The kinetic model of cyclohexene-air combustion over a wide temperature range

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

We present Deep learning for Collective Variables (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.

Experimental and Kinetic Modeling Study on High-Temperature Autoignition of Cyclohexene

Cyclohexene is an important intermediate during the oxidation of cycloalkanes, which comprise a significant portion of real fuels. Thus, experimental data sets and kinetic models of cyclohexene play an important role in the understanding of the combustion of cycloalkanes and real fuels. In this work, an experimental and kinetic modeling study of the high-temperature ignition of cyclohexene is performed. Ignition delay time (IDT) measurements are carried out in a high-pressure shock tube (HPST). The studied pressures are 5, 10, and 20 bar; the equivalence ratios are 0.5, 1.0, and 2.0; and the temperatures range from 980 to 1400 K for IDT in HPST. It is shown that the IDTs of cyclohexene exhibit Arrhenius behaviors as a function of temperature, and the IDTs decrease as the equivalence ratio and pressure increase. The experimental results are simulated using three previous detailed kinetic mechanisms and an updated detailed mechanism in this work. The updated detailed kinetic mechanism exhibits good agreement with experimental results. Reaction path analysis and sensitivity analysis are performed to provide insights into the chemical kinetics controlling the ignition of cyclohexene. The results demonstrate that different detailed kinetic mechanisms are significantly different, and there are still no unified conclusions about the major reaction path for cyclohexene oxidation. However, it is worth noting that the abstraction reaction by oxygen at the allylic site and the submechanism of cyclopentene are of significant importance for the accurate prediction of IDTs of cyclohexene. The present experimental data set and kinetic model should be valuable to improve our understanding of the combustion chemistry of cycloalkanes.