Wall shear stress ( \(\tau _w\) ) quantification is fundamental in fluid dynamics but remains challenging in wind-tunnel experiments. Sensor-based methods offer high accuracy but lack spatial resolution for capturing complex three-dimensional effects. Conversely, oil-film visualization is a simple method to obtain high-resolution surface flow topology by processing a sequence of images using optical flow (OF) techniques. However, leveraging this approach for quantitative analysis suffers from noise and systematic biases. This study introduces SENSE (Sensor-Enhanced Neural Shear Stress Estimation), a data-driven approach that leverages a neural network to enhance OF-based \(\tau _w\) estimation through the integration of sparse, high-fidelity sensor measurements via a multi-objective loss function. SENSE processes oil-film image sequences directly, inherently mitigating temporal noise without explicit averaging. The method is validated in a turbulent separated flow on a one-sided diffuser. Results demonstrate SENSE’s robustness to sequence length and spatial resolution compared to classical optical flow algorithms. Crucially, incorporating sparse sensor data significantly improves quantitative accuracy, achieving over 30% reduction in root-mean-squared error on validation sensors with only 8 strategically distributed sensors. The sensor data provide a global regularization effect, improving estimates far from sensor locations. SENSE offers a promising approach to elevate oil-film visualization to a reliable quantitative measurement technique by combining image sequences and sparse sensor data.
