Automatic image processing of cavitation bubbles to analyze the properties of petroleum products

We have developed a new computer vision method of automatic image processing of cavitation bubbles to classify petroleum products with different octane numbers (ONs) using an artificial neural network (ANN). Ultrasonic irradiation induces cavitation bubbles, which exhibit growth, oscillations, and resonance shapes. Gasoline solutions may have different physical and chemical properties. While a precise understanding of how these properties impact bubble dynamics is challenging, training the ANN algorithm on bubble images allows classification of gasoline bubbles with different ON values. The integration of the ultrasonic cavitation method with computer vision and artificial intelligence techniques offers a promising way for real-time ON assessment in liquid flow. Machine-vision analysis of a frame with a gas bubble in the resonance mode (n = 8).