Frédéric Dias’s research while affiliated with University College Dublin and other places

There are several factors to account for marine growth including but not limited to temperature, salinity, chlorophyll-a content, existing species in the environment and predating. This paper proposes a model of biological growth for hard species on marine structures, which can be compatible with site-specific and realistic ecology while also being able to translate the results for analyses linked to lifetime hydrodynamic or structural effects via commercial software or computing. The model preserves fundamentals of ecological aspects rather than using heuristics or random sampling to data fitting on sparsely collected information. The coefficients used in the proposed model align to the real world, with location-specific values, and can be adapted to new information. The growth model is demonstrated for Mythulis Edulis (blue mussel) colonisation to assess the lifetime hydrodynamic effects for the West Coast of Ireland and the Gulf of Guinea. The model can be extended to any hard growth approach.

Bubble plumes play a significant role in the air–sea interface by influencing processes such as air–sea gas exchange, aerosol production, modulation of oceanic carbon and nutrient cycles, and the vertical structure of the upper ocean. Using acoustic Doppler current profiler (ADCP) data collected off the west coast of Ireland, we investigate the dynamics of bubble plumes and their relationship with sea state variables. In particular, we describe the patterns of bubble plume vertical extension, duration, and periodicity. We establish a power-law relationship between the average bubble penetration depth and wind speed, consistent with previous findings. Additionally, the study reveals a significant association between whitecapping coverage and observed acoustic volume backscatter intensity, underscoring the role of wave breaking in bubble plume generation. The shape of the probability distribution of bubble plume depths reveals a transition toward stronger and more organized bubble entrainment events during higher wind speeds. Furthermore, we show that deeper bubble plumes are associated with turbulent Langmuir number La t ∼ 0.3, highlighting the potential role of Langmuir circulation on the transport and deepening of bubble plumes. These results contribute to a better understanding of the complex interactions between ocean waves, wind, and bubble plumes, providing valuable insights for improving predictive models and enhancing our understanding of air–sea interactions. Significance Statement This research contributes to understanding bubble plume dynamics in the upper ocean and their relationship with sea state variables. The establishment of a power-law relationship between the bubble penetration depth and wind speed, along with the association between whitecapping coverage and acoustic backscatter intensity, contributes to improved predictive capabilities for air–sea interactions and carbon dioxide exchange. The identification of the potential influence of Langmuir circulation on bubble plume dynamics expands our understanding of the role of coherent circulations in transporting bubble plumes. Additionally, this study presents a clear methodology using commercial sensors such as an ADCP, which can be easily replicated by researchers worldwide, leading to potential advancements in our comprehension of bubble plume dynamics.

Knowledge of the directional distribution of a wave field is crucial for a better understanding of complex air–sea interactions. However, the dynamic and unpredictable nature of ocean waves, combined with the limitations of existing measurement technologies and analysis techniques, makes it difficult to obtain precise directional information, leading to a poor understanding of this important quantity. This study investigates the potential use of a wavelet-based method applied to GPS buoy observations as an alternative approach to the conventional methods for estimating the directional distribution of ocean waves. The results indicate that the wavelet-based estimations are consistently good when compared to the framework of widely used parameterizations for the directional distribution. The wavelet-based method presents advantages in comparison with the conventional methods, including being purely data-driven and not requiring any assumptions about the shape of the distribution. In addition, it was found that the wave directional distribution is narrower at the spectral peak and broadens asymmetrically at higher and lower scales, particularly sharply for frequencies below the peak. The directional spreading appears to be independent of the wave age across the entire range of frequencies, implying that the angular width of the directional spectrum is primarily controlled by nonlinear wave–wave interactions rather than by wind forcing. These results support the use of the wavelet-based method as a practical alternative for the estimation of the wave directional distribution. In addition, this study highlights the need for continued innovation in the field of ocean wave measuring technologies and analysis techniques to improve our understanding of air–sea interactions. Significance Statement This study presents a wavelet-based technique for obtaining the directional distribution of ocean waves applied to GPS buoy. This method serves as an alternative to conventional methods and is relatively easy to implement, making it a practical option for researchers and engineers. The study was conducted in a highly energetic environment characterized by high wind speeds and large waves, providing a valuable dataset for understanding the dynamics of marine environment in extreme conditions. This research has implications for improving our understanding of directional characteristics of ocean waves, which is crucial for navigation, offshore engineering, weather forecasting, and coastal hazard mitigation. This study also highlights the challenges associated with understanding wave directionality and emphasizes a need for further observations.

