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Advances in Mechanical Engineering

Volume 2013 (2013), Article ID 654396, 6 pages

http://dx.doi.org/10.1155/2013/654396

## Modeling and Analysing of Air Filter in Air Intake System in Automobile Engine

Department of Mechanical Engineering, Annamalai University, Annamalai Nagar 608002, Tamil Nadu, India

Received 6 November 2012; Revised 6 March 2013; Accepted 11 March 2013

Academic Editor: Hakan F. Oztop

Copyright © 2013 R. Manikantan and E. James Gunasekaran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

As the legislations on the emission and performance of automobiles are being made more stringent, the expected performance of all the subsystems of an internal combustion engine is also becoming crucial. Nowadays the engines are downsized, and their power increased the demand on the air intake system that has increased phenomenally. Hence, an analysis was carried on a typical air filter fitted into the intake system to determine its flow characteristics. In the present investigation, a CAD model of an existing air filter was designed, and CFD analysis was done pertaining to various operating regimes of an internal combustion engine. The numerical results were validated with the experimental data. From the postprocessed result, we can see that there is a deficit in the design of the present filter, as the bottom portion of the filter is preventing the upward movement of air. Hence, the intake passage can be rearranged to provide an upward tangential motion, which can enhance the removal of larger dust and soot particles effectively by the inertial action of air alone.

#### 1. Introduction

The modern engines are designed to be highly efficient and compact and clean. As the engines are downsized to reduce space and increase power, the work of the air filter becomes significant. A properly designed air filter will not only enhance the life of the engine but also reduce the regeneration and maintenance cost of the filter itself. Usually air filters are designed for better flow efficiency and better acoustic performance. The acoustic performance is important, because government regulations dictate the maximum air mass flow level that vehicles can make during a pass by test. The speed of air generated by the intake system can be a significant contributor to the noise levels [1]. A complete understanding of the flow dynamics of an air filter is important, since a properly designed air filter can deflect and distribute air in such a way that major dirt will be separated out before it enters the main filter. If the flow distribution is proper, the loading on the filter will be uniform and the regeneration, replacement intervals can be improved. Mathematical and numerical modeling nowadays plays an important role in the better understanding of filters and designs a better air intake system. In order to better understand and validate CFD result, an experimental setup was made, with a variable speed blower to simulate the different flow characteristics through the filter.

#### 2. Experimental Setup

A test bench was made with air filter, intake and exhaust ducts, and manometers. The eye of the blower was connected to the exit of the air filter to simulate actual working condition of the filter attached to engine. (Figure 1) shows the components of the experimental setup. Manometer tappings were provided at two locations in the experimental setup to measure pressure drop. Anemometer was provided at the exit of the blower to measure the air velocity. Experiments were carried out with different air velocities through the air filter by adjusting the blower speed.

#### 3. Computational Model Description

A geometrical model of the air filter was created using CATIA preprocessing software. In order to save the computational time and cost trivial geometrical details that are important for fluid flow point of view such as fillets blends, stiffness, and steps have been ignored. Ignoring the above mentioned, a cleaned geometry was obtained from solid model.

Figure 2(a) shows CAD sectional model of the air filter, which shows the location of entry port filter medium and exit port.

Figure 2(b) shows the solid model of the air filter where the filter media are approximated to control volume.

To capture the three-dimensional flow inside the domain with reasonable accuracy, one needs good form quality mesh. Multiblock structured hexahedral mesh was considered to be the best for this case and was created using commercial mesh generator Gambit.

The model was approximately 0.55 million hexahedral fluid elements. Boundary layer was resolved for *y+* of 40 to 200 to capture physics inside the complicated regions (Figure 3).

Air was used as fluid media, which was assumed to be steady and compressible. - turbulence model is widely used in industrial applications. The near-wall cell thickness was calculated to satisfy the logarithmic law of the wall boundary. Other fluid properties were taken as constants. Filter media of intake system were modeled as porous media flow through the filter medium follows the Darcy law, and it is assumed that the flow is changed into laminar everywhere inside the porous medium.

For porous media, it is assumed that, within the volume containing the distributed resistance [2], there exists a local balance everywhere between pressure and resistance forces such that where represents the (mutually orthogonal) orthotropic directions. is the permeability, is the superficial velocity in direction , the permeability is assumed to be a quasilinear function of the superficial velocity magnitude of the form where and are user-defined coefficients [3]. Superficial velocity at any cross-section through the porous medium is defined as the volume flow rate divided by the total cross-sectional area (i.e., area occupied by both fluid and solid). In this analysis, and are assumed to be same.

Commercial CFD solver Star-CD was used for this study. It is a finite volume approach based solver which is widely used in the industries. Governing equations solved by the software for this study in tensor Cartesian form are the following.

Continuity: Momentum is the source, cor—coordinate, and cfg—friction.

Where is density, is th Cartesian velocity, is static pressure, and is viscous stress tensor.

#### 4. Discretisation Practices

The differential equations gaveling the conservation of mass, momentum, energy, and so forth within fluid and solid systems are discredited by the finite volume (FV) method.

