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Modeling the Thermal Performance of an Intelligent 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. One of the best methods to ensure reliability is by regularly calibrating the sensor, checking its functionality and accuracy. By integrating a micro-actuator with a pressure sensor, the sensor can self-calibrate, eliminating the complexities and costs associated with traditional sensor calibration methods. 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 model provided insights into the sensor's performance not previously observed in experimental testing, such as the temperature gradient within the sensor and its implications. Furthermore, the model was utilized for two design studies. First, the sensor's inefficiencies were studied, and 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, decreases power consumption and transient response time. The study also showed that decreasing the cavity base dimension has a larger effect on decreasing 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 true self-calibrating pressure sensors. / Master of Science / Pressure sensors are used in most engineering applications, and the demand is ever increasing due to emerging fields such as the Internet of things (IOT), automations, and autonomy. One drawback of current pressures sensor technology is their need to be calibrated, ensuring accuracy and function. Sensor calibration requires equipment, trained personnel, and must be done regularly, resulting in significant costs. Borrowing technology, methods, and materials from the integrated circuit industry, the costs of sensor calibration can be addressed by the development of an intelligent MEMS (micro-electromechanical system) pressure sensor with self-calibration capabilities. The self-calibrating capability is achieved by combining a micro-actuator and a micro- pressures sensor into one system.

This work focuses on complementing previously obtained experimental testing data with a thermal finite element model to provide a deeper understanding and insight. The model is implemented in the commercial software ANSYS and model uncertainties were addressed via model calibration. The model revealed a temperature gradient within the sensor, and insight into its potential effects.

The model is also used as a design tool to reduce energy inefficiencies, decrease the time it takes the sensor to respond, and to study the effects of reducing the sensor size. The studies showed that the power consumption can potentially be decreased up to 92% and the response time can be decreased up to 99% by changing the sensor's substrate material. Furthermore, by halving the sensor reference cavity size, the cavity temperature can be increased by 45% and the time for the sensor to respond can be decrease by 59%.

Identiferoai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/100688
Date23 October 2020
CreatorsDe Clerck, Albrey Paul
ContributorsMechanical Engineering, Ng, Wing Fai, Paul, Mark R., West, Robert L.
PublisherVirginia Tech
Source SetsVirginia Tech Theses and Dissertation
Detected LanguageEnglish
TypeThesis
FormatETD, application/pdf
RightsIn Copyright, http://rightsstatements.org/vocab/InC/1.0/

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