Effects of the fuel blend flow rate on engine combustion performance

1 Introduction

Combustion is said to be a complex phenomenon of dependent factors. The internal combustion engine (ICE) system has gained significant research attention. Wysocki et al. (2018) introduced the technology of a mobile test rig on combustion, promoting the mobility of the performance and emission characterization site, even farm-based locations. Endoscope photographing technology in ICE fuel atomization and flame studies was applied by Mao et al. (2020) and Stepien et al. (2022), while the application of AI on pump and engine performance and emission and optimization studies were also presented (Karami et al., 2019; Ude et al., 2020; Onukwuli et al., 2021; Said et al., 2022; Yamin and Hdaib, 2022; Feng et al., 2023). A study on the mass flow rate effect found expression in the thermal management of the battery fuel cell reaction phenomenon (Zuo et al., 2022). This strengthens the band of the electric vehicle clean power system in its use of the proton exchange membrane and its application and optimization in conjunction with hydrogen combustion technology in ICE (Zuo et al., 2023a; Zuo et al., 2023b; Chen et al., 2023; Zuo et al., 2023c).

Specific contributions of fuel combustion studies in engine performance and emission have been on a secondary and tertiary fuel blending ratio for spark ignition (SI) and compression ignition (CI) engines, as presented by Al-Hasan and Al-Momany (2008); Abdullah et al. (2015); Liu H. et al. (2015); Akar (2016); Deng et al. (2018); Mohammed et al. (2021); and Prasad and Saravanan (2021). The ternary fuel blending effect on pump was studied by Caligiuri et al., (2019). Also antioxidant and nanoparticles additives flow enhancement pay tribute to performance (Shahabuddin et al., 2013; Shaafi and Velraj, 2015; Feroskhan et al., 2017; Perumal Venkatesan et al., 2021; Daud et al., 2022; Küçükosman et al., 2022). Meanwhile, fuel spray and atomization, fuel injection timing, and pressure variation are current issues of study in the literature, as presented by Kumar and Rehman (2014); Liu J. et al. (2015); Rai et al. (2021); Sun et al. (2021); Küçükosman et al. (2022); Pham et al. (2022); and Stępień et al. (2022). The technique of dual-fuel engine mode with little or no adjustment of the engine system was studied by Feroskhan et al. (2017); Mahla et al. (2017); Ambarita, (2018); Parthasarathy et al. (2019); Estrada et al. (2022); and Jamrozik et al. (2022). To date, dual-fuel injection and combustion studies have focused on the gas fuel flow rate and mixing techniques of homogeneous charge compression ignition (HCCI) technology (Kumar and Rehman, 2014; Gnanamoorthi and Vimalananth, 2020; Atelge et al., 2022; Park et al., 2022; Zardoya et al., 2022). All these studies target the manipulation of fluid flow or its properties for investigating the effect on engine combustion and performance. Another significant addition to the aforementioned study is the impact of the flow rate either prior to or within the combustion chamber. Major works done so far focus on the variation in either fuel flow or air flow rates of various fuels accompanied with the blending effect on SI and CI engines. Mahla et al. (2017) and Parthasarathy et al. (2019) investigated the effect of compressed natural gas (CNG) flow rate variation on CI engine performance, while biogas and hydrogen flow rate variations were studied by others (Feroskhan et al., 2017; Ambarita, 2018; Estrada et al., 2022). Majority of the works focused on the flow rate, existing in the dual-fuel engine mode (Feroskhan et al., 2017; Mahla et al., 2017; Ambarita, 2018; Parthasarathy et al., 2019; Estrada et al., 2022) instead of the usual single-engine operation mode. In this regard, Jamrozik et al. (2022) stated the difference between the effect of both modes, i.e., the flame front will usually form around the injected stream and spread to the filled areas with a single engine; however, the limitations of fuel type will hinder this spread in a dual-fuel engine. Gnanamoorthi and Vimalananth (2020) concluded that the heat release rate (HRR), cylinder pressure, and NOx increased by 28.66%, 28.9%, and 7.3%, respectively, under 30 L/min of hydrogen fuel at brake power variation. A microscopic spray investigation discussed the effect of fuel physical properties and control parameters on atomization and submitted that the spray angle and droplet velocity depend on fuel viscosity (Sun et al., 2021). In another study, the effects of the spray droplet, ambient air mixing, and gas flow rate of a dual-fuel engine brake-specific fuel consumption (BSFC), in-cylinder pressure, and heat release rate improve, while brake thermal efficiency (BTE) and brake volumetric efficiency (BVE) generally remain lower compared to that when using traditional diesel (Pham et al., 2022). Furthermore, in a mixing study, the combination of an early pilot and a large squish piston geometrical approach on a natural gas–diesel dual-fuel engine was investigated using a double injection mode on engine BTE and emission. An improvement of 7.8% and 49.7% in BTE and methane emission, respectively, was obtained, which was better than that obtained with the single injection of pilot dual fuel (Park et al., 2022).

