هوش محاسباتی بر روی ویژگی های عملکرد هیدرودینامیک ویژگی های شکستگی دایره ای سوراخ شده
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|52134||2015||7 صفحه PDF||سفارش دهید|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia Engineering, Volume 116, 2015, Pages 118–124
Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwater (EPQCB) is an artificial concrete breakwater consisting of a curved perforated face fronting the waves, a vertical wall on back and a base slab resting on a low rubble mound base. The perforated curved front face is having advantages like energy dissipation and good stability with less material as it is hollow inside. Computational Intelligence (CI) can be adopted for the evaluation of performance characteristics like reflection, dissipation, run-up and rundown which are complex, time consuming and expensive to perform in laboratory. The paper presents the work carried out to predict the reflection coefficient (Kr) for input parameters, wave period (T) beyond the data range used for training and of wave height (H) along with the data on input parameters of water depth (d), spacing-perforation ratio (S/D) and radius (R) of the EPQCB. The data on various parameters are taken in two categories for training and testing of ANN as mentioned below in order to understand the effect of using non-dimensional data in place of parametric values: 1) Input in the form of parametric data (H, T, d, R, S, D), and 2) Input in the form of non-dimensional values (H/gT2, d/gT2, S/D, R/H). Better correlation was found when individual dimensional parametric data was used instead of non-dimensional group values in both the methods of prediction. Similarly, the correlation between the beyond the data range prediction and actual values was found to be good in both methods of prediction.