Air Damping Analysis of a Micro-Coriolis Mass Flow Sensor

A micro-Coriolis mass flow sensor is a resonating device that measures small mass flows of fluid. A large vibration amplitude is desired as the Coriolis forces due to mass flow and, accordingly, the signal-to-noise ratio, are directly proportional to the vibration amplitude. Therefore, it is important to maximize the quality factor Q so that a large vibration amplitude can be achieved without requiring high actuation voltages and high power consumption. This paper presents an investigation of the Q factor of different devices in different resonant modes. Q factors were measured both at atmospheric pressure and in vacuum. The measurement results are compared with theoretical predictions. In the atmospheric environment, the Q factor increases when the resonance frequency increases. When reducing the pressure from 1 to 0.1 , the Q factor almost doubles. At even lower pressures, the Q factor is inversely proportional to the pressure until intrinsic effects start to dominate, resulting in a maximum Q factor of approximately 7200.

Linearisation of flow sensors using evolutionary optimised function-based methods

Purpose This study aims to propose optimised function-based evolutionary algorithms in this research to effectively replace the traditional electronic circuitry used in linearising constant temperature anemometer (CTA) and Microbridge mass flow sensor AWM 5000. Design/methodology/approach The proposed linearisation technique effectively uses the ratiometric function for the linearisation of CTA and Microbridge mass flow sensor AWM 5000. In addition, the well-known transfer relation, namely, the King’s Law is used for the linearisation of CTA and successfully implemented using LabVIEW 7.1. Findings Investigational results unveil that the proposed evolutionary optimised linearisation technique performs better in linearisation of both CTA and Mass flow sensors, and hence finds applications for computer-based flow measurement/control systems. Originality/value The evolutionary optimisation algorithms such as the real-coded genetic algorithm, particle swarm optimisation algorithm, differential evolution algorithm and covariance matrix adopted evolutionary strategy algorithm are used to determine the optimal values of the parameters present in the proposed ratiometric function. The performance measures, namely, the full-scale error and mean square error are used to analyse the overall performance of the proposed approach is compared to a state of art techniques available in the literature.

Fault detection and accommodation of a thermal-type flow sensor in an aircraft bleed air system

A fault in the primary mass flow sensor of an aircraft engine bleed air system can cause significant deterioration of overall system performance. This project uses an analytical model of the bleed air system to create a fault detection and accommodation scheme for the mass flow sensor. The analytical model uses information from the upstream and downstream pressure sensors to predict the output of the mass flow sensor. Faults are detected by comparing the output from the sensor with the predicted output from the analytical model. A fuzzy logic rule base is used to determine the degree of the flow sensor fault. The degree of the sensor fault is used to determine the inaccuracy of the faulty sensor output. A corrected estimation of the flow rate is then created using a weighted algorithm consisting of the predicted flow rate from the analytical model and the flow rate from the faulty sensor. The analytical model is also used to detect and accommodate transient responses from the flow sensor including signal overshoot, oscillations and time constant errors. A MATLAB computer simulation is conducted to evaluate the performance of the bleed air system degrades slightly in the event of a fault of the flow sensor. While the sensor fault will degrade the performance of the bleed air system, the degradation is not significant, and the bleed air system is able to maintain acceptable performance in the presence of faults.

Fault detection and accommodation of a thermal-type flow sensor in an aircraft bleed air system

A fault in the primary mass flow sensor of an aircraft engine bleed air system can cause significant deterioration of overall system performance. This project uses an analytical model of the bleed air system to create a fault detection and accommodation scheme for the mass flow sensor. The analytical model uses information from the upstream and downstream pressure sensors to predict the output of the mass flow sensor. Faults are detected by comparing the output from the sensor with the predicted output from the analytical model. A fuzzy logic rule base is used to determine the degree of the flow sensor fault. The degree of the sensor fault is used to determine the inaccuracy of the faulty sensor output. A corrected estimation of the flow rate is then created using a weighted algorithm consisting of the predicted flow rate from the analytical model and the flow rate from the faulty sensor. The analytical model is also used to detect and accommodate transient responses from the flow sensor including signal overshoot, oscillations and time constant errors. A MATLAB computer simulation is conducted to evaluate the performance of the bleed air system degrades slightly in the event of a fault of the flow sensor. While the sensor fault will degrade the performance of the bleed air system, the degradation is not significant, and the bleed air system is able to maintain acceptable performance in the presence of faults.