arXiv subjects

Jordan K. Pommerenck

Publications and source records attributed to Jordan K. Pommerenck.

3 recordsLinked to original sources

arXiv2021

An upper bound to gas storage and delivery via pressure-swing adsorption in porous materials

Both hydrogen and natural gas are challenging to economically store onboard vehicles as fuels, due to their low volumetric energy density at ambient conditions. One strategy to densify these gases is to pack the fuel tank with a porous adsorbent material. The US Department of Energy (DOE) has set volumetric deliverable capacity targets which, if met, would help enable commercial adoption of hydrogen/natural gas as transportation fuels. Here, we present a theoretical upper bound on the deliverable capacity of a gas in a rigid porous material via an isothermal pressure swing. To provide an extremum, we consider a substrate that provides a spatially uniform potential energy field for the gas. Our bound relies directly on experimentally measured properties of the pure gas. We conclude that the deliverable capacity targets set by the DOE for room-temperature natural gas and hydrogen storage are just barely theoretically possible. The targets are likely to be impossible for any real, rigid porous material because of steric repulsion, which reduces the deliverable capacity below our upper bound. Limitations to the scope of applicability of our upper bound may guide fuel tank design and future material development. Firstly, one could avoid using an isothermal pressure swing by heating the adsorbent to drive off trapped, residual gas. Secondly, our upper bound assumes the material does not change its structure in response to adsorbed gas, suggesting that flexible materials could still satisfy the DOE targets.

physics.chem-ph↗

arXiv2020

Flat histogram method comparison on 2D Ising Model

We compare the convergence of several flat-histogram methods applied to the 2D Ising model, including the recently introduced stochastic approximation with a dynamic update factor (SAD) method. We compare this method with the Wang-Landau (WL) method, the $1/t$ variant of the WL method, and standard stochastic approximation Monte Carlo (SAMC). In addition, we consider a procedure WL followed by a "production run" with fixed weights that refines the estimation of the entropy. To our knowledge, this work is the first to test this approach against other methods. We find that WL followed by a production run \emph{does} converge to the true density of states, in contrast to pure WL. Three of the methods converge robustly: SAD, $1/t$-WL, and WL followed by a production run. Of these, SAD does not require \emph{a priori} knowledge of the energy range. This work also shows that WL followed by a production run performs superior to other forms of WL while ensuring both ergodicity and detailed balance.

cond-mat.stat-mech↗

arXiv2019

Stochastic Approximation Monte Carlo with a Dynamic Update Factor

We present a new Monte Carlo algorithm based on the Stochastic Approximation Monte Carlo (SAMC) algorithm for directly calculating the density of states. The proposed method is Stochastic Approximation with a Dynamic update factor (SAD) which dynamically adjusts the update factor $γ_t$ during the course of the simulation. We test this method on the square-well fluid and the 31-atom Lennard-Jones cluster and compare the convergence behavior of several related Monte Carlo methods. We find that both the SAD and $1/t$-Wang-Landau ($1/t$-WL) methods rapidly converge to the correct density of states without the need for the user to specify an arbitrary tunable parameter $t_0$ as in the case of SAMC. SAD requires as input the temperature range of interest, in contrast to $1/t$-WL, which requires that the user identify the interesting range of energies. The convergence of the $1/t$-WL method is very sensitive to the energy range chosen for the low-temperature heat capacity of the Lennard-Jones cluster. Thus, SAD is more powerful in the common case in which the range of energies is not known in advance.

cond-mat.stat-mech↗