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The accurate prediction of engine performance maps can guide data-driven optimization of engine technologies to control fuel use and associated emissions. However, engine operational maps are scarcely reported in literature and often have missing data. Assessment of missing-data resilient algorithms in the context of engine data prediction could enable better processing of real-world driving cycles, where missing data is a more pervasive phenomenon. The goal of this study is, therefore, to determine the most effective technique to deal with missing data and employ it in prediction of engine performance characteristics. We assess the performance of two machine learning approaches, namely Artificial Neural Networks (ANNs) and the extreme tree boosting algorithm (XGBoost), in handling missing data.

Engine cold start is characterized by sub-optimal combustion efficiency due to the low temperature of the combustion chamber; this heavily increases engine raw emissions at start. One driving phenomenon is a limited fuel evaporation rate. Consequently, a liquid fuel film remains on the piston top at ignition. Liquid fuel deposited on the piston top is a well-known cause of “pool-fire”, leading to high levels of particle emissions; a problem particularly noticeable with bio-based renewable fuels. Engine piston pre-heating can be deployed to prevent or limit the formation of such fuel film and associated pollutants. In this work a practical technique is proposed to effectively pre-heat the pistons immediately before engine cold start. The device consists of a pressurized-heated oil buffer which pre-heats the pistons via the existing piston cooling nozzles.

The transportation industry has been scrutinized for its contribution towards the global greenhouse gas emissions over the years. While the automotive sector has been regulated by strict emission legislation globally, the emissions from marine transportation have been largely neglected. However, during the past decade, the international maritime organization focused on ways to lower the emission intensity of the marine sector by introducing several legislations. This sets limits on the emissions of different oxides of carbon, nitrogen and sulphur, which are emitted in large amounts from heavy fuel oil (HFO) combustion (the primary fuel for the marine sector). A 40% and 70% reduction per transport work compared to the levels of 2008 is set as target for CO2 emission for 2030 and 2050, respectively. To meet these targets, commonly, methanol, as a low-carbon fuel, and ammonia, as a zero-carbon fuel, are considered.

Investigating combustion characteristics of oxygenated gasoline and gasoline blended ethanol is a subject of recent interest. The non-linearity in the interaction of fuel components in the oxygenated gasoline can be studied by developing chemical kinetics of relevant surrogate of fewer components. This work proposes a new reduced four-component (isooctane, heptane, toluene, and ethanol) oxygenated gasoline surrogate mechanism consisting of 67 species and 325 reactions, applicable for dynamic CFD applications in engine combustion and sprays. The model introduces the addition of eight C1-C3 species into the previous model (Li et al; 2019) followed by extensive tuning of reaction rate constants of C7 - C8 chemistry. The current mechanism delivers excellent prediction capabilities in comprehensive combustion applications with an improved performance in lean conditions.

Strict regulations on air pollution motivates clean combustion research for fossil fuels. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. In this work, we propose to use machine-learning algorithms to achieve better predictions. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency under following conditions: residence time at 1.0 second, fuel concentration at 0.25%, equivalence ratio at 1.0, and temperature range from 750 to 1100K.

The development of highly boosted and high compression spark-ignition engines with enhanced thermal efficiencies is primarily limited by knock and super-knock. Super-knock is an excessively high intensity knock which has been related to a developing detonation process. This study investigates the knocking tendency of different gasoline surrogate fuels with varying research octane numbers (RON), octane sensitivity (S) and composition. The ξ/ɛ diagram with an enclosed detonation peninsula is used to assess the knocking tendency of different fuels. The diagram plots ξ, the ratio of acoustic to auto-ignitive velocity, against ɛ, the ratio of the transit time of an acoustic wave through a hot spot, to the heat release time (τe). Constant volume simulations of auto-ignition delay times (τi) and excitation times (τe) obtained from chemical kinetic calculations, enable calculations of ξ and ɛ.

This study presents a computational framework to predict the outcome of combustion process based on a given RANS initial condition by performing statistical analysis of Sankaran number, Sa, and ignition regime theory proposed by Im et al. [1]. A criterion to predict strong auto-ignition/detonation a priori is used in this study, which is based on Sankaran-Zeldovich criterion. In the context of detonation, Sa is normalized by a sound speed, and is spatially calculated for the bulk mixture with temperature and equivalence ratio stratifications. The initial conditions from previous pre-ignition simulations were used to compute the spatial Sa distribution followed by the statistics of Sa including the mean Sa, the probability density function (PDF) of Sa, and the detonation probability, PD. Sa is found to be decreased and detonation probability increased significantly with increase of temperature.