In-cylinder piston frictional mean effective pressure (FMEP) in ICE losses has been said to exist due to the collection of sub-frictional loses; mechanical mean effective pressure (MMEP) is applied to surmount mechanical friction; power-gaining mean effective pressure (PMEP) exists only in four-stroke engines; charging mean effective pressure (CMEP) is used in driving a supercharger or scavenging pump; turbine-added mean effective pressure (TMEP), turbine add-on pressure, and auxiliary-driving mean effective pressure (AMEP) are usually added with brake mean effective pressure (BMEP), thus yielding indicated mean effective pressure (IMEP) (Taylor, 1985b). Amid rundown, shutoff, Willans lines, motoring, strip, indicated and special measuring known methods of friction determination, according to Van Basshuysen and Schäfer (2004), the indicated method was applied in an experimental and simulation study of FMEP determination in a four-cylinder CI engine (Knauder et al., 2019). A similar study was executed numerically in a four-stroke four-cylinder SI engine, validating existing work (Mishra, 2013). The work suggested that an experimented frictional study by reason of higher boundary friction yields larger friction than the predicted friction. Second, frictional force is maximum within compression and power strokes and negligible in suction and exhaust strokes. Feng et al. (2022) presented a comparative study on energy balance on an automated and manual transmission hybrid vehicle to account for energy losses.

Researchers have also focused on the experimental and numerical dimensions of ICE flow. Increasing diesel and air flow rates in flame stability, combustion efficiency, and other parameters increased the combustion flame instability and efficiency (Li et al., 2022). An engine was studied as a two-phase flow-accommodating entity for heat and fluid flow in CFD studies of velocity magnitude, penetration length, swirl and tumble ratios, and heat release rate, and an experimented heat flow characteristic in blends of 100% pure Khaya senegalensis biodiesel blend (B₁₀₀), B₂₀, and pure diesel blend (D₁₀₀) of Jatropha biodiesel (Atgur et al., 2022). A significant difference between B₂₀ and D₁₀₀ within compression and expansion strokes under a no-load condition was reported. A CFD energy analysis of B₁₀₀ and D₁₀₀ was done on combustion heat transfer, inlet, and exhaust flow properties using MATLAB by Potdukhe and Deshmukh, (2015), while a particulate filter on boundary-layer wall flow with biofuel was reported (Orihuela et al., 2019). Numerical studies have been conducted to investigate the influence of piston-bowl geometrical design (Deresso et al., 2022) on the HRR of engine performance, while others calculate the HRR with experimental validation (Mauro et al., 2018; Dubov et al., 2021). The application of a control system to the blending process of fuel and corrosion inhibitor additive blends on a direct injector system was modeled to ensure a stable blending process despite flow velocity perturbation (Mgbemene et al., 2019).

It is necessary to understand how the injected fuel blend characteristics and in-cylinder conditions influence engine combustion outcomes. Hence, this study presents the impact of injector and piston engine elements on the interaction between in-cylinder fluid motion and the combustion chamber environment with respect to the flow effects on engine combustion strength. Attempts to numerically link the effect of fuel blend atomization and mixing strength on the expected performance index were key in this study with experimental validations. The scope of this work majors between compression and power strokes of a single-cylinder CI engine. As was found from the reviewed literature, studies on the fuel flow rate have focused on air and gaseous fuel flows in single- and dual-fuel engines; therefore, this study focused on the liquid fuel flow rate combustion of a homogenous fuel engine.

