Quantum Tunneling Affects Engine Performance

Resolving Discrepancies between State-of-the-Art Theory and Experiment for HO2 + HO2 via Multiscale Informatics

Recent high-level theoretical calculations predict a mild temperature dependence for HO₂ + HO₂ inconsistent with state-of-the-art experimental determinations that upheld the stronger temperature dependence observed in early experiments. Via MultiScale Informatics analysis of the theoretical and experimental data, we identified an alternative interpretation of the raw experimental data that uses HO₂ + HO₂ rate constants nearly identical to theoretical predictions─implying that the theoretical and experimental data are actually consistent, at least when considering the raw data from experimental studies. Similar analyses of typical signals from low-temperature experiments indicate that an HOOOOH intermediate─identified by recent theory but absent from earlier interpretations─yields modest effects that are smaller than, but may have contributed to, the scatter in data among different experiments. More generally, the findings demonstrate that modern chemical theories and experiments have progressed to a point where meaningful comparison requires joint consideration of their data simultaneously.

Permutation-Invariant-Polynomial Neural-Network-Based Δ-Machine Learning Approach: A Case for the HO2 Self-Reaction and Its Dynamics Study

Δ-machine learning, or the hierarchical construction scheme, is a highly cost-effective method, as only a small number of high-level ab initio energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. However, there is no efficient and systematic way to select as few points as possible from the low-level data set. We here propose a permutation-invariant-polynomial neural-network (PIP-NN)-based Δ-machine learning approach to construct full-dimensional accurate PESs of complicated reactions efficiently. Particularly, the high flexibility of the NN is exploited to efficiently sample points from the low-level data set. This approach is applied to the challenging case of a HO₂ self-reaction with a large configuration space. Only 14% of the DFT data set is used to successfully bring a newly fitted DFT PES to the UCCSD(T)-F12a/AVTZ quality. Then, the quasiclassical trajectory (QCT) calculations are performed to study its dynamics, particularly the mode specificity.

Theoretical Study of the Extent of Intersystem Crossing in the O(3P) + C6H6 Reaction with Experimental Validation

The extent of intersystem crossing in the O(³P) + C₆H₆ reaction, a prototypical system for spin-forbidden reactions in oxygenated aromatic molecules, is theoretically evaluated for the first time. Calculations are performed using nonadiabatic transition-state theory coupled with stochastic master equation simulations and Landau-Zener theory. It is found that the dominant intersystem crossing pathways connect the T2 and S0 potential energy surfaces through at least two distinct minimum-energy crossing points. The calculated channel-specific rate constants and intersystem crossing branching fractions differ from previous literature estimates and provide valuable kinetic data for the investigation of benzene and polycyclic aromatic hydrocarbons oxidation in interstellar, atmospheric, and combustion conditions. The theoretical results are supported by crossed molecular beam experiments with electron ionization mass-spectrometric detection and time-of-flight analysis at 8.2 kcal/mol collision energy. This system is a suitable benchmark for theoretical and experimental studies of intersystem crossing in aromatic species.

First-Principles Molecular Dynamics of Monomethylhydrazine and Nitrogen Dioxide

The exploration of chemical reactions preceding ignition is essential for the development of ideal hypergolic propellants. Unexpected reaction pathways of a hypergolic mixture composed of monomethylhydrazine and nitrogen dioxide are predicted through a cooperative combination of (i) spin-unrestricted ab initio molecular dynamics (AIMD) and (ii) wave packet dynamics of protons. Ensembles of AIMD trajectories reveal a sequence of reaction steps for proton transfer and rupture of the C-N bond. The details of proton transfer are explored by wave packet dynamics on the basis of ab initio potential energy surfaces from AIMD trajectories. The possibility of spontaneous ignition of this hypergolic mixture at room temperature is predicted as a quantized feature of proton-transfer dynamics.

Sparsity Facilitates Chemical-Reaction Selection for Engine Simulations

Analysis of large-scale, realistic models incorporating detailed chemistry can be challenging because each simulation is computationally expensive, and a complete analysis may require many simulations. This paper addresses one such problem of this type, chemical-reaction selection in engine simulations. In this computationally challenging case, it is demonstrated how the important concept of sparsity can facilitate chemical-reaction selection, which is the process of finding the most important chemical reactions for modeling a chemical process. It is difficult to perform accurate reaction selection for engine simulations using realistic models of the chemistry, as each simulation takes processor weeks to complete. We developed a procedure to efficiently accomplish this selection process with a relatively small number of simulations using a form of global sensitivity analysis based on sparse regression. The chemical-reaction selection leads to an analysis of the ignition chemistry as it evolves within the compression-ignition engine simulations and allows for the spatial development of the selected chemical reactions to be studied in detail.

Multi-fuel surrogate chemical kinetic mechanisms for real world applications

The most important driving force for development of detailed chemical kinetic reaction mechanisms in combustion is the desire by researchers to simulate practical systems. This paper reviews the parallel evolution of kinetic reaction mechanisms and applications of those models to practical, real engines. Early, quite simple, kinetic models for small fuel molecules were extremely valuable in analyzing long-standing, poorly understood applied ignition and flame quenching problems, and later kinetic models have been applied to much more complex flame propagation, problems including autoignition in spark-ignition engines and issues related to octane numbers and knock in modern, high compression ratio and other engines. The recent emergence of very large, multi-fuel surrogate kinetic mechanisms that can address many different fuel types and real engine applications is discussed as a modern analytical tool that can be used for a wide variety of practical applications.

Global Sensitivity Analysis with Small Sample Sizes: Ordinary Least Squares Approach

A new version of global sensitivity analysis is developed in this paper. This new version coupled with tools from statistics, machine learning, and optimization can devise small sample sizes that allow for the accurate ordering of sensitivity coefficients for the first 10-30 most sensitive chemical reactions in complex chemical-kinetic mechanisms, and is particularly useful for studying the chemistry in realistic devices. A key part of the paper is calibration of these small samples. Because these small sample sizes are developed for use in realistic combustion devices, the calibration is done over the ranges of conditions in such devices, with a test case being the operating conditions of a compression ignition engine studied earlier. Compression-ignition engines operate under low-temperature combustion conditions with quite complicated chemistry making this calibration difficult, leading to the possibility of false positives and false negatives in the ordering of the reactions. So an important aspect of the paper is showing how to handle the trade-off between false positives and false negatives using ideas from the multiobjective optimization literature. The combination of the new global sensitivity method and the calibration are sample sizes a factor of approximately 10 times smaller than were available with our previous algorithm.