optimization of aseptic housing using cfd analysis...using ansys fluent 6.3.26, followed by its...

Optimization of aseptic housing using CFD analysis

Gaurav PatelDepartment of Mechanical Engineering,

ICCT (for Women),New V. V. Nagar

[email protected]

Roshani PatelDepartment of Mechanical Engineering,

ICCT (for Women),New V. V. Nagar

[email protected]

Abstract— The opening and closing of Aseptic valve is performedeither by a pneumatic actuator or by a manual hand-wheel.Aseptic valve can be used in a sterile pharmaceutical applicationenvironment as well as in food processing. The main objective ofthis paper is to optimize the appropriate design of aseptichousing based on aseptic valve serving multiple functions bydetermining the flow coefficient (Cv) from pressure drop usingComputational Fluid Dynamic simulation. The study focuses onthe investigation of steady and statistical properties of flow.Initially, based on the given specifications a conceptual modelwas developed using AUTOCAD. Later on, the inner volume ofthis model was developed using Pro-Engineer Wildfire 4,followed by the meshing of volumes using GAMBIT 2.3.16. Thenafter, the simulation of the volume of aseptic housing was doneusing ANSYS FLUENT 6.3.26, followed by its optimization. A 2Dmodel of the optimized design is created in AutoCAD, followedby a 3D model of the assembly generated in Pro-EngineerWildfire 4. From the model, a prototype is developed by usingdifferent manufacturing processes. Testing of the prototype iscarried out for the validation of simulated results. The results soobtained from test are compared with the simulated results andfound to be similar to each other.

Keywords- Aseptic Housing, Flow Co-efficient (Cv), Pressuredrop (∆p), Kappa Epsilon (K–E) model, 3D CFD Analysis

I. INTRODUCTIONseptic valve finds its application in a sterilepharmaceutical environment as a standard process valve,

dosing valve, sample valve or as a true aseptic drain valve,e.g. in combination with a centrifugal pump. Aseptic valvecan also be used in the biotech, health care and cosmeticindustries as well as in various food applications for pilotplants, as a sample valve or as a dosing valve eg. for addingCO2/Nitrogen to beer or flavors to beverages [4]. Thecharacteristic of the loss coefficient of the flow across theaseptic housing were analyzed from this study.

CFD is a computer based tool for simulating thebehavior of systems involving fluid flow and other relatedphysical processes inside any domain by the numericalsolution of complex partial differential equations usingadvanced numerical techniques and specified boundaryconditions [1].

CFD can be used to determine the performance ofindividual component at design stage or it can be used forimprovement of design of existing components. It makes useof the governing equations of flow for the solution of flowfield in the required flow space called as flow domain [2].Specialized software like Gambit has been developed for thepurpose of mesh and grid generation, and access to a goodsoftware package and expertise in using this software are vitalto the success of modeling effort [3].

Herein, the meshing software GAMBIT 2.3.16 isused for the meshing of all designs. Then after, the simulationof the volumes is done using ANSYS FLUENT 6.3.26.Performance optimization of the design is carried out byvarying the inner volume of the aseptic housing.

Design optimization is the application of numericalalgorithms and techniques to engineering systems to assist thedesigners in improving the system's performance, weight,reliability, and/or cost.

II. RESEARCH PROCEDURE AND DESIGN GENERATIONInitially, the conceptual design was developed in AutoCAD

as per the following technical specifications.

Working Pressure : 25 to 45 PSI (1.75 to 3 bar) Inlet & Outlet diameter : 3 inches Radius of the stem : R200

Figure given below shows the two dimensional drawing ofthe conceptual design.

Fig 1 2D drawing of design

Subsequently, the inner volume of the model was developedusing Pro-Engineer Wildfire 4, as shown below in Fig 2.

Fig 2 Inner Volume of Conceptual Design

A

13-14 May 2011 B.V.M. Engineering College, V.V.Nagar,Gujarat,India

National Conference on Recent Trends in Engineering & Technology

III. CFD MESHINGIn order to analyze fluid flows, flow domains are split into

smaller subdomains which are made up of geometricprimitives like hexahedral and tetrahedral in 3D andquadrilaterals and triangles in 2D. The subdomains are oftencalled elements or cells, and the collection of all elements orcells is called a mesh or grid. The process of obtaining anappropriate mesh (or grid) is termed mesh generation (or gridgeneration), and has long been considered a bottleneck in theanalysis process due to the lack of a fully automatic meshgeneration procedure [5].

