Neural Network Calibration Method For Varans Models To Analyse Wave-Porous Structures Interaction

Abstract

This study develops a calibration method for the porous media to properly model the interaction between waves and coastal structures using VARANS models. The proposed method estimates the porosity, np, and the optimum values of the Forchheimer coefficients, and . Physical tests were conducted in a 2D wave flume for a homogeneous mound breakwater. Numerical tests were carried out using the IH-2VOF model to simulate the corresponding physical tests and incident wave conditions (HI, T). The numerical tests covered a wide range of Forchheimer coefficients found in the literature, and , and the porosity, np, with a total of 555 numerical tests. The results of 375 numerical tests using IH-2VOF were used to train a Neural Network (NN) model with five input variables (HI, T, np, and ) and one output variable . The NN model explained more than 90% (R2 > 0.90) of the variance of the squared coefficient of reflection, . This NN model was used to estimate the in a wide range of np, and , and the error () between the physical measurements and the NN estimations of was calculated. The results of as function of np, and showed that for a given porosity, np, it was difficult to obtain a pair of and values that gave a common low error if few physical tests are used for calibration. The minimum root-mean-square error of ( was calculated to find the optimum values of porosity and Forchheimer coefficients: np = 0.44, = 200 and = 2.825 for the tested structure. Blind tests were conducted with the remaining 180 numerical tests using IH-2VOF to validate the proposed method for VARANS models.