The interaction of water waves with floating bodies can be modeled with linear potential flow theory and numerically solved with the boundary element method (BEM). This method requires the construction of dense matrices and the resolution of the corresponding linear systems. The cost of this method in terms of time and memory grows at least quadratically with the size of the mesh, and the resolution of large problems (such as large farms of wave energy converters) can, thus, be very costly. Approximating some blocks of the matrix with data-sparse matrices can limit this cost. While matrix compression with low-rank blocks has become a standard tool in the larger BEM community, the present paper provides its first application (to our knowledge) to linear potential flows. In this paper, we assess how efficiently low-rank blocks can approximate interaction matrices between distant meshes when using the Green function of linear potential flow. Due to the complexity of this Green function, a theoretical study is difficult, and numerical experiments are used to test the approximation method. Typical results on large arrays of floating bodies show that 99% of the accuracy can be reached with 10% of the coefficients of the matrix.

The main goal of this study is to investigate if the publicly available sea state forecasts for the Aran Islands region in the Republic of Ireland can be improved. This improvement is achieved by using the combination of local scale sea state forecasts and Bayesian Model Averaging techniques. The question of a good forecast has been around since the start of forecasting. With current state-of-the-art numerical models, computational power, and vast data availability, we consider whether it is possible to improve model forecasts only by using the combination of publicly available forecasts, free open-source software, and very moderate computational power. It is shown that it is possible to improve the sea state forecast by at least $1\%$ , and in some cases up to $8\%$ . The reduction of error is between $6\%$ and $48\%$ . With a more careful and specific selection of training parameters, it is possible to improve the forecast accuracy even more. The possibility of extending this local improvement to the whole coastal area around the island of Ireland is explored. Unfortunately, it is currently impossible, due to a lack of live data buoys in the coastal waters. Nonetheless, it is shown that the proposed process is simple and can be implemented by anyone whose livelihood depends on an accurate sea state forecast. It does not require large computational power, model forecasts are publicly available, and there is minimal to no training in forecasting and statistics required to enable one to perform such improvements for one’s area of interest, provided one has access to live wave data.

While the properties and the shape of the ground state of a gas of ultracold bosons are well understood in harmonic potentials, they remain for a large part unknown in the case of random potentials. Here, we use the localization-landscape (LL) theory to study the properties of the solutions to the Gross-Pitaevskii equation (GPE) in one-dimensional (1D) speckle potentials. In the cases of attractive interactions, we nd that the LL allows one to predict the position of the localization center of the ground state (GS) of the GPE. For weakly repulsive interactions, we point out that the GS of the quasi-1D GPE can be understood as a superposition of a nite number of single-particle states, which can be computed by exploiting the LL. For intermediate repulsive interactions, we introduce a Thomas-Fermi-like approach for the GS which holds in the smoothing regime, well beyond the usual approximation involving the original potential. Moreover, we show that, in the Lifshitz glass regime, the particle density and the chemical potential can be well estimated by the LL. Our approach can be applied to any positive-valued random potential endowed with nite-range correlations and can be generalized to higher-dimensional systems.

A model coupling waves and currents is set up for Galway Bay, Ireland, using COAWST. The impact of the coupling on the accuracy of the model is shown to be marginal on the overall statistics. The model is in good agreement with the available in-situ observations that are used for the validation. However a closer look at the data during storms shows a deterioration of the agreement, which is barely improved by the coupling. A special analysis of the different processes ruling the ocean circulation is conducted during Storm Hector (2018/06/14). It shows a strong 7cm wave-induced surge in the back of Galway Bay, and a strong response in terms of currents along the coast of Clare, about 30cms−1. Most of the surge is attributed to the impact of the conservative wave-induced Stokes drift forcing. The response in currents is partly attributed to the Stokes drift forcing terms, but mostly to the wave-enhanced surface roughness. In both cases waves induce an additional transport of mass or momentum, which is then subject to the particular characteristics of the bay.