A fast multi-fidelity method with uncertainty quantification for complex data correlations: Application to vortex-induced vibrations of marine risers

TL;DR

This paper introduces a rapid multi-fidelity modeling approach in modal space for complex data correlations, applied to vortex-induced vibrations of marine risers, with uncertainty quantification and active learning for sensor placement.

Contribution

It presents a novel multi-fidelity method working in modal space to accurately model complex correlations in VIV data, incorporating Bayesian uncertainty quantification and active sensor placement strategies.

Findings

The method accurately predicts amplitude response shapes of marine risers.

Bayesian uncertainty quantification improves prediction reliability.

Active learning optimizes sensor placement for high-fidelity data collection.

Abstract

We develop a fast multi-fidelity modeling method for very complex correlations between high- and low-fidelity data by working in modal space to extract the proper correlation function. We apply this method to infer the amplitude of motion of a flexible marine riser in cross-flow, subject to vortex-induced vibrations (VIV). VIV are driven by an absolute instability in the flow, which imposes a frequency (Strouhal) law that requires a matching with the impedance of the structure; this matching is easily achieved because of the rapid parametric variation of the added mass force. As a result, the wavenumber of the riser spatial response is within narrow bands of uncertainty. Hence, an error in wavenumber prediction can cause significant phase-related errors in the shape of the amplitude of response along the riser, rendering correlation between low- and high-fidelity data very complex. Working in modal space as outlined herein, dense data from low-fidelity data, provided by the semi-empirical computer code VIVA, can correlate in modal space with few high-fidelity data, obtained from experiments or fully-resolved CFD simulations, to correct both phase and amplitude and provide predictions that agree very well overall with the correct shape of the amplitude response. We also quantify the uncertainty in the prediction using Bayesian modeling and exploit this uncertainty to formulate an active learning strategy for the best possible location of the sensors providing the high fidelity measurements.