Herein, the meshing software GAMBIT 2.3.16 is used formeshing of the design according to the following meshspecifications. Mesh type : Map Type of elements : Tetrahedral No of Nodes : 331995 No of Tetrahedral Cells : 1811569 Interior faces : 3563518 Wall Faces : 83721 Velocity Inlet Faces : 4639 Outflow Faces : 4641

The figure given below shows the meshed inner volume ofconceptual design.

Fig 3 Meshed Model of Conceptual Design

IV. BOUNDARY CONDITIONSIn order to perform CFD analysis, several boundary

conditions need to be applied to the geometry. Hence, fourboundary conditions as shown in the following figure areapplied to the conceptual design. Since, the given design issymmetrical the inner volume has been halved to reduce thecomputational time and expenditure.

Fig 4 Boundary Conditions (Design II)

V. ANALYSISAfter the simulations were carried out in prescribed

condition of the flow through aseptic housing, thecharacteristics of flow-fields and the characteristics of flowcoefficient are analyzed.

A. Pressure distributionsFor the aseptic housing of 72.6 mm inlet & outlet diameter,

the simulation for static analysis was done with a flow-rate of300 GPM i.e. inlet velocity of 4.5721 m/s.

Fig 5 Pressure Distributions of Conceptual Design

From the above Figure 5, it is found that in design I, thealmost closed position causes the highest value of stagnationpoint on the surface of stem; therefore it provides the lowestflow rate in this position.

Conversely, for the same flow rate condition, design IIcauses the lowest stagnation value on the surface of stem,therefore it provides the highest flow rate in this position.

B. Velocity distributionsThe velocity distribution, in this part will be represented byvelocity vectors. This helps to display the velocity value andthe direction of fluid around the aseptic housing. Fig 6 showsthe velocity distribution of fluid around the aseptic housingfor an inlet & outlet diameter of 72.6 mm.

Fig 6 Velocity Distributions

It is clear from the above figure that the high gradient offluid velocity appears at the amalgamation of inlet and outletdue to the change of curve.



VI. OPTIMIZATIONOptimization is a mathematical discipline that concerns to

find values of the variables that minimize or maximize theobjective function(s) while satisfying constraints.

• Multiple-objective functions: Often, the user wouldactually like to optimize a number of different objectives atonce. Usually, the different objectives are not compatible;the variables that optimize one objective may be far fromoptimal for the others.

Herein, for the optimization of the given design, a multi-objective task has to be executed:

Objective I: To maximize value of flow coefficient (Cv) Objective II: To minimize pressure drop

A. VariablesVariables are very essential for defining the objective functionand the problem constraints. A design variable is aspecification that is controllable from the point of view of thedesigner. They can be continuous, discrete or boolean. Thevariables to be optimized in the given design are the shape ofaseptic housing and the opening of valve.

B. ConstraintsConstraints are not essential. In fact, the field of unconstrainedoptimization is an important one for which a lot of algorithmsand softwares are available. The dimension constraint for theinner diameter at the inlet and outlet of the aseptic housing is72.6 mm.

Optimization Steps

A. Step I: Change in shape of the stem.

Fig 7 Change in shape of stem

As shown in figure above, the shape of the stem of thedesign is changed by sharpening the edges of the stem.

B. Step II: Change in the inner shape ofaseptic housing

The figure shown below shows the change in the innershape of the aseptic housing.

Fig 8 Change in the inner shape of aseptic housing

C. Step III: Change in the dimensions of aseptic housing.

The below given figure shows the change in the innerdimensions of aseptic housing.

Fig 9 Change in the dimensions of aseptic housing



VII. FLUENT SIMULATION READINGSThe table given below shows all the simulated readings of theoptimized design.

Table 1 Values of pressure drop & flow co-efficient for the optimizationprocess of design

As shown in the table, the pressure-drop (∆P) of ConceptualDesign is sequentially reduced from 9153.225 N/m2 to7618.78720 N/m2 in Opt. Design III, shown in fig 10.

Since, the pressure drop is greatly reduced, the flow co-efficient (Cv) is consecutively increased from 260.37167 inDesign II to 285.38943 in Opt. Design III., as shown in fig 11.

A. Pressure drop for various optimized designs.

Fig 10 pressure drop for various optimized designs

B. Flow Co-efficient for various optimized designs

Fig 11 Flow Coefficient (Cv) for various optimized designs

Hence, it is found from all of the above that Optimized.Design III is more appropriate for modeling &manufacturing.

