Graphical Abstract Figure
Graphical Abstract Figure
Close modal

Abstract

High compression ratio and lean-burn operation of low-octane gasoline-fueled compression ignition engines lead to significantly higher thermal efficiencies. Hence, it has emerged as a potential technology to propel medium and heavy-duty vehicles. Gasoline compression ignition engines use advanced fuel injection timings and gasoline-like low-octane fuels, and their impact on the lubricating oil tribology and particulate emissions must be experimentally assessed. Hence, this experimental study compares these aspects for the gasoline compression ignition and baseline conventional diesel combustion engines. Extreme heat, moisture, contamination by particulate matter, corrosive gases, dirt, fuel dilution, wear debris, and depleted additives can degrade the lubricating oil, resulting in higher engine wear and eventual failure. The experiments were conducted on a medium-duty diesel engine at varying engine loads and speeds, and the effect of fuel injection timing on particulate emissions was investigated. The engine was operated for 20 hours, and lubricating oil samples drawn at fixed intervals were analyzed for changes in lubricating oil using spectroscopic techniques. Transmission electron microscopy and inductively coupled plasma-mass spectroscopy were used to analyze the soot and trace elements in the lubricating oil. Spray droplet distribution in the cylinder in a non-reactive computational fluid dynamics simulation environment was done to understand the fuel dilution to the lubricating oil. Results indicated that gasoline compression ignition emitted more particulates than baseline diesel combustion. The gasoline compression ignition engine's lubricating oil showed higher soot-in-oil and lower trace elements, ash, and carbon contents than baseline diesel combustion. Fuel dilution to the lubricating oil was observed in the simulations.

1 Introduction

Economists have predicted total annual global energy consumption will increase by 1.3% in 2023 [1]. At the same time, renewable energy and natural gas consumption would rise by ∼2.3% and 1.4% per annum, respectively [2]. The share of petroleum-based fuels will continue to increase in the global energy supply [3]. Conventional fuel-powered internal combustion (IC) engines play a vital role in industrial, transportation, and agricultural sectors due to their reliability and performance for various applications. Reciprocating IC and jet engines propel >99% of transport vehicles worldwide [4]. Compression ignition (CI) engines exhibit higher efficiency, whereas spark ignition (SI) engines emit lower harmful emissions. Further research and development of CI engines can significantly improve their efficiency and reduce emissions [5]. Several advanced combustion techniques have been proposed and investigated to control emissions [6]. In this context, low-octane gasoline in CI engines as fuel has the potential to deliver significantly improved engine performance. This can be done by using a novel engine combustion technique called gasoline compression ignition (GCI), which can simultaneously control the emissions of nitrogen oxide (NOx) and particulate matter (PM) as well [7]. GCI engines incur lower operating costs than comparable CI engines, and fewer hardware modifications are required in the production-grade CI engines to operate them in GCI mode [8]. Optimizing the charge stratification, fuel quality, and injection strategy improved the GCI combustion. For stable combustion, preheated air with optimum exhaust gas recirculation (EGR) and boost pressure are required [9]. Boost pressure shows good correlations with the test fuel octane number. A high boost with less EGR is required at high loads to shorten the ignition delay [10]. EGR addition may decrease or increase the PM emissions and particle number concentrations (PNC) by inhibiting/supporting particle growth. However, EGR substitution could affect nucleation mode particle (NMP) and accumulation mode particle (AMP) formations [11].

Mixing-controlled combustion plays a significant role in PM formation because it alters charge homogeneity and combustion temperature [12]. In GCI mode, the combustion phasing is closely associated with the fuel injection strategy. GCI engines show improved combustion stability at mid-loads, but high engine speeds and loads show combustion instability issues [13]. Typically, GCI engines emit lower PM emissions than diesel engines [14]. However, studies on particle size, PNC distribution, and chemical characteristics are scarce and required. These studies could help design an efficient diesel particulate filter (DPF) for further emission control from GCI engines, enabling them to meet the most stringent emission norms. Diffusion combustion of highly aromatic fuel and premixed combustion of low aromatic fuel showed low PNC [15]. Unsaturated hydrocarbon fuels resulted in higher emissions of AMPs from the GCI engines [12].

Advanced fuel injection timing, high EGR, and fuel composition alter the lubricating oil characteristics. Pilot injections in GCI mode lead to engine cylinder wall-wetting and fuel dilution issues [16]. Fuel–air mixture entering the squish region undergoes incomplete combustion and engine blow-by [17]. Gasoline has lower lubricity than diesel, producing higher engine components' wear and tear [18]. This is due to easy oil squeezing between the component's mating surfaces [19]. The thermal decomposition of lubricating oil significantly increases semi-volatile combustion byproducts [20]. The used lubricating oils contain toluene, xylenes, C3 to C4-benzenes, C2 to C4-naphthalene, pyrene, and fluoranthene in low concentrations. Liquid chromatography-mass spectrometry (LC–MS), Fourier transform infrared (FTIR), nuclear magnetic resonance spectroscopy (NMR), and inductively coupled plasma-mass spectroscopy (ICP-MS) are used for lubricating oil analysis. Proton (1H) and carbon (13C) NMR spectroscopy identify aromatic and aliphatic carbon and hydrogen in the lubricating oil [21]. The analysis of lubricating oil compounds using gas chromatography reports ∼76 and 68% acyclic and monocyclic alkanes [22]. Lubricating oil contamination with PM and water increases the OH radical concentration in the lubricating oil [23]. An increase in engine runtime raises trace concentration of alcohols, aromatics and organic acids in the lubricating oil [24]. Sejkorova et al. [25] identified the OH, nitro and sulfate groups in used lubricating oils. Used lubricating oils showed different spectra in the sulfonate detergent peaks (1172 and 1156 cm−1) and zinc dialkyl dithiophosphates (ZDDP) additive peaks (970 and 656 cm−1) than fresh lubricating oils. Changes in lubricating oil characteristics affects fuel consumption, lubricity, and soot formation in the engine.

In an engine, the fuel and soot mix with the lubricating oil film on the cylinder liner, eventually reaching the oil sump [26]. Lubricating oils contain ash and trace metals because of contamination by the combustion byproducts and wear debris. Tekie et al. [27] examined trace metals in lubricating oils using dry ash and ICP-MS and reported reasonably good results. The soot contamination resulted in oil film breakdown in the engine due to increased lubricating oil viscosity [28], which increased the engine wear due to oil pumping issues. Rocca et al. [27] investigated the soot-in-oil and reported that soot aggregates between 50 and 130 nm had a primary particle diameter (dp) of 10–35 nm and an inner core of ∼8–15 nm. Fractal dimension (Df) of soot aggregate informs about soot behavior [29]. More porous and complex aggregates show a higher Df than compact aggregates. High Df exhibits high oxidative reactivity due to increased surface area and more complex structure.

This study investigates the comparative tribological characteristics of lubricating oil and particulates formed in GCI vis-à-vis conventional diesel combustion (CDC) engines. The experiments were performed in a medium-duty CI engine running at 5 and 10-bar brake mean effective pressure (BMEP) at 1500–2500 rpm. Engine exhaust particle sizer (EEPS) was used to measure the PNC. Changes in the lubricating oil properties in GCI and CDC engines were compared. LC–MS, FTIR, NMR, and ICP-MS techniques were used to analyze the changes in the chemical characteristics of lubricating oils drawn from GCI and CDC engines.

2 Experimental Setup and Methodology

Figure 1 shows the schematic of the experimental setup. The test engine is a four-stroke, four-cylinder, turbo-charged, water-cooled compression ignition engine (Table 1). Stock electronic control unit (ECU) with predefined calibration maps operated the engine in CDC mode. An open ECU was used to optimize various parameters (fuel injection pressure, injection timing, mass, etc.) for the GCI mode operation. Detailed installation procedure of open ECU in a production-grade engine can be found in Ref. [30].

Fig. 1
Schematic of the experimental setup
Fig. 1
Schematic of the experimental setup
Close modal
Table 1

Specifications of the test engine and dynamometer

Test engine specifications
Number of cylinders4
Compression ratio17.5:1
Bore × Stroke97 mm × 100 mm
Chamber geometryRe-entrant bowl
Engine capacity2956 cc
Rated torque300 Nm @ 1600–2000 rpm
Rated power84.5 kW @ 3000 rpm
Fuel injection systemCommon rail direct injection
Fuel injectorSix-hole solenoid injector (Delphi)
0.132 mm orifice, 148 deg spray angle
Fuel injection pressure500–1450 bar (baseline diesel)
Eddy current dynamometer specifications
Manufacturer/ModelDynomerk Controls/EC-300
Rated power220 kW @ 2500–8000 rpm
Rated torque702 Nm @ 8000 rpm
Maximum excitation current6 Amp DC
Speed accuracy±5 rpm
Test engine specifications
Number of cylinders4
Compression ratio17.5:1
Bore × Stroke97 mm × 100 mm
Chamber geometryRe-entrant bowl
Engine capacity2956 cc
Rated torque300 Nm @ 1600–2000 rpm
Rated power84.5 kW @ 3000 rpm
Fuel injection systemCommon rail direct injection
Fuel injectorSix-hole solenoid injector (Delphi)
0.132 mm orifice, 148 deg spray angle
Fuel injection pressure500–1450 bar (baseline diesel)
Eddy current dynamometer specifications
Manufacturer/ModelDynomerk Controls/EC-300
Rated power220 kW @ 2500–8000 rpm
Rated torque702 Nm @ 8000 rpm
Maximum excitation current6 Amp DC
Speed accuracy±5 rpm

A 30:70% (v/v) diesel and gasoline blend (G70) was used as GCI mode test fuel, while baseline diesel was used as CDC mode test fuel. The engine had a common rail fuel injection system and solenoid injectors. A triple injection strategy was used in GCI mode to achieve partially premixed combustion (PPC). Fuel injection strategy, intake air temperature (IAT), and EGR rate were varied in each operating condition for maintaining the combustion phasing at the desired crank angle position (Table 2).

Table 2

Experimental test matrix

Load (bar)Speed (rpm)Caseθpilot1θpilot2θmainEGR (%)IAT (°C)EEPSRun time
5 bar BMEP1500M1100301425644 h
M210029132567
M310028122569
CDCNANANANANA4 h
2000M41004119.525754 h
M51004018.52597
M61003917.52588
CDCNANANANANA4 h
2500M7100442125704 h
M810043202572
M910042192576
CDCNANANANANA4 h
10 bar BMEP1500H11003418.510484 h
H21003317.51050
H31003216.51052
CDCNANANANANA4 h
2000H410044.52210633 h
H510043.5211065
H610042.5201067
CDCNANANANANA3 h
2500H710051230741 h
H81005022071
H91004921070
CDCNANANANANA1 h
Load (bar)Speed (rpm)Caseθpilot1θpilot2θmainEGR (%)IAT (°C)EEPSRun time
5 bar BMEP1500M1100301425644 h
M210029132567
M310028122569
CDCNANANANANA4 h
2000M41004119.525754 h
M51004018.52597
M61003917.52588
CDCNANANANANA4 h
2500M7100442125704 h
M810043202572
M910042192576
CDCNANANANANA4 h
10 bar BMEP1500H11003418.510484 h
H21003317.51050
H31003216.51052
CDCNANANANANA4 h
2000H410044.52210633 h
H510043.5211065
H610042.5201067
CDCNANANANANA3 h
2500H710051230741 h
H81005022071
H91004921070
CDCNANANANANA1 h

The GCI engine was operated for the three cases by adjusting the timings of the two pilot injections (θpilot1,θpilot2) and one main (θmain) injection. M1, M2, and M3 were the three operating cases for 1500 rpm at 5 bar BMEP in GCI mode, as shown in Table 2, and CDC was the corresponding baseline case for comparison. Injection timing, fuel quantity, and EGR rate were unknown for the CDC cases since the original equipment manufacturer (OEM)-configured ECU was used. For PNC analysis, EEPS data were acquired for 60 s at 1 Hz frequency, and an average of the 60 data sets was used. Specifications and working principles of the EEPS can be found in Ref. [31]. In GCI and CDC mode experiments, Universal Gold oil 20W40 was used as a lubricating oil. The engine was operated in GCI and CDC modes for 20 h each, per the test matrix shown in Table 2, while keeping the lubricating oil temperature <120 °C. Table 2 shows the engine run time in each of the GCI and CDC modes, totaling 20 h before the lubricating oil samples were drawn for physical and chemical characterization. Fresh lubricating oil was also taken for the baseline physical and chemical characterization.

2.1 Lubricating Oil Tests for Physical Characterization.

GCI and CDC engine lubricating oil samples were analyzed according to ASTM procedures to determine their physicochemical properties. A kinematic viscometer (Stanhope-SETA), copper corrosion bath (Setavis), and a flash point apparatus (SETA-flash) were used to measure the lubricating oil viscosity (ASTM D2270) [32], copper corrosion (ASTM D130) [33] and flash point (ASTM D92) [34], respectively. Furnace with a silica crucible and Ramsbottom test were used to determine the ash content (ASTM D482) [35] and carbon residue (ASTM D189) [36] in the lubricating oil samples. A high-speed oil centrifuge (REMI) was used to determine the pentane and toluene insolubles (ASTM D893) [37] in lubricating oil samples. The test procedures for physical property characterizations of lubricating oil samples are well established [19,38]. The cracking test is a standard laboratory test to indicate the water content in the lubricating oil. In the cracking test, a drop of oil was placed on a hot plate held at ∼150 °C. The dry ash method with ICP-MS was used to determine the trace metals in the lubricating oils [27]. A 2.0 g oil sample was taken in a silica crucible and placed in a furnace at 550 °C for 1.5 h. The sample was then cooled in the furnace, and the ash was weighed. Ash was dissolved in 2 ml of conc. HNO3 and placed on a hot plate at 120 °C till complete evaporation of acid. 10 ml solution (1% HNO3 mixed milli-Q) was added to this dissolved ash and filtered through 0.2-micron PTFE syringe filters. This sample was further diluted with 25 milli-Q (18.2 MΩ). 14 ml of the sample was taken in 15 ml sterile centrifuge tubes and centrifuged for 5 min. Then the tubes were transferred to ICP-MS (Thermo Scientific, X Series) for trace metals analysis.

2.2 Lubricating Oil Tests for Chemical Characterization.

LC–MS with electrospray ionization (ESI-MS) identifies the components, structure, and chemical properties of molecules in the lubricating oil samples [39]. A mass spectrometer (Agilent portfolio-LC single quadrupole) was used to obtain the mass spectra. 5 µl of a lubricating oil sample was injected into a mobile phase with methanol solvent. The inlet capillary and chamber currents were 0.07 and 0.3 µA. The nitrogen drying gas flowrate was maintained at 11 l/min. The gas temperature environment was maintained at 320 °C. MestReNova-14 software was employed for the chromatogram analysis.

FTIR technique was used to investigate the structural changes in lubricating oil's functional groups. FTIR spectrometer (Perkin Elmer) with the KBr pellet method was used for the FTIR analysis of the lubricating oil samples in the wave number range of 4000–400 cm−1. A total of four scans were acquired at a resolution of 4 cm−1 and converted to absorbance spectra. Oil samples (1 wt%) were mixed with KBr powder and placed on a transparent disc to measure the transmittance IR.

Major hydrocarbon components can be quantified using NMR spectroscopy. NMR analysis was conducted at 24 °C on a spectrometer (JEOL 500 MHz) using an inverse detection probe in a z-direction. For proton NMR, 0.2 ml oil was diluted with 0.4 ml of deuterated chloroform (CDCl3) [24]. The lubricating oil sample was diluted with CDCL3 until saturation for carbon NMR. The signals were divided into different regions for classifying the proton-containing functional groups based on chemical shifts (ppm) in 1H NMR. They are aliphatic protons (0.8–1.8 ppm), alpha to unsaturated hydrocarbons {H–C–C = O} (1.8–3.2), alpha to heteroatoms {H–C–O} (3.2–4.4), Anomeric {O–CH–O} (5–6), Aromatics {H–Ar} (6.5–8.5), and Aldehydes (9.5–10.1) [23]. Similarly, the signals were divided into groups, such as Aliphatic (10–60), Oxygenates (50–80), group of Phenols, PAHs and Olefins (120–136), Aromatics (141–160), COO/N–C = O groups (160–188) and R(C = O)R’ groups (188–230) in 13C NMR [40]. The area under resonance peaks in NMR spectra refers to 1H or 13C atoms of corresponding chemical types. Lubricating oils contain many primary, secondary, and tertiary aliphatic and aromatic hydrocarbons [41]. Various chemical shifts and their affiliated chemical groups (Table 3) are found in Ref. [42].

Table 3

Representative 1H and 13C NMR spectrum of groups

1H NMR13C NMR
Spectral region (ppm)Functional groupsInternal regionSpectral region (ppm)Functional groupsInternal region
0.4––1.1Terminal methyl groupsA15–22Primary alkylCH3
1.1–1.7Chain methyleneA220–30Secondary alkylCH2
0.8–1.0Primary alkylRCH330–50Tertiary alkyl>CH
1.2–1.4Secondary alkylRCH220–40AllylicCC=C
∼1.56H2O∼30BenzylicC6C
1.4–2.1CH of paraffinCH25–50ChloroalkaneC10C13
2.1–4.0Proton in α position of aromaticsHα50–90Alcohol, EtherCH2OH
CH2OR
4.0–6.2Olefins protonHO100–150Alkene, AromaticC=C
6.2–9.2Proton in the aromatic ringHa150–190Carboxylic acidRC=O
1H NMR13C NMR
Spectral region (ppm)Functional groupsInternal regionSpectral region (ppm)Functional groupsInternal region
0.4––1.1Terminal methyl groupsA15–22Primary alkylCH3
1.1–1.7Chain methyleneA220–30Secondary alkylCH2
0.8–1.0Primary alkylRCH330–50Tertiary alkyl>CH
1.2–1.4Secondary alkylRCH220–40AllylicCC=C
∼1.56H2O∼30BenzylicC6C
1.4–2.1CH of paraffinCH25–50ChloroalkaneC10C13
2.1–4.0Proton in α position of aromaticsHα50–90Alcohol, EtherCH2OH
CH2OR
4.0–6.2Olefins protonHO100–150Alkene, AromaticC=C
6.2–9.2Proton in the aromatic ringHa150–190Carboxylic acidRC=O

2.3 Soot Imaging and Processing.

The soot reaches the oil sump via blow-by gas or the lubricating oil layer on the cylinder liner, increasing its viscosity [28]. Carbon residue, FTIR, insoluble tests, etc., are used to quantify the presence of soot in the lubricating oils. The morphological structure of soot can be analyzed by TEM using suitable solvents [43]. For this, 1 ml of lubricating oil was diluted with 60 ml n-heptane in a centrifuge vessel. The vessels were placed in the centrifuge (REMI) and centrifuged for 2 h at 1500 rpm. The n-heptane/oil supernatant was removed without disturbing the soot sediment (∼3 ml). The above procedures were repeated six times to remove the oiliness from the soot. The sample was transferred to a 2 ml centrifuge tube, followed by ultrasonication for 5 min. Rocca et al. [43] reported that the aggregates' macro-size increased with centrifugation.

In contrast, ultrasonic bathing breaks the large aggregates into small clusters/chains. Two drops of the sample were dropped on TEM grids using a micropipette. A transmission electron microscope (FEI-Technai G2 Twin 120 KV TEM) was used for soot imaging. Figure 2 shows the methodology for identifying primary particles of soot.

Fig. 2
TEM Images of soot particles (a) soot aggregate, (b) identifying primary particles, (c) measuring the radial distance of particles from CG, and (d) calculating the radius of gyration
Fig. 2
TEM Images of soot particles (a) soot aggregate, (b) identifying primary particles, (c) measuring the radial distance of particles from CG, and (d) calculating the radius of gyration
Close modal
The soot compactness is calculated in terms of fractal dimensions. Equation (1) is used to estimate the fractal dimension (Df) [44,45]:
(1)
where kg is the fractal prefactor, Rg is the radius of gyration, and N is the total number of monomers in aggregate. In studies, Df and kg are calculated from a power law fit of N versus the 2Rgdp. The “N” value is determined by Eq. (2):
(2)
where ka is the constant, a is the empirical projected area exponent, Aa is the projected area of the soot aggregate, and Pa is the mean projected perimeter of the monomer.

2.4 Computational Fluid Dynamic Simulations.

Spray quenching and wall-wetting effects increase HC and CO emissions in GCI engines. Hence, non-reactive split-injection simulations were performed to observe the fuel quenching using Converge computational fluid dynamics (CFD). A summary of models used for engine modeling is given in Table 4. Iso-octane and n-heptane were taken as surrogates for gasoline and diesel, respectively. A reduced primary reference fuel (PRF) mechanism (73 species and 296 reactions) was used for GCI combustion [47], whereas a reduced mechanism (42 species and 168 reactions) was used for CDC [48]. Based on OEM data, a single injection was used in the CDC. A trapezoidal injection rate shape was used for CDC and GCI mode cases [49].

Table 4

Main sub-models used in Converge CFD for non-reacting cases [17,46]

PhenomenonSubmodelParametersInput conditions
GCICDC
Spray breakupKH-RT instabilityIntake charge composition100% N2
Droplet collisionO’ Rourke modelIntake pressure (kPa)100.3
EvaporationFrossilingIntake Temperature (K)310
TurbulenceRNG K-epsilonEngine speed (rpm)1500
Wall interactionRebound/slideSwirl ratio0.7
Wall heat transferO’ Rourke & AmsdenSurrogatePRF 70: 30% v/v iso-octane/n-heptanen-heptane
Near wall treatmentStandard wall functionStart of injection (°bTDC)Pilot 1: 100Main: 12
Pilot 2: 30
Main: 14
Equation of stateRedlich–KwongInjected mass (mg)2424
Injection pressure (bar)500800
PhenomenonSubmodelParametersInput conditions
GCICDC
Spray breakupKH-RT instabilityIntake charge composition100% N2
Droplet collisionO’ Rourke modelIntake pressure (kPa)100.3
EvaporationFrossilingIntake Temperature (K)310
TurbulenceRNG K-epsilonEngine speed (rpm)1500
Wall interactionRebound/slideSwirl ratio0.7
Wall heat transferO’ Rourke & AmsdenSurrogatePRF 70: 30% v/v iso-octane/n-heptanen-heptane
Near wall treatmentStandard wall functionStart of injection (°bTDC)Pilot 1: 100Main: 12
Pilot 2: 30
Main: 14
Equation of stateRedlich–KwongInjected mass (mg)2424
Injection pressure (bar)500800

Figure 3(a) shows the engine's computational domain near TDC. Adaptive mesh refinement was used to refine the grid based on temperature and velocity gradients [46]. The images were taken at two horizontal plane positions in the combustion chamber (Fig. 3(b)). Figure 3(c) shows that the developed model is validated using the experimental in-cylinder pressure data.

Fig. 3
(a) View of the computational grid at 40 degbTDC, (b) horizontal plane positions, P1 = squish volume plane; P2 = piston bowl plane, and (c) Model validation
Fig. 3
(a) View of the computational grid at 40 degbTDC, (b) horizontal plane positions, P1 = squish volume plane; P2 = piston bowl plane, and (c) Model validation
Close modal

3 Results and Discussion

PM emissions from GCI and CDC engines are discussed initially. Changes in the physicochemical properties of lubricating oil samples are investigated after that. Then, the trace elements and soot particles in lubricating oils are discussed in detail. Finally, the modeling study examined fuel distribution in the engine cylinder, and the results are discussed in the following sections.

3.1 Particle Number Concentration.

The particles are generally classified into three categories: nano-particles (NP; mobility diameter (dm)<10nm), nucleation mode particles (NMP; 10 < dm < 50 nm), and accumulation mode particles (AMP; dm > 50 nm) [8,50]. Figure 4 shows low NPs at 5 bar BMEP for the baseline case. A bi-model distribution of NPs and AMPs was observed for GCI cases. Higher PNC was observed for GCI than baseline due to PPC. Increasing engine speed reduces the volumetric efficiency and available time for soot oxidation, increasing the small particles and clusters [51].

Fig. 4
Particle number concentration at different engine loads and speeds
Fig. 4
Particle number concentration at different engine loads and speeds
Close modal

Retarded injection timings (M1 to M3 sweep) showed an insignificant effect on the particulate emissions. However, at 2000 rpm, retarded injection timing (M6) exhibited higher PNC than advanced injection timing (M4). Yang et al. [52] reported that PNC increased with retarded injection timing at no (0%) EGR. PNC increased and then decreased while retarding the injection timing at 20% EGR. PNC emissions depend on in-cylinder dynamics. Increasing engine load increased PNC for baseline and GCI modes. At high loads, more NMPs were generated and transformed into AMPs [53]. Increased fuel–air ratio and temperature resulted in diffusion combustion, producing soot [51]. With increasing EGR, both NMPs and AMPs increased at medium loads and speeds, as reported in Ref. [54]. H4 showed higher PNC than H6 due to incomplete combustion. Advanced injection led to spray wall impingement, causing liquid fuel film formation and subsequent pool fire leading to soot formation [52].

GCI mode showed 97–99.7% and 3–98% higher NP concentrations than baseline CDC mode at 5 and 10 bar BMEP, respectively (Fig. 5). With increasing engine speed, PNC increased, and the peak concentration shifted to AMPs region [55]. GCI mode at high engine load exhibited higher NP due to more fuel burning, oxidation of larger soot particles, and condensation of heavy hydrocarbons in the dilution line [56]. Reduction in EGR rate improved the combustion of fuel. Therefore, lower NPs were observed at 2500 rpm than 2000 rpm at 10 bar BMEP (Table 2). At all engine operating conditions, fuel injection timing significantly impacted the formation of NPs. At 5 bar BMEP, NMPs were significantly higher (10–67 times) than NPs for baseline and GCI modes. Increasing the engine speed increased the NMPs by 0.6–10% and 0–55% for baseline and GCI modes, respectively. Higher engine speed produced heavier hydrocarbons, producing higher NMPs [57]. GCI mode engine operation at 10 bar BMEP showed 4.5–63% higher NMPs than baseline CDC mode, except for H4 and H5, which showed 2–20% lower NMPs. Gasoline PPC combustion produced more NMPs at higher loads and speeds [58]. Typically, with increasing engine speed, NMPs decreased, and AMPs increased due to coagulation of NMPs [59]. In this study, NMPs and AMPs increased with increasing engine speed. Fuel injection timings did not significantly impact NMPs at high engine speeds.

Fig. 5
Particulate emissions (a,b) TPN, (c,d) TPM, and (e,f) CMD
Fig. 5
Particulate emissions (a,b) TPN, (c,d) TPM, and (e,f) CMD
Close modal

AMPs were higher for baseline CDC and GCI modes than NMPs (Figs. 5(e) and 5(f)). At 5 bar BMEP, AMPs were 0.4–4.8 times higher for GCI mode than baseline CDC mode. In contrast, Chen et al. [60] reported higher AMPs for baseline CDC mode than diesel-gasoline blends. 10 bar BMEP showed 0–4.84 and 1.2–7.2-times higher AMPs than 5 bar BMEP for baseline CDC and GCI mode cases. Increasing the engine load increased the AMPs due to higher surface growth and coagulation of soot [57]. 10 bar BMEP and 2000 rpm showed 3–10.3% more AMPs than 1500 rpm. Increasing the engine speed initiated frequent particle collisions, increasing the formation of AMPs [58]. With increasing EGR, AMPs increased due to reduced oxygen availability in fuel-rich combustion zones [61]. Due to the absence of EGR, 2500 rpm engine speed exhibited 18–34% lower AMPs than 2000 rpm. Not-so-strong fuel evaporation, wall-wetting, and pool fires on the piston surface induce large AMPs, increasing the PM mass emission [62].

Figures 5(a) and 5(b) show lower total particle number (TPN) for baseline CDC than GCI mode. With increasing engine speed, primary particles have a shorter time available to form and grow, leading to a smaller count mean diameter (CMD) of the soot formed [56]. TPN remains unaffected by fuel injection timings at lower engine loads and speeds. Too advanced and retarded fuel injection timings and small pilot-main injection intervals can affect the TPN [63]. 10 bar BMEP showed 1.3–1.9 (baseline CDC) and 1–5.6 (GCI mode) times higher TPN than 5 bar BMEP. Fuel-rich high-temperature zones result in pyrolysis of fuel and lubricating oil, increasing the particulate emissions [64]. TPN and CMD of soot particles increased with engine speed at high loads due to the dominance of diffusion combustion [56]. Since GCI engines emitted higher AMPs, the TPM was higher than the baseline CDC engine [58]. A slight and 2.7–8.4 times increment in TPM was observed for baseline CDC and GCI modes upon increasing the engine speed. CMD was higher for GCI mode than baseline CDC mode except for 5 bar BMEP and 1500 rpm. At low loads, CMD showed lesser dependence on engine speed but more dependency on engine load [58]. At 5 bar BMEP, GCI mode showed 30–35% larger CMD for 2500 rpm than 1500 rpm. Typically, larger particles can be trapped easily by the DPF. CMD was slightly and 21–92% higher for baseline CDC and GCI modes at 10 bar than 5 bar BMEPs. Increasing soot formation and aggregation at high engine loads yielded larger particles [64]. The CMD range was between 55 and 149 nm, and the highest values were observed at high engine speeds. Finally, TPN and TPM were higher for GCI mode, requiring a more effective after-treatment device to meet the stringent and upcoming emission norms.

3.2 Lubricating Oil Tribology.

Wear debris, moisture, fuel dilution, and chemical changes due to heating alter the lubricating oil's properties. Dense contaminants, gums, asphaltic and phenolic compounds potentially alter the lubricating oil's viscosity. GCI engine lubricating oil showed higher density than CDC engine lubricating oil (Table 5) due to pyrolyzed fuel components and PM mass mixing. Lower viscosity of the lubricating oil affected the lubricating oil film formation, oil film thickness, and load-bearing capacity, thus increasing the engine component wear.

Table 5

Summary of various lubricating oil tests

S. NoTestLubricating oil in enginesMeasurement standard
Fresh oilCDC engineGCI engine
1Density at 20 °C (kg/m3)867.6842.6853.6
2Viscosity (cSt) at 40 °C113.4111.6101.8ASTM D7042
Viscosity (cSt) at 100 °C16.021.8317.87
3Flashpoint (°C)249236200ASTM D92
4Ash content (mg/g of oil)5.136.035.58ASTM D482
5Carbon residue (wt%)0.881.281.10ASTM D189
6ICP-MS (mg/kg of oil)71.1394.3774.76
7Copper corrosion test1b (Slight Tarnish)3a (Dark Tarnish)3a (Dark Tarnish)ASTM D130
8Pentane and toluene insolubleNegligible amountNegligible amountASTM D893-14
10Cracking testNegligible amountNegligible amount
S. NoTestLubricating oil in enginesMeasurement standard
Fresh oilCDC engineGCI engine
1Density at 20 °C (kg/m3)867.6842.6853.6
2Viscosity (cSt) at 40 °C113.4111.6101.8ASTM D7042
Viscosity (cSt) at 100 °C16.021.8317.87
3Flashpoint (°C)249236200ASTM D92
4Ash content (mg/g of oil)5.136.035.58ASTM D482
5Carbon residue (wt%)0.881.281.10ASTM D189
6ICP-MS (mg/kg of oil)71.1394.3774.76
7Copper corrosion test1b (Slight Tarnish)3a (Dark Tarnish)3a (Dark Tarnish)ASTM D130
8Pentane and toluene insolubleNegligible amountNegligible amountASTM D893-14
10Cracking testNegligible amountNegligible amount

GCI engine lubricating oil showed lower viscosity than CDC engine lubricating oil. Oxidation, fuel dilution, moisture addition, insoluble contamination, depletion of additives, and addition of lighter fractions reduce lubricating oil's viscosity. Lubricating oil's viscosity may increase or decrease with usage, depending on the dominance of any of these factors [65]. A lower flash point temperature was observed for GCI engine lubricating oil than for CDC engine lubricating oil due to higher fuel dilution [65]. This phenomenon was also discovered in the CFD simulation results in Sec. 3.8. Higher ash content and carbon residues were observed for CDC engine lubricating oil than for GCI engine lubricating oil. The ash was 0.51, 17.54, and 8.77 wt% for unused, CDC, and GCI engine lubricating oils, respectively. Atmospheric dust, wear debris from component wear, decomposed fuel, and lubricating oil additives lead to the formation of carbon residues in the lubricating oil. Carbon residues increased by 45.45% and 25% for CDC and GCI engine lubricating oils, respectively, than unused lubricating oil. The copper corrosion test showed a 3a grade (Dark Tarnish) level for CDC and GCI engine lubricating oils. The copper corrosion test indicated the corrosiveness of lubricating oil to the copper-containing components [65]. Pentane- and toluene-insoluble tests can determine suspended contaminants in the lubricating oils [38]. Insignificant insoluble matter was observed in the lubricating oils from both engines. GCI lubricating oil contains more fuel traces and lower wear debris than CDC lubricating oil.

3.3 Electrospray Ionization LC–MS.

C17C35 carbon atoms form significant lubricating base oil components (80–90%), and the rest include organo-metallic additives (10–20%) [22]. Alkanes and olefins are saturated and unsaturated hydrocarbons commonly used as base oils. Various additives are dissolved in the lubricating oil to give it desirable properties. During usage, lubricating oils may get contaminated and undergo physicochemical changes. LC-MS analysis is used to identify the changes or deterioration over time in the lubricating oil. The peak at m/z 422 ([M + H]+) in the methanol atmosphere refers to di-nonyl diphenylamine, an antioxidant [66]. Used lubricating oils from GCI and CDC mode engines showed 75.1 and 77.5% lower m/z 422 count than unused lubricating oil (Fig. 6). Peaks at m/z 296 were mono-nonyl diphenylamine, and m/z 369 and 397 were trimethylolpropane ester additives [66]. With usage, antioxidant decay results in the formation of several undesirable byproducts. GCI engine lubricating oil showed 0.4 and 1.85 times higher m/z 369 and 397 counts, respectively, than unused lubricating oil. They were 14 and 97% higher for CDC mode engine lubricating oil than unused lubricating oil. At the same time, fragmentation of an ion of high m/z can result in the production of multiple small ions with lower m/z values. The base lubricating oils m/z can vary from 50 to more than 1000. Hydrocarbon molecules with an m/z 100–450 are majorly present in the lubricating oil. The concentration of components in the m/z 100–400 range was 17.2% higher and 89.4% lower for GCI and CDC engine lubricating oils, respectively, than unused lubricating oil. The peaks at m/z 196, 224, 280, 322, and 350 correspond to C11,C13,C17,C20,andC22 n-alkanes, respectively [67]. Hence, fuel dilution to lubricating oil can increase the m/z 100–400 components. Alcohols (-OH), organic acids (-COOH), and amides (-CONH2) are minorly present in lubricating oils. Alcohols act as solvents to base oils and additives, whereas organic acids are corrosion inhibitors and pH adjusters [68]. Amines are used as friction modifiers or viscosity improvers. Lubricating oil contains various viscosity improvers (m/z 500–3000), molybdenum compounds (m/z 350–500), corrosion inhibitors (m/z 119–300), and diarylamine antioxidants (m/z 170–207) [69,70]. These components were detected in higher concentrations in the CDC engine lubricating oil than in the GCI engine lubricating oil. The fragmentation of -dioctyl diphenylamine and molybdenum complex can form the peak at m/z 322, representing the benzylic tertiary carbon [71]. Olefins, aromatic components, fatty acid esters, and thiophosphates are present in the base oils, and their peaks occurred between m/z 450–750 [72]. They were 139% higher and 32.8% lower for GCI and CDC engine lubricating oils, respectively than the unused lubricating oil. Dibenzothiophenes, phthalic acid esters, hydrogenated polydecene and alkylated aromatics compounds have ∼m/z 800, and they are present in the base oils. Large molecules such as polyalphaolefins and alkylated naphthalenes have ∼ m/z 1000–2000, detected in the base oils [73].

Fig. 6
ESI mass spectra of glycopeptides from LC/MS analysis: (a) unused lubricating oil, (b) GCI engine lubricating oil, (c) CDC engine lubricating oil, and (d) G70 test fuel for GCI engineESI mass spectra of glycopeptides from LC/MS analysis: (a) unused lubricating oil, (b) GCI engine lubricating oil, (c) CDC engine lubricating oil, and (d) G70 test fuel for GCI engine
Fig. 6
ESI mass spectra of glycopeptides from LC/MS analysis: (a) unused lubricating oil, (b) GCI engine lubricating oil, (c) CDC engine lubricating oil, and (d) G70 test fuel for GCI engineESI mass spectra of glycopeptides from LC/MS analysis: (a) unused lubricating oil, (b) GCI engine lubricating oil, (c) CDC engine lubricating oil, and (d) G70 test fuel for GCI engine
Close modal

Relative concentrations of peaks at m/z 200–400 were higher for GCI mode than baseline CDC mode. The used lubricating oil contains higher polynuclear aromatic hydrocarbons than unused lubricating oils, increasing peaks in m/z 300–500 [74]. Lubricating oils from gasoline engines showed 22 times higher PAHs than diesel engines [75]. Figure 6(d) shows a chromatogram of G70, showing m/z range between 100–1400. Diesel has higher concentrations of compounds with lower m/z than gasoline [76]. Barnett et al. [77] detected gasoline fuel additives at 320–450 °C with m/z 600–810. Peaks at m/z 146, 217, 274, 325, and 517 were observed in G70 and GCI engine lubricating oil. Peaks at m/z 196, 736, 792, and 858 might belong to baseline diesel. Dilution of lubricating oil by diesel led to the detection of these components in oil. The gasoline components can react with lubricating oil whereas diesel components remain suspended [78]. Concentrations of base oil and additives were lower in GCI engine lubricating oil than in CDC engine lubricating oil, requiring more frequent lubricating oil replacements.

3.4 FTIR.

In Fig. 7, 3500–3350 and 1700–1600 cm−1 bands exhibited the O-H stretching and bending vibrations, respectively [79]. Due to moisture and ethanol, GCI engine lubricating oil showed high transmittance in these bands. The lubricating oils showed bands at 3000–2850 cm−1, an intense peak at 1460 cm−1, and a less strong peak at 1377 cm−1 due to hydrocarbon compounds (C–H bands) [78]. Spectral bands at 1760–1710 cm−1 inferences about the in-service oil deterioration kinetics due to radical oxidation [80], and found to be higher in GCI engine lubricating oil. Bands within 1650–1600 cm−1 indicate nitration products in the lubricating oils. Nitration and carbonyl groups (N = O, C = O, etc.) were lower in CDC engine lubricating oil than in GCI engine lubricating oil. GCI engine lubricating oil showed higher transmittance at 1300–1150 cm−1. Bands at 1300–1250 cm−1 are associated with sulfonate salts in the lubricating oils [80]. The peak at 1156 cm−1 related to polymethacrylate, shows a pour-point depressant additive of the lubricating oil [78]. Peaks at 1172 and 1156 cm−1 also indicate the presence of sulfonate groups (SO3) [25]. Bands at 1040–1020 cm−1 confirm the existence of sulfonate groups. Unused lubricating oil contains higher sulfonate group elements than used lubricating oils. During engine combustion, sulfur compounds are combusted, resulting in lower sulfonate components in the lubricating oil.

Fig. 7
FTIR spectra of GCI, CDC, and unused lubricating oils
Fig. 7
FTIR spectra of GCI, CDC, and unused lubricating oils
Close modal

Bands at 1050–920 cm−1 related to the P–O–C in ZDDP are effective antioxidants. The absorption bands at around 990–980 cm−1 are generally associated with the P–O–C starting vibrations of ZDDPs [25]. Lubricating oil undergoes thermal and oxidative stresses, destroying ZDDPs. CDC engine lubricating oil exhibited lower ZDDPs than GCI engine lubricating oil, increasing the component wear and leading to engine degradation. Bands at 990–900 cm−1 characterize the C–H stretching vibration of unsaturated hydrocarbons [80]. GCI engine lubricating oil showed higher transmittance than unused lubricating oil in this band. The peaks at 720 and 654 cm−1 signify the P = S stretching of ZDDPs [81]. Peaks corresponding to ZDDPs were higher for GCI than CDC engine lubricating oils. Bands at around 760–740 cm−1 signify the presence of C = C, -CH = CH-, S–O, and halo components in the lubricating oils. GCI engine lubricating oil contained more hydrocarbon components than CDC engine lubricating oil. Diesel and G70 showed almost similar FTIR spectra (Fig. 8).

Fig. 8
FTIR spectra of diesel and G70
Fig. 8
FTIR spectra of diesel and G70
Close modal

Asymmetric and symmetric stretching of CH,CH2,CH3 were detected at 2924 and 2854 cm−1, respectively [82]. They were higher for G70 than baseline diesel. Similarly, peaks at 1450 and 1375 cm−1 represent CH3 and CH,CH2 bending. Diesel and G70 showed similar carbonyl group bond (C = O) stretching at 1742 cm−1. Axial asymmetric deformation peak (O–C–C) was observed at 1195 cm−1. Diesel contained more C–C bonds than G70. GCI engine lubricating oil showed higher bending vibrations of the aromatic compounds (C–H bond) at 875–825 cm−1 [80]. Gasoline contains aromatics as octane number boosters. The absorption peaks at 810, 780, and 750 ± 20 cm−1 correspond to the C–H bend of alkanes [82]. Diesel and gasoline contain high long-chain and short-chain alkanes, respectively. Short-chain alkanes have higher bending frequencies than long-chain alkanes due to the lower stiffness of C–H bonds [83]. Gasoline contains higher alkenes than baseline diesel, while diesel may contain alkenes as impurities. Peaks at 970, 890, 840–790, 730–665 cm−1 correspond to the C = C bend of alkenes. Hence, the C = C bend was higher for G70 than for diesel. Halo components (850–550, 690–515, and 600–500 cm−1) are impurities in the gasoline and diesel, reducing their stability. However, some fuel additives contain halogenated compounds. Lubricating oils show peaks at 985 and 654 cm−1 corresponding to additives that were not observed in the test fuels. Overall, the GCI engine lubricating oil contains more C–H groups (∼1460, 1380, and 1400 cm−1) and soot. These components could degrade the lubricating oil quality.

3.5 Nuclear Magnetic Resonance Spectroscopy.

Figure 9 shows the proton spectra of unused and used lubricating oils. At 0–2 ppm spectral regions, dominant signals were observed. Lubricating oils contain a diverse range of alkyl chains (saturated and unsaturated) that exhibit variations in size and chemical composition [42].

Fig. 9
Comparison of 1H NMR spectra for unused and used lubricating oils (a) unused lubricating oil, (b) GCI lubricating oil, and (c) CDC lubricating oil
Fig. 9
Comparison of 1H NMR spectra for unused and used lubricating oils (a) unused lubricating oil, (b) GCI lubricating oil, and (c) CDC lubricating oil
Close modal

The signals in the regions of 1.7–3.2 ppm (aliphatic), 3.2–4.4 ppm (oxygenates), 4.4–6.0 ppm (olefins, amines, and esters), and 6.4–8.8 (aromatics) were unseen in the lubricating oils. Signals in aliphatic and oxygenated species regions increase with lubricating oil aging [24]. 13C NMR determines the quaternary carbon atoms and functional groups, and 1H NMR could not detect them [21]. 13C NMR signals were observed at 10–40 ppm (alkanes) (Fig. 10). However, it is challenging to obtain clear information on alkanes and cycloalkanes from 13C NMR [21]. The signals in the regions of 50–90 ppm (alcohols, esters, and alkyne), 100–150 ppm (aromatic), and 190–210 ppm (ketone) were undetected in the lubricating oil samples.

Fig. 10
Comparison of 13C NMR spectra for unused and used lubricating oils (a) unused lubricating oil, (b) GCI lubricating oil, and (c) CDC ubricating oil
Fig. 10
Comparison of 13C NMR spectra for unused and used lubricating oils (a) unused lubricating oil, (b) GCI lubricating oil, and (c) CDC ubricating oil
Close modal

The intensity of an NMR signal corresponds to the number of nuclei contributing to the signal. The area under an NMR signal corresponds to the number of nuclei in a specific chemical environment [42]. Primary alkyl hydrogen-containing functional groups (5–22 ppm) were higher than secondary alkyl (20–30 ppm) counterparts for unused lubricating oil. Alkyl chains in lubricating oils usually differ in size and chemical structure from saturated to unsaturated carbon chains. Despite chemical changes in lubricating oil during usage, there will always be many CH3 and =CH2 groups [24]. GCI and CDC engine lubricating oils showed higher –OH molecules (50–90 ppm) than unused lubricating oil [78]. Metals form metal oxides when exposed to oxygen for a prolonged period [84]. Figure 11 shows proton and carbon NMR for G70. More CH2 and CH3 protons (0.8–1.4 ppm) were observed than CH protons (1.4–2.1 ppm) in G70 (Table 3).

Fig. 11
1H and 13C NMR spectra for G70 fuel: (a) proton NMR_G70 fuel, and (b) carbon NMR_G70 fuel
Fig. 11
1H and 13C NMR spectra for G70 fuel: (a) proton NMR_G70 fuel, and (b) carbon NMR_G70 fuel
Close modal

Significant aromatics (2.1–4.0 ppm) and aromatic ring protons (6.2–9.2 ppm) were observed in G70. Carbon NMR confirms the presence of C = C, CH, CH2, and CH3 in G70. Ether and alcohol carbon (50–90 ppm) were detected in G70 due to 12% ethanol in commercial gasoline used for blending. Finally, no significant changes in hydrogen and carbon functional groups were observed between used and unused lubricating oils from the two engines.

3.6 Trace Element Analysis.

ICP-MS multi-element technique analyzes the trace elements in the lubricating oils [85]. Trace metals are low in new lubricating oils and increase with usage. Around 26 elements were investigated, and 16 were quantified (Fig. 12).

Fig. 12
Concentration of trace elements in lubricating oil
Fig. 12
Concentration of trace elements in lubricating oil
Close modal

Ca, Zn, and P traces were majorly observed in the lubricating oils. These trace elements were 27–34% and 2–4% higher for CDC and GCI engine lubricating oils, respectively, than unused lubricating oil. Mg and Ca are present in detergent additives, alloys, wear particles, dust, and water-based contaminants [86]. P is present in the anti-wear additive (ZDDP) and phosphate ester. Brass, ZDDP additives, and filter canisters contain Zn traces. Lubricating oil decays due to thermal effects and can reduce P, Zn, and Ca trace concentrations. Increasing fuel dilution can further reduce their trace concentrations. Fe, Na, and Al trace elements were 228, 55.5, and 179% higher in CDC engine lubricating oil than unused lubricating oil, whereas they were 92, 504, and 74% higher in GCI engine lubricating oil. High Na traces in the GCI engine lubricating oil infer that it comes from G70 (fuel) via fuel dilution. Cu traces were 19.6 and 7.69 times higher for CDC and GCI engine lubricating oils, respectively, than the unused lubricating oil. Fe and Cu traces were higher for CDC engine lubricating oil than for GCI engine lubricating oil. Pb, Sr, and Cr traces increased by 48, 2.3, and 17 times in CDC engine lubricating oil than unused lubricating oil, whereas they increased by 26, 0.25, and 3.36 times for the GCI engine lubricating oil.

Wear metals (Fe, Cu, Cr, and Pb) trace concentrations increased with engine runtime [86,87]. K, B, Cr, and P traces are generally associated with antifreeze additives ingress in the lubricating oils. Minor amounts of Rb, Mn, Ba, and Bi trace were detected in the lubricating oils. Mn is a metallic element, whereas Ba traces are present in fuel detergent additives and grease. Pb, Cr, and Bi are hazardous elements, observed to be ∼63.6 and 30.3 ng/g of lubricating oil in the CDC and GCI engine lubricating oils, respectively. CDC engine lubricating oil contains higher trace elements than GCI engine lubricating oil. Fuel dilution of the lubricating oil could reduce the trace metals in the lubricating oil. Higher concentrations of trace metals in the lubricating oils can increase engine wear and PM emissions.

3.7 Soot-in-Oil.

The soot aggregates were between 140–650 nm in size and had a modest branched structure (Fig. 13). The average aggregate width and length were 130 and 500 nm, respectively, while the maximum width and length were 200 and 565 nm, respectively (for GCI soot in the lubricating oil).

Fig. 13
TEM images of soot particles in the lubricating oil from (a) CDC mode engine and (b) GCI mode engine
Fig. 13
TEM images of soot particles in the lubricating oil from (a) CDC mode engine and (b) GCI mode engine
Close modal

Apart from chain aggregates, compact aggregates of ∼650 nm were observed. Aggregates length-to-width (L/W) ratio varied between 1.1 to 1.51 and 1.0 to 2.16 for CDC and GCI engines, respectively. Skeleton width (Wsk) and skeleton length (Lsk) give more information about the soot shape, especially for chain-like aggregates [88]. The Lsk/Wsk ratio was between 2 and 10 for the GCI engine, which was higher than the CDC engine. Agglomerates are defined as a cluster when L/W < 2.5; hence, most soot falls into this group. Whenever the skeletal ratio (Lsk/Wsk) is used, more aggregates can be classified as chains.

The aggregate perimeter versus area is shown in Fig. 14(a). More small-size aggregates were observed in CDC engine lubricating oil than in GCI engine lubricating oil. The soot aggregate perimeter for CDC and GCI engine lubricating oils were ∼2000nm and 2500 nm, respectively. The high combustion temperature of CDC emits more small-size particles, whereas low-temperature combustion regions in GCI produce more chain-like aggregates [46]. The number of primary particles per aggregate (N) is plotted against the corresponding normalized radius of gyration (2Rg/dp) [89], in Fig. 14(b). Slope and intercept of logarithmic 2Rg/dp and N provide Df and Kf (fractal prefactor) [46]. The soot Df was observed to be between 1.1 to 1.9 (accepted universal values) and lower Df represents chain-like aggregates [89]. Soot in the GCI and CDC engine lubricating oils showed 1.21 and 1.15 Df, respectively. A high Kf indicated a higher number of primary particles and a larger overall size of soot aggregates [29].

Fig. 14
Plots of (a) perimeter versus area for soot aggregates and (b) statistical determination of Df for the soot in the lubricating oil from GCI and CDC mode engines
Fig. 14
Plots of (a) perimeter versus area for soot aggregates and (b) statistical determination of Df for the soot in the lubricating oil from GCI and CDC mode engines
Close modal

The dp distribution of soot from CDC and GCI engine lubricating oils is shown in Fig. 14. For the CDC engine soot in the lubricating oil, the minimum and maximum dp were 19.6 and 46.7 nm, respectively. These were 18.6 and 49 nm for GCI engine soot in the lubricating oil. Rocca et al. [43] reported that primary particle sizes extracted from the lubricating oil range from 20.2 to 35 nm. With increasing in-cylinder temperature and oxygen, mean dp decreases [88]. The soot in the CDC engine lubricating oil showed smaller primary particles than the primary particles in the soot from GCI engine lubricating oil. Molecular (aromatic) structures influenced the soot formation in direct-injection PPC engines [90]. GCI engine lubricating oil contained more soot, which increased its viscosity, requiring more frequent oil changes.

3.8 Non-Reactive Computational Fluid Dynamic Simulations.

In-cylinder pressure and temperatures were slightly lower for GCI than the CDC mode engine (Fig. 15(a)). Higher latent heat of vaporization of gasoline decreases the intake charge temperature. The chamber temperature was ∼330, 620, and 750 K at 100, 30, and 14 °bTDC injection timings. Iso-octane's critical temperature is ∼550 K, leading to a longer spray for the pilot injections. Figure 15(b) shows that the gasoline spray hits the cylinder walls and is converted into vapors. Le Coz et al. [91] reported gasoline spray penetrated up to 7 cm at 700 K and 11 bar chamber conditions. Ray et al. [49] reported diesel liquid and vapor penetration lengths lie within 4 cm. Fuel trapped in the squish region was negligible in the CDC engine due to the absence of pilot injection. Moreover, n-heptane exhibited lower liquid spray mass due to higher temperature and fuel-rich conditions.

Fig. 15
(a) In-pressure and temperature and (b) Spray penetration length and liquid spray mass for CDC and GCI mode engines
Fig. 15
(a) In-pressure and temperature and (b) Spray penetration length and liquid spray mass for CDC and GCI mode engines
Close modal

Observations were made for in-cylinder fuel mass fractions at different crank angles, starting after the start of injection up to the TDC. Fuel from pilot-1 strikes the cylinder walls, causing the wall-wetting (at 89 °bTDC). Fuel from Pilot-2 injection hits the piston lip and splits into two parts: squish and bowl regions. A portion of the fuel–air mixture trapped in the squish region moves to the bowl region during the compression stroke. A significant fraction of fuel in the squish and crevice regions can undergo incomplete combustion [26]. Fuel from the main injection enters the bowl region and forms a richer fuel–air mixture. The GCI mode engine's spray droplet radius is 10–20 microns. Yan et al. [92] reported Sauter mean diameter (SMD) of iso-octane was between 5 and 12.5 microns. Adding n-heptane to iso-octane increased the SMD of spray droplets and spray droplet velocity. In CDC mode, the fuel was targeted into the bowl region at ∼800 K gas environment, which underwent dominant diffusion combustion. It was observed that n-heptane did not evaporate fully at the TDC position. More fuel was trapped in the piston bowl region, and subsequent combustion led to higher CO emissions [26].

