Vortex-Induced Drag Forecast for Cylinder in Non-uniform Inflow

TL;DR

This paper develops a physics-based data-driven neural network model to predict vortex-induced drag on a cylinder in non-uniform inflow, incorporating upstream velocity and pressure signals for improved accuracy.

Contribution

It introduces an optimized FCNN architecture integrating upstream flow measurements, enhancing drag prediction under complex vortex dynamics at moderate Reynolds numbers.

Findings

Achieved R^2 score of 0.75 in predicting drag fluctuations.

Optimized sensor placement correlates with flow separation dynamics.

Model performance scales exponentially with input signal quality.

Abstract

In this letter, a physics-based data-driven strategy is developed to predict vortex-induced drag on a circular cylinder under non-uniform inflow conditions - a prevalent issue for engineering applications at moderate Reynolds numbers. Traditional pressure-signal-based models exhibit limitations due to complex vortex dynamics coupled with non-uniform inflow. To address this issue, a modified fully connected neural network (FCNN) architecture is established that integrates upstream velocity measurements (serving as an inflow calibration) with pressure-signal-based inputs to enhance predictive capability (R^2 ~ 0 to 0.75). Direct numerical simulations (DNS) at Reynolds number Re = 4000 are implemented for model training and validation. Iterative optimizations are conducted to derive optimized input configurations of pressure sensor placements and velocity components at upstream locations. The optimized model achieves an R^2 score of 0.75 in forecasting high-amplitude drag coefficient fluctuations (C_d=0.2 - 1.2) within a future time window of one time unit. An exponential scaling between model performance and optimized pressure signal inputs is observed, and the predictive capability of sparsely distributed but optimized sensors is interpreted by the scaling. The optimized sensor placements correspond to the physical mechanism that the flow separation dynamics play a governing role in vortex-induced drag generation. This work advances machine learning applications in fluid-structure interaction systems, offering a scalable strategy for forecasting statistics in turbulent flows under real-world engineering conditions.