Learning Optimal Parametric Hydrodynamic Database for Vortex-Induced Crossflow Vibration Prediction

TL;DR

This paper introduces a novel method to efficiently estimate a parametric hydrodynamic database for vortex-induced vibration prediction, reducing experimental complexity and improving prediction accuracy for flexible cylindrical structures.

Contribution

The paper proposes a constraint optimization approach to learn a parametric hydrodynamic database, enabling accurate VIV prediction without extensive experiments.

Findings

Significantly reduces prediction error in VIV simulations.

Robustly applicable to different cylinder types and flow conditions.

Improves efficiency by avoiding exhaustive experimental data collection.

Abstract

The Vortex-induced vibration (VIV) prediction of long flexible cylindrical structures relies on the accuracy of the hydrodynamic database constructed via rigid cylinder forced vibration experiments. However, to create a comprehensive hydrodynamic database with tens of input parameters including vibration amplitudes and frequencies and Reynolds number, surface roughness and so forth is technically challenging and virtually impossible due to the large number of experiments required. The current work presents an alternative approach to approximate the crossflow (CF) hydrodynamic coefficient database in a carefully chosen parameterized form. The learning of the parameters is posed as a constraint optimization, where the objective function is constructed based on the error between the experimental response and theoretical prediction assuming energy balance between fluid and structure. Such a method yields the optimal estimation of the CF parametric hydrodynamic database and produces the VIV response prediction based on the updated hydrodynamic database. The method then was tested on several experiments, including freely-mounted rigid cylinder in large Reynolds number with combined crossflow and inline vibrations and large-scale flexible cylinder test in the Norwegian Deepwater Program, and the result is shown to robustly and significantly reduce the error in predicting cylinder VIVs.