山区铁路路堑边坡滑塌整治技术研究

山区铁路路堑边坡常出现滑塌现象,为保证铁路运营安全,需及时采取整治措施。本文以某铁路滑塌路堑边坡为例,根据现场调查、地质水文资料,结合数值模拟结果剖析路堑边坡稳定性及滑塌机制,并对引起边坡滑塌的因素制定针对性整治措施。结果表明:连续降雨以及长期排水不畅会导致地层性质减弱、滑动面下移,将诱发加固边坡发生二次滑塌;为减少对既有边坡防护的扰动破坏,对二次垮塌边坡挡护进行合理加固补强亦可满足边坡稳定要求。     

船舶低硫柴油系统设计的分析与优化

就新辟航线首制船“汉亚直达”集装箱船的低硫柴油系统,叙述了船舶低硫柴油系统的设计经验,从当前国内外对船用燃油硫含量的要求、应对方案到船舶低硫柴油冷却方式选择、低硫柴油冷却系统设计、高/低硫柴油转换、使用低硫柴油风险分析及处理等方面进行详细叙述,为业内同行提供参考。     

中低速磁浮列车救援支撑装置的优化及冲洗回路参数设定

介绍了中低速磁浮列车救援支撑装置的组成及其工作原理,以及增压缸的工作过程。分析了救援支撑装置的常见问题。阐述了救援支撑装置的气液回路优化措施和控制方式优化措施,并基于长沙长沙磁浮快线的实际情况,通过计算得到冲洗回路工艺参数设定参考值,提高了救援支撑装置的可靠性及可操作维护性。     

国内船舶造修企业的VOCs排放现状及治理措施

船舶造修过程中的预处理及涂装工艺,会产生并排放大量的可挥发性有机化合物(VOCs),严重污染大气环境,危害人体健康。通过对国内相关船舶造修企业的走访和调查,梳理了船舶造修过程中产生VOCs的工艺环节,近年来因VOCs排放超标受到环保职能部门处罚的案例,以及当前企业的涂料用量和VOCs的排放现状。同时,汇总了船舶造修企业常见的VOCs净化处理技术及其适用的情况,并介绍了实际进行VOCs治理的案例。对比国内外先进船舶建造行业,并基于我国船舶造修企业的实际情况和VOCs法规的排放要求,提出若干工艺优化的建议和举措,旨在减少排放且降低成本。     

铁路隧道穿越瓦斯煤系地层的旋喷围桩防突工法探析

为确保铁路隧道能够快速安全地穿越瓦斯突出煤系地层，依据《铁路瓦斯隧道技术规范》的防突规定及旋喷桩工艺特点，从“堵”“排”结合的角度，提出一种旋喷围桩快速防突的新型施工方法。首先，依据防突原理，针对超前综合防突和工作面综合防突，开展了“堵”“排”结合的旋喷围桩防突工艺研究； 然后，以叙毕铁路欧家湾高瓦斯隧道防突为例，进行了旋喷围桩防突工法中关于围桩桩径与瓦斯释放孔数量的多方案设计；最后，依据施工定额，结合工程量统计数据，从施工成本和施工时间方面将旋喷围桩防突工法与传统抽排防突措施进行比较。研究结果表明： 1)封闭的高强度旋喷桩加固了防突区域轮廓周线上煤层的地质结构，隔离了围桩内外的煤体，截断了煤与瓦斯突出向围桩外扩展的途径； 2)仅对围桩内突出煤层实施地质钻钻孔和瓦斯排放，使消突范围大大减小； 3)与传统抽排消突方案相比，旋喷围桩防突工法桩内释放孔数量减少为原设计的1/5～1/3时，地质钻钻孔总工程量可减少13%～22%，施工成本按基价计仅增加1.7%～10.5%，既安全高效，又简单易行。     

基于未确知测度理论的隧道施工瓦斯灾害风险评价

选取煤层厚度、煤层瓦斯含量、回风流平均瓦斯浓度、相对瓦斯涌出量、岩石类型、连通封闭性、深度值、隧道跨度、隧道长度、通风风速和涌水量等11项瓦斯灾害指标作为隧道施工瓦斯灾害风险评价指标体系,并建立各指标的等级标准。将序关系分析法(G1)和反熵权法(AEW)运用拉格朗日乘子法确定组合权重,构建各指标的单指标测度函数,并计算出各指标的综合测度评价向量。为了优化未确知测度置信度识别准则,引进集对分析(SPA)关联系数确定各隧道的瓦斯灾害风险等级。并对24个瓦斯隧道进行瓦斯灾害评价,并和FDA法评价结果和现场实测结果进行对比,精度达到91.7%,基本符合实际情况,表明了该评价方法合理有效,可为隧道施工瓦斯灾害评价提供理论参考。     

长三角地区液化天然气接收站的发展前景

为了剖析长三角地区液化天然气(LNG)市场发展形势,从而为长三角地区科学布局LNG接收站提供参考,开展长三角地区天然气消费现状及发展趋势分析,梳理影响天然气市场发展的因素,对长三角地区天然气需求量进行预测。通过供需能力平衡分析,判定长三角地区LNG接收站承担的进口LNG接卸量还有较大的增长空间,未来2~3年将是LNG接收站的建设热潮。基于未来发展形势分析,从规划布局、功能定位及市场化等方面提出促进长三角LNG接收站发展的建议。     

基于绿色铸造高速动车组齿轮箱箱体产业化研究

采用金属型低压铸造工艺开发出高速动车组齿轮箱体铸件,产品外观光洁、尺寸精度高,实体综合力学性能优。通过建成具有自动化、信息化的高速动车组齿轮箱金属型低压铸造产业化平台,生产效率高,过程可控,节能、高效和环保。通过研究应用新型砂芯无机黏结剂,减少了砂芯浇注过程污染性气体排放,有效实现了高速动车组齿轮箱体铸件生产方式向节能减排、绿色铸造的转型升级。     

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.     

Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.