the design and optimization of a hydraulic washing machine ...the difference between inlet pressure...

The Design and Optimization of a The Design and Optimization of a Hydraulic Washing Machine PumpHydraulic Washing Machine Pump

StepsSteps• Validation of the CFD model using experimental data

• Creation of the parametric model (CATIA v5)

• Mesh creation (ICEMcfd)

• CFD analysis (CFX5)

• Optimization (modeFRONTIER)

Step 1 : Simplification of the Original ModelStep 1 : Simplification of the Original Model

The volute was constructed by joining the interior surfaces of the original solid model.

The rotor was simplified by removing the stiffening ribs of the original model and by closing the 4 axial holes.

The difference between inlet pressure and the outlet pressure was about 0.270atm.

Step 1 : CFD analysis of the original modelStep 1 : CFD analysis of the original model

Step 1 : CFD analysis of the original modelStep 1 : CFD analysis of the original model

The visualization of the flow field at different axial heights showed large recirculation zones at outlet

Step 1 : CFD analysis of the original modelStep 1 : CFD analysis of the original model

The measured hydraulic efficiency was 0.42 while the hydraulic efficiency obtained from the CFD analysis fluctuated between 0.41 and 0.43.

Step 1 : ConclusionsStep 1 : Conclusions

The CFD results match the experimental data tosufficient accuracy

Step 2 : Creation of the parametric modelStep 2 : Creation of the parametric model

The pump model was composed of two main bodies (the rotor and the volute).Several auxiliary surfaces were created to split the fluid domains (inlet, rotor, volute, outlet) into different regions. The angular velocity was only applied to the domain surrounding the rotor.

Step 2 : Creation of the parametric modelStep 2 : Creation of the parametric model

• It was assumed that only 7 of the 23 parameters would have a considerable effect on the performance of the hydraulic pump; these were the parameters used for the optimization. They are listed below with their lower and upper bounds:

– Wrapping angle : value of the blade’s covering angle [50° 90°].– Beta1: angle of incidence at the blade leading edge [13° 33°]– Beta2: angle of incidence at blade trailing edge [[11° 41°]– B2: height of the blade at outlet [6mm 12mm]– D2: diameter of the pump rotor [53mm 59mm]– Blades: Nmber of blades [9 11]– Blade Shape: spline tension defining the blade shape [1.1 1.5]

Step 2 : Validation of the geometrical model automatic updateStep 2 : Validation of the geometrical model automatic update

Step 3 : Automatic grid generation Step 3 : Automatic grid generation

The exported parametric model built using CATIA V5 was easilyimported into ICEM 5.1.1.

• The volute was meshed using tetrahedra with prisms at the wall.

• The rotor and the inlet and outlet tubes were meshed using hexahedra.

Geometrical model imported in ICEMcfd Detail of the Rotor Mesh

Step 3 : Validation of the automatic mesh generationStep 3 : Validation of the automatic mesh generation

Step 4 : Automatic CFD analysis of the parametric modelStep 4 : Automatic CFD analysis of the parametric model

CFX5 pre-processor in batch cfx5pre –batch sole.pre

CFX5 solver in batch cfx5solve –def sole.def

CFX5 post-processor in batch cfx5post –batch sole.cse

StepStep 4 : 4 : ValidationValidation of the of the automatic CFD analysisautomatic CFD analysis(The (The modeFrontiermodeFrontier Workflow)Workflow)

Step 5 : OptimizationStep 5 : Optimization

Optimization ParametersInput Variables:

•Wrapping angle•Beta1•Beta2•B2•D2•Number of blades•Blade shape

Objectives & Constraints

Objective:•Maximize Efficiency

Constraint:•Head ( keep within a defined range)

Optimization StrategyExploration phase:Sobol DOE – 12 Designs

Optimization phase:MOGA II –20 generations

We run 114 designs during the optimization cycle.The optimization took about three weeks on a mixed architecture cluster (CONDOR queuing system – 3 computers in the pool; one Linux, one Windows and one HP-UX 11.00)

Step 5 : OptimizationStep 5 : Optimization

Step 5 : OptimizationStep 5 : OptimizationOptimization history

The squares represent the feasible solutions while the triangles are the unfeasible ones

Step 5 : OptimizationStep 5 : Optimization

Diameter BladeHeight Beta1 Beta2 Blade

Shape Wrapping Number ofBlades Efficiency Pressure (Pa)

55 14 26 32 X X 11 0.43 27000

53 12 13 15 1.5 55 11 0.618 27231

Input pump parameters which had the best efficiency and also met the constraint on the pressure head.

Original

Optimized

*Blade shape parameter and Wrapping Angle parameter are not present in the original model

Step 5 : OptimizationStep 5 : Optimization

Velocity and pressure fields of the optimized hydraulic pump

ConclusionsConclusions

• The automatic analysis of a hydraulic pump was successfully tested (CATIA v5 – ICEM – CFX5)

• The pump efficiency of designs which met the constraint on pressure head improved during the optimization process.

• The suitability of the parameterization was confirmed by the fact that many designs created during the optimization had a higher efficiency than that of the original model.

• The parametric model has been designed to be flexible in order to be reused for pumps having different inlet diameter, outlet diameter or overall dimension, etc…

• modeFrontier proved to be a very powerful optimization tool