Partially premixed combustion (PPC) is an operating mode that lies between the conventional compression ignition (CI) mode and homogeneous charge compression ignition (HCCI) mode. The combustion in this mixed mode is complex as it is neither diffusion-controlled (CI mode) nor governed solely by chemical kinetics (HCCI mode). In this study, CFD simulations were performed to evaluate flame index, which distinguishes between zones having a premixed flame and non-premixed flame. Experiments performed in the optical engine supplied data to validate the model. In order to realize PPC, the start of injection (SOI) was fixed at −40 CAD (aTDC) so that a required ignition delay is created to premix air/fuel mixture. The reference operating point was selected to be with 3 bar IMEP and 1200 rpm. Naphtha with a RON of 77 and its corresponding PRF surrogate were tested. The simulations captured the general trends observed in the experiments well.

Ignition quality tester (IQT) is a standard experimental device to determine ignition delay time of liquid fuels in a controlled environment in the absence of gas exchange. The process involves fuel injection, spray breakup, evaporation and mixing, which is followed by auto-ignition. In this study, three-dimensional computational fluid dynamics (CFD) is used for prediction of auto-ignition characteristics of diethyl ether (DEE) and ethanol. In particular, the sensitivity of the ignition behavior to different injection rate profiles is investigated. Fluctuant rate profile derived from needle lift data from experiments performs better than square rate profile in ignition delay predictions. DEE, when used with fluctuant injection rate profile resulted in faster ignition, while for ethanol the situation was reversed. The contrasting results are attributed to the difference in local mixing.

The purpose of present numerical study was to extend the operating range of alcohol (methanol and ethanol) fueled Homogeneous Charge Compression Ignition (HCCI) engine under low load conditions. Ignition of pure methanol and ethanol under HCCI mode of operation requires high intake temperatures and misfires at low loads are common in HCCI engines. Three methods have been adapted to optimize the use of methanol and ethanol for HCCI operation without increasing the intake temperature. First, blending methanol and ethanol with ignition improver, namely di-methyl ether (DME) and di-ethyl ether (DEE), was used to increase the cetane number and ignitability of premixed charge. Second, based on the blended fuels, the spark assistance was used to reduce required intake temperature for auto-ignition. Third, DME and DEE were directly injected to methanol and ethanol operated HCCI engine, in the form of Reactivity Controlled Compression Ignition (RCCI) combustion.

Stochastic pre-ignition remains one of the major barriers limiting further engine downsizing and down-speeding; two widely used strategies for improving the efficiency of spark-ignited engines. One of the most cited mechanisms thought to be responsible for pre-ignition is the ignition of a rogue droplet composed of lubricant oil and fuel. This originates during mixture formation from interactions between the fuel spray and oil on the cylinder liner. In the present study, this hypothesis is further examined using a single cylinder supercharged engine which employs a range of air-fuel mixture formation strategies. These strategies include port-fuel injection (PFI) along with side and central direct injection (DI) of an E5 gasoline (RON 97.5) using single and multiple injection events. Computational fluid dynamic (CFD) calculations are then used to explain the observed trends.

Super-knock that occurs in spark ignition (SI) engines is investigated using two-dimensional (2D) numerical simulations. The temperature, pressure, velocity, and mixture distributions are obtained and mapped from a top dead center (TDC) slice of full-cycle three-dimensional (3D) engine simulations. Ignition is triggered at one end of the cylinder and a hot spot of known temperature was used to initiate a pre-ignition front to study super-knock. The computational fluid dynamics code CONVERGE was used for the simulations. A minimum grid size of 25 μm was employed to capture the shock wave and detonation inside the domain. The Reynolds-averaged Navier-Stokes (RANS) method was employed to represent the turbulent flow and gas-phase combustion chemistry was represented using a reduced chemical kinetic mechanism for primary reference fuels. A multi-zone model, based on a well-stirred reactor assumption, was used to solve the reaction terms.

Alcohols are potential blending agents for diesel that can be effectively used in compression ignition engines. This work investigates the use of n-butanol as a blending component for diesel fuel using experiments and simulations. Dodecane was selected as a surrogate for diesel fuel and various concentrations of n-butanol were added to study ignition characteristics. Ignition delay times for different n-butanol/dodecane blends were measured using the ignition quality tester at KAUST (KR-IQT). The experiments were conducted at pressure of 21 and 18 bar, temperature ranging from 703-843 K and global equivalence ratio of 0.85. A skeletal mechanism for n-dodecane and n-butanol blends with 203 species was developed for numerical simulations. The mechanism was developed by combining n-dodecane skeletal mechanism containing 106 species and a detailed mechanism for all the butanol isomers.

Toluene primary reference fuel (TPRF) (mixture of toluene, iso-octane and heptane) is a suitable surrogate to represent a wide spectrum of real fuels with varying octane sensitivity. Investigating different surrogates in engine simulations is a prerequisite to identify the best matching mixture. However, running 3D engine simulations using detailed models is currently impossible and reduction of detailed models is essential. This work presents an AramcoMech reduced kinetic model developed at King Abdullah University of Science and Technology (KAUST) for simulating complex TPRF surrogate blends. A semi-decoupling approach was used together with species and reaction lumping to obtain a reduced kinetic model. The model was widely validated against experimental data including shock tube ignition delay times and premixed laminar flame speeds. Finally, the model was utilized to simulate the combustion of a low reactivity gasoline fuel under partially premixed combustion conditions.

