Deforming domains occur in many fields of computational fluid dynamics (CFD), such as interface tracking, simulation of pumps and engines, and fluid/structure interaction. The deformation of the domain presents a challenge to the integrity of the computational mesh; substantial motion of the domain boundaries requires vertex motion and changes in mesh connectivity. For cases of simple boundary motion or structured meshes, predetermined changes to the mesh structure can be sufficient. However, without a priori knowledge of how the domain will change, a more robust solution is required. The present work offers a parallelized solution for simplical meshes that is well-suited to extremely complex geometry. The mesh continuously evolves without user intervention or the use of target meshes. Varying length scales imposed by evolving boundary curvatures and narrow gaps are resolved with a fast length-scale algorithm. The set of algorithms are incorporated into an object-oriented code structure that permits broad application to a range of CFD problems. The robustness and versatility of the algorithm is demonstrated in several examples, representing motion of internal and external boundaries, where the boundary motion may or may not be known a priori.
The insulated gate bipolar transistor (IGBT) is a power transistor that is used in medium to high power, and low frequency applications. These applications include railway traction motors, wind turbines, electric and hybrid vehicles and uninterrupted power supplies. IGBT failures can result in reduced efficiency of the system or lead to system failure. Anomaly detection techniques can provide early warning of IGBT failures leading to cost benefits by avoidance of unscheduled maintenance and improved safety. One approach to detect anomalies in IGBTs is to monitor the collector emitter current and voltage in application. These current and voltage parameters can then be used to compute a distance measure called the Mahalanobis Distance (MD). The MD values with the use of an appropriate threshold enable anomaly detection of these devices. The computation of the MD measure requires calculating the covariance between the parameters monitored. The presence of outliers in the monitored data can lead to the overestimation of the covariance matrix that in turn affects the anomaly detection results.This issue can be addressed by the use of robust covariance estimation techniques. In this study, three different robust covariance estimators are evaluated to determine the impact of the use of these techniques on the anomaly detection time obtained by MD.
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