Numerical analysis of soot emissions from gasoline-ethanol and gasoline-butanol blends under gasoline compression ignition conditions. (1st July 2022)

• Record Type: Journal Article
• Authors: Kalvakala, Krishna C.; Pal, Pinaki; Gonzalez, Jorge Pulpeiro; Kolodziej, Christopher P.; Seong, Hee Je; Kukkadapu, Goutham; McNenly, Matthew; Wagnon, Scott; Whitesides, Russell; Hansen, Nils; Aggarwal, Suresh K.
• Is Part Of: Fuel. Volume 319(2022)
• Publication Date: 2022-07-01
• Subjects: Gasoline-biofuel blends -- Soot emissions -- Gasoline compression ignition -- Mixture stratification -- Numerical simulation -- Computational fluid dynamics -- Mechanism reduction -- Gasoline surrogates -- Machine learning; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.123740
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: A novel skeletal mechanism with PAH chemistry is developed for TPRF + ethanol/n-butanol blends. A CFD model is developed to capture GCI engine combustion and emissions at low-load conditions. Effects of start-of-injection (SOI) timing on combustion behavior and soot emissions are studied. Autoignition propensity is primarily affected by fuel chemistry. Sooting propensity is strongly coupled with both fuel chemistry and physical properties. Abstract: In the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine were performed to investigate the impact of blending two biofuels, ethanol and n-butanol, with gasoline on the trade-off between combustion phasing and soot emissions under low load conditions. In order to represent market gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF) + ethanol (ETPRF) surrogate (with 20% ethanol by mole; E20) was formulated using a neural network based octane predictor such that the surrogate had the same ethanol content, Research Octane Number (RON) and Octane Sensitivity (S). In addition, a novel skeletal kinetic mechanism for ETPRF and TPRF + n-butanol (BTPRF) blends, incorporating polycyclic aromatic hydrocarbon (PAH) chemistry, was developed. A three-dimensional (3D) engine CFD formulation employing the skeletal mechanism, adaptive mesh refinement (AMR), finite-rate chemistry approach, and hybrid method of moments (HMOM) was adopted to capture the in-cylinder combustion phenomena and soot emissions. The engine CFD model was validated against RD5-87 experimental data for a broad range of start-of-injection (SOI) timings (-21/-27/-36/-45 crank angle degrees (CAD) after top-dead-center (aTDC)), with respect to in-cylinder pressure, heat release rate, combustion phasing, and soot emissions. The closed-cycle simulation results were analyzed to elucidate the non-monotonic trend of soot emissions versus SOI timing: SOI-36 > SOI-45 > SOI-21 > SOI-27. Thereafter, the validated CFD model was employed to simulate the combustion of a gasoline-ethanol blend with 45% (by mole) ethanol (E45) and a gasoline-butanol blend with 45% (by mole) n-butanol (B45) under the same operating conditions to study the effects of fuel composition and SOI timing on combustion phasing and soot emissions. The sooting propensity followed the trend: B45 > E20 > E45 at all SOI timings. Overall, it was observed that the autoignition propensity was primarily related to fuel chemistry. On the other hand, sooting propensity showed strong coupling with both fuel chemistry and physical properties, with greater impact of fuel physical properties at advanced SOI timings.