Wall normal velocity and wall pressure data for turbulent channel flow

Data for the manuscript titled: Data-driven Sensor Placement for Predictive Applications: A Correlation-Aware Attribution Framework (CAAF). We propose a machine-learning-based feature attribution framework to identify optimal sensor placement (OSP) for target predictions. Feature attribution quantifies input contributions to a model’s output; however, it struggles with highly correlated input data often encountered in practical applications. To address this, we propose a Correlation-Aware Attribution Framework (CAAF), which introduces a clustering step before performing feature attribution to reduce redundancy and enhance generalizability. We demonstrate its effectiveness in realistic dynamical systems, such as wall-normal velocity estimation for turbulent channel flow. The results show that the CAAF outperforms alternative approaches that typically struggle due to the presence of nonlinear dynamics, chaotic behavior, and multi-scale interactions, and enables the effective application of feature attribution for identifying OSP in real-world environments. Wall-normal velocity (V_data.mat) has shape (#time steps, #x grid points, #y grid points, #z grid points). Wall pressure data (P_w.mat) has shape (#time steps, #x grid points, #z grid points).