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Development of CFD Shape Optimization Technology using the Adjoint Method and its Application to Engine Intake Port Design
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 08, 2013 by SAE International in United States
Citation: Tokuda, S., Kubota, M., and Noguchi, Y., "Development of CFD Shape Optimization Technology using the Adjoint Method and its Application to Engine Intake Port Design," SAE Int. J. Engines 6(2):833-842, 2013, https://doi.org/10.4271/2013-01-0969.
Computational fluid dynamics (CFD) shape optimization technology is playing an increasingly significant role in the development of products that satisfy various demands, including trade-off relationships. It offers the possibility of designing or improving product shape with respect to a given cost function, subject to geometrical constraints. However, conventional CFD shape optimization technology that uses parametric shape modification has two following issues: (1) expensive computational cost to obtain the final shape, (2) performance variations of the obtained shape depends on the skill or experience of the designer who determined the locations to be modified.
In this study, to resolve those problems, an efficient shape optimization technology was developed that uses the adjoint method to perform sensitivity analysis of a cost function on the design parameters. It is composed of a combination of topology optimization and surface geometry optimization. Firstly, topology optimization is used to roughly derive a base shape in the constrained design space. Next, surface geometry optimization is used to further modify the base shape according to the sensitivity distributions of the cost function to each vertex on the surface geometry. This technology was then applied to the design of an engine intake port. The cost function was defined as the balance between maximizing the intake flow rate and in-cylinder tumble moment. As a result, topologically optimized shapes with various performance aspects according to the weighting factor of the cost function were obtained. Then, the topologically optimized shapes were fine-tuned by surface geometry optimization.