Computationally aware surrogate models for the hydrodynamic response characterization of floating spar-type offshore wind turbine

Due to increasing environmental concerns and global energy demand, the development of Floating Offshore Wind Turbines (FOWTs) is on the rise. FOWTs offer a promising solution to expand wind farm deployment into deeper waters with abundant wind resources. However, their harsh operating conditions and lower maturity level compared to fixed structures pose significant engineering challenges, notably in the design phase. A critical challenge is the time-consuming hydromechanics analysis traditionally done using computationally intensive Computational Fluid Dynamics (CFD) models. In this study, we introduce Artificial Intelligence-based surrogate models using state-of-the-art Machine Learning algorithms. These surrogate models achieve CFD-level accuracy (within 3% difference) while dramatically reducing computational requirements from minutes to milliseconds. Specifically, we build a surrogate model for characterizing the hydrodynamic response of a floating spar-type offshore wind turbine (including added mass, radiation damping matrices, and hydrodynamic excitation) using computationally efficient shallow Machine Learning models, optimizing the trade-off between computational efficiency and accuracy, based on data generated by a cutting-edge potential-flow code

Surrogate models to unlock the optimal design of stiffened panels accounting for ultimate strength reduction due to welding residual stress

In this paper, for the first time, a three-step approach for the optimal design of stiffened panels accounting for the ultimate limit state due to welding residual stress is developed. First, authors rely on state-of-the-art analytical approaches coupled with recently data-driven nonlinear finite element methods surrogates characterized by functional which are computationally expensive to build but computationally inexpensive to use. Then, surrogates are used within a design optimization loop to find new optimal designs since nonlinear finite element methods are too computationally demanding for this purpose. Finally, the new designs are reassessed with the original nonlinear finite element methods to verify that substituting them with their surrogates in the optimization loop actually leads to better designs. Results obtained optimizing a series of parameters of a commonly used stiffened panel geometry under different scenarios will support the authors’ novel approach

Air quality forecasting of along-route ship emissions in realistic meteo-marine scenarios

This study introduces a novel framework of metocean prediction and ship performance models that integrate multiple layers of modeling to evaluate the environmental impact of ship emissions. It enables scenario simulations that assess a ship's performance, estimates pollutant emissions, and simulate the fate of these pollutants in the atmosphere. The study analyzes the fate of NOx, SO2, and PM10 pollutants in the atmosphere using spatially distributed concentration maps. It provides a comprehensive approach to assessing the environmental effects of ships and their emissions and contributes to the field of environmental impact assessment. Case studies are presented to demonstrate the framework's functionalities, evaluating the interrelationships between adverse meteo-marine conditions, pollutant emissions, and resulting atmospheric diffusion characteristics

Data-driven and hybrid models for the underwater radiated noise of cavitating marine propellers