2 Materials and methods

The physicochemical properties of Khaya senegalensis biodiesel blends of 5% of biodiesel to 95% of diesel blend (B₅), 15% of biodiesel to 85% of diesel blend (B₁₅), 25% of biodiesel to 75% of diesel blend (B₂₅), B₁₀₀, and D₁₀₀ produced experimentally by Onojowho et al. (2019) are given in Table 1. These binary blends are a percentage volumetric proportion of biodiesel and diesel mixed at a stirring speed of 1.5 using the IKA C-MAG HS 4 for 7 min. Before allowing each sample to stand, the TecQuipment Ltd. small engine test rig fitted with a hydraulic dynamometer, as shown in Figure 1 with specifications in Table 2, was applied for the experiment. Each sample was poured into the engine tank, taking turns to flow through the FLOWave SAW flowmeter type 8098. The conical geometry injector fitted into the engine is a multi-hole swirl injection system, design parameters of which are given in Table 2, and generated a swirl on the piston bowl. Combustion efficiency was determined from flue gases using a gas analyzer. By experimentation, the Willans line approach was applied to determine the least piston frictional loss from three selected blends as the FMEP outcome, as shown in Figure 4, at a constant rate of 2,560 rpm. Its low initialization temperature of 300 K was reflected in the combustion temperature. Ansys Fluent v19.R3 software was used to simulate the combustion, and the engine and biodiesel parameters are summarized in Table 3. The HP Z820 Workstation system model was used in this simulation. The engine geometry model was produced on Ansys SpaceClaim having a piston bowl shape and four manifolds, as shown in Figure 2, which was later decomposed to a sector. Figure 3 presents a 2D piston bowl-shaped engine model. The decomposed geometry was meshed into a tri/tetrahedron element-shape type of mesh, and the fine mesh size and total element numbers are shown in Table 3. Deforming the zone mesh sensitivities by applying smoothing and layering mesh methods of the dynamic mesh ensured accuracies. High computational timing was traded-off for accuracy since the total number of elements increases with transition from coarse to fine mesh types. The entire simulation was between the inlet valve closes (IVC) and exhaust valve closing (EVC); however, the compression stroke period of the IVC (225° crank angle [CA] or 45° after bottom dead center [ABDC]) to the fuel valve closes (FVC) (360° CA or 0° before top dead center [BTDC]) of the fuel injection and mixture interaction within the in-cylinder environment is shown in Figure 4. Here, the static temperature is the temperature indicated by the sensor moving concurrently with the mixture velocity. The profile predicted the possible point at which each blend pitches the maximum temperature. A distinct observation was made with each blend with the in-cylinder temperature, indicating that blending viscosity, surface tension, and density together with the velocity, pressure, and temperature generates swirl, tumble, and turbulence, significantly contributing to combustion. During the injection period, the profile made by blends suggested the distinct swirl influence of each blend on the engine performance as the engine speed increases. The injection pressure is directly proportional to the engine speed square, and it is a vital response factor in atomization such that the higher the pressure, the better the atomization but not beyond a threshold of creating penetration issues (Taylor, 1985a). In this regard, this simulation considers the injection pressure, angle, velocity, timing, angle, and nozzle diameter to be constant, while the aforementioned blend characteristics with engine speed were varied. The injection diameter at the chamber inlet was 0.00185 m, and the temperature boundary conditions of the walls are as follows: piston, 645 K; valve and chamber, 562 K; and cylinder head, 604 K. Temperature and pressure variable conditions at fluid–wall patched zones were maintained at 404 K and 321,259 Pa, respectively.

Table 1

Table 1. Physicochemical properties of Khaya senegalensis blends.

Figure 1

Figure 1. Small engine test rig experiment setup.

Table 2

Table 2. Engine specification.

Figure 2

Figure 2. Engine 3D model.

Figure 3

Figure 3. Piston bowl shape of the engine model in 2D.

Figure 4

Figure 4. Summary of the study focus.

Table 3

Table 3. Combustion simulation parameter summary.

