用电容传感+AI实现多相流模式自动识别，准确率超85%。
Automated Flow Pattern Classification in Multi-phase Systems Using AI and Capacitance Sensing Techniques
- 融合电容传感器与1D SENet深度学习模型进行流型分类。
- 实验数据集准确率达85%以上，模式数据集达71%。
- 适合工业实时监测，突破传统方法依赖透明管道的局限。
在多相流系统中，流型分类对于优化流体动力学和提升系统效率至关重要。当前工业与科研中主要依赖普通相机或人眼观察、高速成像等方法，受限于主观判断且仅适用于透明管道，导致准确率受场景影响大，泛化能力差。本研究提出一种新型平台，结合电容传感器与AI分类方法，并以传统技术为基准进行对比。实验结果表明，所提出的1D SENet深度学习模型在基于实验的数据集上准确率超过85%，在基于模式的数据集上达到71%。该方法显著提升了鲁棒性与可靠性，为工业应用中的实时流型监测与预测建模提供了变革性路径。
原文摘要 · Abstract (English)
In multiphase flow systems, classifying flow patterns is crucial to optimize fluid dynamics and enhance system efficiency. Current industrial methods and scientific laboratories mainly depend on techniques such as flow visualization using regular cameras or the naked eye, as well as high-speed imaging at elevated flow rates. These methods are limited by their reliance on subjective interpretations and are particularly applicable in transparent pipes. Consequently, conventional techniques usually achieve context-dependent accuracy rates and often lack generalizability. This study introduces a novel platform that integrates a capacitance sensor and AI-driven classification methods, benchmarked against traditional techniques. Experimental results demonstrate that the proposed approach, utilizing a 1D SENet deep learning model, achieves over 85\% accuracy on experiment-based datasets and 71\% accuracy on pattern-based datasets. These results highlight significant improvements in robustness and reliability compared to existing methodologies. This work offers a transformative pathway for real-time flow monitoring and predictive modeling, addressing key challenges in industrial applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。