For the purposes of the FV discretization, it is convenient to work with the following general coordinate free form of the conservation equations: where is the fluid velocity vector. stands for any of the dependent variables (i.e., , , , , etc). are associated diffusion and source coefficients.

##### 4.1. Temporal Discretization

In Star-CD, only implicit schemes for time advancement are used. In the case of SIMPLE, predominantly used for steady flows but under some circumstances also recommended for transient flow simulations, the available options are the first order, fully implicit fully scheme, the second order, fullyimplicit scheme with three levels. In the case of PISO which is optimized for transient flow simulations, no choice is available a special implicit scheme is used.

##### 4.2. Boundary Conditions

Various boundary conditions for the different components applied to this study were as follows.

For inlet the mass flow rate was imposed using mass inlet boundary condition. The value of density (1 kg/m^{3}) and turbulence intensity (5%) were specified at the inlet boundary.

For outlet outflow boundary condition was imposed with flow rate weighing 1. No slip boundary condition was applied on all wall surfaces, the velocity of output was changed for main filter media, and porous media boundary was imposed with .

For air sensor porous media boundary was imposed with .

Whole domain was considered at 1 atm and at 298 as initial condition.

#### 5. Results and Discussion

For the CFD simulation to have any real meaning, the simulated results should match with the experimental data. Figure 4 shows the comparison of experimental pressure drop with the calculated pressure drop. As the filter medium acts like a porous material, the flow will follow Darcy law [2]. It is assumed that the filter material is having a permeability and it is defined according to (3).

From the results it is seen that the assumed velocity boundary conditions are changed and it reasonably compares well up to an exit velocity of 8 m/sec the results are slightly different for the exit velocity of 10 m/sec and 12 m/sec. This difference in result may be attributed to the lower resistance offered by permeability factor used in the calculations.

It is evident that the pressure increases as the velocity of air increases.

It is observed from the figure that the mass flow rate is conversed after 500 sec only when is there a slight improvement in mass flow rate as the calculation time is increased to 500 sec. It is seen that at an exit velocity of 12 m/s experimental mass flow rate across the filter is 0.0602 kg/sec, whereas simulated value for the same condition is slightly higher at a value of 0.078 kg/sec. Also it takes 600 sec to reach this steady state. This variation in mass flux, that is, the higher mass flux predicted by the CFD calculations compares well the earlier result shown in Figure 5(a) that this variation is attributed to the permeability coefficients.

#### 6. Evolution of Flow Field inside the Filter

The CFD simulations were carried out for evolve real life transient condition. The flow will evolve and stabilize after a certain time. Figure 6 CAB, DC (vector puts) compares the flow filed.

It is seen that the flow is strongly directed towards the opposite side of the inlet path. Also the filter flux plate blocks the flow directly into the filter domain.

Hence some modification prefers-likes upward projected intake manifold, and replacing the base plate with stiffness and additional filter material at the bottom.

#### 7. Comparison of Contours of Pressure

Figure 7 compares the relative pressure inside the filter domain for various exit velocities. It is evident from the contour plots that the pressure inside the domain increases with exit pressure.

#### 8. Comparison of Particle Track for Various Exit Velocities

Figures 8(a), 8(b), 8(c), and 8(d) in order to understand and track the complete movement of a fluid flow particle, an imaginary particle having mass is introduced at the entry to the filter. It is observed from the track plots of three different velocities that the incoming air reaches the opposite side of the setup before taking an upward. Once the particle reaches the top, again it takes a downward turn to reach the entrance of the delivery duet.

It is clearly seen that there is a deficiency in the design as it would have been a better strategy either to tangentially place the intake port or orient it upward, so that the number of turns the air particle takes is minimized. By doing these modifications the unnecessary stagnation of air on the opposite side of the entrance can be eliminated.

Based on these results, a further analysis is envisaged by the authors incorporating the above-mentioned modifications.

#### 9. Conclusion

Experimental and numerical investigations were conducted on a commercial filter to determine its flow characteristics and hence any deficiency in design.(i)Results reveal that there is a deficiency in the proper orientation of the intake port.(ii)The intake can be rearranged flow of air in a tangential direction to create either tumble type or swirl type of air motions, respectively.(iii)The validation has been done and it is reasonably satisfactory.

Further analysis is envisaged with design modifications like guide vanes and rearrested intake port.

#### Conflict of Interests

The authors certify that there is no conflict of interests with any third-party software or financial organization regarding the material discussed in the paper.

#### References

- R. Yerram, N. Prasad, P. R. Malathkar, V. Halbe, and S. D. Murthy,
*Optimization of Intake System and Filter of an Automobile Using CFD Analysis*, Quality Engineering & Software Technologies (QUEST), Bangalore, India, 2006. - STAR—CD Methodology.
- M. F. Harrison and P. T. Stanev,
*Measuring Wave Dynamics in I.C. Engine Intake System*, School of Engineering, Cranfield University, Cranfield, UK, 2004.