Publications

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The Co-Optimization of Fuels and Engines (Co-Optima) initiative from the US Department of Energy aims to co-develop fuels and engines in an effort to maximize energy efficiency and the utilization of renewable fuels. Many of these renewable fuel options have fuel chemistries that are different from those of petroleum-derived fuels. Because practical market fuels need to meet specific fuel-property requirements, a chemistry-agnostic approach to assessing the potential benefits of candidate fuels was developed using the Central Fuel Property Hypothesis (CFPH). The CFPH states that fuel properties are predictive of the performance of the fuel, regardless of the fuel's chemical composition. In order to use this hypothesis to assess the potential of fuel candidates to increase efficiency in spark-ignition (SI) engines, the individual contributions towards efficiency potential in an optimized engine must be quantified in a way that allows the individual fuel properties to be traded off for one another. This review article begins by providing an overview of the historical linkages between fuel properties and engine efficiency, including the two dominant pathways currently being used by vehicle manufacturers to reduce fuel consumption. Then, a thermodynamic-based assessment to quantify how six individual fuel properties can affect efficiency in SI engines is performed: research octane number, octane sensitivity, latent heat of vaporization, laminar flame speed, particulate matter index, and catalyst light-off temperature. The relative effects of each of these fuel properties is combined into a unified merit function that is capable of assessing the fuel property-based efficiency potential of fuels with conventional and unconventional compositions.

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This is one of a series of reports produced as a result of the Co-Optimization of Fuels & Engines (Co-Optima) project, a Department of Energy (DOE)-sponsored multi-agency project initiated to accelerate the introduction of affordable, scalable, and sustainable biofuels and high-efficiency, low-emission vehicle engines. The simultaneous fuels and vehicles research and development is designed to deliver maximum energy savings, emissions reduction, and on-road performance.

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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.

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In-cylinder flow measurements are necessary to gain a fundamental understanding of swirl-supported, light-duty Diesel engine processes for high thermal efficiency and low emissions. Planar particle image velocimetry (PIV) can be used for non-intrusive, in situ measurement of swirl-plane velocity fields through a transparent piston. In order to keep the flow unchanged from all-metal engine operation, the geometry of the transparent piston must adapt the production-intent metal piston geometry. As a result, a temporally- and spatially-variant optical distortion is introduced to the particle images. To ensure reliable measurement of particle displacements, this work documents a systematic exploration of optical distortion quantification and a hybrid back-projection procedure that combines ray-tracing-based geometric and in situ manual back-projection approaches. The proposed hybrid back-projection method for the first time provides a time-efficient and robust way to process planar PIV measurements conducted in an optical research engine with temporally- and spatially-varying optical distortion. This method is based upon geometric ray tracing and serves as a universal tool for the correction of optical distortion with an arbitrary but axisymmetric piston crown window geometry. Analytical analysis demonstrates that the ignorance of optical distortion change during the PIV laser temporal interval may induce a significant error in instantaneous velocity measurements. With the proposed digital dewarping method, this piston-motion-induced error can be eliminated. Uncertainty analysis with simulated particle images provides guidance on whether to back-project particle images or back-project velocity fields in order to minimize dewarping-induced uncertainties. The optimal implementation is piston-geometry-dependent. For regions with significant change in nominal magnification factor, it is recommended to apply the proposed back-projection approach to particle images prior to PIV interrogation. For regions with significant dewarping-induced particle elongation (Ep > 3), it is recommended to apply the proposed dewarping method to the vector fields resulting from PIV interrogation of raw particle image pairs.

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