2.1 Fundamental models and equations

The renormalized group (RNG) k-ε turbulent kinetic and dissipation energies (Eqs 1, 2) are of the Navier–Stokes viscous model, and the wall treatment from the Menter–Lechner near-wall sub-model was employed in this CFD simulation. The near-wall source term in Eq. 1 accounted for a low Reynolds number effect within the viscous sublayer zone. The dynamic viscosity of B₁₀₀ and D₁₀₀ is 0.00512 and 0.00395 kg/m^-s, respectively. The Eddy-Dissipation Concept was considered for the transport species turbulent interaction activities. The maximum swirl target ratio was 1.3, and the swirl speed varied, as presented in Table 3, at increasing engine speed. Although autoignition modeling was applied to the Hardenburg model, accounting for ignition delay per mass fraction of atomized species. The predicted effective fuel flow rate in Eq. 3 hinged on the pressure-swirl atomizer model proposed by Schmidt et al. (1999) built into Ansys Fluent spray models. It describes the centrifugal motion of the injected fuel through the inlet port, which forms a film in swirl motion around the valve seat. This forms the bases of primary breakup model film formation through sheet breakup and atomization stages. The atomization model used here depends on the liquid sheet wave growth, while the length of the sheet break up, ligaments, and diameter of ligaments are based on Weber’s model in Eq. 6 (Weber, 1931). The Kelvin–Helmholtz–Rayleigh–Taylor (KHRT) spray secondary breakup model was chosen due to its high Weber number and high-speed injector system characteristics. The KHRT model is a combination of the Kelvin–Helmholtz wave that forms a liquid core around the injector nozzle with a Levich liquid core breaking length of Eq. 7 and Rayleigh–Taylor injection instabilities on droplet surfaces of Eq. 8. In Ansys Fluent, the non-premixed combustion reaction model is compactable with the diesel ICE because the fuel and oxidizer enter the chamber at different streams. The stoichiometric combustion reaction of fuels satisfies Eq. 9. It should be noted that Eqs 10–16 were applied in the experimentation where fuel and equipment basic data, as used in predictions, were maintained to aid experimental validation. The combustion efficiency (Eq. 15) was embedded into the analyzer, where the condensate heat at the dew point is zeroed for the ICE. Accounting for the accuracies and uncertainties of instruments used in the experimentation, the root square sum approach was applied to determine the overall uncertainty (U_r) (Holman, 2012). Parameter measurements were repeatedly done, and Eq. 17 provides an estimated overall uncertainty.

$\partial \left(\mathrm{\rho }\mathrm{k}\right)/\partial \mathrm{t}+\partial \left(\mathrm{\rho }\mathrm{k}{\mathrm{u}}_{\mathbf{i}}\right)/\partial {\mathrm{x}}_{\mathrm{i}}=\partial \left[\left\{\mu +\left(\rho C_{\mu }{k}^2/\epsilon \right)/{\sigma }_k\right\}\right]/\partial {\mathrm{x}}_{\mathrm{j}}+{\mathrm{G}}_{\mathrm{k}}-\mathrm{\rho }\mathrm{\epsilon }+{\mathrm{S}}_{\mathrm{\text{near}}-\mathrm{\text{wall}}},(1)$

$\partial \left(\mathbf{\rho }\mathbf{\epsilon }\right)/\partial \mathbf{t}+\partial \left(\mathbf{\rho }\mathbf{\epsilon }{\mathbf{u}}_{\mathbf{i}}\right)/\partial {\mathbf{x}}_{\mathbf{i}}=\partial \left[\left\{\mathbf{\mu }+\left(\frac{\mathbf{\rho }{\mathbf{C}}_{\mathbf{\mu }}{\mathbf{k}}^{\mathbf{2}}}{\mathbf{\epsilon }}\right)/{\mathbf{\sigma }}_{\mathbf{\epsilon }}\right\}\partial \mathbf{k}/\partial {\mathbf{x}}_{\mathbf{j}}\right]/\partial {\mathbf{x}}_{\mathbf{j}}+{\mathbf{C}}_{\mathbf{1}\mathbf{\epsilon }}\mathbf{\epsilon }/\mathbf{K}{\mathbf{G}}_{\mathbf{k}}-{\mathbf{C}}_{\mathbf{2}\mathbf{\epsilon }}\mathbf{\rho }{\mathbf{\epsilon }}^{\mathbf{2}}/\mathbf{K},(2)$

${\dot{\mathbf{m}}}_{\mathbf{\text{eff}}}=\mathbf{\pi }{\mathbf{\rho }}_{\mathbf{f}}\mathbf{\text{ut}}\left({\mathbf{d}}_{\mathbf{i}}-\mathbf{t}\right),(3)$

$\mathbf{u}={\mathbf{v}}_{\mathbf{c}}\sqrt{\mathbf{2}∆\mathbf{P}/{\mathbf{\rho }}_{\mathbf{f}}},(4)$

