- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Díaz-Carrasco, Pilar es_ES
dc.contributor.author Molines, Jorge es_ES
dc.contributor.author GÓMEZ-MARTÍN, M. ESTHER es_ES
dc.contributor.author Medina, Josep R. es_ES
dc.date.accessioned 2024-07-01T18:36:05Z
dc.date.available 2024-07-01T18:36:05Z
dc.date.issued 2024-03 es_ES
dc.identifier.issn 0378-3839 es_ES
dc.identifier.uri http://hdl.handle.net/10251/205607
dc.description.abstract [EN] 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 , 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 (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 and RMSE <5%) 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 with regular waves 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. Then to calibrate properly a VARANS model it seems necessary to check the results obtained for each combination of and with many laboratory {HI, T} tests. 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. In this study, eight or more physical tests were required to find adequate values of np, and for VARANS models related to the best performance of wave-porous structure interaction. es_ES
dc.description.sponsorship The first author is funded through the Juan de la Cierva 2020 program (FJC 2020-044778-I) by " Union Europea - NextGenerationEU en el marco del Plan de Recuperacion, Transformacion y Resiliencia de Espana ", Spanish Ministry of Science and Innovation. This work is supported by two projects (1) PID 2021-126475OB-I00 and (2) PID 2021-128035OA- I00, funded by the MCIN/AEI/10.13039/501100011033 and, as appropriate, by " ERDF A way of making Europe", by the " European Union NextGenerationEU/PRTR". The authors thank Professor Javier L. Lara and the Environmental Hydraulics Institute of Cantabria (IH-Cantabria, Spain) for providing the IH-2VOF numerical model. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Coastal Engineering es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Numerical modelling es_ES
dc.subject IH-2VOF es_ES
dc.subject VARANS equations es_ES
dc.subject Mound breakwaters es_ES
dc.subject Porous media es_ES
dc.subject Neural Network es_ES
dc.subject.classification INGENIERIA E INFRAESTRUCTURA DE LOS TRANSPORTES es_ES
dc.title Neural Network calibration method for VARANS models to simulate wave-coastal structures interaction. es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.coastaleng.2023.104443 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126475OB-I00/ES/REPARACION Y REHABILITACION DE DIQUES EN TALUD/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128035OA-I00/ES/COMPORTAMIENTO HIDRAULICO DE DIQUES HOMOGENEOS DE BAJA COTA DE CORONACION CONSTRUIDOS CON MALLAS DE COLOCACION FACTIBLES (HOLOBRACE)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINISTERIO DE EDUCACION //FJC2020-044778-I//FALTA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports es_ES
dc.description.bibliographicCitation Díaz-Carrasco, P.; Molines, J.; Gómez-Martín, ME.; Medina, JR. (2024). Neural Network calibration method for VARANS models to simulate wave-coastal structures interaction. Coastal Engineering. 188. https://doi.org/10.1016/j.coastaleng.2023.104443 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.coastaleng.2023.104443 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 188 es_ES
dc.relation.pasarela S\506911 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder MINISTERIO DE EDUCACION es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem