Ship design is an iterative process. Each iteration attempts to obtain a balanced ship design that satisfies specific needs and requirements. The design and construction of a service vessel most often takes more than one year and the process can be divided into several stages:
1. Concept design: definition of the ship's basic characteristics (ship type, deadweight, propulsion and service speed).
2. Preliminary design: creation of technical material for tender and contract, estimation of design and material cost, determination of main dimensions.
3. Contract design: detail calculation to ensure ship performance, refinement of the general arrangement (GA), basic design of the hull and systems arrangements.
4. Detail design: creation of detailed material for hull production, support to the assembly.
The most well known representation of the ship design process is the design spiral of Evans (Evans, 1959). While the design spiral converges to the center new design phases are gone through. If one activity takes longer than planned, a whole iteration will be delayed as well. Additionally, another iteration has to be done if a mistake is made, also causing a delay.
Historically, the marine industry heavily relied on statistical approaches and regression analyses. With the emergence of advanced tools, there has been a significant effort to integrate simulation into the ship design process. According to Harries (Harries, 2008) we are going from modeling design towards simulation-driven design. It means that we drive the final solution not from the modeling, but out of the calculations or simulations.
Within each phase, resistance calculations play an important role as they provide insight into how efficient a ship’s design is. Unfortunately, as these types of simulations often require a lot of computation time and effort, they typically get shifted further down the spiral. This is especially true in the case of Computational Fluid Dynamics (CFD).
Let’s demonstrate with a Multi-Purpose Ship (MPS) used to carry a huge variety of bulk cargo. The vessel operates in a big window, so a large number of speeds and drafts need to be considered in order to obtain the full resistance profile. For example, if we want to evaluate speeds of 9 to 21 knots (2 knots increments) and loading drafts of between 4.25 and 8.25 m (1.0 m increments), 105 data points (7 speeds, 5 draughts and 3 design variations) need to be generated. If you had to set up and run each one of these with CFD, it would involve a significant amount of engineering time and most likely cut into your productivity.
This is why Extrality developed a Deep Learning Physics platform that allows you to investigate a full range of configurations and operation points with minimal manual effort. The platform can predict fluid behavior to assess the performance of new hull designs in a few minutes, thus saving some valuable time in the early stages of a design project. It is accessible directly from the web, without any installation needed. All simulations can be run in 3 clicks!
The average lead time for a typical resistance curve is 1 day. Using Extrality to run all 105 simulations takes just under 10 minutes to complete the entire set… this is a hundred times faster, thus saving engineers time for value-added tasks: explore a range of solutions and innovate, but not at the expense of reaching the desired performance and quality targets. For more information about hull form optimization with Extrality, check out this article.
Evans, 1959. Basic Design Concepts. In: Journal of the American Society for Naval Engineers, Volume 71, pp. 671-678.
Harries, 2008. Serious Play in Ship Design. In: Tradition and Future of ship design in Berlin.