${\mathbf{S}}_{\mathbf{r}}=\frac{\mathbf{n}}{\mathbf{N}},(5)$

${\mathbf{d}}_{\mathbf{o}}=\mathbf{1.88}{\mathbf{d}}_{\mathbf{l}}{\left(\mathbf{1}+\mathbf{3}\mathbf{\hslash }\right)}^{\mathbf{1}/\mathbf{6}},(6)$

$\mathbf{L}={\mathbf{C}}_{\mathbf{l}}{\mathbf{d}}_{\mathbf{o}}\sqrt{{\mathbf{\rho }}_{\mathbf{l}}/{\mathbf{\rho }}_{\mathbf{g}}},(7)$

${\mathbf{r}}_{\mathbf{c}}={\mathbf{\pi }}_{{\mathbf{C}}_{\mathbf{\text{rt}}}}/{\mathbf{k}}_{\mathbf{\text{rt}}},(8)$

${\mathbf{C}}_{\mathbf{i}}{\mathbf{H}}_{\mathbf{j}}{\mathbf{O}}_{\mathbf{k}}+\left(\mathbf{i}+\mathbf{j}/\mathbf{4}\right)\left({\mathbf{O}}_{\mathbf{2}}+\mathbf{3.76}{\mathbf{N}}_{\mathbf{2}}\right)\to \mathbf{\text{iC}}{\mathbf{O}}_{\mathbf{2}}+\mathbf{j}/\mathbf{2}{\mathbf{H}}_{\mathbf{2}}\mathbf{O}+\left(\mathbf{i}+\mathbf{j}/\mathbf{4}\right)\mathbf{3.76}{\mathbf{N}}_{\mathbf{2}},(9)$

${\mathbf{m}}_{\mathbf{f}}={\mathbf{\rho }}_{\mathbf{f}}{\mathbf{A}}_{\mathbf{n}}{\mathbf{C}}_{\mathbf{D}}\left(∆\mathbf{\theta }/\mathbf{360}\mathbf{N}\right)\sqrt{\mathbf{2}{\mathbf{\rho }}_{\mathbf{f}}∆\mathbf{P}}/\mathbf{1000},(10)$

${\mathbf{m}}_{\mathbf{a}}={\mathbf{C}}_{\mathbf{D}}\left(\mathbf{\pi }{\mathbf{d}}^{\mathbf{2}}/\mathbf{4}\right)\sqrt{\mathbf{2}{\mathbf{P}}_{\mathbf{a}}∆\mathbf{P}/\mathbf{R}{\mathbf{T}}_{\mathbf{a}}},(11)$

$\mathbf{\text{BSFC}}=\mathbf{3600}\times \mathbf{60}{\mathbf{m}}_{\mathbf{f}}/\mathbf{2}\mathbf{\pi }\mathbf{N}\mathbf{\tau },(12)$

${\mathbf{H}}_{\mathbf{f}}={\mathbf{m}}_{\mathbf{f}}\times {\mathbf{10}}^{\mathbf{6}}\mathbf{\text{LHV}},(13)$

$\mathbf{\text{BVE}}=\mathbf{60}\left(\mathbf{\text{stroke}}/\mathbf{2}\right){\mathbf{m}}_{\mathbf{a}}\mathbf{R}{\mathbf{T}}_{\mathbf{a}}/\mathbf{100}{\mathbf{V}}_{\mathbf{e}}\mathbf{N}{\mathbf{P}}_{\mathbf{a}}\times \mathbf{100}\%,(14)$

${\mathbf{\eta }}_{\mathbf{c}}=\mathbf{100}-\left[\left({\mathbf{T}}_{\mathbf{f}}-{\mathbf{T}}_{\mathbf{a}}\right)\left(\mathbf{A}/{\mathbf{O}}_{\mathbf{21}}-{\mathbf{O}}_{\mathbf{m}}\right)+\mathbf{B}\right]-\mathbf{K},(15)$

