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Abstract
Shape optimization is a very time-consuming and expensive task, especially if experimental tests need to be performed. To overcome the challenges of geometry optimization, the industry is increasingly relying on numerical simulations. These kinds of problems typically involve the interaction of three main applications: a solid modeler, a multi-physics solver, and an optimizer. In this manuscript, we present a shape optimization work-flow entirely based on open-source tools; it is fault tolerant and software agnostic, allows for asynchronous simulations, and has a high degree of automation. To demonstrate the usability and flexibility of the proposed methodology, we tested it in a practical case related to the naval industry, where we aimed at optimizing the shape of a bulbous bow in order to minimize the hydrodynamic resistance. As design variables, we considered the protrusion and immersion of the bulbous bow, and we used surrogate-based optimization. From the results presented, a non-negligible resistance reduction is obtainable using the proposed work-flow and optimization strategy.
Highlights
Engineering design and product development using shape optimization can be a very daunting task, especially if the design approach is experimental, where cost and time constraints usually limit the number of prototypes that are possible to construct and test
The continuous development of numerical methods and the increase in computing performance have suggested the use of simulation software able to model complex multi-physics problems and optimize the design space, thereby reducing the costs related to prototypes, physical experiments, and field/operational tests
Open-source software has the same capabilities as their commercial counterparts, and it is mature enough so that it can be used in the design and certification process of new products. We address in this manuscript the implementation of an open-source framework for shape optimization
Summary
Engineering design and product development using shape optimization can be a very daunting task, especially if the design approach is experimental, where cost and time constraints usually limit the number of prototypes that are possible to construct and test. Even though successful applications with low-fidelity methods have been previously reported in the literature [10], the adoption of high-fidelity solvers (such as finite volume method or finite element method solvers) in the design loop can increase the accuracy and reliability of the design process In this field, many studies have been conducted, for example, Serani et al [13] successfully used a viscous solver for addressing the shape optimization of a high-speed catamaran with excellent results; the computational cost in terms of CPU time (for the whole optimization loop and each single simulation) and the complexity of the optimization loop was too big to be implemented by a manufacturer ( small ones, which have limited resources). We aim at coupling all tools needed for shape optimization studies in a streamlined work-flow, with a high degree of automation and flexibility, and using efficient optimization techniques that allow engineers to explore, exploit, and optimize the design space from a limited number of training observations
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