scholarly journals The Use of Artificial Neural Networks to Determine the Engine Power and Fuel Consumption of Modern Bulk Carriers, Tankers and Container Ships

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4827
Author(s):  
Tomasz Cepowski ◽  
Paweł Chorab
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Design Stage ◽  
Accurate Estimation ◽  
Ship Propulsion ◽  
Fuel Prices ◽  
Bulk Carriers ◽  
Artificial Neural ◽  
Engine Power

The 2007–2008 financial crisis, together with rises in fuel prices and stringent pollution regulation, led to the need to update the methods concerning ship propulsion system design. In this article, a set of artificial neural networks was used to update the design equations to estimate the engine power and fuel consumption of modern tankers, bulk carriers, and container ships. Deadweight or TEU capacity and ship speed were used as the inputs for the ANNs. This study shows that even a linear ANN with two neurons in the input and output layers, with purelin activation functions, offers an accurate estimation of ship propulsion parameters. The proposed linear ANNs have simple mathematical structures and are straightforward to apply. The ANNs presented in the article were developed based on the data of the most recent ships built from 2015 to present, and could have a practical application at the preliminary design stage, in transportation or air pollution studies for modern commercial cargo ships. The presented equations mirror trends found in the literature and offer much greater accuracy for the features of new-built ships. The article shows how to estimate CO2 emissions for a bulk carrier, tanker, and container carrier utilizing the proposed ANNs.

scholarly journals Prediction of a Newbuilding Proce of the Bulk Carriers based on Gross Tonnage GT and Main Engine Power

2017 ◽  
Vol 25 (1) ◽  
pp. 42-45
Author(s):  
Żaneta Cepowska ◽  
Tomasz Cepowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Parametric Design ◽  
Bulk Carrier ◽  
Bulk Carriers ◽  
Artificial Neural ◽  
Main Engine ◽  
Mathematical Relationships ◽  
Engine Power

Abstract The paper presents mathematical relationships that allow us to forecast the newbuilding price of new bulk carriers, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the price based on a gross tonnage capacity and a main engine power The approximations were developed using linear regression and the theory of artificial neural networks. The presented relations have practical application for estimation of bulk carrier newbuilding price needed in preliminary parametric design of the ship. It follows from the above that the use of artificial neural networks to predict the price of a bulk carrier brings more accurate solutions than linear regression.

scholarly journals ESTIMATION OF FUEL CONSUMPTION IN AGRICULTURAL MECHANIZED OPERATIONS USING ARTIFICIAL NEURAL NETWORKS

2017 ◽  
Vol 37 (1) ◽  
pp. 136-147 ◽  
Author(s):  
Pedro H. M. Borges ◽  
Zaíra M. S. H. Mendoza ◽  
João C. S. Maia ◽  
Aloísio Bianchini ◽  
Haroldo C. Fernándes
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Consumption ◽  
Artificial Neural

scholarly journals Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

2020 ◽  
Vol 12 (19) ◽  
pp. 8226
Author(s):  
Jorge Navarro-Rubio ◽  
Paloma Pineda ◽  
Roberto Navarro-Rubio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Design ◽  
Precast Concrete ◽  
Computing Time ◽  
Plate Thickness ◽  
Economic Benefits ◽  
Design Tool ◽  
Design Stage ◽  
Artificial Neural

In the built environment, one of the main concerns during the design stage is the selection of adequate structural materials and elements. A rational and sensible design of both materials and elements results not only in economic benefits and computing time reduction, but also in minimizing the environmental impact. Nowadays, Artificial Neural Networks (ANNs) are showing their potential as design tools. In this research, ANNs are used in order to foster the implementation of efficient tools to be used during the early stages of structural design. The proposed networks are applied to a dry precast concrete connection, which has been modelled by means of the Finite Element Method (FEM). The parameters are: strength of concrete and screws, diameter of screws, plate thickness, and the posttensioning load. The ANN input data are the parameters and nodal stresses obtained from the FEM models. A multilayer perceptron combined with a backpropagation algorithm is used in the ANN architecture, and a hyperbolic tangent function is applied as an activation function. Comparing the obtained predicted stresses to those of the FEM analyses, the difference is less than 9.16%. Those results validate their use as an efficient structural design tool. The main advantage of the proposed ANNs is that they can be easily and effectively adapted to different connection parameters. In addition, their use could be applied both in precast or cast in situ concrete connection design.

Optimization of the Impeller and Diffuser of Hydraulic Submersible Pump using Computational Fluid Dynamics and Artificial Neural Networks

2012 ◽  
pp. 456-474
Author(s):  
Juan Bernardo Sosa Coeto ◽  
Gustavo Urquiza Beltrán ◽  
Juan Carlos García Castrejon ◽  
Laura Lilia Castro Gómez ◽  
Marcelo Reggio
Keyword(s):  
Fluid Dynamics ◽  
Neural Networks ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Networks ◽  
Impeller Speed ◽  
Design Stage ◽  
Submersible Pump ◽  
Pump Performance ◽  
Mathematical Relation ◽  
Artificial Neural

Overall performance of hydraulic submersible pump is strongly linked to its geometry, impeller speed and physical properties of the fluid to be pumped. During the design stage, given a fluid and an impeller speed, the pump blades profiles and the diffuser shape has to be determined in order to achieve maximum power and efficiency. Using Computational Fluid Dynamics (CFD) to calculate pressure and velocity fields, inside the diffuser and impeller of pump, represents a great advantage to find regions where the behavior of fluid dynamics could be adverse to the pump performance. Several trials can be run using CFD with different blade profiles and different shapes and dimensions of diffuser to calculate the effect of them over the pump performance, trying to find an optimum value. However the optimum impeller and diffuser would never be obtained using lonely CFD computations, by this means are necessary the application of Artificial Neural Networks, which was used to find a mathematical relation between these components (diffusers and blades) and the hydraulic head obtained by CFD calculations. In the present chapter artificial neural network algorithms are used in combinations with CFD computations to reach an optimum in the pumps performance.

The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

2020 ◽  
Vol 11 (2) ◽  
pp. 50-71 ◽  
Author(s):  
Anupama Kaushik ◽  
Devendra Kumar Tayal ◽  
Kalpana Yadav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Development ◽  
Agile Software Development ◽  
Accurate Estimation ◽  
Effort Estimation ◽  
Whale Optimization ◽  
Artificial Neural ◽  
Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

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