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KAUST develops radically simplified method for accurately simulating fuel combustion; aid to engine design

Researchers at KAUST, with colleagues at Trinity College Dublin, have developed a new conceptual model for describing a fuel’s composition that can accelerate and simplify combustion simulations, ultimately aiding engine design. A paper on the work is published in the journal Combustion and Flame.

Rather than try to model fuel combustion based on the long list of molecules the fuel contains, the researchers found a shorthand: they now show that they can distill the complexity into a very short list of molecular subunits or functional groups most fuel molecules are made from.

This study presents a new methodology to develop fuel surrogates by matching a group of molecular fragments or “functional groups” that are readily identifiable and quantifiable in the target fuel by NMR, while at the same time minimizing the number of surrogate molecules used to match the functional groups. The methodology is hence denoted as the MFG approach. We hypothesize that the functional group distribution serves as an indicator of distinctly reacting chemical functionalities, which are finite arrangements of several adjacent functional groups that govern chemical reactivity.

… This work is novel because we focus on matching functional groups (i.e. the objective function) making up these hydrocarbon classes, which is a more fundamental approach. We also demonstrate that as little as one or two molecules can be used to match the fuel functional groups to formulate surrogates of practical gasoline fuels.

—Jameel et al.

This radically simplified method for accurately simulating fuel combustion was developed by Abdul Gani Abdul Jameel under the guidance of Mani Sarathy and his team.

The project began with the hypothesis that the combustion behavior of each component of a fuel is dictated by the functional groups it comprises. To corroborate the theory, the team performed high-resolution nuclear magnetic resonance analysis in KAUST’s Core Labs to identify the main functional groups in a series of complex fuels. They then made simple surrogates for each fuel by selecting one or two molecules that contained the functional groups in the same balance as the real fuel.

Comparing key combustion parameters, such as ignition delay time and smoke point in the lab, the researchers confirmed the simple surrogates were faithful mimics of the real fuel. They showed a good surrogate needed to match the average molecular weight and contain the right proportions of just five key carbon-hydrogen functional groups: CH3, paraffinic CH2, paraffinic CH, naphthenic CH–CH2 and aromatic C–CH.

Traditional combustion modeling accurately captures the behavior of fuel mixtures by adding detailed chemical kinetics data for increasingly more of the components in the fuel, but the drawback is that the simulation becomes prohibitively slow to run.

We have shown that adding complexity to models is not necessary, as long as underlying features of simpler molecular parameters, the functional groups, are captured.

—Mani Sarathy

The team’s method for making simple fuel surrogates will directly improve the design of efficient new engines, suggests Abdul Jameel.

Using a minimal number of components significantly reduces the time involved in developing chemical kinetic models and the computational expenses involved in simulating combustion in internal combustion engines.

—Abdul Jameel

The team’s functional-group-based approach will reach far beyond surrogate formulation.

We are currently developing machine-learning-based models to predict the combustion properties of fuels based on their functional groups.

—Abdul Jameel


  • Abdul Jameel, A. G., Nasera, N., Issayeva, G., Touitoub, J., Ghosh, M.K., Emwas, A-H., Farooq, A., Doley, S. & Sarathy, S. M. (2018) “A minimalist functional group (MFG) approach for surrogate fuel formulation.” Combustion and Flame 192, 250-271 doi: 10.1016/j.combustflame.2018.01.036



Engines will be around for a while, improving makes sense.


Sales of cars with an ICE was 99,3% in 2017 in the EU. It makes sense to invest more...

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