Esta tese centra-se na forma do casco dos navios porta-contêineres, que é analisada para identificar as principais curvas de forma e suas principais características geométricas. Neste trabalho, o formato do casco de um navio porta-contêineres é analisado e otimizado para reduzir o arrasto do casco e o EEDI, levando em consideração restrições relacionadas à capacidade de carga.
Introduction
Motivation
The IMO aims to improve the energy efficiency of ships through the mandatory implementation of EEDI. This tool fully serves the purpose of the thesis because it explores to its full potential the parametric design approach in ship design and the very objective optimization essential for an energy efficient ship.
Aim of the dissertation
This tool is GL's software for parametric hull modeling and optimization, called FFW.
Thesis Structure
State of the art
The generational designers are then ranked according to a series of scenarios regarding the designer's decision. A preferred design is chosen to be the baseline for the next optimization run.
Vessel under analysis
Design parameter analysis
EEDI versus the Ship Design Parameters
- Variation in vessel speed
- Variation in length
- Variation in beam
- Variation in depth
- Variation in draft
- Variation in C B
- Effective Power/Displacement curve
Analyzing the space design with respect to vessel length, a low decrease in the achieved EEDI was found as the length increases. Analyzing the design space in relation to the diameter of the vessel, it was found that the achieved EEDI decreased as the diameter increased. By analyzing the design of the room in relation to the depth of the container, there is no visible effect on the achieved EEDI.
Although increasing draft increases resistance to ships, the relationship to cargo, emissions and power leads to a reduction in the EEDI index. CB has a significant impact on EEDI - The increase in CB increases the EEDI - The EEDI shows a linear evolution with the CB. It is clear from Figure 29 that the EEDI curve should have the same trend line as the Effective Power Displacement curve.
The force versus displacement curve follows the achieved EEDI trend line - EEDI does not conflict with the hydrodynamic rules of naval architecture.
Parametric Model
Geometric generation
As previously mentioned, a mathematical model of the entire hull had to be built in order to create a systematic variation of the hull shapes. Using the internal design-oriented language of the software, all the computational geometry had to be converted into the feature definitions of the program. This means that for each abscissa value in the interval of the functions a curve can be generated where the information stems from the input functions.
This is the reason for the explanation of a coordinate system that concerns the functions of the parameters. This curve is the sum of the bottom, bilge and side curves, all NURBS curves. Function curves are then created to describe the section input parameters along the length of the fuselage.
As mentioned earlier, the feature definition is a pre-programmed module that can draw a cross-sectional curve of the surface to be built.
Code description
Each of these curves are defined by start and end points that are set by the Fvector command which is the base vector of FFW. The starting points of the tail curve are determined by the end point of the lower curve and the starting point of the side curve. After that, it is necessary to attribute some references to finish using the F-Spline definition.
The last definition required to build the bilge curve is the area. The zz coordinate from the keel will create the profile rise from the stern to the transom. Then the yy coordinate of the bottom plate is used to define the profile of the bottom.
Due to the fact that the back part of the flat or lower curve is of the 3rd degree, it is necessary to have 3 points to describe it and two FSpline curves merged into a polycurve.
Created features
In short, the front part of the hull is made on the same platform as the rear part, more complicated due to the more complex shapes of the bow and bulb. These functions will provide the necessary hydrostatic data for use in other design phases. The method for calculating the EEDI is obtained from the guidelines on how to calculate the achieved energy efficiency index (IMO, 2012).
In this regard, attention was paid to the cargo capacity of the analyzed container vessel. Since the main data of the ship depended on the level, types and sections of cargo on the ship, the sequence of the project was always. The main design details were determined as a function of the number of levels, rows and bays on the ship.
All the data collected are gathered in Appendix A, along with full reports on the most important ones.
Optimization Sequence
After this run, there is an equal percentage of variants that do not meet the constraints, demonstrating the range of applicability of the parametric model for the research purpose. After completing the DOE, the designer has the application limits and can choose to broaden or narrow the range of design variables. This allows the designer to pseudo predict the outcome of the next trials when a local change is applied to one of the main variables.
In Chapter 2 it was made clear that in modern optimization the designer must have a clear view of the problem at hand and of the tool used to solve it. Regarding the instrument and of course the dependencies between the different parts of the parameter field many combinations of output can be obtained, the question is how to control it and how to understand the different effects of the various variable components on the input vector. After the DOE stage to create a design of experiments baseline, the genetic algorithm NSGA II is used for multi-objective optimization.
Each time NSGA II is run, the population of 50 variants from the previous run is used over a six-generation cycle (Figure 48).
Results Analysis
Without analyzing these pre-outputs it is impossible for the designer to calibrate the optimization and obtain a fair end result. It is possible to see where the highest concentration of results are from the following graphs and if another series were to be run the designer would have to repeat the process of looking where the best result for the desired variable is located and where, according to this particular model, it is more likely to obtain the best value for a given parameter. For the ship length value, Figure 50 shows the AttEEDI value for all feasible ships presented on that specific dimension.
On this case, of observation it is possible to continue the study and closing the boundaries of the field of solution forces the engine of optimization to focus on certain regions for certain variable. Fortunately during this study and thought out the various stages of optimization, the progress of EEDI was always coherent, and is minimized after each step and after all the adjustments of the intervals. In the next chapter, the model of the Pareto front is selected and presented and a table relating all the different steps of the optimization process is shown comparing the evolution of all the design variables as well as the objectives.
The set of possible solutions will be underlined by the Pareto frontier, as mentioned earlier, where the designer will choose the most suitable one for this case.
Design and Optimization
Although the design selected for the maximum reduction in EEDI was the previously presented design (des_0959), another run was performed to obtain the design closer to the EEDI level three cutoff. This was done to understand how the design would evolve as the required EEDI limit was approached. The design data is presented here for the reader's understanding although this exercise was purely for trial purposes and no conclusion is drawn except that for this particular model and combination of optimization parameters and constraints as you move towards the EEDI limit the ship will mainly modify speed and block coefficient.
As mentioned earlier, in Figure 58, the design obtained by the last optimization (des_0959) will travel towards the level 3 limit for EEDI requirements. During this optimization, the carrying capacity of the ship is also reduced, because a free parameter remained for changing the shape of the hull. If the optimization determined the design parameters in terms of dimensions, the optimization engine would simply adjust the speed.
The final ship obtained from run number 3 of the NSGA II algorithm is shown in the figures below.
Conclusions
Using this fully parametric CAD-CAE system, a fully parametric ship was designed, taking into account the complex geometric and programming challenges faced during its formulation. Nevertheless, the parametric model has been shown to produce efficient ship shapes and follows a condensed optimization process running over 2300 ships in a 4-stage hierarchical multi-objective engine that achieves the desired goal it was set out to achieve, Integrated and Multi-Objective optimization approach Ship design used to improve energy efficiency index.
Further Improvements
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT press. Amendments to the Annex to the Protocol of 1997 amending the International Convention for the Prevention of Pollution from Ships, 1973, as amended by the Protocol of 1978 relating thereto (Revised Annex VI of MARPOL): Annex 13 (Resolution MEPC. Study o preliminary method ship design using a deterministic approach and a probabilistic approach, including hull design.
Project: Integrated and multi-objective optimization approach to ship design applied to the improvement of (EEDI).
