Topology Optimization for Large-Scale Unsteady Flow With the Building-Cube Method

IF 2.7 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Ryohei Katsumata, Koji Nishiguchi, Hiroya Hoshiba, Junji Kato
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Abstract

This study proposes a novel framework for solving large-scale unsteady flow topology optimization problems. While most previous studies on fluid topology optimization assume steady-state flows, an increasing number of recent studies deal with unsteady flows, which are more general in engineering. However, unsteady flow topology optimization involves solving the governing and adjoint equations of a time-evolving system, which requires a significant computational cost for topology optimization with a fine mesh. Therefore, we propose a large-scale unsteady flow topology optimization based on the building-cube method (BCM), which is one of the hierarchical Cartesian mesh methods. Although the BCM has been confirmed to have excellent scalability and is suitable for massively parallel computing, there are no studies that have applied it to unsteady flow topology optimization. In the proposed method, the governing and adjoint equations are discretized by a cell-centered finite volume method based on the BCM, which can achieve high parallel efficiency even with a fine mesh. The effectiveness of the proposed method for large-scale computing is discussed through several examples of optimization and verification of computational efficiency by weak scaling.

Abstract Image

大尺度非定常流场的构建立方体拓扑优化
本研究提出了一种求解大规模非定常流场拓扑优化问题的新框架。以往关于流体拓扑优化的研究大多假设稳态流动,而近年来越来越多的研究涉及非定常流动,这在工程上更为普遍。然而,非定常流场拓扑优化涉及求解时变系统的控制方程和伴随方程,对于精细网格的拓扑优化需要大量的计算成本。为此,我们提出了一种基于分层笛卡尔网格法中的构建立方体法(BCM)的大规模非定常流场拓扑优化方法。虽然BCM已被证实具有优异的可扩展性,适合大规模并行计算,但尚未有研究将其应用于非定常流场拓扑优化。该方法采用基于单元中心有限体积法对控制方程和伴随方程进行离散化处理,在精细网格下也能获得较高的并行效率。通过几个弱尺度优化和计算效率验证的实例,讨论了该方法在大规模计算中的有效性。
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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
276
审稿时长
5.3 months
期刊介绍: The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems. The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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