研究目的
To describe the principle, design, fabrication, measurement methodology, and applications of micro hot-film flow sensors, particularly those fabricated on flexible polyimide substrates, for accurate and efficient fluid flow measurements in various fields.
研究成果
Micro hot-film flow sensors on flexible substrates offer simple, cost-effective solutions for various applications, with good sensitivity and ease of integration. Future work should focus on large-area fabrication, improved power efficiency, and broader deployment in wearable and distributed systems.
研究不足
The sensors are sensitive to ambient temperature fluctuations, requiring compensation methods; fabrication uniformity is critical for performance; spatial resolution and sensitivity are trade-offs with sensor size; and applications may be limited by fluid properties (e.g., Prandtl number effects).
1：Experimental Design and Method Selection:
The paper describes the design and fabrication of micro hot-film flow sensors using thermal anemometry principles, including constant temperature difference (CTD) mode operation for high sensitivity and fast response. It covers geometric design of sensing elements, temperature compensation methods, and data fusion algorithms using neural networks for flow parameter extraction.
2：Sample Selection and Data Sources:
Sensors are fabricated on polyimide substrates with metal films (e.g., Pt, Ni) as sensing elements. Applications include surface airflow detection on micro air vehicles, 2D wind detection, and respiratory airflow monitoring, with data collected from wind tunnel tests and real-world scenarios.
3：List of Experimental Equipment and Materials:
Polyimide substrate, metal films (Cr, Ni, Pt), parylene for encapsulation, Wheatstone bridge circuits, signal conditioning circuits, Bluetooth modules, and neural network-based data processing systems.
4：Experimental Procedures and Operational Workflow:
Fabrication involves printing electrodes on polyimide, depositing metal films, patterning, optional parylene coating, and post-treatment annealing. Sensors are operated in CTD mode with Wheatstone bridges, and outputs are processed to deduce flow velocity and direction using calibration and neural networks.
5：Data Analysis Methods:
Data is analyzed using back propagation (BP) neural networks to model relationships between sensor outputs and flow parameters, with statistical error analysis for velocity and angle measurements.
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