Graduate Thesis Or Dissertation


Advancing Renewable Gas Storage using Flat-histogram Methods Public Deposited

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  • This work introduces the novel flat-histogram Monte Carlo (MC) method stochastic approximation with a dynamic update factor (SAD) and explores the convergence properties of a variety of related weight-based MC methods. The new method is applied to a number of physical `test’ systems including the 2D Ising model, a square-well fluid, and a 31 atom Lennard-Jones cluster. We find that SAD performs robustly on all of the systems. Also, SAD efficiently samples the entire energy space defined by a chosen temperature range rather than using unphysical parameters. Additionally, we develop a theoretical upper bound for gas adsorption and delivery in porous materials and compare the upper bound with experimental data. A driving motivation for developing the novel Monte Carlo method SAD is to provide a simulation method for computing the thermodynamic properties of porous materials. By computationally determining a materials deliverable capacity, researchers save countless development and experimental hours. In particular, this body of research heavily contributes to enabling light-vehicle gas storage applications.
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