• Title/Summary/Keyword: modified Ursell number($U_{RP}$)

Search Result 3, Processing Time 0.015 seconds

The Local Scour around a Slender Pile in Combined Waves and Current (파랑과 흐름이 결합된 공존역에서 파일 주변의 국부세굴)

  • Park, Jong-Hwan;Kim, Kyoung-Ho
    • Journal of Korean Society of Coastal and Ocean Engineers
    • /
    • v.22 no.6
    • /
    • pp.405-414
    • /
    • 2010
  • In the study, experiments are performed in the mixing region combined wave and current to investigate the characteristics of local scour around a slender pile. Wave generator and current generator are used for the experiments and currents are co-directions with the waves. The local scour depths around the pipeline are obtained according to the various pipe diameters, wave periods, wave heights, and current velocities. The experiments show that the maximum equilibrium local scour depth increases with pipe diameter, wave period, wave height, and current velocity. Using the experimental results, the correlations of scour depth and parameters such as Shields parameter ($\theta$), Froude number (Fr), Keulegan-Carpenter number (KC), Ursell number ($U_R$), modified Ursell number ($U_{RP}$) and ratio of velocities ($U_c/U_c+U_m$) are analyzed. In the mixing region combined with waves and currents, The Froude number of single parameters is the main parameter to cause the local scour around a slender pile due to waves and current and this means that current governs the scour within any limits of the currents.

The Local Scour around Submarine Pipelines in the Interaction Region Combined with Waves and Currents (파랑과 정상흐름의 공존역에서 해저관로 주변의 국부세굴)

  • Kim, Kyoung-Ho;Lee, Ho-Jin;Kim, Wan-Shik
    • Journal of Korean Society of Coastal and Ocean Engineers
    • /
    • v.20 no.5
    • /
    • pp.510-521
    • /
    • 2008
  • In the study, experiments are performed in the interaction region combined with wave and current to investigate the characteristics of local scour around submarine pipelines. Wave generator and current generator are used for the experiments and two current directions were used; co-direction and counter direction to the wave. The local scour depths around the pipeline are obtained according to the various pipe diameters(D), wave periods(T), wave heights(H), and current velocities(V). The experiments show that the maximum equilibrium local scour depth increases with pipe diameter, wave period, wave height, and current velocity. Using the experimental results, the correlations of scour depth and parameters such as Shields parameter($\theta$), Froude number(Fr), period parameter, Keulegan-Carpenter number(KC), Ursell number($U_R$), modified Ursell number($U_{RP}$) and ratio of velocities($U_{c}/(U_{c}+U_{m})$) are analyzed. In the interaction region combined with waves and currents, Froude number and Shields parameter are found the main parameters to cause the local scour around the submarine pipelines and this means that current governs the scour within any limits of the currents.

Prediction of the Scour Depth around the Pipeline Exposed to Waves using Neural Networks (신경망을 이용한 파랑하 관로주변의 세굴심 예측)

  • Kim, Kyoungho;Cho, Junyoung;Lee, Hojin;Oh, Hyunsik
    • Journal of the Korean GEO-environmental Society
    • /
    • v.14 no.5
    • /
    • pp.15-22
    • /
    • 2013
  • The submarine pipe, which is one of the most important coastal structures, is widely used in the development of coastal and ocean engineering. The scour of the submarine pipe occurs due to the wave and the current according to the state of the sea bed. The scour affects the submarine pipe and causes it to undergo settlement and fatigue. It is difficult to predict the local scour under complicated and various conditions of the coastal environment, even though many researches on the scour of the submarine pipe have been studied in recent years. This study analyzed the scour depth around a submarine pipe by using the Neural Network technique. The back-propagation algorithms was used to train the Neural Network. The 58 simulating experimental data for the performance and validation of the Neural Network technique were analyzed in this study. Then, the regression analysis for the same data was performed in this study to predict and compare with the Neural Network technique for the scour depth.