$\mathbf{\text{FMEP}}=\mathbf{\text{IMEP}}-\mathbf{\text{BMEP}},(16)$

${\mathbf{U}}_{\mathbf{r}}={\left[{\displaystyle \sum _{\mathbf{i}=\mathbf{1}}^{\mathbf{l}}}{\left({\mathbf{u}}_{{\overline{\mathbf{x}}}_{\mathbf{i}}}\partial \mathbf{r}/{\partial \mathbf{x}}_{{\mathbf{i}}_{\mathbf{x}=\overline{\mathbf{x}}}}\right)}^{\mathbf{2}}\right]}^{\mathbf{1}/\mathbf{2}}={\left[{\mathbf{\text{BSFC}}}^{\mathbf{2}}+{\mathbf{\text{BVE}}}^{\mathbf{2}}+{\mathbf{\text{HRR}}}^{\mathbf{2}}+{{\mathbf{\eta }}_{\mathbf{c}}}^{\mathbf{2}}+{{\mathbf{m}}_{\mathbf{f}}}^{\mathbf{2}}+{\mathbf{\text{FMEP}}}^{\mathbf{2}}\right]}^{\mathbf{1}/\mathbf{2}}=\pm \mathbf{5.88}.(17)$

3 Results and discussion

3.1 Mixing quality parameter effects

A few mixing quality factors highlighted are in view of revealing their progressive input with respect to the flow rate of K. senegalensis biodiesel blends in connection with variation in density, viscosity, and surface tension of fuels on the combustion performance.

3.1.1 Swirling flow attribute

In this direct injection system, the aerodynamics flow nature is promoted by the helical nature of the inlet manifolds and the shape of the valve seat as the external swirl generator and the piston bowl shape as the internal swirl generator. Since strong air motion is desirable irrespective of the injection rate, engine speed variation became a determinant in this simulation. The swirl rapid fluctuation flow period is observed to be so significant during the compression stroke between injection and combustion periods, as shown in Figures 5C, D. The prediction in Figure 5C reveals the buttressing effect of D₁₀₀ higher in-cylinder pressure, irrespective of the engine speed than B₁₀₀. Keeping a threshold swirl factor of 0.07, D₁₀₀ at 2,300 rpm achieved the highest peak swirl ratio of 0.0272 at 336.15 CA, persisting around that value than the engine speeds of 1,800 and 2,800 rpm, which promoted a longer mixing period. B₁₀₀ at 1,800 rpm achieved the highest peak swirl ratio of 0.0668 at 341.3 CA closer to a threshold of 0.07, followed by the performance of 2,800 rpm, as shown in Figure 5D. It can also be observed that 2,300 rpm yielded the least swirl performance, which may affect the mixing length. The notable decrease, similar to that observed by Atgur et al. (2022), observed in both blend swirl ratios is due to the piston movement from the BTDC.

Figure 5

Figure 5. CFD predictions of (A,B) heat release rate and (C,D) swirl ratios for D₁₀₀ and B₁₀₀.

3.1.2 Air–fuel mix effect

The equivalent ratio is ratio of the mass flow rate of air to fuel. The mixture grade is determined by the air–fuel ratio. The CI engine has a grade band of 18–70 (Pulkrabek, 1999); a lower value than this range indicates a very rich mixture, while a value above this range indicates a very lean mixture. However, good combustion could exist around the very rich mixture zone, according to Taylor, (1985a), as shown in Figure 6.

Figure 6

Figure 6. Fuel blend flow rate relationship to BVE and air–fuel ratio experiments.

This experiment presents that the air–fuel ratio of all blends reads below 18 even at an increasing flow rate. Increasing the mass fuel flow rate decreases the air–fuel ratio, irrespective of the blends. An implication of this highly rich mixture combustion is that it may result in a decreased in-cylinder combustion pressure and wall temperature and, consequently, increase the ignition delay if the engine depends so much on a high chamber temperature for an ignition. On the other hand, the reduction in the air–fuel ratio reduces the percentage exergy loss during combustion (Van Basshuysen and Schäfer, 2004).

3.1.3 ICE numerical effluence velocity impact of blends

Here, effluence was better studied numerically under the interactive impact of the in-cylinder injection rate, swirl, tumble, squish, and piston motion. As the engine speed increases, the fuel mass injection rate from the single-point nozzle will increase, thus causing turbulence. The piston harmonic motion on the mixture also increases the turbulence result of the above interactions. In Figure 7, the maximum mass fraction of percentage O₂ consumption decreases as speed increases for D₁₀₀ and B₁₀₀, upholding the decrease response in the air–fuel ratio to the fuel flow rate. However, the higher consumption rate of B₁₀₀ in (b), which negates the experimented expectation of Figure 6, resulted from the O₂ content in B₁₀₀, whereas D₁₀₀ has a void.

