FEM-Based Preliminary Design of a Vibration Monitoring System in the Context of Decommissioned and Reinstalled Wind Towers
PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON BEHAVIOUR OF STEEL STRUCTURES IN SEISMIC AREAS, VOL 2, STESSA 2024(2024)
Univ Salerno
Abstract
In the context of steel structures, the structural longevity of wind turbines amidst aging poses a significant challenge, underscoring the need for sustainable solutions to enhance their lifespan and enable efficient reuse in line with a circular economy. However, the adoption of circular solutions, based on the decommissioning and re-installation of older towers requires monitoring solutions that could guarantee the structural health and safety management of the structure. This study proposes an application of Finite Element Method (FEM) aimed at producing a first guess on the potentially-suitable technical and installation requirements for a vibration monitoring system to be operated for a steel wind turbine. The research is applied to a wind tower, being originally 65 m high, which was disassembled and reassembled at a reduced height of 45 m to increase its service lifetime, adopting a circular economy approach. The described application is part of a method, consisting in a continuous integration and mutual optimization of the monitoring system and its digital model counterpart. Such an integration avoids the parallelization of initial modelling and monitoring for validation, while it goes beyond the step of matching the model and the experimental data, with the aim of producing a tuned FEM. The application of the proposed approach to steel structures in the strategic industrial sector of renewable energy production proved again to be effective, as in the previous context of cultural heritage structures, implying the possibility of expanding the use of this method to different context, such as structural health monitoring systems, structural safety, critical structures and infrastructures monitoring and management, and long-term monitoring for structures with a prolonged service lifetime, contributing to an increased adoption of sustainable and circular solution.
MoreTranslated text
Key words
Finite Element Model (FEM),Structural Dynamics,Applied Physics,Sensor,Environmental monitoring
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined