Evaluation of computational fluid dynamic methods for reactor safety analysis (ECORA)

Investigation on Safety Dynamic Evolution Mechanism of a Distributed Low Carbon Manufacturing System with Large Time Delay

In order to reveal the inducing factors and safety dynamic evolution mechanism of frequent personal injury accidents under a low carbon manufacturing process, a nonlinear safety dynamic evolution model of a distributed low carbon manufacturing system with large time delay is established. The established model is then verified by simulation results from mathematical analysis and dynamic evolution. Moreover, qualitative analysis on nonlinear safety dynamic evolution and the trend of human–machine safety under a low carbon manufacturing process is investigated. Finally, an application case of the established model is studied. The key results are as follows: (1) There are four dynamic regions, namely the safety area I, the deterioration area II, the asymptotically stable safety area III, and the enhancement area IV of the safety ability in the interaction evolution model of carelessness and safety levels; (2) There are two singularities in the dynamic evolution model of the man–machine safety system with large time delay under a low carbon manufacturing process; (3) The equilibrium points of the human–machine safety system are El = (0, 0) and E2 = (0.5333, 0.2489), while changes in the carelessness level have a serious block effect on safety development with time; (4) For the radial tire casing process, the low carbon development trend of the technological process of radial tire casing is good, but low carbon structure and management have slightly lower low carbon levels. This work provides a theoretical basis for the safety evaluation and control of the distributed low carbon manufacturing human–machine safety system with large time delay.

A Mass Conservative Kalman Filter Algorithm for Computational Thermo-Fluid Dynamics

This paper studies Kalman filtering applied to Reynolds-Averaged Navier⁻Stokes (RANS) equations for turbulent flow. The integration of the Kalman estimator is extended to an implicit segregated method and to the thermodynamic analysis of turbulent flow, adding a sub-stepping procedure that ensures mass conservation at each time step and the compatibility among the unknowns involved. The accuracy of the algorithm is verified with respect to the heated lid-driven cavity benchmark, incorporating also temperature observations, comparing the augmented prediction of the Kalman filter with the Computational Fluid-Dynamic solution found on a fine grid.