| As a large-scale low-frequency structure,jacket platform is the most widely used offshore oil and gas production facility in the world.It has served in harsh marine environment for a long time and bears loads such as wind,waves and currents,especially the structural state of jacket platform after typhoon,which directly affects the safety of operators returning to Taiwan and production facilities on the platform.How to accurately obtain the parameters reflecting the structural state of the jacket platform and evaluate the status of the platform,so as to achieve early warning is particularly necessary.Many scholars have carried out early warning research based on single parameters such as displacement and frequency,but there may be false alarm problems in practical application.At the same time,in the research of parameter acquisition,the existing methods have the problems that the test equipment is expensive or difficult to apply to large-scale low-frequency structures.In view of the above problems,this paper studies the early warning conditions of jacket platform based on displacement and frequency by integrating theoretical analysis,numerical simulation,experimental verification and other technical means,which improves the early warning fault tolerance of the platform.The specific research contents are as follows:(1)The finite element model of jacket platform structure is established by ABAQUS software,and the static analysis of the platform is carried out considering the influence of wind,wave and current loads,which verifies the accuracy of the finite element model and provides a benchmark model for the later early warning research.(2)The classification method of early warning grade of jacket platform based on displacement and frequency is put forward.Firstly,the finite element model of jacket platform is analyzed linearly,and the collapse analysis method is determined as the ultimate bearing capacity analysis method.Then,the dynamic wave and current loads are replaced by static loads by load replacement method,and the nonlinear collapse analysis of the platform is carried out on the basis of considering the material nonlinearity and geometric nonlinearity of the platform by increasing the load step by step.According to the changes of displacement and frequency corresponding to the failure forms of the platform in the process of collapse analysis,it is divided into three early warning levels.Finally,the residual intensity coefficient is used to evaluate the rationality of early warning level,which verifies the effectiveness of the proposed method.(3)The problem of obtaining displacement signal by integrating acceleration signal twice is studied.Combining the low frequency characteristics of jacket platform structure and the advantages of EMD method which is suitable for analyzing low frequency signal,a displacement reconstruction method based on EMD is proposed.Through simulation analysis,experimental verification and application of measured acceleration signal of offshore platform,this method can effectively obtain displacement signal from low frequency acceleration signal of offshore platform,which provides technical support for early warning research of offshore platform.(4)The method of extracting working modal parameters based on ODS FRF is studied.Through cantilever beam test,the modal frequency obtained by hammering test modal analysis based on PolyMAX is compared,which verifies the effectiveness of the method and can be used to obtain frequency early warning parameters of jacket platform.(5)Taking the aforementioned jacket platform reference model as the research object,the platform model is excited by the wind and wave environmental loads measured during typhoon in the South China Sea,and the acceleration responses at three measuring points are calculated by ABAQUS software implicit dynamics method.According to the aforementioned method,the displacement and frequency early warning parameters are obtained.Compared with the early warning level,the structure of the platform is in a safe state after typhoon crossing. |