Port flow, In-cylinder, Intake port, Exhaust port, Optimization, CFD. SUMMARY. Development of any internal combustion engine is driven primarily by fuel.
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DESIGN OPTIMIZATION OF AN IN-CYLINDER ENGINE INTAKE PORT
DESIGN OPTIMIZATION OF AN IN-CYLINDER ENGINE INTAKE PORT Padmesh Mandloi, Gunjan Verma ANSYS Fluent India Pvt. Ltd., India Arnaud Boland ANSYS, Belgium
THEME Optimization
KEYWORDS Port flow, In-cylinder, Intake port, Exhaust port, Optimization, CFD
SUMMARY Development of any internal combustion engine is driven primarily by fuel efficiency and emission requirements. This requires refinement of the incylinder flow, mixture formation and combustion processes. Design optimization of the intake/exhaust port, valves and piston bowl is essential to realize the above mentioned requirements. The use of Computational Fluid Dynamics (CFD) along with optimization tools can help shorten the design optimization cycle time. Traditional approach of experiments using flow bench testing is very costly as well as time consuming. Moreover CFD allows insight into the minute flow details which otherwise are not capture using flow bench tests. The present study demonstrates the use of optimization technology in improving the intake port design. Design exploration from ANSYS Workbench is used in the study. The objective of the study was to maximize the effective flow area in order to optimize the Brake Specific Fuel Consumption (bsfc). Variations in the design parameters like guide curve angle, guide curve radius etc. were studied. The optimization study provided response charts of the different design variables on the output. Sensitivity analysis of the input variables helped in identifying the importance of each design variable and their
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respective effects on the output. Finally the different design points were rated based on a goal-driven optimization study and the best design is chosen. The entire study took less than a day compared to few weeks which would be required in the absence of well integrated optimization and CFD tools. No customization was required. Single Graphical User Interface allowed quick learning and ease-of-use.
1: INTRODUCTION Development of internal combustion engine involves resolving several conflicting issues. One of the most important of them being reducing the fuel consumption and exhaust emissions at the same time improving the performance and power output of the engine [3]. To solve such conflicting issues, efficient analysis using CFD simulations and optimization tools is required. Simulation Driven Product Development (SDPD) has gained huge significance in the last two decades and today plays a very effective role in bridging the gap between conceptual design and mass production. Figure 1 below explains the same using a schematic. SDPD essentially combines several analysis processes together with simulation and design optimization being two of the many such processes. CAD-based design optimization is now-a-days widely used in industry, specifically the automotive industry. A look at some of the recent publication in this area reveals that in most of the cases design optimization is achieved by establishing links between a CAD package, a CFD mesher and solver and an optimizer [2], [3], [4]. Most often these different packages are offerings from different software vendors and the user has to write codes to establish links between these packages. Sometimes this causes loss in accuracy of the data that is transferred from one package to the other. In the present paper, ANSYS Workbench 2 is used to perform all the steps from CAD handling to creating CFD mesh and solving the cases to performing optimization studies. Workbench 2 is a new offering from ANSYS Inc. and it provides a unified environment for all types of analysis ranging from structural to fluids and electro-magnetic to even design optimization. DesignXplorer (DX) within Workbench 2 is a tool for designing and understanding response of parts and assemblies. DX allows users to perform analysis ranging from the basic "what-if" scenarios to deterministic, multiple goal-driven optimization to six-sigma analysis and even varaitional analysis.
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2009
1990's
Figure 1:
Increasing role of simulation in product design and development
The sections to follow will first talk about the design optimization process followed by Workbench. Subsequent to that, the CAD modelling process will be described. The CFD meshing and case-setup procedure will be discussed thereafter. Optimization techni techniques ques used in the current study will be explained in some detail after that. Finally results and conclusion will be presented.
