Journal
WATER
Volume 12, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/w12020386
Keywords
Accropode armored breakwater; non-hydrostatic wave model; mean overtopping discharge; equivalent roughness coefficient
Categories
Funding
- National Key Research and Development Program of China [2017YFC1404200]
- Science Fund for Creative Research Groups of the National Natural Science Foundation of China [51621092]
- National Natural Science Foundation of China [51609152, 51509177, 51978440]
- Key Programme of the Natural Science Foundation of Tianjin, China [19JCZDJC39700]
- Programme of the Natural Science Foundation of Tianjin, China [16JCQNJC07700]
- Natural Science Foundation of Tianjin Education Commission [2019ZD16, 2019KJ108]
- State Scholarship Fund of China [201808120025]
- Open fund for State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University [HESS-1912]
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In this paper, a new method for predicting wave overtopping discharges of Accropode armored breakwaters using the non-hydrostatic wave model Simulating WAves till SHore (SWASH) is presented. The apparent friction coefficient concept is proposed to allow the bottom shear stress term calculated in the momentum equation to reasonably represent the effect of comprehensive energy dissipation caused by the roughness and seepage during the wave overtopping process. A large number of wave overtopping cases are simulated with a calibrated SWASH model to determine the values of equivalent roughness coefficients so that the apparent friction coefficients can be estimated to achieve the conditions with good agreement between numerical overtopping discharges and those from the EurOtop neural network model. The relative crest freeboard and the wave steepness are found to be the two main factors affecting the equivalent roughness coefficient. A derived empirical formula for the estimation of an equivalent roughness coefficient is presented. The simulated overtopping discharges by the SWASH model using the values of the equivalent roughness coefficient estimated from the empirical formula are compared with the physical model test results. It is found that the mean error rate from the present model predictions is 0.24, which is slightly better than the mean error rate of 0.26 from the EurOtop neural network model.
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