The sustainability of anthropogenic activities is a fundamental problem requiring a multidisciplinary approach in order to be properly addressed. Recently, underwater radiated noise has been categorized as a form of pollution, due to the substantial increase of underwater noise levels on oceans worldwide, with severe effects on the marine ecosystem. For propeller-driven vessels, cavitation is the most dominant noise source, producing both structure-borne and radiated noise. As such accurate predictions of the noise signature are fundamental for the design of silent, yet efficient, propellers. In this respect, this work investigates a novel hybrid (combined physics-based and data driven) model for the prediction of underwater radiated noise of marine propellers. By relying on both the engineering knowledge (through the physics-based model), and advanced statistical inference procedures (through the data-driven model), the hybrid model will be capable of providing an accurate, yet computationally cheap, assessment of the noise levels emitted by a cavitating marine propeller. The proposed model relies on a novel hybridization strategy that is able to truly blend the knowledge of the underlying physical phenomena with information contained in historical data. This strategy allows the development of that models able to properly, i.e., physically plausibly, extrapolate as physics-based models, while being extremely accurate and computationally inexpensive as data-driven models. In particular, knowledge of the underlying physical phenomena is leveraged during model structure, model building, and in model enrichment: a dedicated feature engineering process is considered to extract meaningful information from available experimental data, as well as the noise estimates of a computationally efficient physics based model. Everything is empowered by state-of-the-art learning algorithms from the field of Machine Learning that take advantage of all information sources. The proposed model is tested on a series of complex extrapolation scenarios, in which the numerical predictions are compared with measurements collected in an extensive experimental campaign conducted at the Emerson Cavitation Tunnel of Newcastle University. The results support the feasibility of the proposed approach in all scenarios considered. The proposed model shows enhanced capabilities in predicting the underwater radiated noise levels: It commits low errors that are certainly acceptable during the early stage design process, and delivers predictions that are in agreement with state-of-the-art engineering knowledge of the underlying physical phenomena.The sustainability of anthropogenic activities is a fundamental problem requiring a multidisciplinary approach in order to be properly addressed. Recently, underwater radiated noise has been categorized as a form of pollution, due to the substantial increase of underwater noise levels on oceans worldwide, with severe effects on the marine ecosystem. For propeller-driven vessels, cavitation is the most dominant noise source, producing both structure-borne and radiated noise. As such accurate predictions of the noise signature are fundamental for the design of silent, yet efficient, propellers. In this respect, this work investigates a novel hybrid (combined physics-based and data driven) model for the prediction of underwater radiated noise of marine propellers. By relying on both the engineering knowledge (through the physics-based model), and advanced statistical inference procedures (through the data-driven model), the hybrid model will be capable of providing an accurate, yet computationally cheap, assessment of the noise levels emitted by a cavitating marine propeller. The proposed model relies on a novel hybridization strategy that is able to truly blend the knowledge of the underlying physical phenomena with information contained in historical data. This strategy allows the development of that models able to properly, i.e., physically plausibly, extrapolate as physics-based models, while being extremely accurate and computationally inexpensive as data-driven models. In particular, knowledge of the underlying physical phenomena is leveraged during model structure, model building, and in model enrichment: a dedicated feature engineering process is considered to extract meaningful information from available experimental data, as well as the noise estimates of a computationally efficient physics based model. Everything is empowered by state-of-the-art learning algorithms from the field of Machine Learning that take advantage of all information sources. The proposed model is tested on a series of complex extrapolation scenarios, in which the numerical predictions are compared with measurements collected in an extensive experimental campaign conducted at the Emerson Cavitation Tunnel of Newcastle University. The results support the feasibility of the proposed approach in all scenarios considered. The proposed model shows enhanced capabilities in predicting the underwater radiated noise levels: It commits low errors that are certainly acceptable during the early stage design process, and delivers predictions that are in agreement with state-of-the-art engineering knowledge of the underlying physical phenomena

Low temperature waste heat recovery of marine Diesel engine using organic Rankine cycle

183 σ.Σκοπός της παρούσας μελέτης είναι η βελτίωση της απόδοσης ενός ναυτικού συστήματος πρόωσης. Αυτό θα επιτευχθεί μέσω της εγκατάστασης ενός συστήματος Οργανικού Κύκλου Rankine (ΟΚR), στο οποίο θα πραγματοποιείται ανάκτηση της απορριπτόμενης θερμότητας του συστήματος πρόωσης.The main purpose of the thesis is to enhance the efficiency of a marine propulsion system, via the installation of an Organic Rankine Cycle, which will recover low temperature waste heat.Μιλτιάδης Κ. Καλικατζαράκη

Development of a zero-dimensional model and application on a medium-speed marine four-stoke diesel engine

The present study deals with the development of a zero-dimensional model for marine diesel engines of the four-stroke type, as well as a methodology to automate its calibration process. The modelling and calibration approach is validated using experimental and operational data from a Wärtsilä engine, covering several model input and outputs. Validation results suggest that the modelling approach suggested, can predict the stationary and transient responses of the engine within 3.5% on measurements taken during the vessel's operation for a variety of loads ranging between 20% to 100%, with minimal calibration effort from the modeller

