Monday, June 16, 2025

How Optical Microwaves Measure Gas-Liquid Flow #Sciencefather #researchawards #computervision


 Multiphase flow refers to the simultaneous movement of two or more distinct phases, such as gas, liquid, or solid, with clearly defined interfaces. Among them, gas-liquid two-phase flow is one of the most common and significant types, widely present in industries like power generation, petroleum, chemical processing, refrigeration, and aerospace. Applications include steam-water flow in turbines, gas-liquid mixing in heat exchangers, and oil-gas transportation in pipelines. To ensure safe and efficient operation, accurate real-time measurement of gas-liquid flow rates is essential. Traditional methods such as the separation method, dual parameter method, and cross-correlation technique have been used for this purpose. While these methods provide some level of accuracy, they often suffer from drawbacks like complex equipment, sensitivity to flow patterns, limited measurement range, and inability to provide real-time results.

To address these limitations, the Optical Carrier Microwave Interferometry (OCMI) technique has emerged as a promising alternative. OCMI combines the advantages of optics and microwaves, offering features like distributed sensing, high positioning accuracy, and insensitivity to optical polarization. It has been successfully used for measuring physical parameters like temperature, strain, pressure, and vibration. However, conventional demodulation methods used with OCMI—such as dip frequency tracking and phase demodulation—are typically limited to detecting one parameter at a time and may struggle with low sensitivity or resolution, especially when applied to complex systems like gas-liquid two-phase flow. These limitations highlight the need for an improved approach that can handle the simultaneous measurement of multiple flow parameters under dynamic industrial conditions.

In response, this study proposes a new sensing method that integrates OCMI technology with machine learning, specifically artificial neural networks (ANN). By learning from data patterns, ANN models can simultaneously predict gas and liquid flow rates, overcoming the limitations of traditional OCMI demodulation. This approach not only enhances measurement accuracy and adaptability to different flow patterns but also provides a foundation for real-time monitoring in complex flow environments. The effectiveness of different input schemes to the neural network is also investigated, demonstrating the potential of combining advanced sensing techniques with intelligent data processing to revolutionize multiphase flow measurement in industrial applications.

 

International Conference on Computer Vision

The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.


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