Flexibility prediction of thin-walled parts based on finite element method and K-K-CNN hybrid model

Li, Wangfei; Ren, Junxue; Shi, Kaining; Lu, Yanru; Zhou, Jinhua; Zheng, Huan

doi:10.1007/s00170-024-13657-x

Int J Adv Manuf Technol

2024

DOI: 10.1007/s00170-024-13657-x

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Flexibility prediction of thin-walled parts based on finite element method and K-K-CNN hybrid model

Wangfei Li,

Junxue Ren,

Kaining Shi

et al.

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Predictive digital twin for wind energy systems: a literature review

Kandemir,

Hasan,

Kvamsdal

et al. 2024

Energy Inform

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In recent years, there has been growing interest in digital twin technology in both industry and academia. This versatile technology has found applications across various industries. Wind energy systems are particularly suitable for digital twin platforms due to the integration of multiple subsystems. This study aims to explore the current state of predictive digital twin platforms for wind energy systems by surveying literature from the past five years, identifying challenges and limitations, and addressing future research opportunities. This review is structured around four main research questions. It examines commonly employed methodologies, including physics-based modeling, data-driven approaches, and hybrid modeling. Additionally, it explores the integration of data from various sources such as IoT sensors, historical databases, and external application programming interfaces. The review also delves into key features and technologies behind real-time systems, including communication networks, edge computing, and cloud computing. Finally, it addresses current challenges in predictive digital twin platforms. Addressing these research questions enables the development of hybrid modeling strategies with data fusion algorithms, which allow for interpretable predictive digital twin platforms in real time. Filter methods with dimensionality reduction algorithms minimize the computational resource demand in real-time operating algorithms. Moreover, advancements in high-bandwidth communication networks facilitate efficient data transmission between physical assets and digital twins with reduced latency.

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Predictive digital twin for wind energy systems: a literature review

Kandemir,

Hasan,

Kvamsdal

et al. 2024

Energy Inform

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Flexibility prediction of thin-walled parts based on finite element method and K-K-CNN hybrid model

Cited by 1 publication

References 28 publications

Predictive digital twin for wind energy systems: a literature review

Predictive digital twin for wind energy systems: a literature review

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