A 3-D Multiphysics Modeling and Simulations for Uncertainty Reduction in the Performance of Electrostatic Sensor

Electrostatic (ES) sensors have gained significant use in engine health monitoring (EHM) systems due to their real-time particle detection, noncontact operation, hightemperature resistance, and cost-effective operation in the complex environment of the exhaust gas path. The response of an ES sensor is dependent on a series of uncertain inherent parameters of gas particles, including their trajectory, velocity, size, concentration, temperature, and release conditions. This series of dependencies on uncertain parameters leads to uncertainty in the performance of ES sensors and increases the chances of false alarms in real-time EHM systems. To this end, herein, a 3-D Multiphysics approach is presented by integrating heat transfer, fluid flow, particle tracing, and ES sensing models. The Multiphysics numerical model demonstrates good accuracy compared to existing models while being validated with the experimental results. The 3-D Multiphysics simulations have shown the coupling effect of models, the influence of inherent uncertain parameters on charge accumulation, and the induced charge on the ES sensor. Thereby, the developed 3-D Multiphysical model facilitates avoiding false alarms in the EHM system due to uncertain inherent parameters of exhaust gas.