• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.743-752
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• 2019

The flow in the meandering channel is characterized by the spiral motion of secondary currents that typically cause the erosion along the outer bank. Hydraulic structures, such as spur dike and groyne, are commonly installed on the channel bottom near the outer bank to mitigate the strength of secondary currents. This study is to investigate the effects of submerged vanes installed in a $90^{\circ}$ meandering channel on the development of secondary currents through three-dimensional numerical modeling using the hybrid RANS/LES method for turbulence and the volume of fluid method, based on OpenFOAM open source toolbox, for capturing the free surface at the Froude number of 0.43. We employ the second-order-accurate finite volume methods in the space and time for the numerical modeling and compare numerical results with experimental measurements for evaluating the numerical predictions. Numerical results show that the present simulations well reproduce the experimental measurements, in terms of the time-averaged streamwise velocity and secondary velocity vector fields in the bend with submerged vanes. The computed flow fields reveal that the streamwise velocity near the bed along the outer bank at the end section of bend dramatically decrease by one third of mean velocity after the installation of vanes, which support that submerged vanes mitigate the strength of primary secondary flow and are helpful for the channel stability along the outer bank. The flow between the top of vanes and the free surface accelerates and the maximum velocity of free surface flow near the flow impingement along the outer bank increases about 20% due to the installation of submerged vanes. Numerical solutions show the formations of the horseshoe vortices at the front of vanes and the lee wakes behind the vanes, which are responsible for strong local scour around vanes. Additional study on the shapes and arrangement of vanes is required for mitigate the local scour.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.469-484
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• 2021

Rock mass classification results have a great influence on construction schedule and budget as well as tunnel stability in tunnel design. A total of 3,526 tunnels have been constructed in Korea and the associated techniques in tunnel design and construction have been continuously developed, however, not many studies have been performed on how to assess rock mass quality and grade more accurately. Thus, numerous cases show big differences in the results according to inspectors' experience and judgement. Hence, this study aims to suggest a more reliable rock mass classification (RMR) model using machine learning algorithms, which is surging in availability, through the analyses based on various rock and rock mass information collected from boring investigations. For this, 11 learning parameters (depth, rock type, RQD, electrical resistivity, UCS, V_p, V_s, Young's modulus, unit weight, Poisson's ratio, RMR) from 13 local tunnel cases were selected, 337 learning data sets as well as 60 test data sets were prepared, and 6 machine learning algorithms (DT, SVM, ANN, PCA & ANN, RF, XGBoost) were tested for various hyperparameters for each algorithm. The results show that the mean absolute errors in RMR value from five algorithms except Decision Tree were less than 8 and a Support Vector Machine model is the best model. The applicability of the model, established through this study, was confirmed and this prediction model can be applied for more reliable rock mass classification when additional various data is continuously cumulated.