| Title: |
Automated Local Measurement of Wall Shear Stress with AI-Assisted Oil Film Interferometry |
| Authors: |
Mehdizadeh Youshanlouei, Mohammad; Lazzarini, Lorenzo; Talamelli, Alessandro; Bellani, Gabriele; Rossi, Massimiliano |
| Contributors: |
Mehdizadeh Youshanlouei, Mohammad; Lazzarini, Lorenzo; Talamelli, Alessandro; Bellani, Gabriele; Rossi, Massimiliano |
| Publication Year: |
2026 |
| Collection: |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
| Subject Terms: |
Oil-Film Interferometry; VGG16; YOLO; deep learning; optical sensing; wall shear stress |
| Description: |
Accurate measurement of wall shear stress (WSS) is essential for both fundamental and applied fluid dynamics, where it governs boundary-layer behavior, drag generation, and the performance of flow-control systems. Yet, existing WSS sensing methods remain limited by low spatial resolution, complex instrumentation, or the need for user-dependent calibration. This work introduces a method based on artificial intelligence (AI) and Oil-Film Interferometry, referred to as AI-OFI, that transforms a classical optical technique into an automated and sensor-like platform for local WSS detection. The method combines the non-intrusive precision of Oil-Film Interferometry with modern deep-learning tools to achieve fast and fully autonomous data interpretation. Interference patterns generated by a thinning oil film are first segmented in real time using a YOLO-based object detection network and subsequently analyzed through a modified VGG16 regression model to estimate the local film thickness and the corresponding WSS. A smart interrogation-window selection algorithm, based on 2D Fourier analysis, ensures robust fringe detection under varying illumination and oil distribution conditions. The AI-OFI system was validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE), showing excellent agreement with reference pressure-drop measurements and conventional OFI, with an average deviation below 5%. The proposed framework enables reliable, real-time, and operator-independent wall shear stress sensing, representing a significant step toward next-generation optical sensors for aerodynamic and industrial flow applications. |
| Document Type: |
article in journal/newspaper |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/41600495; info:eu-repo/semantics/altIdentifier/wos/WOS:001671426500001; volume:26; issue:2; firstpage:1; lastpage:15; numberofpages:15; journal:SENSORS; https://hdl.handle.net/11585/1042973 |
| DOI: |
10.3390/s26020701 |
| Availability: |
https://hdl.handle.net/11585/1042973; https://doi.org/10.3390/s26020701; https://www.mdpi.com/1424-8220/26/2/701 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Creative commons ; license uri:http://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.B61AE43 |
| Database: |
BASE |