Applied Neural Network-Based Active Control for Vortex-Induced Vibrations Suppression in a Two-Degree-of-Freedom Cylinder

TL;DR

This paper introduces a neural network-based active control method for suppressing vortex-induced vibrations in cylindrical structures, demonstrating up to 99% vibration reduction under uncertain conditions.

Contribution

A novel model-based active control strategy using neural networks is developed for VIV suppression, incorporating adaptive feedback and uncertainty modeling.

Findings

Achieved up to 99% vibration reduction.

Enhanced control stability under uncertainties.

Validated effectiveness through controllability analysis.

Abstract

Vortex-Induced Vibrations (VIVs) of cylindrical structures present significant challenges in various engineering applications, including marine risers, tall buildings, and renewable energy systems. Hence, it is vital to control Vortex-Induced Vibrations of cylindrical structures. For this purpose, in this study a novel approach is introduced to VIV control, based on a model-based active control strategy integrated with a Neural Network (NN) in the presence of uncertainty modeling. The proposed method utilizes a closed-loop control system, where feedback from the system's dynamic state is used to generate adaptive control commands, enabling the system to respond to changing flow conditions and nonlinearities. Then, the controllability analysis is conducted to assess the efficiency of the control strategy in mitigating VIV. Two control approaches are implemented: simple learning and composite learning. Both strategies significantly enhance vibration suppression, achieving up to 99% reduction in vibrations despite uncertainties in the system. The results demonstrate the potential of the proposed method to enhance the efficiency, stability, and lifespan of structures subject to VIV.