Co-Optimization of Fuels &amp; Engines: Efficiency Merit Function for Spark-Ignition Engines; Revisions and Improvements Based on FY16-17 Research

Farrell, John T.; Zigler, Bradley T.; Ratcliff, Matthew A.; Miles, Paul C.; Kolodziej, Christopher P.; Sjöberg, Magnus; Sluder, C. Scott; Szybist, Jim; Wagner, Scott C.; Splitter, Derek; Pihl, Joshua; Toops, Todd J.; Moses-DeBusk, Melanie; Storey, John; Vuilleumier, David

doi:10.2172/1463450

Cited by 9 publications

(15 citation statements)

References 29 publications

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“…Leveraging the natural advantages of enzymatic synthesis enables deployment of fuel molecules with highly tunable properties unachievable via petrochemical routes -enabling potential improvements in octane/cetane number, melting point, energy density, and sooting tendency (Figure 2). A recent survey of bioblendstocks for light duty gasoline engines utilized a computational merit function (Farrell et al 2018) to generate a top 10 list of blendstocks likely to enable fuel efficiency exceeding that of E10 premium gasoline, including seven alcohols, cyclo-pentanone, di-isobutylene, and mixed furan derivatives (Gaspar 2019) . These bioblendstocks may generate additional value via synergistic blending with lower cost fuel mixtures.…”

Section: Future Perspectivesmentioning

confidence: 99%

Microbial production of advanced biofuels

et al. 2021

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Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels are biofuels produced from a renewable carbon source using engineered microorganisms. Two biofuels, ethanol and biodiesel, have been made inroads to displacing petroleum-based fuels, but their penetration has been limited by the amounts that can be used in conventional engines and by cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure, but have had limited penetration due to costs. In this review, we will discuss the advances in engineering microbial metabolism to produce advanced biofuels and prospects for reducing their costs.

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Section: Future Perspectivesmentioning

confidence: 99%

Microbial production of advanced biofuels

et al. 2021

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“…Considering the CRs of modern engines, ,, as well as modern vehicle calibration and drive-cycle conditions, the results suggest that there is no strong synergy between low-RON fuels and this combustion system in a multimode implementation. Conversely, a strong synergy is expected between high-RON and high- S fuels for both MMC at applicable conditions as well as for boosted stoichiometric SI operation, which extends to beyond-RON conditions and therefore also benefits from fuels with higher RON and higher S . ,− …”

Section: Resultsmentioning

confidence: 99%

Octane Requirements of Lean Mixed-Mode Combustion in a Direct-Injection Spark-Ignition Engine

et al. 2022

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This study investigates the octane requirements of a hybrid flame propagation and controlled autoignition mode referred to as mixed-mode combustion (MMC), which allows for strong control over combustion parameters via a spark-initiated deflagration phase. Due to the throughput limitations associated with both experiments and 3-D computational fluid dynamics calculations, a hybrid 0-D and 1-D modeling methodology was developed, supported by experimental validation data. This modeling approach relied on 1-D, two-zone engine simulations to predict bulk in-cylinder thermodynamic conditions over a range of engine speeds, compression ratios, intake pressures, trapped residual levels, fueling rates, and spark timings. Those predictions were then transferred to a 0-D chemical kinetic model, which was used to evaluate the autoignition behavior of fuels when subjected to temperature–pressure trajectories of interest. Finally, the predicted autoignition phasings were screened relative to the progress of the modeled deflagration-based combustion in order to determine if an operating condition was feasible or infeasible due to knock or stability limits. The combined modeling and experimental results reveal that MMC has an octane requirement similar to modern stoichiometric spark-ignition engines in that fuels with high research octane number (RON) and high octane sensitivity (S) enable higher loads. Experimental trends with varying RON and S were well predicted by the model for 1000 and 1400 rpm, confirming its utility in identifying the compatibility of a fuel’s autoignition behavior with an engine configuration and operating strategy. However, the model was not effective in predicting (nor designed to predict) operability limits due to cycle-to-cycle variations, which experimentally inhibited operation of some fuels at 2000 rpm. Putting the operable limits and efficiency from MMC in the context of a state-of-the-art engine, the MMC showed superior efficiencies over the range investigated, demonstrating the potential to further improve fuel economy.

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“…In this work, we present an optimization-based method to design fuels for advanced highly boosted spark-ignition engines with dedicated high compression ratios using the recently developed engine efficiency merit function , as an objective function (Figure ). We evaluate engine efficiency as well as technical and practical feasibility of each candidate fuel by combining quantum chemistry-based predictive thermodynamics and machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

et al. 2023

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Co-design of alternative fuels and future spark-ignition (SI) engines allows very high engine efficiencies to be achieved. To tailor the fuel's molecular structure to the needs of SI engines with very high compression ratios, computer-aided molecular design (CAMD) of renewable fuels has received considerable attention over the past decade. To date, CAMD for fuels is typically performed by computationally screening the physicochemical properties of single molecules against property targets. However, achievable SI engine efficiency is the result of the combined effect of various fuel properties, and molecules should not be discarded because of individual unfavorable properties that can be compensated for. Therefore, we present an optimization-based fuel design method directly targeting SI engine efficiency as the objective function. Specifically, we employ an empirical model to assess the achievable relative engine efficiency increase compared to conventional RON95 gasoline for each candidate fuel as a function of fuel properties. For this purpose, we integrate the automated prediction of various fuel properties into the fuel design method: Thermodynamic properties are calculated by COSMO-RS; combustion properties, indicators for environment, health and safety, and synthesizability are predicted using machine learning models. The method is applied to design pure-component fuels and binary ethanol-containing fuel blends. The optimal pure-component fuel tert-butyl formate is predicted to yield a relative efficiency increase of approximately 8% and the optimal fuel blend with ethanol and 3,4-dimethyl-3-propan-2-yl-1-pentene of 19%.

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Co-Optimization of Fuels & Engines: Efficiency Merit Function for Spark-Ignition Engines; Revisions and Improvements Based on FY16-17 Research

Cited by 9 publications

References 29 publications

Microbial production of advanced biofuels

Microbial production of advanced biofuels

Octane Requirements of Lean Mixed-Mode Combustion in a Direct-Injection Spark-Ignition Engine

Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning

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