Numerical Investigation of a Central Fuel Property Hypothesis Under Boosted Spark-Ignition Conditions

Abstract

In the present work, a central fuel property hypothesis (CFPH), which states that fuel properties are sufficient to provide an indication of a fuel's performance irrespective of its chemical composition, was numerically investigated. In particular, the objective of the study was to determine whether Research Octane Number (RON) and Motor Octane Number (MON), as fuel properties, are sufficient to describe a fuel's knock-limited performance under boosted spark-ignition (SI) conditions within the framework of CFPH. To this end, four TPRF-bioblendstock surrogates having different compositions but matched RON (= 98) and MON (= 90), were first generated using a non-linear regression model based on artificial neural network (ANN). Three unconventional bioblendstocks were included in the analysis: Di-isobutylene (DIB), Isobutanol and Anisole. Skeletal reaction mechanisms were generated for the TPRF-DIB, TPRF-isobutanol and TPRF-anisole blends from a detailed kinetic mechanism. Thereafter, numerical simulations were performed for the fuel surrogates using the skeletal mechanisms and a virtual cooperative fuel research (CFR) engine model, under a representative boosted operating condition. In the computational fluid dynamics (CFD) model, the G-equation approach was employed to track the turbulent flame front and the well-stirred reactor model combined with multi-zone binning strategy was used to capture auto-ignition in the end-gas. In addition, laminar flame speed was tabulated for each blend as a function of pressure, temperature and equivalence ratio a priori, and the lookup tables were used to prescribe laminar flame speed as an input to the G-equation model. Parametric spark timing sweeps were performed for each fuel blend to determine the corresponding knock-limited spark advance (KLSA) and 50% burn point (CA50) at the respective KLSA timing. It was observed that despite same RON, MON and engine operating conditions, the TPRF-Anisole blend exhibited markedly different knock-limited performance from the other three blends. This deviation from the octane index (OI) expectation was shown to be caused by differences in laminar flame speed (LFS). However, it was found that relatively large fuel-specific differences in LFS (> 20%) would have to be present to cause any appreciable deviation from the OI framework. Otherwise, RON and MON would still be robust enough to predict a fuel's knock-limited performance.

Fuel-air mixing in motored CFR engine at research octane number (RON) relevant condition

“…Similar to Szybist et al, 6 Pal et al 20 also showed that along with the RON and MON, laminar burning velocity has a significant influence on the knocking phenomena as well. However, the effects of HoV on knocking was not investigated by Pal et al 17,20 Bestel et al 21 performed experiments and 3-D CFD modeling of CFR engine, adapting the realistic CFR engine model from Pal et al 17 The end gas auto ignition was compared between pipeline natural gas and reference methane/hydrogen blends. The 3-D CFD results in this work 21 revealed that (a) the end gas temperature is higher for methane/hydrogen blend and (b) the turbulent flame velocity is similar for both pipeline natural gas and reference methane/hydrogen blends.…”

“…Probst et al 19 utilized the virtual CFR engine model 17 to demonstrate a concurrent perturbation method (CPM) to speed up CFD simulations for predicting cycle-to-cycle variability (CCV) in engine knock. In another recent work, Pal et al 20 used the virtual CFR engine model to determine the corresponding knock-limited spark advance (KLSA) and 50% burn point (CA50) at the respective KLSA timing for gasoline surrogates blended with di-isobutylene (DIB), isobutanol, and anisole.…”

Numerical Study of PAHs and Soot Emissions from Gasoline–Methanol, Gasoline–Ethanol, and Gasoline–n-Butanol Blend Surrogates

“…This model demonstrates good predictive capability in terms of root-mean-square errors (RMSEs) in RON/MON of ∼1 octane number (ON). Further details of the design, implementation, and validation of the neural network can be found in ref . In contrast, TPRF–ethanol (TPRFE) blends were generated utilizing the correlation developed by Al Ramadan et al The data set for TPRFE blends was adopted from our previous study .…”

“…This allows for better characterizing the fuel–engine interactions. Further, it is well established in the literature that the combustion and emission characteristics of gasoline-like fuels to a large extent are governed by their physical and chemical properties, such as vapor pressure, viscosity, heat of vaporization (HoV), specific heat, research octane number (RON), and octane sensitivity ( S ). − Here, octane sensitivity ( S ) is defined as the difference in RON and motor octane number (MON). Van Dam et al performed a global sensitivity analysis to characterize the importance of various physical properties of fuel on the performance of a direct-injection spark-ignition (DISI) engine.…”

“…Sluder et al conducted a literature review to identify that fuels with higher HoV and S help in expanding the thermodynamic working conditions of DISI engines by lowering the knocking tendency. Further, S alone has significant importance in determining the performance of ACEs. ,,− To characterize the importance of S on soot emissions, Kalvakala et al tested 90 different TPRF–ethanol blends under laminar partially premixed flame conditions. It was observed that the PAH and soot emissions were closely coupled to S , while the effect of ethanol on soot emissions was not directly proportional to its concentration in the fuel mixture but was highly interwoven with the fuel composition as a whole.…”