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%.

A growing industry demand for smart pressure sensors that can be quickly calibrated to compensate for sensor drift, nonlinearity effects, and hysteresis without the need for expensive equipment has led to the development of a self-calibrating pressure sensor. Pressure sensor inaccuracies are often resolved with sensor calibration, which typically requires the use of laboratory equipment that can produce a known, standard pressure to actuate the sensor. The developed MEMS-based, self-calibrating pressure sensor is a piezoresistive-type sensor with a sensing element made from a silicon on insulator (SOI) wafer using deep reactive-ion etching to create a hollow reference cavity. Using a micro-heater to heat the small, air-filled reference cavity of the sensing element, a standard pressure is generated to actuate the sensor's pressure-sensitive membrane, creating a self-calibration effect. Previous work focused on modeling and improving the thermal performance of the sensor identified potential solutions to extend the sensor's calibration and operating range without increasing the micro-heater's power consumption. This report focuses on using a water liquid-to-vapor phase change inside the sensor's reference cavity to increase the sensor's effective range and response time without increasing power demands. A combination of Ansys Fluent CFD modeling and benchtop experiments were used to guide the development of the two-phase, self-calibrating pressure sensor. A two-phase benchtop testing rig was built to demonstrate the anticipated effects of a liquid-to-vapor phase change in a closed domain and to provide experimental data to anchor CFD models. Due to the complexity of modeling a phase-change within a closed domain with Ansys Fluent R21.1, the CFD modeling was performed in two stages. First, the two-phase benchtop rig was modeled, and validated using benchtop test data to verify the Volume of Fluid multiphase model setup in Ansys Fluent. Then, a 2D Ansys Fluent model of the self-calibrating pressure sensor's reference cavity using the validated multiphase model was made, demonstrating the potential temperature, pressure, and density gradients inside the reference cavity at steady state. Using the guidance from the benchtop testing and CFD modeling, a prototype two-phase, self-calibrating pressure sensor was fabricated with a water volume fraction of at least 0.1 in the reference cavity. Testing the prototype two-phase sensor showed that the addition of a water liquid-to-vapor phase change inside the sensor's reference cavity can nearly triple the sensor's effective range of operation and self-calibration without increasing the power consumption of the cavity micro-heater. / Master of Science / Highly sensitive pressure sensors are essential to many modern engineering applications. For a pressure sensor to be accurate and functional, it must be properly calibrated with a known, standard pressure range that overlaps with the sensor's intended operating range. Mechanical wear, material aging, and thermal effects all reduce a pressure sensor's accuracy over time, requiring recalibration which often involves expensive equipment and long downtimes. To eliminate the need for additional equipment and the removal of the pressure sensor from its use-site for calibration, the authors have developed a pressure sensor capable of self-calibration. The self-calibrating sensor uses a MEMS sensing element with an integrated micro-actuator in the form of a small heating element to create the standard pressure range necessary for calibration. Previous work focused on modeling the thermal performance of the sensor identified potential solutions to extend the sensor's calibration and operating range without increasing the micro-heater's power consumption. This report focuses on using a water liquid-to-vapor phase change inside the sensor's reference cavity to increase the sensor's effective range and response time without increasing power demands. To help guide the development of the two-phase, self-calibrating sensor, a benchtop testing rig and CFD model were used to examine the effects of heating a liquid inside of a closed domain. A 2D CFD model of the sensor's reference cavity was also used to provide insight into the expected temperature and pressure gradients inside the sensing element after heating with the micro-actuator. Using the guidance from the CFD models, a prototype two-phase, self-calibrating pressure sensor was fabricated. Testing the prototype two-phase sensor showed that the addition of a water liquid-to-vapor phase change inside the sensor's reference cavity can nearly triple the sensor's effective range of operation and self-calibration without increasing the power consumption of the cavity micro-heater.

Engine Management Systems (EMS) is getting more complicated each year with new functions being introduced due to tighter emission regulations of both air quality and CO2. This directly a ects the calibration process of a powertrain because the number of vehicle parameters has increased about 10 times in the last 10 years. Self-calibrating feature such as proposed in this thesis has the potential to increase the e ciency of calibrating a complex EMS. The feature is intended to adapt itself to the engine behaviour and performance by continuously updating its calibration maps as the engine is being operated. This process will reduce the needs for new calibration data and additional ne-tuning when an EMS is being carried over to a di erent vehicle. The self-calibrating feature automatically adjusts the air path calibration maps of an engine. It adjusts the air path setpoint maps in real-time for steady state operating conditions.