Analysis of twin screw ships\u27 asymmetric propeller behaviour by means of free running model tests

Twin screw ships may experience considerably asymmetric propeller functioning during manoeuvres. This phenomenon may result in large power fluctuations during tight manoeuvres, with increases of shaft torque up to and over 100% of the steady values in straight course and considerable unbalances; this, in its turn, may be potentially dangerous, especially in case of particularly complex propulsion plant configurations, such as those with coupled shaftlines. A joint research project supported by the Italian Navy has been set up in order to deeply investigate the phenomenon, by means of large scale model testing and related numerical simulations. In the present work, the extensive experimental campaign results on a free running model of a twin-screw ship are presented, allowing to obtain a deeper insight of the problem. In particular, tests have been carried out simulating different simplified control schemes, starting from the most common constant rate of revolution tests and including different control strategies (constant torque and power). Usual standard manoeuvres (turning circle, zigzag and spiral) have been carried out, providing results for asymmetric shaft functioning and ship manoeuvrability behaviour. Results from the present analysis allow to obtain the complete model for the time domain simulation of asymmetric shaft functioning

Vibration edge computing in maritime IoT

IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process

Rigid body dynamic response of a floating offshore wind turbine to waves : identification of the instantaneous centre of rotation through analytical and numerical analyses

Floating Offshore Wind Turbines (FOWT) can harness the abundant offshore wind resource at reduced installation requirements. However, a further decrease in the development risks through higher confidence in the design and analysis methods is needed. The dynamic behaviour of FOWT systems is complex due to the strong interactions between the large translational and rotational motions and the diverse loads, which poses a challenge. While the methods to study the FOWT’s general responses are well established, there are no methods to describe the highly complex time-dependent rotational motion patterns of FOWT. For a rigid body in general plane motion, an Instantaneous Centre of Rotation (ICR) can be identified as a point at which, at a given moment, the velocity is zero. However, it is common to assume a centre of rotation fixed in space and time, arbitrarily set at the centre of floatation or gravity. Identification of the ICR is crucial as it may lead to better motion reduction methods and can be leveraged to improve the designs. This includes better-informed fairlead placement and the reduction of aerodynamic load variability. In this paper, we propose a two-fold approach for the identification of the ICR: an analytical solution in the initial static equilibrium position, and a time-domain numerical approach for dynamic analysis in regular and irregular waves to understand the motion patterns and ICR sensitivity to environmental conditions. Results show that the ICR of FOWT depends on wave frequency and, at low frequencies, on wave height, due to the nonlinear viscous drag and mooring loads. An unexpected but interesting result is that the surge-heave-pitch coupling introduced by the mooring system leads to a dynamic phenomenon of signal distortion known as ”clipping” in the nonlinear audio signal processing area, which, through the introduction of higher harmonics, is responsible for the ICR sensitivity to motion amplitude

Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents

Induction motors are fundamental components of several modern automation system, and they are one of the central pivot of the developing e-mobility era. The most vulnerable parts of an induction motor are the bearings, the stator winding and the rotor bars. Consequently, monitoring and maintaining them during operations is vital. In this work, authors propose an Induction Motors bearings monitoring tool which leverages on stator currents signals processed with a Deep Learning architecture. Differently from the state-of-the-art approaches which exploit vibration signals, collected by easily damageable and intrusive vibration probes, the stator currents signals are already commonly available, or easily and unintrusively collectable. Moreover, instead of using now-classical data-driven models, authors exploit a Deep Learning architecture able to extract from the stator current signal a compact and expressive representation of the bearings state, ultimately providing a bearing fault detection system. In order to estimate the effectiveness of the proposal, authors collected a series of data from an inverter-fed motor mounting different artificially damaged bearings. Results show that the proposed approach provides a promising and effective yet simple bearing fault detection system

A framework for optimal sensor placement to support structural health monitoring

Offshore or drydock inspection performed by trained surveyors is required within the integrity management of an in-service marine structure to ensure safety and fitness for purpose. However, these physical inspection activities can lead to a considerable increase in lifecycle cost and significant downtime, and they can impose hazards for the surveyors. To this end, the use of a structural health monitoring (SHM) system could be an effective resolution. One of the key performance indicators of an SHM system is its ability to predict the structural response of unmonitored locations by using monitored data, i.e., an inverse prediction problem. This is highly relevant in practical engineering, since monitoring can only be performed at limited and discrete locations, and it is likely that structurally critical areas are inaccessible for the installation of sensors. An accurate inverse prediction can be achieved, ideally, via a dense sensor network such that more data can be provided. However, this is usually economically unfeasible due to budget limits. Hence, to improve the monitoring performance of an SHM system, an optimal sensor placement should be developed. This paper introduces a framework for optimising the sensor placement scheme to support SHM. The framework is demonstrated with an illustrative example to optimise the sensor placement of a cantilever steel plate. The inverse prediction problem is addressed by using a radial basis function approach, and the optimisation is carried out by means of an evolutionary algorithm. The results obtained from the demonstration support the proposal

Multidisciplinary design analysis and optimisation frameworks for floating offshore wind turbines : state of the art

