Krithika Narayanaswamy

Surrogates for Biodiesel: Review and Challenges

Biodiesel is being considered as a renewable fuel candidate to completely or partially replace fossil diesel. Understanding its combustion is key to assess its applicability in practical compression ignition engines. Significant progress has been made in understanding biodiesel combustion through experimental studies, development of reaction kinetics to describe its oxidation, and simulations in typical engine environments. The use of surrogates in place of the real biodiesels plays a crucial role in this endeavour. This chapter reviews the existing studies revolving around surrogate fuels for biodiesels. Thereafter, the challenges ahead in this context to further enhance our knowledge of biodiesel combustion are presented, and possible options to address these are discussed where appropriate.

Simulation-driven formulation of transportation fuel surrogates

An alternative way to formulate transportation fuel surrogates using model predictions of gas-phase combustion targets is explored and compared to conventional approaches. Given a selection of individual fuel components, a multi-component chemical mechanism describing their oxidation kinetics, and a database of experimental measurements for key combustion quantities such as ignition delay times and laminar burning velocities, the optimal fractional amount of each fuel is determined as the one yielding the smallest error between experiments and model predictions. Using a previously studied three-component jet fuel surrogate containing n-dodecane, methyl-cyclohexane, and m-xylene as a case study, this article investigates in a systematic manner how the surrogate composition affects model predictions for ignition delay time and laminar burning velocities over a wide range of temperature, pressure and stoichiometry conditions, and compares the results to existing surrogate formulation techniques, providing new insights on how to define surrogates for simulation purposes. Finally, an optimisation algorithm is described to accelerate the identification of optimal surrogate compositions in this context.