Experimental study on the mechanical property of carbon fiber reinforced polymer with the combined application of digital twin and reduced order algorithm

Author:

Ji Xiukun1ORCID,Wang Jinlong2,Gong Yongjun1ORCID,Bao Yongjie2ORCID,Yang Yuxing2ORCID

Affiliation:

1. Naval Architecture and Ocean Engineering College Dalian Maritime University China

2. Marine Engineering College Dalian Maritime University China

Abstract

AbstractRecognizing the anisotropy of mechanical property transfer between carbon fiber reinforced polymer (CFRP) layers, a unidirectional reduced‐order model (UROM) is proposed to comprehensively express the mechanical properties of each layer. A crucial focus is on effectively analyzing the real‐time mechanical properties of CFRP specimens, particularly in accurately identifying and predicting the interlaminar mechanical states of anisotropic CFRP laminated specimens. Moreover, the multi fidelity surrogate (MFS) is employed to replace the computationally intensive model, amalgamating high‐fidelity sensor data with low‐fidelity data obtained from the UROM. The UROM MFS is used to reduce the amount of data in the same layer of CFRP laminates, while preserving the characteristics between different layers and preserving the interlaminar information of CFRP laminates. Finally, the stress prediction results of the UROM MFS and the unreduced MFS model were compared and analyzed. The real‐time response speed of the u‐ROM MFS digital twin (DT) model was increased by 658%. The UROM MFS DT model effectively captures the mechanical properties of each layer of CFRP laminates, enables quick calculation of model parameters, and provides accurate predictions.Highlights First, the interlayer mechanical property of CFRP is analyzed with the combined application of experimental data, DT model, and UROM MFS. Second, MFS is employed to merge high‐fidelity sensor data of CFPR laminate with low‐fidelity data obtained from UROM. Third, the digital twin framework for CFPR laminate with high response speed and calculation accuracy is proposed.

Funder

Dalian Science and Technology Innovation Fund

National Natural Science Foundation of China

Publisher

Wiley

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