Scour depth prediction of offshore pipelines is of great significance to the design and construction of the submarine pipeline projects. In this paper, based on the CFD software package FLUENT and User Defined Functi...Scour depth prediction of offshore pipelines is of great significance to the design and construction of the submarine pipeline projects. In this paper, based on the CFD software package FLUENT and User Defined Function （UDF）, an Eulerian two-phase model, which includes an Euler-Euler coupled model for water and sediment phases, and a turbulent model for the fluid phase, is adopted to predict the scour depth around pipelines. The model is verified by observation data obtained from laboratory experiments. On the basis of the simulations, the factors affecting the scour depth, including the effects of incipient velocity, pipe diameter and sediment particle size and so on, were investigated. Meanwhile, according to formulas of incipient velocity of various sediments, approximate calculation on theoretical scour depths is developed for pipelines of seven stations in the South China Sea, where engineering application information is available.展开更多
A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improv...A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.展开更多
基金Foundation item： Supported by the State Key Laboratory of Hydraulic Engineering Simulation and Safety （Grant No. HESS-1401）, the National Natural Science Foundation of China （Grant No. 51279124）, the Marine Science and Technology Foundation of South China Sea Branch, State Oceanic Administration （Grant No. 1417）, and the National High Technology Research and Development Program of China （Grant No. 2008AA09A401）. Acknowledgement We wish to thank Prof. Bai YC for stimulating discussions and reviewers for their helpful comments that led to considerable improvements to the paper.
文摘Scour depth prediction of offshore pipelines is of great significance to the design and construction of the submarine pipeline projects. In this paper, based on the CFD software package FLUENT and User Defined Function （UDF）, an Eulerian two-phase model, which includes an Euler-Euler coupled model for water and sediment phases, and a turbulent model for the fluid phase, is adopted to predict the scour depth around pipelines. The model is verified by observation data obtained from laboratory experiments. On the basis of the simulations, the factors affecting the scour depth, including the effects of incipient velocity, pipe diameter and sediment particle size and so on, were investigated. Meanwhile, according to formulas of incipient velocity of various sediments, approximate calculation on theoretical scour depths is developed for pipelines of seven stations in the South China Sea, where engineering application information is available.
基金Projects 50774080 supported by the National Natural Science Foundation of China200348 by the Foundation for the National Excellent Doctoral Dis-sertation of China
文摘A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.
