Key Takeaways & Executive Findings
- •• • The flexible sensing patch integrates a capacitive differential pressure sensor array and a vector flow sensor, enabling simultaneous measurement of AOA, AOS, and airspeed in a single device, overcoming the integration limitations of rigid sensors. • • The dual-layer capacitive differential pressure sensor achieves a resolution of 12 mPa within the low-pressure range of 0–1 Pa, demonstrating superior sensitivity and linearity compared to traditional single-layer sensors, critical for detecting subtle pressure changes at leading edges. • • The PVF algorithm introduces spanwise velocity (Vz) based on the classic three-sensor algorithm, decoupling AOA and AOS and reducing computational complexity, which is essential for real-time flight control in miniaturized UAVs. • • CFD simulations validated the PVF algorithm's reliability under wind tunnel conditions, confirming its potential for high-precision flight parameter detection in complex aerodynamic environments.
Abstract
Precise measurement of flight parameters—including angle of attack (AOA), angle of sideslip (AOS), and airspeed—is critical for stabilized control of unmanned aerial vehicles (UAVs) in complex aerodynamic environments. Conventional rigid sensors fail to conform to curved leading edges, disrupting local flow and degrading aerodynamic performance, and are ill-suited for UAV miniaturization. Emerging flexible sensing technologies using hot-film, piezoresistive, or capacitive transduction offer potential solutions. This work presents a flexible pressure-velocity sensing patch developed by Professor Jiang's team at Beihang University. The patch integrates a capacitive differential pressure sensor array and a vector flow sensor, fabricated via a multi-layer polyimide bonding process. The capacitive differential pressure sensor features dual-layer chambers, achieving a resolution of 12 mPa within 0–1 Pa, superior to traditional single-layer designs. The vector flow sensor operates on the calorimetric principle, using a micro-heater and thermistor array to characterize flow velocity and direction. A novel pressure-velocity fusion (PVF) algorithm introduces spanwise velocity (Vz) to decouple AOA and AOS, reducing computational complexity compared to classic three-sensor algorithms. Computational fluid dynamics (CFD) simulations validated the algorithm's reliability under wind tunnel conditions. The sensing patch enables simultaneous measurement of multiple flight parameters with high precision and low computational cost, addressing the limitations of rigid sensors and advancing UAV aerodynamic sensing.
1. Introduction
Conventional rigid sensors for flight parameter detection, such as pitot tubes and vane sensors, struggle to conform to curved leading edges of UAV wings, disrupting local airflow and degrading aerodynamic performance. Moreover, their bulky form factors conflict with the stringent size and integration requirements of modern miniaturized UAVs. These limitations hinder precise measurement of critical parameters like angle of attack (AOA), angle of sideslip (AOS), and airspeed, which are essential for stabilized control in complex aerodynamic environments.
To address these bottlenecks, flexible sensing technologies have emerged, employing hot-film, piezoresistive, or capacitive transduction mechanisms. However, existing flexible solutions often suffer from high computational complexity or limited multi-parameter measurement capability. The presented pressure-velocity fusion (PVF) algorithm, integrated with a flexible sensing patch, introduces spanwise velocity to decouple AOA and AOS, achieving high precision with low computational cost. This innovation directly tackles the trade-off between measurement accuracy and computational efficiency, offering a viable path for real-time flight control in next-generation UAVs.
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Zhichao Ma, Yingli Shi, Guozhen Shen (2026). Flight Parameter Detection via a Pressure-Velocity Fusion Algorithm with a Flexible Sensing Patch. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3866-y
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Frequently Asked Questions
What is the operational pressure range and resolution of the capacitive differential pressure sensor, and how does it compare to traditional single-layer sensors?
The dual-layer capacitive differential pressure sensor operates within a low-pressure range of 0–1 Pa and achieves a resolution of 12 mPa. This represents a significant improvement in sensitivity and linearity over traditional single-layer capacitive sensors, which typically exhibit lower resolution and nonlinearity in positive and negative pressure changes. Such high resolution is critical for detecting subtle pressure variations at the wing leading edge, enabling accurate AOA and AOS estimation.
How does the PVF algorithm reduce computational complexity compared to the classic three-sensor algorithm, and what are the implications for real-time UAV control?
The PVF algorithm introduces spanwise velocity (Vz) based on the classic three-sensor algorithm, which allows decoupling of AOA and AOS without iterative or complex numerical methods. This reduces computational complexity, enabling faster processing and lower power consumption, which is essential for real-time flight control in miniaturized UAVs with limited onboard computing resources.
What are the key fabrication steps and materials used in the flexible sensing patch, and how do they affect mechanical flexibility and durability?
The patch is fabricated using a multi-layer polyimide bonding process, which provides excellent mechanical flexibility and thermal stability. The capacitive pressure sensor incorporates upper and lower pressure chambers, a microchannel layer, and a reference pressure hole, all patterned on polyimide substrates. This construction ensures conformability to curved surfaces while maintaining sensor integrity under aerodynamic loads.
How was the PVF algorithm validated, and what are the specific CFD simulation conditions?
The PVF algorithm was validated using computational fluid dynamics (CFD) simulations of leading edge airflow under wind tunnel conditions. The simulations modeled the airflow around the wing, with pressure measurement ports located at 50°, 30°, and −50° relative to the leading edge. The algorithm's reliability was confirmed by accurately computing AOA, AOS, and airspeed from simulated pressure and velocity data.
What are the potential failure mechanisms of the flexible sensing patch under prolonged aerodynamic stress, and how does the design mitigate them?
Potential failure mechanisms include delamination of polyimide layers, fatigue of capacitive membranes, and drift in thermistor calibration due to thermal cycling. The multi-layer bonding process enhances adhesion, while the dual-layer pressure sensor design distributes stress, improving mechanical robustness. The calorimetric flow sensor's stable temperature gradient minimizes thermal drift, ensuring long-term reliability.
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