Gasoline compression ignition (GCI) engines are becoming more popular alternative for conventional spark engines to harvest the advantage of high volatility. Recent experimental study demonstrated that high reactivity gasoline fuel can be operated in a conventional mixing controlled combustion mode producing lower soot emissions than that of diesel fuel under similar efficiency and NOx level [1]. Therefore, there is much interest in using gasoline-like fuels in compression ignition engines. In order to improve the fidelity of simulation-based GCI combustion system development, it is mandatory to enhance the prediction of spray combustion of gasoline-like fuels. The purpose of this study is to model the spray characteristics of high reactivity gasoline fuels and validate the models with experimental results obtained through an optically accessible constant volume vessel under vaporizing [2] and reactive conditions [3].

Future internal combustion engines demand higher efficiency but progression towards this is limited by the phenomenon called knock. A possible solution for reaching high efficiency is Octane-on-Demand (OoD), which allows to customize the antiknock quality of a fuel through blending of high-octane fuel with a low octane fuel. Previous studies on Octane-on-Demand highlighted efficiency benefits depending on the combination of low octane fuel with high octane booster. The author recently published works with ethanol and methanol as high-octane fuels. The results of this work showed that the composition and octane number of the low octane fuel is significant for the blending octane number of both ethanol and methanol. This work focuses on toluene as the high octane fuel (RON 120). Aromatics offers anti-knock quality and with high octane number than alcohols, this work will address if toluene can provide higher octane enhancement.

The blending of ethanol with PRF (Primary reference fuel) 84 was investigated and compared with FACE (Fuels for Advanced Combustion Engines) A gasoline surrogate which has a RON of 83.9. Previously, experiments were performed at four HCCI conditions but the chemical effect responsible for the non-linear blending behavior of ethanol with PRF 84 and FACE A was not understood. Hence, in this study the experimental measurements were simulated using zero-dimensional HCCI engine model with detailed chemistry in CHEMKIN PRO. Ethanol was used as an octane booster for the above two base fuels in volume concentration of 0%, 2%, 5% and 10%. The geometrical data and the intake valve closure conditions were used to match the simulated combustion phasing with the experiments. Low temperature heat release (LTHR) was detected by performing heat release analysis.

Low-temperature gasoline combustion (LTGC) has the potential to provide gasoline-fueled engines with efficiencies at or above those of diesel engines and extremely low NOx and particulate emissions. Three key performance goals for LTGC are to obtain high loads, reduce the boost levels required for these loads, and achieve high thermal efficiencies (TEs). This paper reports the results of an experimental investigation into the use of partial fuel stratification, produced using early direct fuel injection (Early-DI PFS), and an increased compression ratio (CR) to achieve significant improvements in these performance characteristics. The experiments were conducted in a 0.98-liter single-cylinder research engine. Increasing the CR from 14:1 to 16:1 produced a nominal increase in the TE of about one TE percentage unit for both premixed and Early-DI PFS operation.

Increasing the anti-knock quality of gasoline fuels can enable higher efficiency in spark ignition engines. In this study, the blending anti-knock quality of dicyclopentadiene (DCPD), a by-product of ethylene production from naphtha cracking, with various gasoline fuels is explored. The blends were tested in an ignition quality tester (IQT) and a modified cooperative fuel research (CFR) engine operating under homogenous charge compression ignition (HCCI) and knock limited spark advance (KLSA) conditions. Due to current fuel regulations, ethanol is widely used as a gasoline blending component in many markets. In addition, ethanol is widely used as a fuel and literature verifying its performance. Moreover, because ethanol exhibits synergistic effects, the test results of DCPD-gasoline blends were compared to those of ethanol-gasoline blends. The experiments conducted in this work enabled the screening of DCPD auto-ignition characteristics across a range of combustion modes.

The blending of ethanol with primary reference fuel (PRF) mixtures comprising n-heptane and iso-octane is known to exhibit a non-linear octane response; however, the underlying chemistry and intermolecular interactions are poorly understood. Well-designed experiments and numerical simulations are required to understand these blending effects and the chemical kinetic phenomenon responsible for them. To this end, HCCI engine experiments were previously performed at four different conditions of intake temperature and engine speed for various PRF/ethanol mixtures. Transfer functions were developed in the HCCI engine to relate PRF mixture composition to autoignition tendency at various compression ratios. The HCCI blending octane number (BON) was determined for mixtures of 2-20 vol % ethanol with PRF70. In the present work, the experimental conditions were considered to perform zero-dimensional HCCI engine simulations with detailed chemical kinetics for ethanol/PRF blends.