Bioderived feedstocks offer the opportunity to design transportation fuels with dramatically improved efficiency, emissions, and carbon footprint but detailed chemical kinetic mechanisms of biofuel blendstocks are not well understood. We develop to construct reduced kinetic models that accurately describe soot precursor formation at a localized level and use YSI (Yield Sooting Index, https://ysi.ml.nrel.gov) for network-embedded validation. This project will enable us to provide one of the fundamental links in the chain of models connecting fuel chemical structure to particulate matter. The most significant advantage is the quick building of reduced kinetic mechanisms for any target molecule which doesn’t have a detailed mechanism. Moving beyond the prediction of molecular properties, we explore the potential to use a machine learning approach in automating QM calculations for large-scale kinetic simulations. This is an expansion of our bond dissociation energy (BDE) machine learning model (https://bde.ml.nrel.gov/). These efforts will dramatically expand our ability to design fuel blendstocks that optimally leverage bioderived functionality in new, efficient engine designs.
https://bioenergy-kimlab.org/wp-content/uploads/2021/01/fig3-v2-fi-400.png 400 400 Academic Web Pages http://bioenergy-kimlab.org/wp-content/uploads/2021/02/colorado-logo.png Academic Web Pages2021-01-01 16:45:192021-01-29 19:12:07Bottom-up Prediction/Design of (Bio)Fuel and Engine Performance