VIV modelled using simplified cable dynamics coupled to sub-critical cylinder flow simulations in a moving reference frame-Reference-Cited by-同舟云学术

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VIV modelled using simplified cable dynamics coupled to sub-critical cylinder flow simulations in a moving reference frame

Published:2021-01 Issue: Volume:85 Page:214-231
ISSN:0997-7546
Container-title:European Journal of Mechanics - B/Fluids
language:en
Short-container-title:European Journal of Mechanics - B/Fluids

Author:

Zhang Lei,Redford John,Hafid Fikri,Ghidaglia Jean-Michel,Gueguin Maxime

Funder

RTE (Réseau de transport d’électricité) R&D, France

Publisher

Elsevier BV

Subject

General Physics and Astronomy,Mathematical Physics

Reference65 articles.

1. Computational analysis of aeolian conductor vibration with a stockbridge-type damper;Vecchiarelli;J. Fluids Struct.,2000

2. CIGRE SC 22 WG 01: Report on aeolian vibration, ELECTRA, 41-77,1989

3. A monolithic approach to fluid-structure interaction using space-time finite elements;Hübner;Comput. Methods Appl. Mech. Engrg.,2004

4. Fixed-point fluid-structure interaction solvers with dynamic relaxation;Küttler;Comput. Mech.,2008

5. Partitioned solver for strongly coupled fluid-structure interaction;Habchi;Comput. & Fluids,2013

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. General Modelling of Cable Vibrations Using Data Extracted from Physical Simulation;Lecture Notes in Civil Engineering;2024

2. How statistical modeling and machine learning could help in the calibration of numerical simulation and fluid mechanics models? Application to the calibration of models reproducing the vibratory behavior of an overhead line conductor;Array;2022-09

3. Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor;International Journal of Information and Communication Sciences;2022-05-13

4. Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model;IEEE Access;2021

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