1. Introduction
The environmental pollution due to the exponential growth in the consumption of the fossil fuels as energy sources has become a serious challenge and threat to humanity in modern times. These fuels are in finite amounts. In addition, several experts have agreed that these fuels are exhaustible [
1,
2]. Indeed, in 2015 the British Petroleum (BP) global center revealed that oil reserves would last just 53 years at the current consumption rate [
3]. Hence, the creation of renewable sources of energy and the development carbon-neutral energy systems have been widely recommended as strategic solutions to the threat of fuel shortage and CO
2 concentration in the environment [
4].
Among the newly introduced alternatives, hydrogen was reported to be a promising source of clean energy [
5] as it can mitigate the problems of pollution and scant energy supplies which plague the environment and the economy [
6,
7,
8].
In particular, hydrogen, with its immense thermal efficiency and non-carbonic nature, was selected as an ideal and a potential alternative fuel for internal combustion engines [
8,
9,
10]. However, it is difficult to produce and store it in sufficient amounts at affordable prices. Due to this constraint, the majority of the current usages of hydrogen gas have been limited to its use as an additive to other fuels [
11,
12,
13]. Indeed, hydrogen was recommended as a good additive to natural gas, one of the most available and cheap fossil fuels. However, [
14,
15,
16] showed that the low flame speed and the high combustion temperature of natural gas reduced its thermal efficiency and increased NO
X concentrations. Hence, researchers such as Ma and Wang [
11]; Verhelst and Wallner [
6] and Dong et al. [
17], proved that the enrichment of natural gas with hydrogen succeeded in overcoming this shortcoming.
The Advanced Power Generation Technology forum (UK) (APGFT) meeting (London 2014) emphasized the importance of using hydrogen encouraging for carbon capture and storage (CCS). The studies made by the APGTF were well appreciated by the major manufacturers especially those of the automobile. These studies presented strategies to be adapted in the medium and long terms for better and less efficient energy efficiency. The recommendations of the meeting in London in 2014 strongly pushed for the use of high content hydrogen with natural gas in combustion [
8].
Unfortunately, Delorme and Rousseau [
15], and Verhelst and Wallner [
6] demonstrated that inside the cylinder, it is difficult to reduce the heat due to the high combustion temperature of the hydrogen enriched mixture. As a result, thermal efficiency necessarily resulted in increased NO
X. Moreover, Liu et al. [
18], and Kawahara and Tomita [
19] detected two serious problems when using hydrogen as an additive; namely, backfire and auto-ignition [
8]. These were caused by the high flame speed of hydrogen and the very low combustion activation energy. To solve these problems, Lee et al. [
20] opted for the use of a retarding intake valve with an opening timing and a lean mixture while Yang et al. [
21] recommended injecting water into the intake manifold.
On the other hand, hydrogen as an additive to gasoline was investigated by Kahraman et al. [
9]. These scholars conducted an experimental study that assessed the performance and emission of a spark ignited engine bi-fuelled with hydrogen and gasoline with a conventional ignition system. Their work showed that despite the satisfactory results at high-speed operation, hydrogen and gasoline fueling showed a great power loss at a low-speed operation.
Several works [
11,
14,
19,
22] have carried out investigations on Lean-Burn for engines fuelled with hydrogen compressed natural gas (HCNGX; x indicates the percentage of hydrogen) for the purpose of obtaining a low NO
X concentration. The engines were operated near the lean limit which was significantly expanded for natural gas by adding hydrogen. Besides, a small amount of hydrogen present in the blend remained insignificant as it played the role of a good anti-knock agent [
14]. The effect of hydrogen concentration, in HCNG blends were discussed in many studies and results showed that the CO
2, CO, and HC concentrations were reduced by the addition of hydrogen to the CNG compared to combustion of pure CNG [
10,
11,
12,
13].
Typically, many studies treating the in-cylinder flow while analyzing the intake manifold and around the vicinity of the intake valve were carried out under the consideration of a constant filling flow and a fixed intake valve position [
23,
24]. The techniques of optical diagnostics and analysis such as Laser Doppler Anemometry (LDA), magnetic resonance velocimetry (MRV) and Particle image velocimetry (PIV) achieved a remarkable progress, but always they presented an insufficiency in the description and resolution around the intake valve. LDA techniques were applied in the experiments that using either air or a liquid as an intake fluid in the I.C engines benches [
25,
26]. Freudenhammer et al. [
26] adopted the MRV technique to investigate the flow within the valve and cylinder during intake stroke for a single-cylinder optical engine. The measurements were performed based on the average flow velocity and the manifold diameter while using water as a working fluid. The flow showed important velocity fluctuation through the valve curtain, due to difficulty of flow passage in the valve seat. In these regions the local mass flow filling the cylinder considerably decreased, thus having a strong effect on the in-cylinder flow pattern. In spite of the considerable development in optical diagnostic techniques, the inefficiency to visualize the manifold and valve regions and the limitation in scanning the flow field with sufficient spatial resolution, restricted the detailed investigation of the flow in these areas [
27]. The development of the numerical and CAD software provided researchers with great possibilities to investigate the engine behavior. The numerical simulations took into account the real operating parameters of the engine and a more detailed visualization of the flow [
26,
27,
28,
29,
30,
31,
32].
