A Digital Twin Framework Embedded with POD and Neural Network for Flow Field Monitoring of Push-Plate Kiln

Pin Wu (), Lulu Ji, Wenyan Yuan, Zhitao Liu and Tiantian Tang
Additional contact information
Pin Wu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Lulu Ji: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Wenyan Yuan: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Zhitao Liu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Tiantian Tang: Chengdu Xingyun Zhilian Technology Co. Ltd., Chengdu 610000, China

Future Internet, 2023, vol. 15, issue 2, 1-20

Abstract: The push-plate kiln is a kind of kiln equipment widely used in the oxygen-free sintering of high-temperature alloy materials. Its flow field monitoring has an important application value for the manufacturing industry. However, traditional simulation methods cannot meet the requirements of real-time applications due to the high computational cost and being time-consuming. The rapid development of artificial intelligence technology will empower the traditional manufacturing industry. In this paper, we propose a data-driven digital twin framework for real-time flow field prediction by combining the CFD modeling simulation, IoT, and deep learning technologies. The framework integrates geometric, rule, physical, and neural network models to achieve the real-time simulation of physical and twin objects. The proper orthogonal decomposition (POD) and multiscale convolutional neural network (MCNN) are innovatively embedded into the framework. The POD is used to map high-dimensional data to low-dimensional features, and the MCNN is used to construct models predicting low-dimensional features for fast flow field prediction. The effectiveness of the proposed model is verified by the push-plate kiln case. The results show that the digital twin can quickly predict multi-physics fields based on the perceptual data to achieve the real-time evaluation of the operating state of the push-plate kiln.

Keywords: proper orthogonal decomposition; convolutional neural network; computational fluid dynamics; digital twin (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/15/2/51/pdf (application/pdf)
https://www.mdpi.com/1999-5903/15/2/51/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:15:y:2023:i:2:p:51-:d:1049937

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().