VIII. RESULTS AND DISCUSSION

A. Simulated Results for Different Flow RatesTable 2 Simulated Results of pressure drop & flow co-efficient for differentFlow Rates

From the above given table, it is seen that the pressure-drop(∆P) for a flow rate of 100 GPM is 826.7079 N/m2, whereas itis 7618.7872 N/m2 for a flow rate of 300 GPM. Hence, thepressure drop increases successively with the increase in flowrate. It is also found that the maximum value of flow co-efficient is 289.4922 which is obtained at a flow rate of 150GPM. Whereas the minimum value of flow co-efficient is281.2873 at a flow rate of 250 GPM.

B. Experimental Results for Different Flow RatesTable 3 Experimental Results of pressure drop & flow co-efficient for differentFlow Rates

From the experimental results tabulated above, it is found thatthe pressure-drop (∆P) for a flow rate of 100 GPM is 846.8789N/m2, whereas it is 7597.0023 N/m2 for a flow rate of 300GPM. Hence, the pressure drop increases successively with theincrease in flow rate.

Also, it is found that the maximum value of flow co-efficient is 288.1952 which is obtained at a flow rate of 150GPM. Whereas the minimum value of flow co-efficient is282.3642 at a flow rate of 250 GPM.

C. Comparison of Experimental & Simulated Results

Fig 12 Pressure Drop (∆P) for Experimental & Simulated Results



From the above Figure 12, it is interpreted that for differentvalues of flow rates, the values of pressure drop (∆P) obtainedby simulation using FLUENT are almost similar to theexperimental results obtained by hydro testing of the prototype.It is also found that the pressure drop (∆P) gradually increaseswith the increase in flow rate for both the cases.

Hence, it is concluded that the simulation results ofpressure drop (∆P) obtained for different flow rates are true forthe prototype.

Fig 13 Flow Co-efficient (Cv) for Experimental & Simulated Results

From the above Figure 13, it is interpreted that for differentvalues of flow rates, the values of flow co-efficient (Cv)obtained by simulation using FLUENT are almost similar tothe experimental results obtained by hydro testing of theprototype. It is also found that, in both the cases, the maximumvalue of flow co-efficient (Cv) is obtained at a flow rate of 150GPM (i.e. 2.2861 m/s).

Hence, it is concluded that the simulation results of flowco-efficient (Cv) obtained for different flow rates are true forthe prototype. Thus, it is observed that maximum variation inthe simulated value of flow co-efficient (Cv) is 1.2 % of theexperimental result, which is adequate as per the companyrequirements.

IX. CONCLUSIONThe primary objective of the paper was to obtain an

optimum value of flow co-efficient (Cv) and pressure drop (∆P)by CFD analysis. The CFD analysis of aseptic housing wasdone using the commercial software FLUENT, wherein aconceptual design was created, and later on was optimized.

As a part of optimization, initially the shape & then thedimensions of aseptic hosing were altered and the value of Cv,so found by CFD analysis was desirable. According to theoptimized design, 2D and 3D models were created inAutoCAD and Pro-E. Then after, a prototype was createdbased on the above model.

Experimentation of the prototype was carried out for thevalidation of simulated results. The results so obtained fromtest were compared with the simulated results and found to bealmost similar. Hence, it is concluded that the simulation ofaseptic housing done using FLUENT is relevant.

REFERENCES

[1] Tu J. Y., Yeoh G. H., Liu C, 2008, “Computational Fluid Dynamics - APractical Approach”, Butterworth Heinemann, USA.

[2] Shah N. S., Rajgor P. J., 2008, “Analysis and Development of Non-Return Valve by CFD Simulation and Finite Element Analysis”, ADIT,New V. V. Nagar, India.

[3] Roach P. J., 1998, “Fundamentals of Computational Fluid Dynamics”,Hermosa Publisher, New Mexico.

[4] Zappe R. W., Smith P., 2004, “Valve Selection Handbook”, 5th Edition,Gulf Professional Publishing, USA.

[5] Chaiworapuk W., 2007, “The Engineering Investigation of the WaterFlow Past the Butterfly Valve”, INSA-Lyon, France.



IntroductionResearch procedure and design generationCFD MeshingBoundary ConditionsAnalysisPressure distributionsVelocity distributions

OptimizationVariablesConstraintsOptimization StepsStep I: Change in shape of the stem.Step II: Change in the inner shape ofaseptic housingThe figure shown below shows the change in the inner shape of the aseptic housing.

fluent simulation readingsPressure drop for various optimized designs.

Flow Co-efficient for various optimized designsresults and discussionSimulated Results for Different Flow Rates Experimental Results for Different Flow RatesComparison of Experimental & Simulated Results

conclusionReferences

optimization of aseptic housing using cfd analysis...using ansys fluent 6.3.26, followed by its...

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