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Vlachos, D. G.

Publications and source records attributed to Vlachos, D. G..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Acceleration and sensitivity analysis of lattice kinetic Monte Carlo simulations using parallel processing and rate constant rescaling

Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Steady state likelihood ratio sensitivity analysis for stiff kinetic Monte Carlo simulations

Kinetic Monte Carlo simulation is an integral tool in the study of complex physical phenomena present in applications ranging from heterogeneous catalysis to biological systems to crystal growth and atmospheric sciences. Sensitivity analysis is useful for identifying important parameters and rate-determining steps, but the finite-difference application of sensitivity analysis is computationally demanding. Techniques based on the likelihood ratio method reduce the computational cost of sensitivity analysis by obtaining all gradient information in a single run. However, we show that disparity in time scales of microscopic events, which is ubiquitous in real systems, introduces drastic statistical noise into derivative estimates for parameters affecting the fast events. In this work, the steady-state likelihood ratio sensitivity analysis is extended to singularly perturbed systems by invoking partial equilibration for fast reactions, that is, by working on the fast and slow manifolds of the chemistry. Derivatives on each time scale are computed independently and combined to the desired sensitivity coefficients to considerably reduce the noise in derivative estimates for stiff systems. The approach is demonstrated in an analytically solvable linear system.

Núñez, M.↗
DOE OSTI2014

Hydrodeoxygenation of HMF over Pt/C in a continuous flow reactor

The three‐phase hydrodeoxygenation reaction of 5‐hydroxymethylfurfural (HMF) with H 2 was studied over a 10 wt % Pt/C catalyst using both batch and flow reactors, with ethanol, 1‐propanol, and toluene solvents. The reaction is shown to be sequential, with HMF reacting first to furfuryl ethers and other partially hydrogenated products. These intermediate products then form dimethyl furan (DMF), which in turn reacts further to undesired products. Furfuryl ethers were found to react to DMF much faster than HMF, explaining the higher reactivity of HMF when alcohol solvents were used. With the optimal residence time, it was possible to achieve yields approaching 70% in the flow reactor with the Pt/C catalyst. Much higher selectivities and yields were obtained in the flow reactor than in the batch reactor because side products are formed sequentially, rather than in parallel, demonstrating the importance of choosing the correct type of reactor in catalyst screening. © 2014 American Institute of Chemical Engineers AIChE J , 61: 590–597, 2015

Luo, Jing↗