2: Theory of Optimization Process Design exploration within Workbench is used for design optimization. Design of Experiments (DOE) technique was used in the optimization. DOE is a technique used to determine the location of sampling points. There are several versions of design of experiments available in engineering literature. These techniques all have one common characteristic: they try to locate the sampling points such that the space of random input parameters is explored in the most efficient way, or obtain the required information with a minimum of sampling points. Sample points in efficient locations will not only reduce the required number of sampling points, but also increase the accuracy of the response surface that is derived from the results of the sampling points. By default the deterministic method uses a central composite design (CCD), which combines one center point, points along the axis of the input parameters, and the points determined by a fractional factorial design. In Central Composite Design (CCD), a Rotatable (spherical) design is preferred since the prediction variance
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is the same for any two locations that are the same distance from the design center. An optimal CCD design should minimize both the degree of nonorthogonality of term coefficients and the opportunity of sample points having abnormal influence. If N is the number of input parameters, then a central composite design consists of: •
One center point.
•
2xN axis point located at the -α and +α position on each axis of the selected input parameters.
•
2x(N-f) factorial points located at the -1 and +1 positions along the diagonals of the input parameter space.
The fraction f of the factorial design and the resulting number of automatic design points are given in the following table: Table-1: Automatic Point Generation by CCD Algorithm
Number of Input Parameters
Factorial Number f
Number of Automatic Design Points
1 2 3 4 5 6
0 0 0 0 1 1
5 9 15 25 27 45
Once the deterministic analysis is carried out to obtain finite design points, statistical techniques are used to perform multiple objective optimization. Goal Drive Optimization (GDO) which is a constrained, multi-objective optimization (MOO) technique is performed in the current paper. Optimization techniques like MOGA and NLPQL were used. The MOGA used in GDO is a hybrid variant of the popular NSGA-II (Non-dominated Sorted Genetic Algorithm-II) based on controlled elitism concepts. The first Pareto front solutions are archived in a separate sample set internally and are distinct from the evolving sample set. This ensures minimal disruption of Pareto front patterns already available from earlier iterations.
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3: Work Flow Figure 2 shows the entire workflow. CAD creation was the only external step in the entire process whereas all the other steps were performed within the Workbench framework.
Figure 2:
Workflow
4: Intake Port Optimization Optimization process of a intake port is described in this section. The goal is to optimize the shape of the baseline 2-valve intake port with no-swirl shown in Figure 3. The. objective function is effective flow area defined as EFA =
m
2 ρ∆P where m is the mass flow-rate in kg/s. ∆P is the pressure drop which was set to be 5000 Pa in the current study. The valve lift was kept constant at 5 mm.
Figure 3:
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Baseline intake port model
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4.1: CAD Modeling CATIA v5 R18 was used to create the parametric baseline geometry shown in Figure 4. The shape of the intake port plenum was defined using a master guide curve and several sectional curves orthogonal to the master guide curve. Figure 5 shows the three prime geometric parameters that were considered as inputs in the present study. These geometric design parameters were guidecurve-angle, guide-curve-radius and section-1-length. In the baseline design, the values of these three parameters was 63 deg., 41 mm and 51 mm respectively. 4.2: Analysis Process The CATIA model of baseline design was read in DesignModeler. Using a third-party communicator called CAPRI, DesignModeler is able maintain bidirectional connectivity with CATIA. A full tetrahedral CFD mesh with both curvature and proximity based sizing function is automatically created on the baseline design. Figure 6 shows some images of mesh on the baseline geometry. The typical size of the mesh was of the order of 800K cells. It was possible to grow boundary layers from the surfaces of the ports and other components to resolve flow near the wall more accurately, but in the present study it was decided not to do so since mesh with boundary layers would have grown the overall size of the mesh considerably and would have slowed the entire DOE process.
Figure 4:
Parametric CATIA model
FLUENT was used to perform steady state CFD simulations. An output parameter equivalent to EFA was created using custom-field-functions. Realizable k-epsilon turbulence model was used. Using journaling capabilities, the solution was first converged with first order and later second order schemes.
