Neural Network calibration method for VARANS models to simulate wave-coastal structures interaction.

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, n p , and the optimum values of the Forchheimer coefficients, α and β , that best represent the wave-structure interaction for a complete set of laboratory tests. Physical tests were conducted in a 2D wave flume for a homogeneous mound breakwater under regular wave conditions. Numerical tests were carried out using the IH-2VOF model to simulate the corresponding physical tests and incident wave conditions (H I , T). The numerical tests covered a wide range of Forchheimer coefficients found in the literature, α and β , and the porosity, n p , 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 (H I , T , n p , α and β) and one output variable (K R 2). The NN model explained more than 90% (R2 > 0.90 and RMSE <5%) of the variance of the squared coefficient of reflection, K R 2. This NN model was used to estimate the K R 2 in a wide range of n p , α and β , and the error (ε a) between the physical measurements with regular waves and the NN estimations of K R 2 was calculated. The results of ε a as function of n p , α and β showed that for a given porosity, n p , it was difficult to obtain a pair of α and β values that gave a common low error if few physical tests are used for calibration. Then to calibrate properly a VARANS model it seems necessary to check the results obtained for each combination of α and β with many laboratory { H I , T } tests. The minimum root-mean-square error of K R 2 (ε r m s) was calculated to find the optimum values of porosity and Forchheimer coefficients: n p = 0.44 , α = 200 and β = 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. In this study, eight or more physical tests were required to find adequate values of n p , α and β for VARANS models related to the best performance of wave-porous structure interaction. • A Neural Network model to predict K R 2 on mound breakwaters much faster than any numerical VARANS model. • The physical measurement of the porosity, n p , is not reliable and it should be calibrated. • The selection of few physical tests (H I , T) to calibrate Forchheimer coefficients, α and β , is not sufficient to obtain the best performance of wave-porous structure interaction. • The proposed method based on a Neural Network model is a robust, accurate and computational efficient tool to calibrate {n p , α and β } in VARANS models. • The proposed calibration method obtains the optimum combination of {n p , α and β } related to the best performance of wave-porous structure interaction. [ABSTRACT FROM AUTHOR]