Novel time-efficient approach to calibrate VARANS-VOF models for simulation of wave interaction with porous structures using Artificial Neural Networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021-09-01

Autores

Filipe Vieira (Autor) Outra; Francisco Taveira Pinto (Autor) FEUP; Paulo Rosa Santos (Autor) FEUP

Revista

Título: Ocean Engineering

Vol. 235

Páginas: 1-12

ISSN: 0029-8018

Editora: Elsevier

Indexação

ISI Web of Knowledge

ISI Web of Science

Scopus

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Outras Informações

DOI: 10.1016/j.oceaneng.2021.109375

Abstract (EN): Numerical models are valuable tools to provide information on wave-structure interaction processes that are difficult to measure in a physical model. The current level of accuracy of numerical models is relatively high but an adequate validation to establish the models’ empirical parameters is required to ensure a correct representation of the phenomena to be investigated. To this end, a “trial and error” approach is typically adopted, potentially resulting in a large number of simulations. In this study, a methodology based on Artificial Neural Networks (ANN) is presented, to obtain the optimal combination of values for the empirical coefficients that characterize the porous media in a VARANS-VOF model for predicting mean overtopping discharges. From an initial reduced set of simulations, input and output data are obtained for the training of the ANN. The ANN is then used to estimate the best values for the combination of coefficients that describe the three breakwater layers (armour, filter, and core), which are subsequently applied in the numerical model. The method was successfully applied to a set of laboratory experiments aimed at obtaining overtopping discharges for a Single-Layer Cube armoured breakwater. It resulted in a large reduction of computational effort when compared to the simulation of all possible combinations of values. © 2021 Elsevier Ltd

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12