Impact of fuel chemical function characteristics on spark assisted and kinetically controlled compression ignition performance focused on multi-mode operation

Chuahy, Flavio Dal Forno; Powell, Tommy; Curran, Scott; Szybist, James P.

doi:10.1016/j.fuel.2021.120844

Cited by 7 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even more so than τ 1 , LTHR is a key indicator of low- T reactivity in engines , and can be used to condition the P – T trajectory for optimal phasing and reduced knock propensity. − The correlation-based approach identified here can again be an important screening tool to estimate the engine performance of potential fuel candidates.…”

Section: Results and Discussionmentioning

confidence: 99%

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

et al. 2022

View full text Add to dashboard Cite

Using chemical kinetic modeling and statistical analysis, we investigate the possibility of correlating key chemical “markers”typically small moleculesformed during very lean (ϕ ∼ 0.001) oxidation experiments with near-stoichiometric (ϕ ∼ 1) fuel ignition properties. One goal of this work is to evaluate the feasibility of designing a fuel-screening platform, based on small laboratory reactors that operate at low temperatures and use minimal fuel volume. Buras et al. [Combust. Flame 2020, 216, 472–484] have shown that convolutional neural net (CNN) fitting can be used to correlate first-stage ignition delay times (IDTs) with OH/HO2 measurements during very lean oxidation in low-T flow reactors with better than factor-of-2 accuracy. In this work, we test the limits of applying this correlation-based approach to predict the low-temperature heat release (LTHR) and total IDT, including the sensitivity of total IDT to the equivalence ratio, ϕ. We demonstrate that first-stage IDT can be reliably correlated with very lean oxidation measurements using compressed sensing (CS), which is simpler to implement than CNN fitting. LTHR can also be predicted via CS analysis, although the correlation quality is somewhat lower than for first-stage IDT. In contrast, the accuracy of total IDT prediction at ϕ = 1 is significantly lower (within a factor of 4 or worse). These results can be rationalized by the fact that the first-stage IDT and LTHR are primarily determined by low-temperature chemistry, whereas total IDT depends on low-, intermediate-, and high-temperature chemistry. Oxidation reactions are most important at low temperatures, and therefore, measurements of universal molecular markers of oxidation do not capture the full chemical complexity required to accurately predict the total IDT even at a single equivalence ratio. As a result, we find that ϕ-sensitivity of ignition delay cannot be predicted at all using solely correlation with lean low-T chemical speciation measurements.

show abstract

Section: Results and Discussionmentioning

confidence: 99%

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

“…The implemented algorithm has been applied to the formulation of the surrogates for four real gasolines fully experimentally characterized from literature [28,29]. From [28], two Co-Optima gasoline fuels, namely Co-Optima Aromatic and Co-Optima Cycloalkane, have been selected. From [29], the EU6 compliant gasolines Fuel-15 (hydrocarbon blend with properties in line with the current commercial RON95 gasoline) and Fuel-11 (E10 gasoline-ethanol blend) are selected among the available fuels according to Author's defined criteria i.e., ethanol-blending ≤ E10; RON ≥ 95, olefins volume content ≤ 10%.…”

Section: Resultsmentioning

confidence: 99%

“…From [29], the EU6 compliant gasolines Fuel-15 (hydrocarbon blend with properties in line with the current commercial RON95 gasoline) and Fuel-11 (E10 gasoline-ethanol blend) are selected among the available fuels according to Author's defined criteria i.e., ethanol-blending ≤ E10; RON ≥ 95, olefins volume content ≤ 10%. The main properties of the validation gasolines collected from [28,29] are resumed in Table A1. It is underlined that the EPIONA of Fuel-11 and Fuel-15 are calculated by the present Authors based on the Detailed Hydrocarbon Analysis (DHA) available in [29].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

et al. 2021

View full text Add to dashboard Cite

Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A Bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the ‘unicity’ of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the ‘unicity’ of the surrogate composition has revealed that (i) the unicity is strongly correlated with the accuracy and that (ii) both ‘unicity’ and accuracy of the prediction are very sensitive to the high presence of aromatics.

show abstract

Experimental study of a dual-fuel spark-assisted compression ignition engine with polyoxymethylene dimethyl ether and methanol as fuels

Zhu

Gao

Qiu

et al. 2023

Applied Thermal Engineering

View full text Add to dashboard Cite

Impact of fuel chemical function characteristics on spark assisted and kinetically controlled compression ignition performance focused on multi-mode operation

Cited by 7 publications

References 29 publications

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

Prospects and Limitations of Predicting Fuel Ignition Properties from Low-Temperature Speciation Data

Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

Experimental study of a dual-fuel spark-assisted compression ignition engine with polyoxymethylene dimethyl ether and methanol as fuels

Contact Info

Product

Resources

About