Modelling Glass Dissolution with a Monte Carlo Technique

1995 ◽  
Vol 412 ◽  
Author(s):  
Marc Aertsens ◽  
Pierre Van Iseghem

AbstractWe present a Monte Carlo simulation method for modelling glass dissolution in aqueous solutions. This simulation method is consistent with transition state theory, and therefore also with the glass dissolution rate law, used for instance in the Grambow model. The simulation method allows to add dynamics (kinetics) to the existing thermodynamic models for glass dissolution. Using this method, it is possible to model non stoichiometric dissolution of the glass.Besides, we introduce a simple, first version of a model in which we use the simulation method. In this model, we approximate the glass by a lattice. We assume that the glass contains two components: a network former and a network modifier. Bonds between two network formers are assumed to be much stronger than any other bond in the system. We observe that above a threshold value for the concentration of network modifiers, the glass dissolves fast. No surface layer develops and the dissolution rate is constant (linear stoichiometric dissolution). Below this threshold, the glass is more durable and surface layers are formed. As time goes on, the thickness of the surface layers grows. The dissolution of the glass is not stoichiometric. This behaviour agrees with experimental results.

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.


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