Predictive Engineering Tools for Novel Fuels

Abstract

This project was funded through the Campus Executive Fellowship at University of California (UC) Berkeley, and had two principal aims. First, it sought to explore predictive tools for estimating fuel properties based on molecular structure, with the goal of identifying promising candidates for new fuels to be synthesized. Second, it sought to investigate the possibility of increasing engine efficiency by substituting air for a working fluid with higher efficiency potential employed in a closed loop, namely a mixture of argon and oxygen. In pursuing the predictive tool for novel fuels, a new model was built that proved to be highly predictive of autoignition characteristics for a wide variety of hydrocarbons, esters, ethers and alcohols, and reasonably predictive for furan and tetrahydrofuran compounds, the target class of novel fuels. Obtaining more “training data” for the model improved its predictive capabilities, and further reductions in the uncertainty of the predictions would be possible with more training data. In investigating the concept of a closed-loop engine cycle using an argon-oxygen working fluid, substantial progress was made. Initial engineering models were built showing the feasibility of the concept; numerous collaborations were formed with industry and academic partners; external funding was secured from the California Energy Commission (CEC) to build a dedicated engine platform for research; and this engine platform was designed and constructed. Experimental work and associated modeling studies will take place in late 2016 and early 2017.