As can be clearly seen from this brief review, research was focused on the effect of using hydrogen as an additive to gasoline, biogas, and natural gas. In addition, there was a particular interest in its enrichment effect on the combustion characteristics and the behavior of the converted engines. Most of the realized advancement was in idle or slow speeds. Nevertheless, the CNG–Hydrogen blend effect of the direct filling flow in-cylinder during the intake stroke on the engine volumetric efficiency was under-investigated. Furthermore, the engine output at low, medium, and high speeds did not receive much attention. Finally, most of the works on the process of hydrogen enrichment suffered from two serious shortcomings. Firstly, studies of this topic were restricted to the way of injecting hydrogen. Secondly, the techniques adopted were not commercialized due to their intricacy; i.e., their application never went beyond the laboratory scale. For these reasons, this study purported to investigate the effects of hydrogen as an enriching additive to CNG on in-cylinder flow and to determine the engine emission levels. The ultimate objectives were to find out the maximum allowable hydrogen ratio to CNG for an optimum engine operation and to attempt the supply of this blend to the engine by the carburetion way. It would be expected that such technique allows a flexible transition and facilitates its commercial installation.
2. CFD Simulation
Kacem et al. [
7] as well as Hamzehloo and Aleiferis [
33], Harshavardhan and Mallikarjuna [
34], Rahiman et al. [
28], Schmitt et al. [
31] and Giannakopoulos et al. [
32] recommended the numerical simulation approach as an important tool to investigate the engine in-cylinder flow. The SolidWorks Flow Simulation (SWFS) code was used in many researches treating different topics in Computational Fluid Dynamics (CFD) [
7,
27,
35,
36,
37,
38,
39]. In this study, the SolidWorks Flow Simulation (SWFS) code was used to analyze the in-cylinder flow characteristics. Similar to other CFD codes, “SWFS” associates a noteworthy of functionality and accuracy with ease-of-use. This code uses Finite Volume, multi grid, Multi block time averaged Navier-Stokes equations. The methodology of the flow solver subsists in splitting the domain around each node in the grid into sub-volumes; it assures the flow continuity betwixt the nodes. The spatial discretization is obtained by following a procedure for tetrahedral interpolation scheme. As for the temporal discretization, the implicit formulation is adopted [
38]. The transport equation is integrated over the control volume [
38,
39].
The studied model consisted of the Hyundai Sonata engine inlet system devices (intake manifold, intake valve, engine head pipe, and cylinder). One of the main advantages of this code was its ability to import and mesh the required geometry directly from the CAD software. In addition, SWFS allowed studying a wide range the heat and mass transfer phenomena for complex geometries [
8].
As can be seen in
Figure 1, the simulation setup comprised the designing of the model part, performing the mesh resolution, defining the fluid properties and the boundary condition before running the simulation. As we studied an internal flow, the geometry of the model part defined the computational domain limits.
2.1. Studied Geometry
The studied model was a Hyundai Sonata intake manifold.
Table 1 illustrates the main characteristics of this engine. Due to the gazes’ proprieties as shown in
Table 2 and
Table 3, the studied engine was modified and converted into CNG bi-fueled engine. Then, it was adapted to HCNG operation. The CFD study aimed to find out the effect of the hydrogen volume fraction on the in-cylinder flow through the intake shape around the intake valves.
To perform a series of experiments, a test bench was designed and installed according to the specifications shown in
Table 1. This test bench allowed to determine the behavior of the experimental engine and to extract a series of results that would be analyzed and discussed later on.
The diagram presented in
Figure 2 shows the apparatus used in the experimental system consisting of an internal combustion engine, a hydraulic brake, an acquisition card, an exhaust gas analyzer, a gas/petrol switch, an emulator, a lambda analyzer, and a computer.
2.2. Mathematical Formulation
The CFD SWFS code was used in this study to achieve the numerical simulation. Our study was limited to the intake stroke of the engine. Because our objective was to characterize the flow along the intake manifolds and at the valve seat. The interaction with the walls and combustion process were out of the scope of the study. Therefore, the
k-ε model was considered more adequate for our purpose than other models such as
k-w and Random Number Generator (RNG)
k-ε. For this reason, we adopted it. In addition, the
k-ε model was widely used in engine in-cylinder gaseous flow studies [
7,
25,
27,
31]. This model was adopted to describe the in-cylinder flow behavior for the intake manifold. To predict the turbulent flows, the Favre-averaged Navier–Stokes equations were used. The mass conservation, momentum conservation and the energy conservation are described according to the following equations:
where “
ρ” is the fluid density; “
u” is the fluid velocity; “
h” is the thermal enthalpy; “
τij” is the viscous shear stress tensor; “
Si” is a mass-distributed external force per unit mass; “
qi” is the diffusive heat flux and “
QH” is a heat source. The subscripts were used to denote summation over the three coordinate directions. The energy equation is presented as follows [
7,
40]:
where “
e” is the internal energy.
