外部燃料重整掺氢对天然气发动机性能的影响

利用外部燃料重整系统对天然气燃料进行了重整反应,燃料重整率为12％,将反应后的重整气体通入进气管内与新鲜空气混合,再通入气缸内燃烧,以此来比较外部燃料重整掺氢燃烧对天然气发动机性能的影响.试验结果显示:结合外部燃料重整掺氢燃烧,发动机的循环变动明显改善,燃烧持续期缩短,相同工况下的燃料消耗率变化很小;THC排放量在各工...     

南京大胜关长江大桥施工钢围堰中节制作技术

通过工程实例,介绍大型桥梁施工双壁钢围堰的制作加工技术,可供类似工程设计、施工借鉴、参考。并介绍大型钢结构有关细部的施工方法。     

塑料排水板加固高速公路软土路基效果分析

以河北沿海高速公路软土路基塑料排水板超载预压法处理深厚软基的试验结果为基础,对用该方法处理深厚软基的沉降、孔隙水压力、及深层水平位移的变化规律及固结系数进行了分析。     

表面状态对60Si2CrVAT弹簧钢疲劳性能的影响

对表面脱碳与喷丸状态不同的60Si2CrVAT弹簧钢进行了疲劳性能对比试验,结果表明:860℃-50min加热,试样表层形成约0.1mm的脱碳层,但未观察到对疲劳寿命产生明显影响;适度喷丸在试样表层形成压应力使疲劳寿命大大提高;过度喷丸虽使表面粗糙度减小,但使表层材料受到损伤,因此疲劳寿命平均值降低.     

猎豹智能车无人驾驶系统总体设计

介绍猎豹智能车无人驾驶系统总体设计及其硬件和软件的设计特点和实现方法。系统采用机器视觉来识别高速公路车道线,利用拟人控制算法来实现车辆自动驾驶控制,最高时速为95km/h的初步试验结果验证了系统的可行性。     

航运市场预测方法研究

引入灰色理论和回归预测模型,提出预测海运市场容量的方法,运用马尔科夫法预测未来公司的市场占有率,并运用敏感性分析搜寻盈利的关键因素。结合实例计算分析,证明该方法的衫性与有效性。     

浅谈日本“来岛海峡”的航行方法

对往返于中国和日本的“联裕”轮多次安全通过日本“来岛海峡”进行了相关分析,浅谈如何根据水文气象资料和有关法规法则安全通过日本“来岛海峡”的航行方法及所采取的航行措施。     

纯电动汽车能量回收控制策略研究

纯电动汽车的能量回收技术有很高的研究价值,是提高整车经济性的有效手段之一。以一种并联能量回收方案为研究对象,从再生制动原理进行分析,在电池回收能力、电机回收能力及负载功率消耗的限制前提下,提出一个合理的能量回收控制策略,兼顾整车的经济性及乘客的舒适性,并通过ADVISOR进行NEDC工况仿真验证。试验表明,与无能量回收车型对比,相同时间内该策略下整车电池SOC值明显下降缓慢,该研究为制动能量回收策略算法的进一步优化开发提供参照。     

Calculation of strain amplification matrix for strain invariant failure theory based on representative volume element models with periodical boundary condition

Strain invariant failure theory (SIFT) is a micro-mechanics-based failure theory for multi-scale failure analysis of composite materials originally proposed by Gosse and Christensen. In this paper, the approach for obtaining strain amplification matrix which is a key step for the execution of SIFT is improved by adopting representative volume element (RVE) finite element models considering periodical boundary condition, based on which more actual deformation mode is reflected. The deformation modes and strain data at the characteristic points of the centroid cell of multi-cell RVE model are analyzed and taken as a reference. It can be concluded that more reasonable deformation mode and relationship between the micro-mechanical and macro-mechanical strain states are obtained by employing the new model. Finally, numerical examples are provided to illustrate the determination of strain amplification factors within the RVEs considering periodical boundary condition at the characteristic points.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.