Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods

Li, Jinxing; Liu, Tianyuan; Zhu, Guangya; Li, Lili; Xie, Yonghui

doi:10.1016/j.energy.2023.127289

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…The modeling requires the appropriate technology based on the physical characteristics and application requirements of the actual system. For fluid dynamics systems, computational fluid dynamics (CFD) modeling 88 precisely depicts fluid movement and heat transfer processes to optimize performance against exceptions and monitor operational faults. Finite element modeling (FEM) is for structural analysis by dividing structures into manageable cells to approximate mechanical properties, enabling numerical failure simulation of complex systems.…”

Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

Digital twin-driven prognostics and health management for industrial assets

Xiao,

Zhong,

Bao

et al. 2024

Sci Rep

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As a facilitator of smart upgrading, digital twin (DT) is emerging as a driving force in prognostics and health management (PHM). Faults can lead to degradation or malfunction of industrial assets. Accordingly, DT-driven PHM studies are conducted to improve reliability and reduce maintenance costs of industrial assets. However, there is a lack of systematic research to analyze and summarize current DT-driven PHM applications and methodologies for industrial assets. Therefore, this paper first analyzes the application of DT in PHM from the application field, aspect, and hierarchy at application layer. The paper next deepens into the core and mechanism of DT in PHM at theory layer. Then enabling technologies and tools for DT modeling and DT system are investigated and summarized at implementation layer. Finally, observations and future research suggestions are presented.

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Section: Dt Modeling Tech Tool and Dt System For Phmmentioning

confidence: 99%

Digital twin-driven prognostics and health management for industrial assets

Xiao,

Zhong,

Bao

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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A review of graph neural network applications in mechanics-related domains

Zhao,

Li,

Zhou

et al. 2024

Artif Intell Rev

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Mechanics-related tasks often present unique challenges in achieving accurate geometric and physical representations, particularly for non-uniform structures. Graph neural networks (GNNs) have emerged as a promising tool to tackle these challenges by adeptly learning from graph data with irregular underlying structures. Consequently, recent years have witnessed a surge in complex mechanics-related applications inspired by the advancements of GNNs. Despite this process, there is a notable absence of a systematic review addressing the recent advancement of GNNs in solving mechanics-related tasks. To bridge this gap, this review article aims to provide an in-depth overview of the GNN applications in mechanics-related domains while identifying key challenges and outlining potential future research directions. In this review article, we begin by introducing the fundamental algorithms of GNNs that are widely employed in mechanics-related applications. We provide a concise explanation of their underlying principles to establish a solid understanding that will serve as a basis for exploring the applications of GNNs in mechanics-related domains. The scope of this paper is intended to cover the categorisation of literature into solid mechanics, fluid mechanics, and interdisciplinary mechanics-related domains, providing a comprehensive summary of graph representation methodologies, GNN architectures, and further discussions in their respective subdomains. Additionally, open data and source codes relevant to these applications are summarised for the convenience of future researchers. This article promotes an interdisciplinary integration of GNNs and mechanics and provides a guide for researchers interested in applying GNNs to solve complex mechanics-related tasks.

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