Making Shipping Greener Part II: Hull-Shape Optimization Using Fine Marine

In a world grappling with the urgent challenges of climate change, industries across the board are actively seeking methods to reduce their environmental footprint. The shipping sector, pivotal to the global economy, is no exception and progressively focuses on sustainability practices. Central to this green shipping revolution stands the International Maritime Organization (IMO), aiming to cut the industry’s greenhouse gas emissions by half by 2050. Achieving such an ambitious goal hinges on refining vessel design, particularly through hull shape optimization—crafting designs for maximum hydrodynamic efficiency. In this sequel of our blog series, we'll explore the role of advanced simulation and optimization tools such as Fidelity Fine Marine and Fidelity Fine Design 3D in propelling the shipping industry toward lower emissions and a more sustainable future.

In the context of hull-shape optimization, it is crucial to remember that the primary goal is to maximize one or more chosen objectives while considering any imposed constraints. In the schematic representation of the design optimization process, one can pursue either singular or multiple objectives constrained by various factors. These factors include reducing ship drag in calm waters and waves, improving the flow around the bulb, increasing propeller efficiency, and minimizing dynamic trim.

When dealing with constraints, it's vital to consider hydrostatics. This means ensuring that the vessel's displacement remains constant, keeping the draft below a specific limit, and preventing any significant shift in the center of gravity due to either cargo or structural factors. Furthermore, stability is a key concern involving maintaining the metacentric height within certain limits.

Three Key Components for Hull-Shape Optimization

The three key components are:

1. Parametric Modeler: The parametric modeler is used to parameterize geometry. Any modeler can be used for efficient and streamlined optimization processes.

2. Fine Design 3D (with MINAMO): Once a parametric model is obtained, one can use Fine Design 3D, an efficient surrogate-based optimizer that automatically explores the design space. This is achieved by generating a large number of strategically placed blue points that cover the design space based on the set of parameters that the user wants to vary in the design. The data is analyzed to develop surrogate models when the results are run. These models are then further optimized with each iteration to minimize uncertainty, a process known as uncertainty quantification (UQ), and to achieve the most robust design, known as robust design optimization (RDO).

3. Fidelity Fine Marine: C-Wizard, with its automation capabilities, is used for preprocessing and meshing. Fine Marine generates numerous resistance computations using the adaptive grid refinement technique. The computation time is optimized by reducing cell count while maintaining high accuracy. Adaptive grid refinement is particularly useful in this case, as it helps create large matrices of computations without creating unnecessary refinement.

Hull-Shape Optimization Workflow

Here is the general workflow to optimize a design: First, a parametric model is created. This model is then used by Fine Design 3D to achieve a robust design. Finally, the design is sent for meshing and preprocessing in Fine Marine.

In the preliminary stages of the project, extensive research was conducted to gather the necessary data. This data was used to establish a database with specific parameters. The primary goal was to create a ferry, and to achieve this, a parametric model was developed. A comprehensive analysis of 15 design parameters, such as bulb length, width, deadrise angle, propeller clearance, and transom inclination, was carried out. The objective was to minimize the propeller power under four different operating conditions, i.e., two loading conditions and two operational speeds. These objectives were subject to constraints, such as hard points due to scantling/structural design, constant displacement, and intact stability requirements.

Results from the Hull-Shape Optimization Workflow

Several experiments were carried out to ascertain the number of necessary design parameters. The calculations indicated a need for 1.7 times more parameters than were available. The analysis of variance (ANOVA) technique was utilized to determine which parameters affected the propeller power output. Self-organizing maps were also implemented to expose correlations and trends between the design parameters, objectives, and constraints.

Four different operating conditions were tested during the experiment, including two speeds and two loading conditions. Self-organizing maps helped identify the best design solution and were consistent across all four conditions. This meant that instead of creating four separate designs for each condition, only one design had to be optimized. After reaching convergence, the final computer-aided design (CAD) for the fast ferry was obtained.

The image above depicts two designs, with the top one being the initial design and the bottom one being the optimized version. The optimized design was achieved by utilizing comprehensive optimization techniques in a semi-automated way, resulting in an astounding 16.5% reduction in fuel consumption. Therefore, utilizing optimization techniques can result in significant benefits for greener shipping.

Conclusion

Leveraging sophisticated computational technologies alongside an advanced grasp of hydrodynamic principles sets the stage for notable improvements in ship efficiency and environmental compatibility. The employment of state-of-the-art design optimization and marine CFD tools, such as Fidelity Fine Design 3D and Fidelity Fine Marine, facilitates the achievement of significant reductions in fuel usage via hull optimization. This, in turn, propels the maritime industry closer to meeting the ambitious greenhouse gas emission reduction goals established by IMO.

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