Ultrasound technology has been recognized as the mainstream approach for the identification of gas-liquid two-phase flow patterns, which holds great value in engineering domain. However, commercial rigid probes are bulky, limiting their adaptability to curved surfaces. Here, we propose a strategy for autonomous identification of flow patterns based on flexible ultrasound array and machine learning. The array features high-performance 1–3 piezoelectric composite material, stretchable serpentine wires, soft Eco-flex layers and a polydimethylsiloxane (PDMS) adhesive layer. The resulting ultrasound array exhibits excellent electromechanical characteristics and offers a large stretchability for an intimate interfacial contact to curved surface without the need of ultrasound coupling agents. We demonstrated that the flexible ultrasound array combined with machine learning can accurately identify gas-liquid two-phase flow patterns, in a circular pipeline. This work presents an effective tool for recognizing gas-liquid two-phase flow patterns, offering engineering opportunities in petroleum extraction and natural gas transportation.

超声技术已被公认为识别气液两相流模式的主流方法，在工程领域具有重要价值。然而，商用刚性探针体积庞大，限制了它们对曲面的适应性。在这里，我们提出了一种基于柔性超声阵列和机器学习的流型自主识别策略。该阵列采用高性能 1-3 压电复合材料、可拉伸蛇形线、柔软的 Eco-flex 层和聚二甲基硅氧烷 （PDMS） 粘合层。所得超声阵列表现出优异的机电特性，并提供很大的可拉伸性，无需超声耦合剂即可与曲面紧密界面接触。我们证明，灵活的超声阵列与机器学习相结合，可以准确识别圆形管道中的气液两相流模式。这项工作为识别气液两相流模式提供了一种有效的工具，为石油开采和天然气运输提供了工程机会。