Vincenzo Fiamma

Integrating renewable energy sources with aquaculture systems on floating multi-use platforms presents an innovative approach to developing sustainable and resilient offshore infrastructure, utilizing the ocean’s considerable potential. From March 2021 to January 2022, a 1:15-scale prototype was tested in Reggio Calabria, Italy, which gave crucial insights into how these structures behave under different wave conditions. This study investigates the application of Artificial Neural Networks (ANNs) to predict changes in mooring loads, particularly at key points of the structure. By analyzing metocean data, several ANN models and optimization techniques were evaluated to identify the most accurate predictive model. With a Normalized Root Mean Square Error (NRMSE) of 1.7–4.7%, the results show how ANNs can effectively predict offshore platform dynamics. This research highlights the potential of machine learning in developing and managing sustainable ocean systems, setting the stage for future advancements in data-driven marine resource management.

A small-scale field experiment was conducted on a U-OWC incorporated into a caisson breakwater at the NOEL laboratory of Reggio Calabria (Italy). The U-Oscillating Water Column (U-OWC) or REWEC (REsonant Wave Energy Converter) is a device belonging to the family of OWCs. Such a device is very innovative, being able to absorb a very high percentage of incoming sea waves energy and to produce electrical power via proper PTO. The focus of the paper has been the analysis of the impact wave loads acting on the modified U-OWC structure during extreme wave events. A total of 250 records of pure wind waves were analyzed to verify the behaviors of wave loads acting on a U-OWC breakwater during operating conditions. The occurrence of both “quasi-standing wave” loads due to non-breaking waves and “impulsive wave loads”, exerted by a wave breaking against the U-OWC model, were observed. Then, Goda’s model was applied to predict the wave pressure distribution on the external wall of the U-OWC pneumatic chamber, and the theoretical results were compared to those obtained via small-scale field experiment.

The article presents the results of an investigation of water composition and quality in sprinkler installations, as well as the influence of temperature changes on the corrosion process. The physical and chemical components of the water were measured to ascertain the influence of its properties on the corrosion process in a wet pipe sprinkler system operating in significantly changing ambient temperature conditions. The article presents the results of measurements of the wall thickness of galvanized pipes and changes in the chemical composition of water occurring under the influence of variable ambient temperature. The range and variability of temperatures corresponded to the seasons of the year: spring and summer in a temperate climate. Changes in the corrosive aggressiveness of water were assessed using the Langelier saturation index (LSI), the Ryznar stability index (RSI), and the general acidity intensity index (I). The tests revealed that the tap water used demonstrated strong corrosive properties for galvanized pipes. The calculated indices showed the tendency of water to cause corrosion (−1.1 > LSI < 1.0; RSI = 7.1–12.8). The chemical parameters that significantly influenced the corrosion of galvanized pipes are chloride (Cl⁻), sulphate (SO₄²⁻), and bicarbonate (HCO³⁻) ions. An important factor contributing to the intensification of corrosion is the roughness and heterogeneity of the pipe surface.

Contractions and expansions are common features in various types of pipeline systems. The purpose of this study is to investigate the influence of a sudden cross section change on transient pressure waves. The paper presents laboratory data and numerical calculations of pressure oscillations during the valve-induced water hammer in serially connected steel pipes. Five different variants of experiments were conducted which included recording pressure changes at the downstream end of the pipeline system. The more sections with different diameters there are connected in series, the more complex the transient wave recorded is. Laboratory data indicate a significant influence of individual pipeline sections on the final course of pressure oscillations. Transient equations were solved using the explicit MacCormack scheme. In order to numerically simulate water hammers in pipe series, the improved junction boundary condition was established. It involves assigning two sets of values, which describe flow parameters, to the connection node thus causing it to act as two separate nodes. The numerical model was calibrated with the unsteady friction factor. The derivation of equations that take into account a sudden change in diameter in the connected pipes allowed the reproduction of the wave nature of the water hammer phenomenon, results were satisfactory as compared to experimental data. The numerical model correctly reproduced pressure wave interactions and pressure amplitudes.

System identification of offshore floating platforms is usually performed by testing small-scale models in wave tanks, where controlled conditions, such as still water for free decay tests, regular and irregular wave loading can be represented. However, this approach may result in constraints on model dimensions, testing time, and costs of the experimental activity. For such reasons, intermediate-scale field modelling of offshore floating structures may become an interesting as well as cost-effective alternative in a near future. Clearly, since the open sea is not a controlled environment, traditional system identification may become challenging and less precise. In this paper, a new approach based on Frequency Domain Decomposition (FDD) method for Operational Modal Analysis is proposed and validated against numerical simulations in ANSYS AQWA v.16.0 on a simple spar-type structure. The results obtained match well with numerical predictions, showing that this new approach, opportunely coupled with more traditional wave tanks techniques, proves to be very promising to perform field-site identification of the model structures.

About 6,300,000 pressure head waves beneath the sea surface were recorded by an array of 26 transducers aligned orthogonally to the coastline of Reggio Calabria, Italy. The data set covered spectra from very narrow unimodal to very broad multimodal and waves from shallow to deep waters. Herein, the quasi-determinism (QD) theory is proven to be very effective in predicting the probability of large-wave heights as well as the period and particle acceleration of these waves both on deep and shallow waters. In addition, the Tayfun model for predicting the probability of large-wave heights is also proven to be highly effective.