4 Conclusions

This study investigated the effects of GCI and CDC mode engines on particulate emissions and lubricating oil deterioration. Advanced fuel injection timings led to wall-wetting and interaction with the cylinder liner and the lubricating oil. GCI engine burns more lubricating oil, resulting in higher particulate emissions. GCI mode engine exhibited 97–99.7% and 4.5–58% higher NPs and NMPs than the baseline CDC mode engine. AMPs increased with the engine load and speed. A small fraction of fuel mixed with the lubricating oil causes crankcase dilution, reducing the lubricating oil's viscosity. Particulates also interact with lubricating oil, increasing its viscosity. GCI engine lubricating oil showed 8.78%, 7.46%, and 14% lower viscosity, ash, and carbon residue than CDC engine lubricating oil. Unused lubricating oil showed higher base oil and additive components, whereas used lubricating oils contained more diesel molecules than gasoline. Due to their chemical characteristics, gasoline molecules evaporated or reacted with lubricated oils. CDC engine lubricating oil showed higher base oil components than GCI engine lubricating oil. FTIR analysis revealed that hydroxide or moisture was lower in CDC engine lubricating oil than in GCI engine lubricating oil. NMR showed that primary alkyl hydrogens were 57.9% and 68.34% higher than secondary alkyl hydrogens for GCI and CDC engine lubricating oils. GCI and CDC engines showed dominant clusters and chain-like aggregates in the TEM images of soot in the lubricating oils. Larger primary particles and fractal dimensions were observed in the soot from the GCI engine lubricating oil than from the CDC engine lubricating oil. Sixteen trace elements were detected in the lubricating oil samples. GCI engine lubricating oil showed lower (∼ 20 mg/kg of oil) trace metals than the CDC engine lubricating oil. GCI engine showed a longer fuel spray penetration length due to lower in-cylinder temperature, resulting in the possibility of wall-wetting by the fuel sprays, which will adversely affect the lubricating oil, as seen in the oil characterization studies. Therefore, GCI combustion engines would either require special lubricating oil formulations to address these issues or more frequent oil changes.

Acknowledgment

This work is supported by the J C Bose Fellowship by the Science & Engineering Research Board, Government of India (Grant No. EMR/2019/000920) and SBI endowed Chair Professorship from the State Bank of India to Professor Avinash Kumar Agarwal. Financial support from the SERB, under the National Postdoctoral Fellowship scheme to Dr M. Krishnamoorthi (PDF/2021/001209) is acknowledged. The authors are grateful to the Department of Chemistry, Center for Environmental Science and Engineering, and the Advanced Imaging Centre of IIT Kanpur for allowing us to use their sophisticated analytical facilities. The authors gratefully acknowledge the assistance of Harsimran Singh, Abhijit Saha, Vaibhav Singh, Ankur Kalwar, Sam Joe, and Vasudev Chaudhari in this work. The assistance of the Engine Research Laboratory staff in conducting the exhaustive series of experiments is gratefully acknowledged.

Conflict of Interest

There are no conflicts of interest. This article does not include research in which human participants were involved. Informed consent was obtained for all individuals. Documentation is provided upon request. This article does not include any research in which animal participants were involved.

Data Availability Statement

The datasets generated and supporting the findings of this article are obtainable from the corresponding author upon reasonable request.

Appendix

Figures 16 and 17.

Fig. 16
(a) Pentane insoluble, (b) ash content, (c) flash point, and (d) and viscosity tests
Fig. 16
(a) Pentane insoluble, (b) ash content, (c) flash point, and (d) and viscosity tests
Close modal
Fig. 17
Copper corrosion, carbon residue and cracking tests: (a) copper corrosion, (b) carbon residue Ramsbottom and (c) crackling test
Fig. 17
Copper corrosion, carbon residue and cracking tests: (a) copper corrosion, (b) carbon residue Ramsbottom and (c) crackling test
Close modal

References

1.
The Economist Intelligence Unit
,
2022
,
Energy Outlook 2023 Surviving the Energy Crisis
,
The Economist Intelligence Unit
,
London
.
2.
US EIA
,
2017
,
Total in Energy
,
Energy Inf Adm
,
Washington, DC
.
3.
Report
, and
International Energy Agency
,
2022
,
World Energy Outlook 2022
,
International Energy Agency
,
France
.
4.
Kale
,
A. V.
, and
Krishnasamy
,
A.
,
2023
, “
Experimental Study of Homogeneous Charge Compression Ignition Combustion in a Light-Duty Diesel Engine Fueled With Isopropanol–Gasoline Blends
,”
Energy
,
264
(
1
), p.
126152
.
5.
Qian
,
Y.
,
Zhang
,
Y.
,
Mi
,
S.
,
Wu
,
H.
,
Li
,
Z.
, and
Lu
,
X.
,
2023
, “
Efficient and Clean Combustion of Intelligent Charge Compression Ignition (ICCI) Engine at Low Load Conditions
,”
Fuel
,
332
, p.
126002
.
6.
Krishnamoorthi
,
M.
,
Malayalamurthi
,
R.
,
He
,
Z.
, and
Kandasamy
,
S.
,
2019
, “
A Review on Low-Temperature Combustion Engines: Performance, Combustion and Emission Characteristics
,”
Renewable Sustainable Energy Rev.
,
116
, p.
109404
.
7.
Kalghatgi
,
G. T.
,
Risberg
,
P.
, and
Ångström
,
H.-E.
,
2006
, “
Advantages of Fuels With High Resistance to Auto-Ignition in Late-Injection, Low-Temperature, Compression Ignition Combustion
,”
J. Fuels Lubr.
,
115
(
4
), pp.
623
634
.
8.
Agarwal
,
A. K.
,
Solanki
,
V. S.
, and
Krishnamoorthi
,
M.
,
2023
, “
Gasoline Compression Ignition (GCI) Combustion in a Light-Duty Engine Using Double Injection Strategy
,”
Appl. Therm. Eng.
,
223
, p.
120006
.
9.
Roberts
,
J.
,
Chuahy
,
F. D. F.
,
Kokjohn
,
S. L.
, and
Roy
,
S.
,
2019
, “
Isolation of the Parametric Effects of Pre-Blended Fuel on Low Load Gasoline Compression Ignition (GCI)
,”
Fuel
,
237
, pp.
522
535
.
10.
Pickett
,
L. M.
,
Siebers
,
D. L.
, and
Idicheria
,
C. A.
,
2005
, “
Relationship Between Ignition Processes and the Lift-Off Length of Diesel Fuel Jets
,”
J. Engines
,
114
(
3
), pp.
1714
1731
.
11.
Zhang
,
Y.
,
Zhao
,
W.
,
Wu
,
H.
,
He
,
Z.
,
Qian
,
Y.
, and
Lu
,
X.
,
2022
, “
Potential of EGR and Intake Heating for Load Extension Using Gasoline-Ethanol Blends as Low Reactivity Fuel in an Intelligent Charge Compression Ignition Engine
,”
Fuel
,
314
, p.
122785
.
12.
Qian
,
Y.
,
Li
,
Z.
,
Yu
,
L.
,
Wang
,
X.
, and
Lu
,
X.
,
2019
, “
Review of the State-of-the-Art of Particulate Matter Emissions From Modern Gasoline-Fueled Engines
,”
Appl. Energy
,
238
, pp.
1269
1298
.
13.
Kobashi
,
Y.
,
Wang
,
Y.
,
Shibata
,
G.
,
Ogawa
,
H.
, and
Naganuma
,
K.
,
2019
, “
Ignition Control in a Gasoline Compression Ignition Engine With Ozone Addition Combined With a Two-Stage Direct-Injection Strategy
,”
Fuel
,
249
, pp.
154
160
.
14.
Cung
,
K.
,
Moiz
,
A.
,
Smith
,
M.
,
Bitsis
,
C.
,
Briggs
,
T.
, and
Miwa
,
J.
,
2021
, “
Gasoline Compression Ignition (GCI) Combustion of Pump-Grade Gasoline Fuel Under High Compression Ratio Diesel Engine
,”
Transp. Eng.
,
4
, p.
100066
.
15.
Chuahy
,
D. F.
,
Moses-DeBusk
,
M.
,
Curran
,
S. J.
,
Storey
,
J. M. E.
, and
Wagnon
,
S. W.
,
2021
, “
The Effects of Distillation Characteristics and Aromatic Content on Low-Load Gasoline Compression Ignition (GCI) Performance and Soot Emissions in a Multi-Cylinder Engine
,”
Fuel
,
299
, p.
120893
.
16.
Yang
,
C.
,
Yang
,
Z.
,
Zhang
,
G.
,
Hollebone
,
B.
,
Landriault
,
M.
,
Wang
,
Z.
, et al
,
2016
, “
Characterisation and Differentiation of Chemical Fingerprints of Virgin and Used Lubricating Oils for Identification of Contamination or Adulteration Sources
,”
Fuel
,
163
, pp.
271
281
.
17.
Perini
,
F.
,
Dempsey
,
A.
,
Reitz
,
R. D.
,
Sahoo
,
D.
,
Petersen
,
B.
, and
Miles
,
P. C.
,
2013
, A Computational Investigation of the Effects of Swirl Ratio and Injection Pressure on Mixture Preparation and Wall Heat Transfer in a Light-Duty Diesel Engine. SAE Technical Paper No. 2013-01–1105.
18.
Thomas
,
D.
,
Cyrille
,
C.
, and
Ludovic
,
M.
,
2019
, “
Investigating the impact of gasoline lubricity on the high pressure pumps operation
,”
SAE Technical Paper
,
01
.
19.
Kumar Agarwal
,
A.
, and
Dhar
,
A.
,
2012
, “
Wear, Durability, and Lubricating Oil Performance of a Straight Vegetable Oil (Karanja) Blend Fueled Direct Injection Compression Ignition Engine
,”
J. Renew. Sustainable Energy
,
4
(
6
), p.
063138
.
20.
Agarwal
,
A. K.
, and
Agarwal
,
D.
,
2021
, “
Field-Testing of Biodiesel (B100) and Diesel-Fueled Vehicles: Part 2 – Lubricating Oil Condition Monitoring
,”
ASME J. Energy Resour. Technol.
,
143
(
4
), p.
042308
.
21.
Rakhmatullin
,
I.
,
Efimov
,
S.
,
Varfolomeev
,
M.
, and
Klochkov
,
V.
,
2018
, “
High-Resolution NMR Study of Light and Heavy Crude Oils: “Structure-Property” Analysis
,”
IOP Conf. Ser. Earth Environ. Sci.
,
155
(
1
), p.
012014
.
22.
Alam
,
M. S.
,
Zeraati-Rezaei
,
S.
,
Liang
,
Z.
,
Stark
,
C.
,
Xu
,
H.
,
MacKenzie
,
A. R.
, et al
,
2018
, “
Mapping and Quantifying Isomer Sets of Hydrocarbons (≥C12) in Diesel Exhaust, Lubricating Oil and Diesel Fuel Samples Using GC×GC-ToF-MS
,”
Atmos. Meas. Tech.
,
11
(
5
), pp.
3047
3058
.
23.
Kumar
,
V.
,
Goel
,
A.
, and
Rajput
,
P.
,
2017
, “
Compositional and Surface Characterisation of HULIS by UV-Vis, FTIR, NMR and XPS: Wintertime Study in Northern India
,”
Atmos. Environ.
,
164
, pp.
468
475
.
24.
Förster
,
E.
,
Becker
,
J.
,
Dalitz
,
F.
,
Görling
,
B.
,
Luy
,
B.
,
Nirschl
,
H.
, et al
,
2015
, “
NMR Investigations on the Ageing of Motor Oils
,”
Energy Fuels
,
29
(
11
), pp.
7204
7212
.
25.
Sejkorová
,
M.
,
Šarkan
,
B.
,
Veselík
,
P.
, and
Hurtová
,
I.
,
2020
, “
FTIR Spectrometry With PLS Regression for Rapid TBN Determination of Worn Mineral Engine Oils
,”
Energies
,
13
(
23
), p.
6438
.
26.
Xu
,
L.
,
Bai
,
X.-S.
,
Li
,
Y.
,
Treacy
,
M.
,
Li
,
C.
,
Tunestål
,
P.
, et al
,
2020
, “
Effect of Piston Bowl Geometry and Compression Ratio on In-Cylinder Combustion and Engine Performance in a Gasoline Direct-Injection Compression Ignition Engine Under Different Injection Conditions
,”
Appl. Energy
,
280
, p.
115920
.
27.
Tekie
,
H. A.
,
McCrindle
,
R. I.
,
Marais
,
P. J. J.
, and
Ambushe
,
A. A.
,
2015
, “
Evaluation of Six Sample Preparation Methods for Determination of Trace Metals in Lubricating Oils Using Inductively Coupled Plasma-Optical Emission Spectrometry
,”
S. Afr. J. Chem.
,
68
, pp.
76
84
.
28.
George
,
S.
,
Balla
,
S.
,
Gautam
,
V.
, and
Gautam
,
M.
,
2007
, “
Effect of Diesel Soot on Lubricant Oil Viscosity
,”
Tribol. Int.
,
40
(
5
), pp.
809
818
.
29.
Liu
,
F.
,
Snelling
,
D. R.
, and
Smallwood
,
G. J.
,
2009
, “
Effects of the Fractal Prefactor on the Optical Properties of Fractal Soot Aggregates
,”
Proceedings of the ASME 2009 Second International Conference on Micro/Nanoscale Heat Mass Transfer, ASMEDC
,
Shanghai, China
,
Dec. 18–21
, pp.
363
371
.
30.
Kumar Agarwal
,
A.
,
Solanki
,
V. S.
, and
Krishnamoorthi
,
M.
,
2023
, “
Experimental Evaluation of Pilot and Main Injection Strategies on Gasoline Compression Ignition Engine – Part 1: Combustion Characteristics
,”
SAE Int. J. Engines
,
16
(
6
), pp.
809
832
.
31.
Singh
,
A. P.
,
Kumar
,
D.
, and
Agarwal
,
A. K.
,
2020
, “
Particulate Characteristics of Laser Ignited Hydrogen-Enriched Compressed Natural Gas Engine
,”
Int. J. Hydrogen Energy
,
45
(
35
), pp.
18021
18031
.
32.
D2270 A
,
2004
,
Standard Practice for Calculating Viscosity Index From Kinematic Viscosity at 40 °C and 100 °C
,
ASTM International
,
West Conshohocken, PA
.
33.
D130 A
,
2019
,
Standard Test Method for Corrosiveness to Copper From Petroleum Products by Copper Strip Test
,
ASTM International
,
West Conshohocken, PA
.
34.
ASTM D92
,
2004
,
Standard Test Method for Flash and Fire Points by Cleveland Open Cup Tester
,
ASTM International
,
West Conshohocken, PA
.
35.
D482 A
,
2007
,
Standard Test Method for ash From Petroleum Products
,
ASTM International
,
West Conshohocken, PA
.
36.
D189 A
,
1998
,
Standard Test Method for Ramsbottom Carbon Residue of Petroleum Products
,
ASTM International
,
West Conshohocken, PA
.
37.
ASTM D893
,
2002
,
Standard Test Method for Insolubles in Used Lubricating Oils
,
ASTM International
,
West Conshohocken, PA
.
38.
Gupta
,
J. G.
, and
Agarwal
,
A. K.
,
2021
, “
Engine Durability and Lubricating Oil Tribology Study of a Biodiesel Fuelled Common Rail Direct Injection Medium-Duty Transportation Diesel Engine
,”
Wear
,
486–487
, p.
204104
.
39.
Porter
,
D. J.
,
Mayer
,
P. M.
, and
Fingas
,
M.
,
2004
, “
Analysis of Petroleum Resins Using Electrospray Ionisation Tandem Mass Spectrometry
,”
Energy Fuels
,
18
(
4
), pp.
987
994
.
40.
Cao
,
X.
,
Ro
,
K. S.
,
Chappell
,
M.
,
Li
,
Y.
, and
Mao
,
J.
,
2011
, “
Chemical Structures of Swine-Manure Chars Produced Under Different Carbonisation Conditions Investigated by Advanced Solid-State 13C Nuclear Magnetic Resonance (NMR) Spectroscopy
,”
Energy Fuels
,
25
(
1
), pp.
388
397
.
41.
Rakhmatullin
,
I. Z.
,
Efimov S
,
V.
,
Klochkov A
,
V.
,
Gnezdilov
,
O. I.
,
Varfolomeev
,
M. A.
, and
Klochkov V
,
V.
,
2022
, “
NMR Chemical Shifts of Carbon Atoms and Characteristic Shift Ranges in the Oil Sample
,”
Pet. Res.
,
7
(
2
), pp.
269
274
.
42.
Hao
,
N.
,
Ben
,
H.
,
Yoo
,
C. G.
,
Adhikari
,
S.
, and
Ragauskas
,
A. J.
,
2016
, “
Review of NMR Characterisation of Pyrolysis Oils
,”
Energy Fuels
,
30
(
9
), pp.
6863
6880
.
43.
La Rocca
,
A.
,
Di Liberto
,
G.
,
Shayler
,
P. J.
, and
Fay
,
M. W.
,
2013
, “
The Nanostructure of Soot-in-Oil Particles and Agglomerates From an Automotive Diesel Engine
,”
Tribol. Int.
,
61
, pp.
80
87
.
44.
Wang
,
Y.
,
Liu
,
F.
,
He
,
C.
,
Bi
,
L.
,
Cheng
,
T.
,
Wang
,
Z.
, et al
,
2017
, “
Fractal Dimensions and Mixing Structures of Soot Particles During Atmospheric Processing
,”
Environ. Sci. Technol. Lett.
,
4
(
11
), pp.
487
493
.
45.
Brasil
,
A. M.
,
Farias
,
T. L.
, and
Carvalho
,
M. G.
,
1999
, “
A Recipe for Image Characterisation of Fractal-Like Aggregates
,”
J. Aerosol Sci.
,
30
(
10
), pp.
1379
1389
.
46.
Krishnamoorthi
,
M.
,
Sreedhara
,
S.
, and
Prakash Duvvuri
,
P.
,
2020
, “
Experimental, Numerical and Exergy Analyses of a Dual Fuel Combustion Engine Fuelled With Syngas and Biodiesel/Diesel Blends
,”
Appl. Energy
,
263
, p.
114643
.
47.
Wang
,
H.
,
Yao
,
M.
, and
Reitz
,
R. D.
,
2013
, “
Development of a Reduced Primary Reference Fuel Mechanism for Internal Combustion Engine Combustion Simulations
