COUPLING EFFICIENT MULTIVARIATE ANALYSIS AND OPTIMIZATION STRATEGIES WITH AN INNOVATIVE FLOW SOLVER FOR HULL HYDRODYNAMICS

(1)

[email protected] | Tel: +39 040 9776236 | Fax: +39 040 46063448 | www.engys.eu

COUPLING EFFICIENT

MULTIVARIATE ANALYSIS AND OPTIMIZATION STRATEGIES WITH

AN

INNOVATIVE FLOW SOLVER FOR HULL HYDRODYNAMICS

Paolo Geremia, Engys Srl, Italy

Kevin J. Maki, University of Michigan, USA Gianpiero Lavini, Fincantieri SpA, Italy Harpo Genuzio, Fincantieri SpA, Italy

modeFRONTIER 2012 Users’ Meeting, 21-22 May 2012, Trieste, Italy

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

(3)

Company Details

• Registered in UK, Germany, Italy and Australia

• CAE services company:

 Consultancy

 Software & Methods Development

 User support & Training

• Open Source engineering software for industry:

 CFD

 Optimisation

• Extensive expertise (> 10 years)

OPENFOAM is a registered trademark of OpenCFD Ltd.

(4)

Background | Resistance Prediction Methods

Method Pros Cons

Experiments Methodology is well tested Costly, scaling issues Potential Flow Methods Low computational cost

(boundary vs. volume)

Neglect viscous effects

CFD Most accurately models the

physics

Captures non-linear effects - wave-breaking

High computational cost - grid generation &

computation

Empirical Data / Regression Powerful for early design Rely on geometric similarity to parent data

→ Idea: combine benefits of potential flow methods with RANS CFD into a new flow

solver for fast and accurate drag prediction

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

(6)

Flow Solution | Solver Description

• Method:

 RANS viscous flow solution + steady free-surface effect

• Benefits:

 Accurate viscous and pressure drag prediction

 Support for sinkage and trim calculation

 Well-suited for optimisation due to robust formulation that

employs automatic grid

generation and quick solution

time

(7)

Flow Solution | Wigley Hull Validation

• SRI 4m model

• 120K cells

• CPU time ~ 40 mins per speed on a laptop

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Flow Solution | DTMB 5415 Hull Validation

• INSEAN and DTMB Experiments

• 185K cells

• CPU time ~ 60 mins per

speed on a laptop

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Geometry Update Geometry Update

Meshing Meshing

Pre-processing Pre-processing

Post-processing Post-processing

Solver Solver OPTIMISER

OPTIMISER

Flow Solution | Optimisation Workflow

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

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Parametric Geometry

• Different geometrical shapes are generated via

parametric morphing model starting from a baseline model

• Robust morphing of existing surface mesh

• Parameterisation is quick and easy to setup

• No CAD parameterisation required

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Parametric Geometry

• Surface morphing and simple deformation

operations can be applied to the existing baseline geometry

• Deformation applies an efficient B-Spline driven

surface morphing

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Parametric Geometry | Morphing in ANSA

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

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CFD Model Setup | Meshing

• Hex-dominant mesh of 500,000 cells

• 5 near-wall extrusion layers

• Accurate feature-edge resolution

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CFD Model Setup | Meshing

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CFD Model Setup | Meshing

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CFD Model Setup | Validation

• The flow solution was validated against a standard interface- capturing VOF method

• Simulation was carried out at Froude number 0.22

• VOF simulations performed on 2 discretizations:

 Coarse grid: 171,187 cells

 Fine grid: 1,185,150 cells

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CFD Model Setup | Validation

• The VOF solver on a fine grid requires about 400 CPU-hrs

• The new method converged in about 3 CPU-hrs

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CFD Model Setup | Free-surface Elevation

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CFD Model Setup | Streamwise Velocity

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

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Optimisation Workflow

Design Parameters Input Variables

Translation along X,Y and Z of 10 independent control points

Output Variables

Total resistance coefficient at Froude number 0.22 Displacement

Design Objectives Minimise resistance Keep displacement fixed Optimization Setup Exploration Phase:

Initial LHS sampling DOE algorithm –No. of samples: 100 Hierarchical Clustering and selection of optimal cluster Final LHS sampling DOE algorithm –No. of samples: 50 Design Parameters

Input Variables

Translation along X,Y and Z of 10 independent control points

Output Variables

Total resistance coefficient at Froude number 0.22 Displacement

Design Objectives Minimise resistance Keep displacement fixed Optimization Setup Exploration Phase:

Initial LHS sampling DOE algorithm –No. of samples: 100 Hierarchical Clustering and selection of optimal cluster Final LHS sampling DOE algorithm –No. of samples: 50

