针对家庭护理问题,设计并实现了基于CAN总线的嵌入式智能防褥疮气垫床.阐述了该智能气垫床的硬件系统设计,软件系统设计及实现方法.气垫床控制模块由AT90CAN128单片机、气垫床、压力传感器、气泵和电磁阀门等构成.采用S3C2440作为主控制模块,以WindowsCE操作系统为核心开发的人机交互界面(HMI)通过CAN总线实时传递各个功能模块命令,能够实现气垫床的预定功能,给出了实验实测数据,并且能够实现即插即用的模块化设计.     

基于模糊神经网络的电弧炉控制系统及仿真

电极调节系统是电弧炉炼钢过程不可缺少的基本装备。目前常用的电极调节器大多是基于单相意识,导致电极调节中电极往往误动作,大大影响了电弧炉的运行效益。在模糊控制和神经网络的基础上,探索研究一种基于三相意识的电弧炉控制新方法,采用模糊神经网络调节器控制对电弧炉电极进行调节,控制弧流弧压稳定在一定范围之内,使电弧炉冶炼达到有功功率最大化,进一步提高电弧炉的综合运行效益,降低能耗、减轻对电网危害。并用matlab对模糊神经网络进行仿真。结果表明,本网络具有较快的训练速度和较高的泛化能力,满足电弧炉控制的要求,其控制性能优于常规电弧炉控制系统。     

Prediction of scouring around an arch-shaped bed sill using Neuro-Fuzzy model

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used to model local scouring depth and pattern scouring around concave and convex arch shaped circular bed sills. The experimental part of this research study includes seven sets of laboratory test cases which were performed in an experimental flume under different flow conditions. A data set consists of 2754 data points of scouring depth were collected to use in the ANFIS model. The ratio of arch diameter, D, to flume width, W, is used as a non dimensional variables in all test cases. The results from ANFIS model were compared with the results of ANN model obtained by Homayoon et al. [24] and previously presented models. The results indicated that for D/W equal to 1 and 1.2, the ANFIS models produced a good performance for convex and concave bed sills. As a result, the ANFIS models can be used as an alternative to ANN for estimation of scour depth and scour pattern around a concave bed sill installed with a bridge pier.     

基于注意力机制和卷积神经网络的异步电动机三相电压不平衡损耗研究

配电网中三相电压不平衡对异步电动机损耗会造成较大影响。运用等效电路公式分析三相电压不平衡影响下电动机损耗存在精度不稳定、需要参数过多且数学模型过于复杂等问题。针对以上问题,提出一种基于注意力机制和卷积神经网络(CNN)的异步电动机损耗评估方法。该方法将实测电机数据作为输入,引入注意力机制为输入特征赋予不同权重;采用卷积层和全连接层组成的CNN构架对异步电动机实测数据进行学习,最后完成损耗评估。以现场试验得到的电机损耗数据作为实际算例,该方法评估损耗与实测损耗平均误差仅为0.717%和0.549%,并与其他典型机器学习算法进行对比,结果表明所提方法具有更好的损耗评估性能。