Open Access

ARTICLE

Research and Application of a Multi-Field Co-Simulation Data Extraction Method Based on Adaptive Infinitesimal Element

Changfu Wan^1,2, Wenqiang Li^1,2,*, Sitong Ling^1,2, Yingdong Liu^1,2, Jiahao Chen^1,2

1 School of Mechanical Engineering, Sichuan University, Chengdu, 610065, China
2 Innovation Method and Creative Design Key Laboratory of Sichuan Province, Sichuan University, Chengdu, 610065, China

* Corresponding Author: Wenqiang Li. Email:

Computer Modeling in Engineering & Sciences 2024, 138(1), 321-348. https://doi.org/10.32604/cmes.2023.029053

Received 29 January 2023; Accepted 05 May 2023; Issue published 22 September 2023

Abstract

Regarding the spatial profile extraction method of a multi-field co-simulation dataset, different extraction directions, locations, and numbers of profiles will greatly affect the representativeness and integrity of data. In this study, a multi-field co-simulation data extraction method based on adaptive infinitesimal elements is proposed. The multi-field co-simulation dataset based on related infinitesimal elements is constructed, and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction. Based on the fireworks algorithm, the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length. The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace. Compared with traditional data extraction methods for multi-field co-simulation, the approximate model constructed by the data extracted from the proposed method has higher construction efficiency. Meanwhile, the relative maximum absolute error, root mean square error, and coefficient of determination of the approximation model are better than those of the approximation model constructed by the data extracted from traditional methods, indicating higher accuracy, it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.

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