Search for: All records

The use of thin films made of alloys, i.e., containing multiple metal species, can enhance their properties. However, as with single-element films, residual stress in the films can limit their performance. A model is proposed for relating the stress in alloy thin films to the processing conditions (growth rate, temperature, and sputter-gas pressure), material properties (composition, atomic and defect mobilities, and elastic moduli), and microstructure (grain size and grain growth kinetics). The model is based on stress-generating processes that occur during film growth at grain boundaries and due to energetic particle impacts. While the equations are similar to those proposed for single-element films, the alloy kinetic parameters now contain the effects of the different atomic species. The model is used to explain the growth rate and composition dependence of in situ stress evolution during the deposition for various concentrations in the tungsten–vanadium system. 

This paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained power system transient stability preventive control method to deal with uncertain renewable energy and loads-induced stability risk. The traditional transient stability-constrained preventive control is reformulated as a chance-constrained optimization problem. To deal with the computational bottleneck of the time-domain simulation-based probabilistic transient stability assessment, the DSPP is developed. DSPP is a parametric Bayesian approach that allows us to predict system transient stability with high computational efficiency while accurately quantifying the confidence intervals of the predictions that can be used to inform system instability risk. To this end, with a given preset confidence probability, we embed DSPP into the primal dual interior point method to help solve the chance-constrained preventive control problem, where the corresponding Jacobian and Hessian matrices are derived. Comparison results with other existing methods show that the proposed method can significantly speed up preventive control while maintaining high accuracy and convergence.