Figure 7

Figure 7. In-cylinder effluence flow plots and oxygen consumption at combustion for both blends at varied speeds.

Instantaneous average velocity during compression and expansion strokes is shown in Figures 7C, D. The engine speed positively increased the effluence flow. The injection flow is significant at a speed of 1,800 rpm and negligible at 2,800 rpm due to the increase in turbulence for D₁₀₀ and B₁₀₀. The turbulence is significantly rapid between 225° CA and 290° CA as the piston moves from the BDC to TDC. This rapid flow increases with increase in the engine speed, especially around the wall, as presented in the contours. Immediately after the compression stroke, the minimum effluence existed for both blends at all varied speed. The least is 3.05414 m/s at 365° CA, and the highest is 3.99833 m/s at 364° CA for B₁₀₀ of 1,800 and 2,800 rpm, respectively, while the least is 3.93886 m/s at 365° CA and the highest is 4.14176 m/s at 364.75° CA for B₁₀₀ at 2,300 and 2,800 rpm, respectively. The contours shown in Figure 8 provides clearer visualization. Piston upward motion from the BTDC creates higher turbulence at the wall from the tumble and squish of fluid in (a) and (k). During the injection period, the swirl produced from the nozzle through the piston surface is higher. The interaction of fuel density, viscosity, and surface tension plays out here. At 2,300 and 2,800 rpm, B₁₀₀ creates higher mixing characteristics due to its higher fuel physical properties. This proper mixing, together with higher cetane number and lower heating value (LHV) of B₁₀₀, promoted a longer combustion period than D₁₀₀, as shown in Figure 4. During combustion, the effluence severity appears to be greater for both fuels, massive on the wall and piston surface. Immediately after the compression stroke at 369° CA, B₁₀₀ seemed to possess higher turbulence, which could have stemmed from prolonged combustion.

Figure 8

Figure 8. Velocity contour depicting in-cylinder effluence. Injection period: (B,E,H,L,O,R). During combustion period: (C,F,I,M,P,S). After compression stroke: (D,G,J,N,Q,T). (A–T) are comparison of B100 and D100 blends at specific CA and speeds respectively).

3.2 Blends effect on fuel consumption

Increasing the load or torque on the engine causes a higher fuel mass flow rate. Applying the fuel flow rate to the BSFC relationship on the blending effect, as shown in Figure 9, presents that the blending increase in pure biodiesel causes a reduction effect on fuel consumption as the fuel flow rate increases. B₅ relatively made the highest consumption rate. Ambarita (2018) obtained a similar trend with this study. BSFC is a derived parameter like others in Table 4. Hence, their range and accuracy were not specified on the instrument. All uncertainties were within their 95% level of acceptance. This indicates that errors were quite small for each parameter and even the overall system.

Figure 9

Figure 9. Fuel flow rate and blending effect on engine consumption.

Table 4

Table 4. Accuracies and uncertainty of measurements.

3.3 Engine performance expectation

3.3.1 Mixture efficiency: BVE

It is expected that the BVE positively responds to the increasing air mass flow rate and inversely to engine speed. This provides a brief idea of the engine breathing capacity in the combustion process. Figure 6 shows blend mixing performance on the fuel flow rate relation from the experiment. The BVE of blends increases as the flow rate increases, where B₅ yielded the lowest response even at 0.0001125 kg/s, thus yielding 10.9% efficiency. Relatively, the highest performing blend is B₁₀₀ with 24.8% efficiency, followed by D₁₀₀ with 22.7% efficiency, specifically at 0.0001038 kg/s.

3.3.2 Combustion efficiency outcome

On a holistic performance, all fuel blends were well-burned. The engine is well capable of combusting at a higher fuel flow rate as the burning efficiency increases. The combustion trend is in coherence with the BVE of fuel mixtures, projecting combustion efficiencies to respond positively to the increasing blending proportion of biodiesel, as shown in Figure 10. The increasing trend of blend efficiency is similar to that studied by Li et al. (2022). The least burned fuel blend was B₅ with 54.5%, while B₁₀₀ had 81.47%, and D₁₀₀ had 88.06% at 0.0001125 kg/s or 75% loading capacity.

Figure 10

Figure 10. Fuel blend effect on combustion efficiency.

