AURORA
Coordinated research integrating numerical and experimental modelling on breakwater Armour Unit Resilience and Optimisation for Real engineering Applications
Call: Proyectos de Generación de Conocimiento 2024
Funding agency: Ministerio de Ciencia, Innovación y Universidades
Summary
The AURORA project aims to significantly advance our understanding of breakwater stability by synergistically combining state-of-the-art numerical modelling (Universidade de Vigo) with rigorous physical modelling (Universitat Politècnica de Catalunya - BarcelonaTech). This coordinated approach leverages the strengths of both institutions, enabling the development and validation of advanced numerical tools for predicting the stability of breakwater armour units under various wave and hydrodynamic conditions. The project will develop and validate high-fidelity numerical models for the simulation of breakwater behaviour. Comprehensive physical model tests at the iCIEM-MARHIS ICTS facility will be conducted to provide ground truth data for model calibration and validation. The project is structured around two distinct objectives, each of which is assigned to a different subproject. The primary subproject entails the development and application of a validated tool to real-world case studies, thereby enabling Port Authorities to assess and enhance the resilience of breakwaters against evolving environmental challenges. The second subproject aims to establish a comprehensive database of stability metrics for various armour units and environmental conditions. Leveraging a Machine Learning (ML) model based on Bayesian Updating, this database will finally enable accurate predictions of the stability of rubble mound breakwaters. By seamlessly integrating numerical modelling and experimental expertise, AURORA will drive the development of more robust and resilient coastal infrastructure, addressing the critical needs of modern port and coastal management.
Subprojects
AURORA-NUM
Title: Armour Unit Resilience and Optimisation for Real engineering Applications Using NUMerical modelling
Reference: PID2024-155465OB-C21
Funding recipient: Universidade de Vigo Q8650002B
Duration: 01/09/2025 – 31/08/2028
Budget: 153.125,00 €
Summary of subproject 1
This subproject focuses on the development, validation and application of high-fidelity numerical models for predicting the stability of breakwater armour units. Using state-of-the-art computational techniques, the project will develop and implement an advanced numerical tool incorporating detailed descriptions of wave-structure interaction, hydrodynamic forces and armour unit movements. The developed tool will be validated using high quality experimental data generated by Subproject 2. Applications include assessing the stability of existing breakwaters, informing the design of new structures, and contributing to the development of a comprehensive database of stability metrics for different armour types and environmental conditions. The results of this subproject will provide valuable tools for coastal engineers to assess and improve the resilience of breakwater structures.
AURORA-UPC
Title: Armour Unit Resilience and Optimisation for Real engineering Applications Using Physical model Conditions and machine learning
Reference: PID2024-155465OB-C22
Funding recipient: Universitat Politècnica de Catalunya – BarcelonaTech Q0818003F
Duration: 01/09/2025 – 31/08/2028
Budget: 216.250,00 €
Summary of subproject 2
Subproject 2 (AURORA-UPC) focuses on advancing the stability analysis and design of artificial breakwaters for sloping dikes, combining experimental research with advanced Machine Learning (ML) techniques. Rigorous tests will be conducted at the iCIEM-MARHIS ICTS facilities, using different types and sizes of concrete blocks under a wide range of wave conditions. These tests will examine scale effects and the impact of material friction on stability through both large-scale and small-scale wave flume experiments. High-quality data (including wave forces, real-time block movements, and a detailed characterization of wave hydrodynamics) will be collected using both intrusive and non-intrusive measurement techniques. The goal is to identify the key factors influencing dike stability, and these data will serve as a critical input for Subproject 1, facilitating model calibration and validation. Finally, a predictive model based on ML and grounded in Bayesian Updating will be developed. This model will improve the accuracy of stability predictions for sloping dikes and will allow for evaluating the performance of existing stability and damage assessment models in light of the new knowledge generated by the project.