混凝土箱梁设计参数对温度效应的影响

在对水府庙混凝土箱梁桥温度场的实测和分析基础上,进行设计参数的研究,包括箱梁翼缘悬臂长度、腹板高度和桥梁轴线方位角对温度场和温度应力的影响,得到最大横向温差与（腹板高度/翼缘悬臂长度）的曲线图.     

沥青混凝土搅拌机燃用重渣油的尝试

为降低沥青混凝土搅拌机的生产成本,采用ZZR重渣油燃烧机替代柴油燃烧机取得了很好的经济效益,给出了燃渣油时渣油含水及油路阻塞的判断方法,并介绍了出现故障的排除方法.     

计算机仿真研究平整度指标与路面状态的定量关系

参照平整度测量仪的结构及原理,用MATLAB科学计算语言对平整度的测量过程进行了模拟仿真,分析平整度指标与路面状态参数之间的定量关系,并阐述了此项研究对提出新的摊铺机自动找平系统的控制方式,提高路面平整度具有的实际意义与可行性.     

复杂地质和环境条件下水闸围堰工程设计技术

在河流中建设水闸、船闸,其围堰工程是保证主体工程安全干施工的重要前提和保障。结合某水闸工程设计,针对围堰设计遇到的地质和环境条件复杂、施工场地狭窄、施工期不断流等难题,开展围堰总体布置、纵横向围堰结构设计、支护围堰结构设计、围堰止水墙设计等一系列关键技术问题研究。采用研究成果设计的方案不仅可以减少围堰工程量、节约工期,而且能确保项目顺利安全实施。     

某散货船试航中主机滑油低压故障的排除

文章分析了某散货轮在试航中主机多次出现因滑油低压而报警停车的故障现象,并将该故障出现的原因及排除过程进行了总结和思考,以期为船厂、船东以及试航的相关人员提供参考。     

5000 PCC“SHANGHAI HIGHWY”船主机启动分析与处理

分析5 000 PCC"SHANGHAI HIGHWAY"船主机伺服油压低引起主机启动故障的原因、介绍调查与处理过程,在实施了恰当的处理方法后,解决了问题。以期为类似问题的处理与解决提供借鉴和参考。     

基于BP网络的球鼻首参数化优化

本文以某集装箱船为研究对象,对降速航行后的球鼻首进行优化。采用Catia建立船体三维模型,为了产生不同形状的球鼻首,选取球鼻特征参数来描述其基本结构;采用拉丁超立方试验抽样方法得到12组不同形状的球鼻首,提出运用非线性拟合能力较强的BP网络构建球鼻首参数和阻力系数之间的关系模型;采用遗传算法对训练后的网络进行极值寻优。结果显示,优化船型的阻力系数显著降低,说明该方法对球鼻首的优化有一定的借鉴意义。     

Experimental investigation of evaporation rate and exhaust emissions of diesel engine fuelled with cotton seed methyl ester and its blend with petro-diesel

An experimental study to measure the evaporation rates, engine performance and emission characteristics of cotton seed biodiesel (cotton seed oil methyl ester) and its blends in different volumetric proportions with diesel is presented. The thermo-physical properties of all the fuel blends have been measured and presented. Evaporation rates of neat cotton seed biodiesel, neat diesel and their bends have been measured under slow convective environment of air flowing with a constant temperature. Evaporation constants have been determined by using the droplet regression rate data. The neat fuels and fuel blends have been utilized in a test engine with different load conditions to evaluate the performance, combustion and emission characteristics of the fuels. The specific fuel consumption values of the two blends, viz. B25 and B75 are found to be same. At the highest load, B0 records the lowest CO volume followed by B100. From the observed evaporation, performance and emissions characteristics, it is suggested that a blend of B50 and B75 can be optimally used in standard diesel engine settings.     

基于Matlab油船意外泄油性能的程序设计

基于概率方法的意外泄油性能参数计算是2010年1月1日及以后新造船破舱稳性需要评估的性能之一,满足一定的衡准要求方可。根据油船的分舱分别进行船底破损概率和船侧破损概率的插值,然后按照一定的公式求出船底破损后与船侧破损后的泄油量的加权之和,即意外泄油性能参数。由于手工计算费时费力,文章基于Matlab平台,开发了意外泄油性能参数的自动计算程序模块。计算结果与文献结果相比较,证明了本程序的计算精度,具有较好的工程实用价值。     

Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.