Several compression-ignition and low-temperature combustion strategies require a fuel where the ignition-delay-time (IDT) is highly sensitive to the fuel-to-air equivalence-ratio (φ). Quantitative prediction of φ-sensitivity (i.e., the change in IDT with respect to φ) would enable rapid screening of the numerous possible (bio)fuel candidates for this desired high φ-sensitivity characteristic. In this work, we propose a new φ-sensitivity metric (η), which is primarily a function of only temperature (T) and pressure (P). We assess the reliability of 0-D (perfectly homogeneous) simulation and state-of-the-art reaction mechanisms for several well-studied fuels by comparing simulation results with experimental φ-sensitivity measurements using the Advanced Fuel Ignition Delay Analyzer (AFIDA). We investigate the impact of molecular structure (i.e., functional groups and branching sites) on φ-sensitivity for alkanes, ethers, and ketones. Assigning uncertainties to the 0-D simulation results is facilitated by training artificial neural networks to rapidly predict η for randomly perturbed sets of the key low- and intermediate-temperature reaction rate parameters. The primary implications of this study are that experimental φ-sensitivity data can be used to refine and validate proposed reaction mechanisms, while machine learning and uncertainty quantification of 0-D simulations are essential for quantitative prediction of φsensitivity in order to rapidly screen fuel candidates.

Quantitative prediction of ignition-delay-time φ-sensitivity: Role of 0-D simulations, machine learning, and uncertainty quantification

Macromolecular Chemistry: The Second Century

[Author Benefits](https://scimeetings.acs.org/?utm_source=pubs_content_marketing&utm_medium=website&utm_campaign=0320_MCF_NPI_Launch_Spring_Homepage&ref=pubs_content_marketing)

[How to use SciMeetings](https://storage.googleapis.com/dl.morressier.com/acs/SciMeetings-ACSSpring2020-HowToGuide_M3020-Final.pdf)

[Frequently Asked Questions](https://storage.googleapis.com/dl.morressier.com/acs/SciMtng_ACSNMSp2020_FAQ_M3120_Final.pdf)

ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.