Publicacións

@article{hilo_computational_2026,

title = {Computational modelling of hydrodynamic loads on offshore monopiles in concurrent wave and current conditions},

author = {Ali Kareem Hilo-Al-Behadili and Félix Nieto-Mouronte and Gabriel Barajas and Javier L. Lara and Byoung-Kwon Ahn and Antonio J. Álvarez-Naveira and Hrvoje Kozmar},

url = {https://www.sciencedirect.com/science/article/pii/S0029801826000387},

doi = {10.1016/j.oceaneng.2026.124204},

issn = {0029-8018},

year  = {2026},

date = {2026-04-01},

urldate = {2026-08-17},

journal = {Ocean Engineering},

volume = {352},

pages = {124204},

abstract = {Offshore monopile wind-turbine systems face significant design challenges due to complex environmental loading conditions, including wind, waves, current, and their intricate coupling effects. Computational Fluid Dynamics (CFD) simulations of complex nonlinear wave-structure interactions have become essential for understanding offshore systems. However, high-fidelity (HF) CFD analyses are computationally demanding, making it impractical to use them alone for extensive parametric studies. Surrogate models provide a promising alternative, as they can deliver efficient and accurate predictions once trained. This study presents a methodology for combining HF computational models and a data-driven approach to assess hydrodynamic loads on an offshore monopile subjected to waves and current. A three-dimensional numerical wave tank was developed in the OpenFOAM environment and validated using experimental and theoretical data. This model accurately accounts for complex wave-current interactions and the resulting force variations on the monopile. The obtained results indicate that the hydrodynamic forces on the monopile are significantly affected when waves and current act concurrently. To reduce the extensive computational HF simulations, a Gaussian Process Regression (GPR)-based surrogate model was developed using HF simulations sampled via Latin Hypercube Sampling. The surrogate model was trained to predict peak inline and transverse forces for various wave heights, frequencies, and current velocities. The GPR model exhibits high accuracy with R2 = 0.98 (coefficient of determination) and MAE = 0.03 (Mean Absolute Error) for the inline force, and R2 = 0.94 and MAE = 0.01 for the transverse force, thus demonstrating its ability to accurately assess complex nonlinear responses, while reducing the computational demand significantly. These findings provide a practical and applicable solution for offshore structural design, especially during early-stage assessments or probabilistic load evaluations.},

keywords = {Computational fluid dynamics, Current, Gaussian process regression, Hydrodynamic forces, Offshore monopile, Surrogate model, Wave},

pubstate = {published},

tppubtype = {article}

}

@article{bergillos_coastal_2020,

title = {Coastal cliff exposure and management},

author = {Rafael J. Bergillos and Cristobal Rodriguez-Delgado and Luis Medina-Rodríguez and Gregorio Iglesias},

url = {https://www.sciencedirect.com/science/article/pii/S0964569120302945},

doi = {10.1016/j.ocecoaman.2020.105387},

issn = {0964-5691},

year  = {2020},

date = {2020-12-01},

urldate = {2026-08-06},

journal = {Ocean & Coastal Management},

volume = {198},

pages = {105387},

abstract = {Coastal cliffs cover about 75% of the world's coastlines. Many of these cliffs suffer severe erosion problems, which are mainly caused by wave action and lead to important damages. The main objective of this work is to propose and apply a methodology to characterize and map wave exposure levels for the management of cliff coasts. The methodology proposed consists of a number of steps. First, a statistical characterization of the wave climate in deep water is performed. Second, relevant sea states are propagated towards the cliff by means of a numerical model. Then, the results are used to assess the distribution of significant wave heights and wave power along the cliff toe. Finally, four exposure levels are defined based on the wave power values at the cliff, and exposure zones are mapped accordingly along the cliff. This approach is illustrated with a case study – the Catedrales Cliffs and Beach, a tourism hotspot in Galicia (NW Spain) where erosion caused loss of human life. We find that exposure levels are primarily influenced by the nearshore bed morphology, which controls wave refraction, shoaling and breaking, as well as by the geometry and orientation of the cliff, with some parts more exposed than others to the prevailing wave direction. On the islets off the cliff the windward faces have a greater level of exposure, since the leeward faces are protected by the islets themselves and lower water depths. The rocky outcrops that occur at the cliff toe in some sections act as emerged (submerged) breakwaters under low (high) tide conditions, dissipating wave power and thus reducing wave exposure. The methodology proposed in this work enables coastal managers to prevent the personal and material damages derived from cliff erosion by focusing remedial measures where they are most needed.},

keywords = {Cliff, Erosion, Exposure, Management, Mapping, Wave},

pubstate = {published},

tppubtype = {article}

}

@article{falcon-suarez_comparison_2019,

title = {Comparison of stress-dependent geophysical, hydraulic and mechanical properties of synthetic and natural sandstones for reservoir characterization and monitoring studies},

author = {Ismael Himar Falcon-Suarez and Kelvin Amalokwu and Jordi Delgado-Martín and Ben Callow and Katleen Robert and Laurence North and Sourav K. Sahoo and Angus I. Best},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2478.12699},

doi = {10.1111/1365-2478.12699},

issn = {1365-2478},

year  = {2019},

date = {2019-01-01},

urldate = {2026-08-04},

journal = {Geophysical Prospecting},

volume = {67},

number = {4},

pages = {784–803},

abstract = {Synthetic rock samples can offer advantages over natural rock samples when used for laboratory rock physical properties studies, provided their success as natural analogues is well understood. The ability of synthetic rocks to mimic the natural stress dependency of elastic wave, electrical and fluid transport properties is of primary interest. Hence, we compare a consistent set of laboratory multi-physics measurements obtained on four quartz sandstone samples (porosity range 20–25%) comprising two synthetic and two natural (Berea and Corvio) samples, the latter used extensively as standards in rock physics research. We measured simultaneously ultrasonic (P- and S-wave) velocity and attenuation, electrical resistivity, permeability and axial and radial strains over a wide range of differential pressure (confining stress 15–50 MPa; pore pressure 5–10 MPa) on the four brine saturated samples. Despite some obvious physical discrepancies caused by the synthetic manufacturing process, such as silica cementation and anisotropy, the results show only small differences in stress dependency between the synthetic and natural sandstones for all measured parameters. Stress dependency analysis of the dry samples using an isotropic effective medium model of spheroidal pores and penny-shaped cracks, together with a granular cohesion model, provide evidence of crack closure mechanisms in the natural sandstones, seen to a much lesser extent in the synthetic sandstones. The smaller grain size, greater cement content, and cementation under oedometric conditions particularly affect the fluid transport properties of the synthetic sandstones, resulting in lower permeability and higher electrical resistivity for a similar porosity. The effective stress coefficients, determined for each parameter, are in agreement with data reported in the literature. Our results for the particular synthetic materials that were tested suggest that synthetic sandstones can serve as good proxies for natural sandstones for studies of elastic and mechanical properties, but should be used with care for transport properties studies.},

keywords = {Elastics, Resistivity, Rock physics, Wave},

pubstate = {published},

tppubtype = {article}

}