Abstract
Multidisciplinary design optimization has become a powerful technique to facilitate continuous improvement of complex and multidisciplinary products. Parametric modeling is an essential part with tremendous impact on the flexibility and robustness of multidisciplinary design optimization. This article investigates the effect of relational and non-relational parameterization techniques on the robustness and flexibility of the conceptual design of a multidisciplinary product. Bench marking between relational and non-relational parameterization and their effect on flexibility and robustness indicate that the relational parameterization is an efficient method in the multidisciplinary design optimization process. The inherent properties of the method contribute to an efficient parametric modeling with improved communication between different disciplines. This enhances the performance of the multidisciplinary design optimization process and allows a more flexible and robust design. The considered disciplines are computer-aided design, computational fluid dynamics, finite element analysis, and dynamic simulation. A high-fidelity geometry created in a computer-aided design environment is computer-aided design centric approach and later used in computational fluid dynamics, finite element analysis for a better understanding of the product as it leads to precise outcomes. The proposed approach is implemented for the conceptual design of a novel product, a tidal power plant developed by Minesto AB using a multidisciplinary design optimization process.
Highlights
A parametric geometry primarily explores possible design alterations of a product under consideration
Flexibility and robustness of geometry have a direct impact on the efficiency of a computer-aided design (CAD)-centric multidisciplinary design optimization (MDO) framework
Weight is one of the objectives of this study directly provided by a CAD model
Summary
A parametric geometry primarily explores possible design alterations of a product under consideration. The parametric modeling history, development aspects, and classification are enumerated by Shah[1] and Davis.[2] The three benefits of this methodology are automatic change propagation, geometry re-use, and embedded design knowledge. Parametric modeling follows associativity, that is, the modifications propagate automatically to all the entities (e.g. point, line, curve, surfaces, etc.) connected to the parameters. Most of the Department of Management and Engineering (IEI), Linkoping University, Linkoping, Sweden. Advances in Mechanical Engineering software are either built-up based on non-uniform rational B-splines (NURBS) or support parametric modeling.[3,4,5] Parametric modeling is an essential part of an multidisciplinary design optimization (MDO) process and gradually becoming the industry standard, in particular for design and modeling of mechanical components. Many studies have indicated the rewards and implementation of this methodology.[7,8,9,10,11,12,13,14]
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