Multifidelity Data Fusion Mechanism for Digital Twins via the Internet of Things

被引：1
作者：
Wang, Hao [1 ]
Song, Xueguan [2 ]
Zhang, Chao [3 ]
机构：
[1] Dalian Univ Technol, Dalian 116024, Peoples R China
[2] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China
[3] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China
来源：
IEEE INTERNET COMPUTING | 2025年 / 29卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Finite element analysis; Real-time systems; Data models; Internet of Things; Digital twins; Boundary conditions; Numerical models; Data integration; Technical requirements; Simulation; Industrial facilities; Job shop scheduling; FIDELITY;
D O I：
10.1109/MIC.2024.3483831
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Digital twins (DTs) build the real-time digital mirrors of physical entities and play an important role in various industrial scenarios. The Internet of Things (IoT) serves as the backbone of collecting real-time data for building DTs to meet the technical requirements on real-time responsiveness and modeling precision. We propose a multifidelity data fusion (MDF) mechanism for digital twins via IoT, called MDF-DT. This mechanism establishes the digital twin of a physical entity by fusing real-time sensor data collected via IoT and historical finite-element method simulation data. An improved hierarchical regression for multifidelity data fusion (IHR-MDF) method is proposed to predict high-fidelity (HF) responses based on the low-fidelity samples taken from multiple sources and a small size of HF samples. Numerical experiments show that the normalized root-mean-square error is less than 0.4, and the computational time is about 0.2 ms/point. The proposed MDF-DT mechanism has high applicability in various DT applications.
引用
收藏
页码：16 / 23
页数：8
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