软刚臂结构现场监测、风险评估与安全预警

发布时间：2018-05-09 02:45

本文选题：软刚臂 + 现场监测　；参考：《大连理工大学》2014年硕士论文

【摘要】：由于渤海水深较浅且存在冰期,软刚臂系泊系统成为作业于该海域浮式海洋平台主要的系泊形式。软刚臂系泊系统是承担浮体系泊定位和油气电力输送等功能的关键设施,一旦发生失效,将会对浮式平台甚至整个油田造成巨大影响。由于海洋环境的恶劣多变及结构形式的复杂新颖,软刚臂系泊系统在服役期间发生了多起结构失效事故,造成了巨大的经济损失。为了保障海上能源开发的顺利进行,需要对软刚臂系统进行结构风险评估,并提出相应的风险管控措施。由于失效因素的不确定性和失效事故资料的不完备,用传统方法对软刚臂结构进行风险评估,存在着诸多困难。本文利用现场监测,获取软刚臂系统真实的环境荷载信息和结构响应信息,通过对现场数据进行分析,评估结构的风险,并提出相应的风险管控措施。主要内容包括：首先,通过对系泊系统设计原理进行分析,建立了针对软刚臂系泊系统的完整的监测体系,通过对环境荷载信息、结构响应信息进行全面测量,为结构风险评估提供了数据基础。同时,为了保证测量数据的正确性,通过将现场实测的数据和通过理论、数值模拟得到的数据进行对比,论证了测量数据的可靠性。其次,基于软刚臂系泊系统的长期监测,对系统存在的风险进行识别,构建了系统风险因素的递阶层次结构体系,并以系统过载失效为例,通过现场监测数据的分析,建立了结构受力的概率分布模型,计算了系统服役期发生过载的概率,并对过载发生后果进行量化分析,判断了该风险的概率等级和后果等级,然后通过风险矩阵,评估过载的风险等级为高风险。最后,根据风险分析的结果,提出通过系泊力预报来减小系统过载的风险。针对传统预报方法预报过程复杂、精度低等缺点,提出通过RBF神经网络预报系泊力的方法。通过对样本数据进行预处理、调整网络参数等措施,提高了学习效率和预报精度,并将预测结果与样本外实测数据进行对比,论证了该方法的可行性。研究成果成功应用于我国渤海某软刚臂系统受力的预报。
[Abstract]:Because of the shallow water depth and ice age in Bohai Sea, the flexible rigid arm mooring system becomes the main mooring form of floating offshore platform in this sea area. The soft rigid arm mooring system is the key facility to perform the functions of floating system positioning and oil and gas power transmission. Once failure occurs, it will have a great impact on the floating platform or even the whole oil field. Due to the variety of marine environment and the complexity and novelty of the structure, there are many structural failure accidents in the period of service of the soft rigid arm mooring system, resulting in huge economic losses. In order to ensure the smooth development of offshore energy, it is necessary to carry out structural risk assessment of soft rigid arm system and put forward corresponding risk control measures. Due to the uncertainty of failure factors and the incompleteness of failure accident data, it is difficult to evaluate the risk of soft rigid arm structure by traditional method. In this paper, the real environmental load information and structural response information of the soft rigid arm system are obtained by field monitoring, and the risk of the structure is evaluated by analyzing the field data, and the corresponding risk control measures are put forward. The main elements include: Firstly, by analyzing the design principle of the mooring system, a complete monitoring system for the soft rigid arm mooring system is established, and the environmental load information and the structural response information are comprehensively measured. It provides a data base for structural risk assessment. At the same time, in order to ensure the correctness of the measured data, the reliability of the measured data is demonstrated by comparing the field measured data with the data obtained through theoretical and numerical simulation. Secondly, based on the long-term monitoring of the soft rigid arm mooring system, the risk of the system is identified, and the hierarchical structure system of system risk factors is constructed, and the system overload failure is taken as an example, and the field monitoring data are analyzed. The probability distribution model of structural force is established, and the probability of overloading in the service period of the system is calculated, and the result of overloading is analyzed quantitatively, the probability grade and consequence grade of the risk are judged, and then the risk matrix is adopted. Assess the risk of overload as high risk. Finally, according to the results of risk analysis, a mooring force prediction is proposed to reduce the risk of system overload. In view of the disadvantages of the traditional forecasting method, such as complicated forecasting process and low precision, a method of predicting mooring force by RBF neural network is proposed. By preprocessing the sample data and adjusting the network parameters, the learning efficiency and prediction accuracy are improved. The feasibility of this method is demonstrated by comparing the prediction results with the measured data outside the sample. The research results have been successfully applied to the prediction of the force of a soft rigid arm system in Bohai Sea, China.
【学位授予单位】：大连理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：P751

【参考文献】