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Guide Curve Angle
Section-1 Length
Guide Curve Radius
Figure 5:
Figure 6:
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Design parameters
Mesh in the port geometry
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Once the entire CFD process was set, Goal Driven Optimization was initiated. The first step towards Goal Driven Optimization was to create a DOE. The range in which the three design parameters should vary in order to get the most optimized design were decided. Table 2 shows these ranges. A Central Composite Design (CCD) based DOE was setup, which created 15 Design Points for the specified three input design parameters. These 15 Design Points were then run automatically to generate output at these points. No manual intervention was required to perform calculations at these points and right from CAD modification to calculating the value of the output parameter, the entire process was performed automatically. Figure 7 shows some designs which lie in the range in which the input parameters were varied. Table-2: Input Parameters with Range Input Design Parameters
Minimum Range
Maximum Range
guide-curve-angle (deg) guide-curve-radius (mm) section-1-length (mm)
50 30 50
70 50 65
Figure 7:
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Intake port design modifications
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5: Results
As mentioned above, the goal of the study was to optimize the intake port shape in the permissible range of input design parameters to obtain maximum effective flow area. A DOE with 15 design points was initiated and the results were analyzed. Response surfaces in 2D and 3D were generated from the results of the 15 design points. These response surfaces were generated based on the full second order polynomial method which uses different regression analysis to find a polynomial. The quality of th this is polynomial is improved using transformation functions like Yeo-Johnson and Box-Cox functions.
Figure 8 shows three different 3D response surfaces. Each response surface shows variation of the output parameter EFA as a result of variation of two input parameters at a fixed value of the third input parameter. It can be seen that EFA peaks up as the guide-curve-radius is increased and then it falls as the value of guide-curve-radius is increase further. Similarly, EFA dips down to a minimum value with increase in the section-1-length and then it increases as the value of section-1-length increases further. The variation of EFA with guide-curve-radius was found to be more or less linear.
Figure 8:
Response surfaces
Figure 9 shows local sensitivity bar charts at baseline and mid-range values of the design. It is evident from these charts that guide-curve-radius is the most sensitive input parameter and guide-curve-angle is the least sensitive
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parameter. The charts also reveal that the max maximum imum EFA can be achieved by both increasing and decreasing the guide-curve-radius.
baseline
mid-range
Figure 9:
Local sensitivity charts
5.1 Goal driven optimization. The objective of the optimization process was to maximize the EFA. Local sensitivity charts gave pointers that the EFA would be maximum for higher values of guide-curve-radius and lower values of guide-curve-angle and section-1-length. However, due to some design constraints a relatively lower value of guide-curve-radius was desired. Multi-Objective-Genetic-Algorithm -Algorithm (MOGA) was tried first with a initial sample size of 10000 and with 100 samples per iteration. Multiple objectives with weights can be assigned to the output as well as input parameters. Figure 10 shows the goals along with wei weights ghts and the best 3 candidates that are obtained using MOGA.
Figure 10: Results from MOGA
The candidate points from MOGA have a higher value of EFA which is desired, but the guide-curve-radius values are quite low. This is because MOGA first tried to optimize the output output parameter and then considers the input parameters. To refine the candidates even further, Candidate A from MOGA
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was selected as the initial guess to perform another optimization known as the NLPQL (Non-Linear Programming by Quadratic Lagrangian) algorithm algorithm which is quite accurate algorithm for single output objective type of problems. Results from the NLPQL analysis resulted in one design point which was, in principle, very close to Candidate A, which was chosen as the initial guess point.
