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Numerical Study of the Thermal Performance of a Mems Pressure Sensor with Self-Calibration Capabilities

Recent industry trends toward more complex and interconnected systems have increased the demand for more reliable pressure sensors. By integrating a microactuator with a pressure sensor, the sensor can self-calibrate, eliminating the complexities and costs associated with traditional sensor calibrat...

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Detalles Bibliográficos
Autores principales: de Clerck, Albrey, Kang, Yuhong, Sibold, Ridge, Mouring, Scott, Ruan, Hang, Ng, Wing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147540/
https://www.ncbi.nlm.nih.gov/pubmed/35632237
http://dx.doi.org/10.3390/s22103828
Descripción
Sumario:Recent industry trends toward more complex and interconnected systems have increased the demand for more reliable pressure sensors. By integrating a microactuator with a pressure sensor, the sensor can self-calibrate, eliminating the complexities and costs associated with traditional sensor calibration methods to ensure reliability. The present work is focused on furthering understanding and improving the thermal performance of a thermopneumatic actuated self-calibrating pressure sensor. A transient numerical model was developed in ANSYS and was calibrated using experimental testing data. The numerical model provided insights into the sensor’s performance not previously observed in experimental testing. Furthermore, the model was utilized for two design studies. First, it was found that a substrate with low thermal conductivity and high thermal diffusivity is ideal for both the sensor’s efficiency and a faster transient response time. The second design study showed that decreasing the size of the sealed reference cavity lowers power consumption and transient response time. The study also showed that reducing the cavity base dimension has a greater effect on lowering power consumption and response time. Overall, the present work increases understanding of the self-calibrating pressure sensor and provides insight into potential design improvements, moving closer to optimized self-calibrating pressure sensors.