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Traditionally, subsea pipelines designed for the transportation of oil, gas, and water are constructed using carbon steel due to its strength, toughness, and ability to operate at temperatures up to 427 °C. However, polyethylene (PE), especially its high-density variant (HDPE), presents advantages such as reduced installation costs, diminished water leakage, and superior corrosion resistance. As research endeavours to enhance PE properties, its adoption for subsea applications is anticipated to rise. This study first delineates the mechanical behaviour of HDPE pipelines for offshore installation, identifying pulling tension, dimension ratio, water depth, and air fill ratio as the paramount lay parameters. Subsequently, a theoretical bend radius equation was derived from pipelaying mechanics using a purely geometric approach. Within this equation, two determinants, parameter X and parameter Y, dictate the sagbend bend radius. Regression analysis elucidated the relationships of lay parameters with both X and Y, yielding a general equation for X in terms of pull tension, water depth, and air fill ratio and another for Y as a function of water depth. Together, these geometric determinants underpin the sagbend bend radius estimation model. For overbend bend radius prediction, a lay index (IL) was fashioned from the aforementioned three parameters. Correlation assessments between the lay index and overbend bend radius revealed R2 values of 0.940, 0.836, and 0.712 for pipes with diameters of 2.0, 2.5, and 3.0 metres, respectively. This underscores the model's proficiency in predicting the bend radius, albeit with decreasing precision for larger-diameter pipelines. [ABSTRACT FROM AUTHOR]

Traditionally, subsea pipelines designed for the transportation of oil, gas, and water are constructed using carbon steel due to its strength, toughness, and ability to operate at temperatures up to 427 °C. However, polyethylene (PE), especially its high-density variant (HDPE), presents advantages such as reduced installation costs, diminished water leakage, and superior corrosion resistance. As research endeavours to enhance PE properties, its adoption for subsea applications is anticipated to rise. This study first delineates the mechanical behaviour of HDPE pipelines for offshore installation, identifying pulling tension, dimension ratio, water depth, and air fill ratio as the paramount lay parameters. Subsequently, a theoretical bend radius equation was derived from pipelaying mechanics using a purely geometric approach. Within this equation, two determinants, parameter X and parameter Y, dictate the sagbend bend radius. Regression analysis elucidated the relationships of lay parameters with both X and Y, yielding a general equation for X in terms of pull tension, water depth, and air fill ratio and another for Y as a function of water depth. Together, these geometric determinants underpin the sagbend bend radius estimation model. For overbend bend radius prediction, a lay index (IL) was fashioned from the aforementioned three parameters. Correlation assessments between the lay index and overbend bend radius revealed R2 values of 0.940, 0.836, and 0.712 for pipes with diameters of 2.0, 2.5, and 3.0 metres, respectively. This underscores the model’s proficiency in predicting the bend radius, albeit with decreasing precision for larger-diameter pipelines.

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

This research aims to study the ultimate limit state (ULS) behaviour of stiffened panel under longitudinal compression by a non-linear finite element method (NLFEM). There are different types of stiffeners mainly being used in shipbuilding, i.e., T-bar, flat-bar, and angle-bar. However, this research focuses on the ultimate compressive strength behaviour of flat-bar stiffened panel. A total of 420 reliable scenarios of flat-bar stiffened panel were selected for numerical simulation by the ANSYS NLFEM. The ultimate strength behaviours obtained were used as data for the development of closed form shape empirical formulation. Recently, our group proposed an advanced empirical formulation for T-bar stiffened panel, and the applicability of the proposed formulation to flat-bar stiffened panel is confirmed by this study. The accuracy of the empirical formulation obtained for flat-bar stiffened panel was validated by finite element (FE) simulation results of statistical analysis (R2 = 0.9435). The outcome obtained will be useful for ship structural designers in predicting the ultimate strength performance of flat-bar type stiffened panel under longitudinal compression.

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines. [ABSTRACT FROM AUTHOR]

This research aims to study the ultimate limit state (ULS) behaviour of stiffened panel under longitudinal compression by a non-linear finite element method (NLFEM). There are different types of stiffeners mainly being used in shipbuilding, i.e., T-bar, flat-bar, and angle-bar. However, this research focuses on the ultimate compressive strength behaviour of flat-bar stiffened panel. A total of 420 reliable scenarios of flat-bar stiffened panel were selected for numerical simulation by the ANSYS NLFEM. The ultimate strength behaviours obtained were used as data for the development of closed form shape empirical formulation. Recently, our group proposed an advanced empirical formulation for T-bar stiffened panel, and the applicability of the proposed formulation to flat-bar stiffened panel is confirmed by this study. The accuracy of the empirical formulation obtained for flat-bar stiffened panel was validated by finite element (FE) simulation results of statistical analysis (R2 = 0.9435). The outcome obtained will be useful for ship structural designers in predicting the ultimate strength performance of flat-bar type stiffened panel under longitudinal compression. [ABSTRACT FROM AUTHOR]