,”
Energy Fuels
,
27
(
12
), pp.
7843
7853
.
48.
Nordin
,
N.
,
1998
,
Numerical Simulations of Non-Steady Spray Combustion Using a Detailed Chemistry Approach
,
Chalmers University of Technology
,
Sweden
.
49.
Ray SC
,
S.
,
Naito
,
S.
,
Andersson
,
M.
,
Nishida
,
K.
, and
Ogata
,
Y.
,
2023
, “
Evaluation of Vaporising Diesel Spray With High-Speed Laser Absorption Scattering Technique for Measuring Vapour and Liquid Phase Concentration Distributions
,”
Fuels
,
4
(
1
), pp.
75
91
.
50.
Agarwal
,
A. K.
,
Solanki
,
V. S.
, and
Krishnamoorthi
,
M.
,
2023
, “
Experimental Evaluation of Pilot and Main Injection Strategies on Gasoline Compression Ignition Engine – Part 2: Performance and Emissions Characteristics
,”
SAE Int. J. Engines
,
16
(
6
), pp.
809
832
.
51.
Brahma
,
I.
, and
Ofili
,
O.
,
2022
, “
Nucleation-Accumulation Mode Trade-Off in Non-Volatile Particle Emissions From a Small Non-Road Small Diesel Engine
,”
Environ. Sci. Pollut. Res.
,
29
(
59
), pp.
89449
89468
.
52.
Yang
,
B.
,
Liu
,
L.
,
Jia
,
S.
,
Zhang
,
F.
, and
Yao
,
M.
,
2021
, “
Experimental Study on Particle Size Distribution of Gasoline Compression Ignition (GCI) at Low-Load Condition
,”
Fuel
,
294
, p.
120502
.
53.
Puzun
,
A.
,
Wanchen
,
S.
,
Guoliang
,
L.
,
Manzhi
,
T.
,
Chunjie
,
L.
, and
Shibao
,
C.
,
2011
, “
Characteristics of Particle Size Distributions About Emissions in A Common-Rail Diesel Engine With Biodiesel Blends
,”
Procedia Environ. Sci.
,
11
, pp.
1371
1378
.
54.
Lou
,
D.
,
Lou
,
G.
,
Wang
,
B.
,
Fang
,
L.
, and
Zhang
,
Y.
,
2021
, “
Effect of LP-EGR on the Emission Characteristics of GDI Engine
,”
Machines
,
10
(
1
), p.
7
.
55.
Gupta
,
T.
,
Kothari
,
A.
,
Srivastava
,
D. K.
, and
Agarwal
,
A. K.
,
2010
, “
Measurement of Number and Size Distribution of Particles Emitted From a Mid-Sized Transportation Multipoint Port Fuel Injection Gasoline Engine
,”
Fuel
,
89
(
9
), pp.
2230
2233
.
56.
Lu
,
T.
,
Cheung
,
C. S.
, and
Huang
,
Z.
,
2012
, “
Effects of Engine Operating Conditions on the Size and Nanostructure of Diesel Particles
,”
J. Aerosol Sci.
,
47
, pp.
27
38
.
57.
Giechaskiel
,
B.
,
Ntziachristos
,
L.
,
Samaras
,
Z.
,
Scheer
,
V.
,
Casata
,
R.
, and
Vogt
,
R.
,
2005
, “
Formation Potential of Vehicle Exhaust Nucleation Mode Particles On-Road and in the Laboratory
,”
Atmos. Environ.
,
39
(
18
), pp.
3191
3198
.
58.
Sciortino
,
D.
,
Bonatesta
,
F.
,
Hopkins
,
E.
,
Yang
,
C.
, and
Morrey
,
D.
,
2017
, “
A Combined Experimental and Computational Fluid Dynamics Investigation of Particulate Matter Emissions From a Wall-Guided Gasoline Direct Injection Engine
,”
Energies
,
10
(
9
), p.
1408
.
59.
Mathis
,
U.
,
Ristimäki
,
J.
,
Mohr
,
M.
,
Keskinen
,
J.
,
Ntziachristos
,
L.
,
Samaras
,
Z.
, et al
,
2004
, “
Sampling Conditions for the Measurement of Nucleation Mode Particles in the Exhaust of a Diesel Vehicle
,”
Aerosol Sci. Technol.
,
38
(
12
), pp.
1149
1160
.
60.
Chen
,
H.
,
Su
,
X.
,
Li
,
J.
, and
Zhong
,
X.
,
2019
, “
Effects of Gasoline and Polyoxymethylene Dimethyl Ethers Blending in Diesel on the Combustion and Emission of a Common Rail Diesel Engine
,”
Energy
,
171
, pp.
981
999
.
61.
Lee
,
H.
, and
Jeong
,
Y.
,
2012
, “
The Effect of Dynamic Operating Conditions on Nano-Particle Emissions From a Light-Duty Diesel Engine Applicable to Prime and Auxiliary Machines on Marine Vessels
,”
Int. J. Nav. Archit. Ocean Eng.
,
4
(
4
), pp.
403
411
.
62.
Jeon
,
J.
,
2020
, “
Spatiotemporal Flame Propagations, Combustion and Solid Particle Emissions From Lean and Stoichiometric Gasoline Direct Injection Engine Operation
,”
Energy
,
210
, p.
118652
.
63.
Liu
,
B.
,
Cheng
,
X.
,
Liu
,
J.
, and
Pu
,
H.
,
2018
, “
Investigation Into Particle Emission Characteristics of Partially Premixed Combustion Fueled With High n-Butanol-Diesel Ratio Blends
,”
Fuel
,
223
, pp.
1
11
.
64.
Srivastava
,
D. K.
,
Agarwal
,
A. K.
, and
Gupta
,
T.
,
2011
, “
Effect of Engine Load on Size and Number Distribution of Particulate Matter Emitted From a Direct Injection Compression Ignition Engine
,”
Aerosol Air Qual. Res.
,
11
(
7
), pp.
915
920
.
65.
Agarwal
,
A. K.
, and
Dhar
,
A.
,
2010
, “
Karanja Oil Utilisation in a Direct-Injection Engine by Preheating. Part 2: Experimental Investigations of Engine Durability and Lubricating Oil Properties
,”
Proc. Inst. Mech. Eng. Part D J. Automob. Eng.
,
224
(
1
), pp.
85
97
.
66.
WaterCom
,
n.d.
, Analysing Lubricating Oil Formations Without Sample Preparation, https://www.waters.com/content/dam/waters/en/app-notes/2011/720004025/720004025-ja.pdf, Accessed April 26, 2023.
67.
Tzing
,
S.-H.
,
Chang
,
J.-Y.
,
Ghule
,
A.
,
Chang
,
J.-J.
,
Lo
,
B.
, and
Ling
,
Y.-C.
,
2003
, “
A Simple and Rapid Method for Identifying the Source of Spilled Oil Using an Electronic Nose: Confirmation by Gas Chromatography With Mass Spectrometry
,”
Rapid Commun. Mass Spectrom.
,
17
(
16
), pp.
1873
1880
.
68.
Al-Amiery
,
A. A.
,
Isahak
,
W. N. R. W.
, and
Al-Azzawi
,
W. K.
,
2023
, “
Corrosion Inhibitors: Natural and Synthetic Organic Inhibitors
,”
Lubricants
,
11
(
4
), p.
174
.
69.
Kiw
,
Y. M.
,
Adam
,
P.
,
Schaeffer
,
P.
,
Thiébaut
,
B.
, and
Boyer
,
C.
,
2022
, “
Molecular Evidence for Sulfurisation of Molybdenum Dithiocarbamates (MoDTC) by Zinc Dithiophosphates: A Key Process in Their Synergetic Interactions and the Enhanced Preservation of MoDTC in Formulated Lubricants?
,”
RSC Adv.
,
12
(
6
), pp.
3542
3553
.
70.
Qian
,
X. L.
,
2017
, Lubricating Oil Compositions Containing Amidine Antioxidants. US Patent No. US9752092B2.
71.
Hu
,
J.-Q.
,
Wei
,
X.-Y.
,
Dai
,
G.-L.
,
Liu
,
C.-C.
,
Fu
,
Y.
,
Zong
,
Z.-M.
, et al
,
2007
, “
Study Demonstrating Enhanced Oxidation Stability When Arylamine Antioxidants Are Combined With Organic Molybdenum Complexes
,”
Tribol. Trans.
,
50
(
2
), pp.
205
210
.
72.
Waters
,
2022
, Rapid, Direct Analysis of Additives in Mineral Oils With Radian ASAP. pp.
1
10
. https://lcms.cz/labrulez-bucket-strapi-h3hsga3/720007685_en_b40fe85313/720007685-en.pdf, Accessed April 26, 2023.
73.
EFSA Panel on Contaminants in the Food Chain (CONTAM)
,
2012
, “
Scientific Opinion on Mineral Oil Hydrocarbons in Food
,”
EFSA J.
,
10
(
6
), p.
2704
.
74.
Sullivan
MJ.
Oil, Lubricating. Encycl. Toxicol.
,
Elsevier
,
New York
;
2005
, pp.
295
297
.
75.
Carmichael
,
P. L.
,
Jacob
,
J.
,
Grimmer
,
G.
, and
Phillips
,
D. H.
,
1990
, “
Analysis of the Polycyclic Aromatic Hydrocarbon Content of Petrol and Diesel Engine Lubricating Oils and Determination of DNA Adducts in Topically Treated Mice by 32 P-Postlabelling
,”
Carcinogenesis
,
11
(
11
), pp.
2025
2032
.
76.
Suppajariyawat
,
P.
,
de Andrade
,
A. F. B.
,
Elie
,
M.
,
Baron
,
M.
, and
Gonzalez-Rodriguez
,
J.
,
2019
, “
The Use of Chemical Composition and Additives to Classify Petrol and Diesel Using Gas Chromatography-Mass Spectrometry and Chemometric Analysis: A UK Study
,”
Open Chem.
,
17
(
1
), pp.
183
197
.
77.
Barnett
,
I.
,
Bailey
,
F. C.
, and
Zhang
,
M.
,
2019
, “
Detection and Classification of Ignitable Liquid Residues in the Presence of Matrix Interferences by Using Direct Analysis in Real-Time Mass Spectrometry
,”
J. Forensic. Sci.
,
64
(
5
), pp.
1486
1494
.
78.
Kupareva
,
A.
,
Mäki-Arvela
,
P.
,
Grénman
,
H.
,
Eränen
,
K.
,
Sjöholm
,
R.
,
Reunanen
,
M.
, et al
,
2013
, “
Chemical Characterisation of Lube Oils
,”
Energy Fuels
,
27
(
1
), pp.
27
34
.
79.
Ng
,
E.-P.
, and
Mintova
,
S.
,
2011
, “
Quantitative Moisture Measurements in Lubricating Oils by FTIR Spectroscopy Combined With Solvent Extraction Approach
,”
Microchem. J.
,
98
(
2
), pp.
177
185
.
80.
Wolak
,
A.
,
Molenda
,
J.
,
Zając
,
G.
, and
Janocha
,
P.
,
2021
, “
Identifying and Modelling Changes in Chemical Properties of Engine Oils by Use of Infrared Spectroscopy
,”
Measurement
,
186
, p.
110141
.
81.
Al-Sheikh Omar
,
A.
,
Salehi
,
F. M.
,
Farooq
,
U.
,
Neville
,
A.
, and
Morina
,
A.
,
2022
, “
Effect of Zinc Dialkyl Dithiophosphate Replenishment on Tribological Performance of Heavy-Duty Diesel Engine Oil
,”
Tribol. Lett.
,
70
(
1
), p.
24
.
82.
Zzeyani
,
S.
,
Mikou
,
M.
,
Naja
,
J.
, and
Elachhab
,
A.
,
2017
, “
Spectroscopic Analysis of Synthetic Lubricating Oil
,”
Tribol. Int.
,
114
, pp.
27
32
.
83.
Clark
,
R. N.
,
Curchin
,
J. M.
,
Hoefen
,
T. M.
, and
Swayze
,
G. A.
,
2009
, “
Reflectance Spectroscopy of Organic Compounds: 1. Alkanes
,”
J Geophys. Res.
,
114
(
E3
), p.
E03001
.
84.
Zhao
,
S.
,
Tie
,
L.
,
Guo
,
Z.
, and
Li
,
J.
,
2020
, “
Water Deteriorates Lubricating Oils: Removal of Water in Lubricating Oils Using a Robust Superhydrophobic Membrane
,”
Nanoscale
,
12
(
21
), pp.
11703
11710
.
85.
Aucélio
,
R. Q.
,
de Souza
,
R. M.
,
de Campos
,
R. C.
,
Miekeley
,
N.
, and
da Silveira
,
C. L. P.
,
2007
, “
The Determination of Trace Metals in Lubricating Oils by Atomic Spectrometry
,”
Spectrochim. Acta, Part B
,
62
(
9
), pp.
952
961
.
86.
Wolak
,
A.
,
Zając
,
G.
, and
Gołębiowski
,
W.
,
2019
, “
Determination of the Content of Metals in Used Lubricating Oils Using AAS
,”
Pet. Sci. Technol.
,
37
(
1
), pp.
93
102
.
87.
Zali
,
M. A.
,
Ahmad
,
W. K. W.
,
Retnam
,
A.
, and
Catrina
,
N.
,
2015
, “
Concentration of Heavy Metals in Virgin, Used, Recovered and Waste Oil: A Spectroscopic Study
,”
Procedia Environ. Sci.
,
30
, pp.
201
204
.
88.
Sharma
,
N.
, and
Agarwal
,
A. K.
,
2019
, “
Particle Characterisation of Soot Aggregates Emitted by Gasohol Fueled Direct Injection Engine
,”
Energy Fuels
,
33
(
1
), pp.
420
428
.
89.
Dobbins
,
R. A.
,
2007
, “
Hydrocarbon Nano-Particles Formed in Flames and Diesel Engines
,”
Aerosol Sci. Technol.
,
41
(
5
), pp.
485
496
.
90.
Tree
,
D. R.
, and
Svensson
,
K. I.
,
2007
, “
Soot Processes in Compression Ignition Engines
,”
Prog. Energy Combust. Sci.
,
33
(
3
), pp.
272
309
.
91.
Le Coz
,
J.-F.
,
Lemenand
,
C.
, and
Bruneaux
,
G.
,
2003
, “
Gasoline Injection and Spray Combustion in a Cell With Conditions Typical of Direct Injection Engines
,”
Int. J. Fuels Lubr.
,
112
(
4
), pp.
2154
2166
.
92.
Yan
,
J.
,
Gao
,
S.
,
Liu
,
W.
,
Chen
,
T.
,
Lee
,
T. H.
, and
Lee
,
C.-F.
,
2021
, “
Experimental Study of Flash Boiling Spray With Iso-Octane, Hexane, Ethanol and Their Binary Mixtures
,”
Fuel
,
292
, p.
120415
.