In this study since the specific heat ratio
γ = cp/cv was constant, the pressure can be written as follows:
2.3. Turbulence
The description of the turbulence in the intake flow provided a solid understanding of the engine behavior. To decrease emissions and to improve the engine performance, turbulence modeling was essential for characterizing the mixing and combustion in an engine. The turbulence model focuses on the calculation of the turbulent kinetic energy k and the turbulent dissipation function ε.
The turbulent kinetic energy and its dissipation rate are described by the following equations:
and are the source terms;
is the turbulence generated by the buoyancy forces.
The empirical constants in SWFS code have the following values:
2.4. Computational Setup
2.4.1. Boundary Conditions
The boundary and initial conditions were defined in function of each running case. Some simplifications were taken for the calculation. The cylinder walls were supposed to be adiabatic walls. This study took into account only the critical cylinder N°4 as in the intake stroke. The
Figure 3 and
Figure 4 resume the boundaries and initial conditions setup. The top head piston surface was considered as a moving wall and its speed was considered as an output condition. The piston displacement computed in function of the following equation:
The intake valves were considered as real walls with dependency translation velocity.
The pressure at the entrance of the intake manifold was maintained at the atmospheric pressure (i.e., 1.013 bar).
2.4.2. Mesh Settings
The SWFS code automatically generated the computational mesh in the Computational Domain function of the specified input.
First of all, the parameters governing the automatic procedure of establishing the initial computational mesh called Global Mesh had to be specified. In order to create the Global Mesh the computational domain within the coordinate system planes was divided into slices. Thus, a Basic Mesh consisting of rectangular cells was obtained. In order to better resolve the solid geometry and to attain a rigorous solution the cells of this Basic Mesh were further subdivided into smaller rectangular cells within the regions selected in function of the specified instructions.
SWFS has the capability to fit the computational mesh to the solution during the calculation. The SWFS refine the mesh cells in the high-gradient flow regions and merges the cells in the low-gradient flow regions. The mesh starts from an initial state than it is modified during the calculation based on user defined level.
The code offers seven levels of mesh refinement. The mesh refinement level specifies how many times the initial mesh cells can be split to attain the solution-adaptive refinement criteria. The solve time of a flow simulation is highly dependent on the number of cells in the computational domain. For precise results and to minimize the computation time we opted for a sufficient minimum level of mesh. Our approach consisted in simulating the pressure in the intake manifold plenum for different mesh resolutions and in comparing the numerical results with the measured ones. The increasing of the mesh resolution level engenders a higher mesh refining, which generates a larger number of cells. The mesh grid sizes for the tested refining level are summarized in the following tables.
Table 4 shows that the fluid cells size changes clearly between the different mesh resolution levels but the fluid cells contacting solids are practically the same for the 4th and 5th grid level.
Figure 5 presents the superposition of different numerical and experimental results in function of the engine speed for different mesh levels.
Figure 5 shows clearly that the meshes resolution for levels 4 and 5 yielded simulation values practically identical to the measured ones. The 4th mesh grid level was chosen in order to have valid results in the shortest computation time possible without affecting the quality of the results.
Figure 6 shows the mesh analysis resolution that was chosen to achieve the calculation.
The follow-up of the physical parameters was recorded for three critical points goals (PG). The first point, denoted PG1, was recorded just at the intake valve. The second point, called PG2, was recorded in the combustion chamber. The third one, called PG3, was recorded in the vicinity of the intake valve.
Figure 7 below illustrates the location of the points goals.
5. Conclusions
This work attempted a numerical simulation of in-cylinder flow in an internal combustion engine. For this purpose, the SWFS code was adopted for an initial CFD investigation. In addition, it conducted an empirical study of the optimum hydrogen ratio that can be added to HCNG fuel for a good performance and an acceptable emission rate.
The simulation revealed the following results. Firstly, Hydrogen enrichment would offer an improvement of the in-cylinder flow characteristics. Secondly, the addition of more hydrogen amounts to the CNG would improve the cylinder filling and therefore enhance the engine volumetric efficiency. Thirdly, above a certain hydrogen ratio in the blend, the agitation would gain a critical high level around the valve. This would generate an abnormal combustion that would generate backfire problems.
The empirical study resulted in the following findings. Firstly, the maximum BT produced by the engine depended on the proportion of hydrogen added to CNG. Secondly, the optimum ratio of hydrogen added to CNG to obtain a normal engine operation was equal to 40%. Mixtures containing 50% hydrogen or more resulted in problems of backfire ranging between seriously disturbing the engine operation and stopping it completely.
Finally, the emission aspects resulting from every mixture proportion were determined in function of various engine speeds. Firstly, NOX concentration values increased with hydrogen proportions higher than 40%.at speeds inferior to 3800 rpm. This was probably due to the high combustion temperature required by hydrogen to be totally burnt. However, at speeds above 3800 rpm, NOX concentration levels decreased for the mixture containing more than 40% hydrogen. Secondly, HC and CO concentration levels decreased with increasing hydrogen percentage. Therefore, this study ended by suggesting the use of 40% of hydrogen as an additive to CNG when an alternative to gasoline is sought. It could represent a potential solution to energetic, environmental and economic problems.