3.3.3 Engine heat liberation rate outcome

Figures 5A, B show the engine modeling of the HRR for D₁₀₀ and B₁₀₀ respectively. It was a zero-heat loss-based assumption; hence, it is called the apparent HRR. Heat transfer from the mixture to the chamber was indicated by the negative decrease from zero. Rapid combustion was predicted to start at 335° CA for both blends, irrespective of the engine speed to approximately 380° CA for D₁₀₀ and 430° CA for B₁₀₀. The HRR was predicted to occur in two phases. The first phase was at the spontaneous ignition period, as shown in Figure 4. The second phase was characterized with a significant swirl-amplified ripple occurring at the heart of the combustion period. The highest peak of the HRR D₁₀₀ was predicted as 0.36892 kJ/s at 342.1° CA, 0.26226 kJ/s at 342.3° CA, and 0.2261 kJ/s at 343.05° CA for 1,800 rpm, 2,300 rpm, and 2,800 rpm, respectively. These peaks occurred at about the ignition period, and the profile of D₁₀₀ is an inverse of that of B₁₀₀ at all speeds. This explains the lower activation energy requirement of B₁₀₀; hence, the higher compression temperature and pressure of D₁₀₀ yielded a rapid ignition rate, as obtained by Atgur et al. (2022). However, B₁₀₀ achieved the highest peaks of the HRR with 0.78063 kJ/s at 373.25° CA, 0.69362 kJ/s at 373° CA, and 0.6811 kJ/s at 373° CA prior to combustion decay for a speed of 1,800 rpm, 2,300 rpm, and 2,800 rpm, respectively. This HRR profile may have also been influenced by the piston bowl-shaped design, as obtained by Deresso et al. (2022). The influence of the engine speed on the HRR in D₁₀₀ states and an increase in speed provided an increase in heat release, whereas B₁₀₀ obtained an opposite effect. From experimental validation, the fuel flow rate and heating value are directly proportional to the heat of combustion, as shown in Figure 11. Heat liberated from B₁₀₀ is higher than that in D₁₀₀; however, a steady increase was observed with D₁₀₀ as against B₁₀₀ with respect to the flow rate pointing to the aforementioned speed effect. The blend with the best yield was B₅ as it yields the highest heat liberation under an increasing fuel flow rate, as specified on the profile.

Figure 11

Figure 11. Engine heat liberation rate.

3.3.4 Fuel blends and engine losses

During the compression of a mixture, the piston loses some useful energy in overcoming friction. As shown in Figure 12, D₁₀₀ lost useful pressure force of 3 bar to in-cylinder friction, while B₁₀₀ and B₅ lost 3.136 bar and 3.135 bar, respectively, at constant speed. This suggests that diesel possesses a more lubricating effect and would offer a better exergy advantage than B₅ and B₁₀₀ blends.

Figure 12

Figure 12. Piston frictional losses.

4 Conclusion

This study successfully presented a numerical and experimentally validated behavior of the fuel blend flow rate on the ICE, showcasing specific observations on combustion. In other words, the fuel mass flow rate generated by an injector and piston motion significantly affected the engine combustion strength.

➢ Fuel–air mixing quality impacts the ICE combustion performance indexing. Fuel consumption reduces when the biodiesel blending ratio and mass flow rate increase.

➢ Engine breathing efficiency increases at a higher fuel mass flow rate, causing a high burning efficiency of blends. This is the engine stability response to the loading effect.

➢ Engine combustion efficiency could be effectively determined via flue gas up to an 88% accuracy level as the fuel mass flow rate increases.

➢ An ignition delay period of 3^° CA CFD prediction will be common to diesel and biodiesel, irrespective of the speed effect; however, biodiesel will have a longer combustion duration.

➢ The fuel mass flow rate increase increases the heat of fuel combustion experimentally. The engine speed effect on the heat liberation rate causes a contrary behavior on biodiesel and diesel fuels when compared numerically.

➢ Lastly, D₁₀₀ offers lower frictional loss than B₁₀₀ and B₅ in an engine. By the FMEP approach, D₁₀₀ projects better lubricating and exergy advantages.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Author contributions

EO: funding acquisition, investigation, and writing–original draft. AA: supervision and writing–review and editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was self-funded.

Acknowledgments

The authors thank the staff and management of the Mechanical Engineering Department, University of Benin, for granting access to their laboratory and equipment.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Nomenclature