Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks

We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. This paper explains the rationale behind the selection of the ML network architecture, along with other model hyperparameters, in an effort to demystify the process of arriving at a useful ML model. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Comment: 13 pages, 7 figure

Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multiagent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application

A Review on Applications of Machine Learning in Shipping Sustainability

The shipping industry faces a significant challenge as it needs to significantly lower the amounts of Green House Gas emissions at the same time as it is expected to meet the rising demand. Traditionally, optimising the fuel consumption for ships is done during the ship design stage and through operating it in a better way, for example, with more energy-efficient machinery, optimising the speed or route. During the last decade, the area of machine learning has evolved significantly, and these methods are applicable in many more fields than before. The field of ship efficiency improvement by using Machine Learning methods is significantly progressing due to the available volumes of data from online measuring, experiments and computations. This amount of data has made machine learning a powerful tool that has been successfully used to extract information and intricate patterns that can be translated into attractive ship energy savings. This article presents an overview of machine learning, current developments, and emerging opportunities for ship efficiency. This article covers the fundamentals of Machine Learning and discusses the methodologies available for ship efficiency optimisation. Besides, this article reveals the potentials of this promising technology and future challenges

Marine gas turbine monitoring and diagnostics by simulation and pattern recognition

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK\uae model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model

Using artificial neural networks and strain gauges for the determination of static loads on a thin square fully constrained composite marine panel subjected to a large central displacement

Current methods of estimating the behaviour of marine composite structures under pressure due to slamming as a result of high waves are based on trial and error or oversimplification. Normally under these conditions the nonlinearities of these structures are often neglected and in order to compensate, an overestimated safety factor is employed. These conservative approaches can result in heavier and overdesigned structures. In this paper a new semi-empirical method is proposed that overcomes some of these problems. This work involved the use of Artificial Neural Network (ANN) combined with strain gauge data to enable real-time in-service load monitoring of large marine structural panels. Such a tool has other important applications such as monitoring slamming or other transient hydrostatic loads that can ultimately affect their fatigue life. To develop this system a Glass Fibre Reinforced Polymer (GFRP) composite panel was used due to its potential for providing a nonlinear response to pressure or slamming loads. It was found the ANN was able to predict normal loads applied at different locations on the panel accurately. This method is also capable of predicting loads on the marine structure in real-time

Ship Propulsion Plant Performance Assessment Using An Artificial Neural Network

Nowadays, more than in the past, the attention towards the environmental impact of shipping has gained great interest. In particular, several international rules were issued to set new standards in terms of ship energy efficiency and emissions. Most of the actual worldwide fleets are not compliant with the new standards, and it is unthinkable that ship-owners will replace the existing ships with new buildings in a short time. According to this, the retrofit of either the propulsion plant or auxiliary system is the good compromise choice. The first task that the designer has to face is the evaluation of the actual propulsion plant performance to detect where to act. On the view of this, the authors present two different approaches to identify the performance of an existing ship propulsion plant equipped with a four-stroke diesel engine and a controllable pitch propeller. The first approach is the standard approach, relying on the static performance assessment of the required power and fuel consumption, starting from the design data of the hull and machinery, not always available several years past ship fabrication. The second approach is based on the application of an artificial neural network, trained using the results of sea trials. Ship speed, shaft revolution speed, pitch angle, engine torque and fuel consumption have been recorded, then part of the data have been used as a training set for the artificial neural network, and the remaining as a validation set to compare the two approaches. The main idea is to evaluate the best strategy, in term of developing time and accuracy, to obtain the global, even if static, evaluation of the propulsion plant performance, with the final aim to have a handy tool to be used to assess potential energy saving solutions. Eventually, a comparison between the two methodologies and sea trials is shown and critically discussed