Optimizing fuel consumption in thrust allocation for marine dynamic positioning systems

In offshore maritime operations, automated systems capable to maintain the vessel’s position and heading using its own propellers and thrusters to compensate exogenous disturbances, like wind, waves and currents, are referred to as Marine Dynamic Positioning (DP) Systems. DP systems play a central role in assuring the mission of the vessels, such as for drilling, pipe-laying, coring, and ocean observation operations. At the same time, vessels operations are the primary cause of fuel consumption, having a strong impact on the overall footprint of the vessel. For this reason, in this paper, we will face the problem of optimising the propellers thrust allocation, namely determining thrust and direction of each propeller and thruster in an overactuated vessel, to maintain its position and heading, while minimising the fuel consumption. State of the art approaches simplify this problem by roughly approximating it and obtain a simple, mostly convex, optimisation problem. This allows to solve it in near-real time allowing its exploitation on-board during operation by simply integrating it in the automation system. In this paper, we deal with the problem of improving the current approaches with a twofold contribution. On one hand, we will exploit a detailed modelling approach of the physical system, resulting in an high fidelity representation of the optimisation problem. On the other hand, we will study and manipulate the resulting optimisation problem in such a way that it is still possible to solve it in near-real time on conventional on-board computing platform. Authors will leverage on a Platform Supply Vessel, equipped with 6 thrusters, as case study to evaluate the quality of the proposal. Results will show that, leveraging on the proposed approach, it is possible to achieve up to 5% of fuel savings with respect to conventional approaches

Optimizing Fuel Consumption in Thrust Allocation for Marine Dynamic Positioning Systems

In offshore maritime operations, automated systems capable of maintaining the vessel's position and heading using its own propellers and thrusters to compensate exogenous disturbances, like wind, waves, and currents, are referred to as marine dynamic positioning (DP) systems. DP systems play a central role in several marine operations, such as drilling, pipe-laying, coring, and ocean observation. These operations are the primary cause of fuel consumption, having a strong impact on the overall footprint of the vessel. For this reason, we will face the problem of optimal thrust allocation of an over-actuated vessel to maintain position and heading with minimal fuel consumption. State-of-the-art approaches simplify this problem by roughly approximating it and obtain a simple, mostly convex, optimization problem that can be solved in near-real time by the automation system. In this article, we improve current approaches with the following contributions. We will exploit a higher fidelity representation of the physical system, and we will manipulate the resulting optimization problem accordingly, to allow for near-real-time solutions on conventional computing platforms on-board. We evaluate the quality of the proposal with a case study on a drilling unit equipped with six thrusters. The results will show that it is possible to achieve up to 5% of fuel savings with respect to conventional approaches. Note to Practitioners - This article was motivated by the problem of minimizing fuel consumption in thrust allocation of DP systems. The current approaches simplify this issue by adopting simpler, yet related, optimization problems as surrogates, keeping the problem tractable for near-real-time control. We propose, instead, to solve the original problem with state-of-the-art modelization of the physical system and exploit reasonable and theoretical proprietaries to achieve optimal solutions in near-real-time. The results on a drilling unit will show additional fuel savings of up to 5% with respect to alternative state-of-the-art approaches

Data science and advanced analytics for shipping energy systems

The purpose of this chapter is to provide an overview of the state-of-the-art and future perspectives of Data Science and Advanced Analytics for Shipping Energy Systems. Specifically, we will start by listing the different static and dynamic data sources and knowledge base available in this particular context. Then we will review the Data Science and Advanced Analytics technologies that can leverage these data to extract and synthesize new additional actionable information, suggestions, and actions. We will then review the current exploitation strategies of these technologies aiming at improving the current Shipping Energy Systems. In conclusion, we will depict our vision on the future perspectives of the application and adoption of Data Science and Advanced Analytics for shaping the next generations of Shipping Energy Systems