Meeting climate and air quality targets, while preserving the focus on the reliability and cost effectiveness of energy, became a central issue for offshore wind turbine engineers. Floating offshore wind turbines, which allow harnessing the large untapped wind resources in deep waters, are highly complex and coupled systems. Subsystem-level optimisations result in suboptimal designs, implying that an integrated design approach is important. Literature saw a few attempts on multidisciplinary design analysis and optimisation of floating wind turbines, with varying results, proving the need for an efficient, and sufficiently accurate, integrated approach. This paper reviews the state-of-the-art approaches to multidisciplinary design analysis and optimisation of floating support structures. The choice of the optimisation framework architecture, support platform design variables, constraints and objective functions are investigated. The techno-economic analysis models are closely examined, focusing on the approaches to achieving the optimum accuracy-efficiency balance. It is shown that the representation of the fully coupled system within the optimisation framework requires the introduction of a more complex multidisciplinary analysis workflow. Methods to increase the efficiency of such frameworks are indicated. Nonconventional support structure configurations can be conceived through the application of more advanced parametrisation schemes, which is feasible together with design space size reduction techniques. The set of design criteria should be extended by operation and maintenance cost, and power production metrics. The main technical limitations of the frameworks adopted so far include the inability to accurately analyse a diverse range of support structure topologies in multiple design load cases within a common framework. The cost approximation models should be extended by the chosen aspects of pre-operational phases, to better explore the benefits of the floating platforms

Parametrisation scheme for multidisciplinary design analysis and optimisation of a floating offshore wind turbine substructure – OC3 5MW case study

Abstract: The development of novel energy technologies is considered imperative in the provision of solutions to meet an increasing global demand for clean energy. Floating Offshore Wind Turbine (FOWT) is one of the emerging technologies to exploit the vast wind resources available in deeper waters. To lower the levelized cost of energy (LCOE) or optimise the performance response associated with a FOWT system, a detailed understanding of the different disciplines (Aero-Hydro-Servo-Elastic) within the system and the relationship between the FOWT system and the dynamics of the marine environment is required. This requires an efficient Multidisciplinary Design, Analysis and Optimisation (MDAO) framework for FOWT systems to reduce the capital cost and increase dynamic performance. A key component of any MDAO framework is the shape parameterisation scheme, as it enables the modelling of a large array of platform designs with different geometric shapes using limited number of parameters. This work focuses on the B-Spline parameterisation modelling technique of OC3 spar-buoy and the use pattern search optimization algorithm to select the optimal design variants. The parametrisation technique is implemented in an analysis framework, where a B-spline library from Sesam GeniE is used to model each design representation, and a potential flow frequency domain analysis solver (HydroD/Wadam) is used for the hydrodynamic analysis. Validation of the selected designs within the design space is conducted with a benchmark NREL5MW spar-buoy hydrodynamic response results in literature with the hydrodynamic response of the frequency domain modelling approach using Sesam GeniE and HydroD/Wadam. This analysis process shows a high accuracy in response results between the OC3 spar-buoy in literature and the OC3 spar-buoy model design using B-Spline parametrization technique. Key performance metrics like the cost of materials and root mean square (RMS) of the nacelle acceleration also show improvement with the design variants compared to estimation from OC3 design in literature

Parametric curve comparison for modelling floating offshore wind turbine substructures

The drive for the cost reduction of floating offshore wind turbine (FOWT) systems to the levels of fixed bottom foundation turbine systems can be achieved with creative design and analysis techniques of the platform with free-form curves to save numerical simulation time and minimize the mass of steel (cost of steel) required for design. This study aims to compare four parametric free-form curves (cubic spline, B-spline, Non-Uniform Rational B-Spline and cubic Hermite spline) within a design and optimization framework using the pattern search gradient free optimization algorithm to explore and select an optimal design from the design space. The best performance free-form curve within the framework is determined using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The TOPSIS technique shows the B-spline curve as the best performing free-form curve based on the selection criteria, amongst which are design and analysis computational time, estimated mass of platform and local shape control properties. This study shows that free-form curves like B-spline can be used to expedite the design, analysis and optimization of floating platforms and potentially advance the technology beyond the current level of fixed bottom foundations

A review and analysis of optimisation techniques applied to floating offshore wind platforms

The deployment of offshore wind in the UK has seen a rapid increase in the past decade and will continue to increase with the securement of the recent Scotwind sites. Floating platforms will be utilised for 60% of these new sites, creating opportunities to try new platform typologies and further solidify the validity of existing concepts. Since there is no consensus on the platform typology, the cost will vary; however, it is predicted to be double the price of traditional fixed platforms. Finding the most optimal solution in terms of cost and performance is key to keeping cost low, allowing the technology to be more competitive. A technique which has been used in other industries is multi-objective optimisation, searching a large design space much more quickly than traditional methods. By carrying out a multi-objective approach, the optimal platform geometry can be identified over the Pareto Frontier, considering conflicting objectives such as cost and performance. The aim of this work is to review the existing literature on multi-objective optimisation of floating offshore wind (FOW) platforms, highlighting the gaps and shortfalls in the current literature. This review highlights the majority of work has been carried out for the 5 MW NREL turbine on a SPAR platform, utilising a genetic algorithm. Cost reduction has been noted as the main objective, however, the models found within the literature are simplistic, with a number of assumptions. The overall findings of this work highlight future work that could be improved: cost models, the inclusion of an energy production model linked to the platform motion, the requirement for analysis of larger turbines and the potential for a concept selection tool to reduce computational time

Marine safety and data analytics : vessel crash stop maneuvering performance prediction

Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal