A reanalysis-based multi-fidelity (RBMF) surrogate framework for efficient structural optimization. (December 2022)

• Record Type: Journal Article
• Authors: Lee, Mingyu; Jung, Yongsu; Choi, Jaehoon; Lee, Ikjin
• Is Part Of: Computers & structures. Volume 273(2022)
• Publication Date: 2022-12
• Subjects: Structural reanalysis method -- Multi-fidelity surrogate model -- Prior knowledge -- Reanalysis-based multi-fidelity (RBMF) surrogate framework -- Sampling strategies for RBMF surrogate framework; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2022.106895
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: An efficient reanalysis-based multi-fidelity (RBMF) surrogate framework is developed. The proposed method utilizes the prior knowledge when building the surrogate model. Each fidelity sample is divided into two types: (i) exact sample, (ii) reanalysis sample. Sample classification and infilling methods are combined with the proposed framework. Local convergence criteria for the efficient reanalysis method are proposed. Abstract: In recent years, research on multi-fidelity (MF) surrogate modeling, which integrates high-fidelity (HF) and low-fidelity (LF) models, has been conducted to improve efficiency in structural optimization. However, even the latest well-developed MF surrogate models inevitably require a certain number of samples to maintain the fidelity of the surrogate models. To overcome this issue, in this paper, a reanalysis-based multi-fidelity (RBMF) surrogate framework, which combines the MF surrogate modeling and a structural reanalysis method, is developed to reduce the computational cost for each sample, not the number of samples. The core idea of the developed framework is to approximately obtain a large number of samples based on a small number of exactly calculated data as prior knowledge. Each sub-strategy for RBMF surrogate framework is developed to maximize the performances of the reanalysis method. Specifically, a reanalysis sample classification method, reanalysis sample infilling method, and local convergence criteria are proposed to effectively reflect the characteristics of the reanalysis method. Therefore, the coupling between MF surrogate modeling and reanalysis method is strengthened. Finally, two numerical examples show that the developed framework outperforms the conventional one.