Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation.How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problem...Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation.How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solved to ensure the safe operation of the platform.To address the practical engineering problem that it is difficult to monitor the stress response of the tubular joints of jacket platforms online,a digital twin reduced-order method for real-time prediction of the stress response of tubular joints is proposed.In the offline construction phase,multi-scale modeling and multi-parameter experimental design methods are used to obtain the stress response data set of the jacket structure.Proper orthogonal decomposition is employed to extract the main feature information from the snapshot matrix,resulting in a reduced-order basis.The leave-one-out cross-validation method is used to select the optimal modal order for constructing the reduced-order model(ROM).In the online prediction phase,a digital twin model of the tubular joint is established,and the prediction performance of the ROM is analyzed and verified through using random environmental load and field environmental monitoring data.The results indicate that,compared with traditional numerical simulations of tubular joints,the ROM based on the proposed reduced-order method is more efficient in predicting the stress response of tubular joints while ensuring accuracy and robustness.展开更多
Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended perio...Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.11472076).
文摘Jacket platforms constitute the foundational infrastructure of offshore oil and gas field exploitation.How to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solved to ensure the safe operation of the platform.To address the practical engineering problem that it is difficult to monitor the stress response of the tubular joints of jacket platforms online,a digital twin reduced-order method for real-time prediction of the stress response of tubular joints is proposed.In the offline construction phase,multi-scale modeling and multi-parameter experimental design methods are used to obtain the stress response data set of the jacket structure.Proper orthogonal decomposition is employed to extract the main feature information from the snapshot matrix,resulting in a reduced-order basis.The leave-one-out cross-validation method is used to select the optimal modal order for constructing the reduced-order model(ROM).In the online prediction phase,a digital twin model of the tubular joint is established,and the prediction performance of the ROM is analyzed and verified through using random environmental load and field environmental monitoring data.The results indicate that,compared with traditional numerical simulations of tubular joints,the ROM based on the proposed reduced-order method is more efficient in predicting the stress response of tubular joints while ensuring accuracy and robustness.
基金financially supported by the Natural Science Foundation of Heilongjiang Province of China (Grant No. LH2020E016)the National Natural Science Foundation of China (Grant No.11472076)。
文摘Offshore platforms are susceptible to structural damage due to prolonged exposure to random loads,such as wind,waves,and currents.This is particularly true for platforms that have been in service for an extended period.Identifying the modal parameters of offshore platforms is crucial for damage diagno sis,as it serves as a prerequisite and foundation for the process.Therefore,it holds great significance to prioritize the identification of these parameters.Aiming at the shortcomings of the traditional Fast Bayesian Fast Fourier Transform(FBFFT) method,this paper proposes a modal parameter identification method based on Automatic Frequency Domain Decomposition(AFDD) and optimized FBFFT.By introducing the AFDD method and Powell optimization algorithm,this method can automatically identify the initial value of natural frequency and solve the objective function efficiently and simply.In order to verify the feasibility and effectiveness of the proposed method,it is used to identify the modal parameters of the IASC-ASCE benchmark model and the j acket platform structure model,and the Most Probable Value(MPV) of the modal parameters and their respective posterior uncertainties are successfully identified.The identification results of the IASC-ASCE benc hmark model are compared with the identification re sults of the MODE-ID method,which verifies the effectivene ss and accuracy of the proposed method for identifying modal parameters.It provides a simple and feasible method for quantifying the influence of uncertain factors such as environmental parameters on the identification results,and also provide s a reference for modal parameter identification of other large structures.