Meshing Meshing Geometry Update

Geometry Update

modeFRONTIER modeFRONTIER

Flow Solution

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Optimisation Workflow

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

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Initial Exploration | Correlation

• Global correlations between input and output

variables are detected

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Initial Exploration | Clustering

• Design data are grouped into different clusters using Hierarchical clustering method (Ward approach)

• 19 clusters we identified

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Initial Exploration | Clustering

• An optimal cluster having displacement close to

the baseline model was found

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Final Run | Results

• The solutions obtained during the final LHS run

improved the baseline design

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CAP Analysis

• 4 clusters were identified in the final set of the

optimal solutions

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CAP Analysis

• To detect local correlation among variables in the optimal cluster, CAP tool was employed

• 2 cluster were considered in the CAP analysis

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Final Run | Local Correlation

• When moving from the lowest displacement cluster to the highest displacement cluster:

• dx1, dx2, dy2, dy3, dy4, dz1, dz2 dz4 are to be decreased

• dy1 and dz3 are to be increased

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Optimisation Results

• A candidate optimal solution was selected from the final dataset

• Flow resistance is minimized whereas displacement value is almost the same

Displacement Ct at Fn 0.22

Delta -0.57% -7.43%

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Optimization Results

Optimised

Baseline

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Optimisation Results

Baseline

Optimised

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Validation Results

• The optimised solution results were compared to a 121K, 1M

and 10M cells grids solved with interFoam VOF solver

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Validation Results

• The new solver overpredicts force

• The “delta” between baseline and optimised solutions is quite

accurately predicted

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• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

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Conclusions

• Different shape parameterisation techniques were evaluated.

• Benefits of new flow solver scalability are huge

for product development speed-up and reduction in costs.

• Unique service for ship hydrodynamics optimisation:

 New formulation for combination of free-surface and viscous hydrodynamics

 Accurate drag prediction with fast turn simulation time

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Thank you very much!

COUPLING EFFICIENT MULTIVARIATE ANALYSIS AND OPTIMIZATION STRATEGIES WITH AN INNOVATIVE FLOW SOLVER FOR HULL HYDRODYNAMICS

[email protected] | Tel: +39 040 9776236 | Fax: +39 040 46063448 | www.engys.eu

COUPLING EFFICIENT

MULTIVARIATE ANALYSIS AND OPTIMIZATION STRATEGIES WITH

AN

INNOVATIVE FLOW SOLVER FOR HULL HYDRODYNAMICS

Paolo Geremia, Engys Srl, Italy

Kevin J. Maki, University of Michigan, USA Gianpiero Lavini, Fincantieri SpA, Italy Harpo Genuzio, Fincantieri SpA, Italy

modeFRONTIER 2012 Users’ Meeting, 21-22 May 2012, Trieste, Italy

Contents

• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

Company Details

• Registered in UK, Germany, Italy and Australia

• CAE services company:

 Consultancy

 Software & Methods Development

 User support & Training

• Open Source engineering software for industry:

 CFD

 Optimisation

• Extensive expertise (> 10 years)

Background | Resistance Prediction Methods

Method Pros Cons

Experiments Methodology is well tested Costly, scaling issues Potential Flow Methods Low computational cost

(boundary vs. volume)

Neglect viscous effects

CFD Most accurately models the

physics

Captures non-linear effects - wave-breaking

High computational cost - grid generation &

computation

Empirical Data / Regression Powerful for early design Rely on geometric similarity to parent data

→ Idea: combine benefits of potential flow methods with RANS CFD into a new flow

solver for fast and accurate drag prediction

Contents

• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup

• Optimisation Results

• Conclusions

Flow Solution | Solver Description

• Method:

 RANS viscous flow solution + steady free-surface effect

• Benefits:

 Accurate viscous and pressure drag prediction

 Support for sinkage and trim calculation

 Well-suited for optimisation due to robust formulation that

employs automatic grid

generation and quick solution

time

Flow Solution | Wigley Hull Validation

• SRI 4m model

• 120K cells

• CPU time ~ 40 mins per speed on a laptop

Flow Solution | DTMB 5415 Hull Validation

• INSEAN and DTMB Experiments

• 185K cells

• CPU time ~ 60 mins per

speed on a laptop

Geometry Update Geometry Update

Meshing Meshing

Pre-processing Pre-processing

Post-processing Post-processing

Solver Solver OPTIMISER

OPTIMISER

Flow Solution | Optimisation Workflow

Contents

• Background

• Method Description

• Parametric Geometry

• CFD Model Setup

• Optimisation Setup