Since both MOGA and NLPQL analysis did not give due respect to the objective set on the input parameter guide-curve-radius, it was decided to perform Screening based analysis. A Screening based analysis was done for the present case. 10000 sample points were generate generated. d. It uses the properties of the input as well the output parameters, and uses the Decision Support Process on a sample set generated by the Shifted Hamersley technique to rank and report the candidate designs. Thus, the candidate designs in this case corr correspond espond to both the input and output objectives specified by the user. Figure 11 shows the results of the Screening method. It can be seen that the results from this analysis are quite different from the MOGA and NLPQL analysis. The EFA from Screening analysis is lesser than that from the MOGA and NLPQL analysis because Screening analysis respected the goals set on the input parameters. Figure 12 shows the tradeoff charts with blue points as the most feasible points
Figure 11:
Figure 12:
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Results from Screening method
Sample points generated from screening method
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Further analysis was carried out to obtain the best design with maximum EFA when guide-curve-radius is in the range of 35 to 45 mm whereas the guidecurve-angle is in the range of 50 to 55 degrees. Pareto chart chartss were created to perform this analysis. Figure 13 shows three different Pareto charts. The first Pareto chart shows 5000 sample points. When the guide-curve-angle and guide-curve-radius were restricted in the said ranges, the number of Pareto fronts lying in this range could be seen. Further, the number of Pareto fronts were reduced to 1 to show the best design satisfying all the goals. Table 3 summarizes best designs from the MOGA/NLPQL analysis along with that from the Screening analysis.
Figure 13:
Pareto charts
CFD simulation was performed at these two optimal points and it was found that the CFD results were within 2 % accuracy of the statistical results reported by the optimization techniques. Figure 14 shows the baseline design along with the other two optimal designs.
Table-3: Optimized designs using MOGA/NLPQL and Screening Analysis
Analysis MOGA/NLPQL Screening
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guide-curveangle (deg) 50 50.069
guide-curveradius (mm) 30 35.314
section-1length (mm) 60.499 58.519
EFA (square mm) 1180.4 1153.8
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Baseline
MOGA/NLPQL
Figure 14:
Screening
Design change due to optimization
The entire optimization process took 20 hours on a Windows xp 64 bit machine with dual core and 4GB RAM. Very little manual intervention was required once the entire process was set for the baseline design. 6: Conclusions
CFD based design optimization of an automotive intake port is described. ANSYS Workbench framework was used for design exploration. Using three design parameter, the effective flow area was maximized. The entire optimization process was fully automatic and streamlined. No third party connectivity was required to perform this analysis except for setting bidirectional connectivity between CATIA and DesignModeler. CCD based design of experiment technique required only 15 CFD simulations to be performed and therefore the entire optimization process from CAD import to achieving the optimal designs took less than a day of manual as well as computational efforts. Acknowledgement
The authors would like to thank:
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• Tushar Sambharam and Sudesh Palase, ANSYS Fluent India, for helping with creating a generic CAD model of the intake port. • Sourabh Shrivastava, ANSYS Fluent India, for helping with setting the bi-directional CAD connectivity of Workbench. • Laurent Gerboud and Michel Rochette, ANSYS Lyon France, for answering several questions on the usage of Design exploration within Workbench 2 and also on the theory of optimization.
REFERENCES 1. HEYWOOD, J B – Internal Combustion Engine Fundamentals, McGrawHill, New York, 1988 2. CHO SU K, KORIVI V M – Port Design Optimization Using CFD Analysis, Journal of Advanced Manufacturing Systems, Vol. 3, No. 1, pp 21-32, 2004 3. KARCH M, DURST B, BUCHER S, EBNER JOACHIM – Implementation of Automatic Optimization of Cylinder Head Components, Conference Proceedings, 3rd European Automotive CFD Conference, Frankfurt, Germany, 2007 4. HORVATH Z, MORAUSZKI T, TOTH K, ISTVAN S – Automated CADbased CFD-Optimization and Applications in Diesel Engine Design, Conference Proceedings, 3rd European Automotive CFD Conference, Frankfurt, Germany, 2007 5. LAUNDER B E, SPALDING D B – The numerical computation of turbulent flows, Computer Methods in Applied Mechanics and Engineering, Vol. 3, pp 269-289, 1974 6. FLUENT 6.3 User's Guide, ANSYS Inc., Canonsburg PA (2007) 7. Release 11.0 documentation for ANSYS Workbench, ANSYS Inc., Canonsburg PA (2007)
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