Enterprise AI Analysis
Automated Local Measurement of Wall Shear Stress with AI-Assisted Oil Film Interferometry
Authors: Mohammad Mehdizadeh Youshanlouei, Lorenzo Lazzarini, Alessandro Talamelli, Gabriele Bellani, Massimiliano Rossi
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.
Revolutionizing Fluid Dynamics: AI-OFI's Impact on WSS Measurement
The advent of AI-Assisted Oil Film Interferometry (AI-OFI) marks a paradigm shift in Wall Shear Stress (WSS) measurement, transforming a complex optical technique into a real-time, autonomous sensor platform. This innovation addresses critical limitations of traditional methods, promising profound implications for research and industrial applications where precision and efficiency are paramount.
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Average Deviation
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Real-Time Automation
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Operator Independence
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Localized WSS Detection
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Real-time Droplet Detection (YOLO-v8)
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Smart Interrogation Window Selection (2D DFT)
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Fringe Pattern Regression (VGG16)
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Local Film Thickness Estimation
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Wall Shear Stress (WSS) Calculation
The AI-OFI framework leverages a sophisticated multi-stage AI pipeline to automate the entire WSS measurement process. This includes real-time identification of oil droplets, intelligent selection of optimal analysis regions, and accurate interpretation of complex interference patterns, all culminating in precise WSS data.
2.4%
Mean Absolute Deviation in WSS Prediction
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Oil Film Interferometry (OFI) is a direct, non-invasive method for measuring Wall Shear Stress (WSS). It relies on observing the thinning of a thin oil film due to the tangential stress exerted by fluid flow. The film thickness is determined by analyzing interference patterns (Fizeau fringes) generated when monochromatic light reflects off the oil-air and oil-solid interfaces. The rate of change in fringe spacing is directly proportional to the local WSS.
Historically, the accuracy and practical applicability of OFI have been constrained by the need for meticulous manual selection of interrogation windows and sensitivity to experimental variations. The core principle, however, provides a robust physical foundation for WSS measurement, making it an ideal candidate for AI enhancement.
Case Study: CICLoPE Long Pipe Facility Validation
The AI-OFI system was rigorously validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE). This validation demonstrated excellent agreement with reference pressure-drop measurements and conventional OFI results, achieving an average deviation below 5%.
The system proved capable of fully automatic quantitative WSS sensing in turbulent pipe flow across a broad range of Reynolds numbers, showcasing its reliability and potential for real-world aerodynamic and industrial flow applications.
< 5%
Average Deviation from Reference Measurements
Calculate Your Enterprise ROI
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Projected Annual Savings
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Annual Hours Reclaimed
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AI-OFI Implementation Roadmap
A structured approach to integrating AI-OFI into your operational framework, ensuring a seamless transition and maximum benefit.
Needs Assessment & Customization
Evaluate current WSS measurement challenges, define integration points, and tailor AI-OFI solution parameters.
Data Integration & Model Training
Integrate existing OFI data (if available), fine-tune AI models with specific experimental conditions, and establish initial validation.
Pilot Deployment & Testing
Deploy AI-OFI in a controlled environment, run pilot experiments, and gather feedback for iterative refinement.
Full-Scale Integration & Monitoring
Roll out AI-OFI across all relevant applications, provide training for operational teams, and establish continuous performance monitoring.
Performance Optimization & Expansion
Continuously optimize AI models, explore new applications (e.g., flow control), and expand deployment across the enterprise.
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