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

Zhang, Lei; Redford, J.A.; Hafid, Fikri; Ghidaglia, Jean‐Michel; Gueguin, Maxime

doi:10.1016/j.euromechflu.2020.09.013

Cited by 4 publications

(5 citation statements)

References 45 publications

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“…The mode 2 peak is about twice as high and wide as the mode 1 peak. Both peaks demonstrate a sharp falloff at the lower velocities and a curved falloff at higher velocities, which is inline with observations in the literature [1]. The mode 2 peak is at a velocity that is slightly less than twice the velocity of the mode 1 peak, which indicates a change in Strouhal number between the two peaks.…”

Section: A) Laminar Flowsupporting

confidence: 89%

“…Otherwise, the Strouhal number at both peaks is ≈ 0.24, while the Strouhal number away from the peaks is slightly lower at 0.23, which better corresponds with the expected value of 0.221[3, Table 1], with a total variation of about 5%. The mode 1 peak for H = 2000 N has a sharp falloff at the lower velocities and a curved falloff at higher velocities, which is inline with observations in the literature [1]. The mode 2 peaks seem to have a similar form, but more points are necessary.…”

Section: A) Laminar Flowsupporting

confidence: 89%

“…Figure 3 shows a simulation initialised in this way and demonstrates how two inverse fluid flow simulations are created. The cable is modelled using (1). For a fluid with density ρ and velocity U over a cylinder with diameter D and from the definition of the lift coefficient C L , F L = 1 2 ρU 2 DC L , which is related to the flow variable q via q = 2C L /C L0 , where C L0 is the reference lift coefficient found from experiment or simulation.…”

Section: C) Initialisation Of Higher Modesmentioning

confidence: 99%

“…Based on this observation, RTE (Reseau de Transport d'Electricité) the French TSO (Transmission System Operator),has launched several research projects aimed at establishing physical models capable of reproducing the vibratory behavior of conductors under such conditions. This research has notably led to the implementation of two models, making it possible to have the movements of a conductor in time and space for a given wind speed [1].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

Self Cite

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In this paper, we present a new approach to use machine learning (ML) for the calibration of a physical model allowing the reproduction of the vibratory behavior of an overhead line conductor. This physical model known as Strip Theory (ST) has the advantage of being very precise but very complicated and cumbersome in its software operations and manipulations. A second model known as the Wake Oscillator (WO) has been implemented in order to meet the limitations of the ST model. In order to be able to use the WO model instead of the ST model, very heavy manual adjustments are required, which makes its use complicated.Precisely, the WO must be able to generate a time series similar to a time series generated by the ST model. In order to respond to this limitation, a machine learning model known as ENS has been proposed. The machine learning model will therefore take as input the data from the WO model and output the data from the ST model. A series of Machine learning models have been implemented and tested. The ENS algorithm was retained with a best Pearson's linear coefficient of determination (R2 Score) of almost 0.7 and a Root mean square deviation (RMSE) of 7.57e-09. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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Section: A) Laminar Flowsupporting

confidence: 89%

Section: A) Laminar Flowsupporting

confidence: 89%

Section: C) Initialisation Of Higher Modesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

Self Cite

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show abstract

“…In this context, and particularly in the field of numerical simulation for fluid mechanics, different strategies for combining physical and ML approaches have been established very recently in the literature in order to match an approximate or simplified model to a certain model considered. how being a reference [3][4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

Self Cite

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One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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General Modelling of Cable Vibrations Using Data Extracted from Physical Simulation

Redford,

Gueguin,

Hafid

et al. 2024

Lecture Notes in Civil Engineering

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

Cited by 4 publications

References 45 publications

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

General Modelling of Cable Vibrations Using Data Extracted from Physical Simulation

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