WD+MS Systems as the Progenitors of Type Ia Supernovae with Different Metallicities

The single-degenerate model for the progenitors of type Ia supernovae (SNe Ia) is one of the two most popular models, in which a carbon-oxygen white dwarf (CO WD) accretes hydrogen-rich material from its companion, increases its mass to the Chandrasekhar mass limit, and then explodes as a SN Ia. Incorporating the prescription of Hachisu et al. (1999a) for the accretion efficiency into Eggleton's stellar evolution code and assuming that the prescription is valid for \emph{all} metallicities, we carried out a detailed binary evolution study with different metallicities. We show the initial and final parameter space for SNe Ia in a ($\log P-M_{\rm 2}$) plane. The positions of some famous recurrent novae in the ($\log P-M_{\rm 2}$) plane, as well as a supersoft X-ray source (SSS), RX J0513.9-6951 are well explained by our model, and our model can also explain the space velocity and mass of Tycho G, which is now suggested to be the companion star of Tycho's supernova . Our study indicates that the SSS, V Sge, is a potential progenitor of supernovae like SN 2002ic if the delayed dynamical-instability model in Han & Podsiadlowski (2006) is appropriate.Comment: 10 pages, 12 figures, accepted for publication in PAS

A data-based approach for multivariate model predictive control performance monitoring

An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system