multi-objective optimization coupling modefrontier and · pdf filea. clarich, z. wen* esteco,...

A. Clarich, Z. Wen* ESTECO, Trieste, (Italy)

Multi-objective optimization coupling modeFRONTIER and CST MICROWAVE STUDIO

Summary

• Introduction to modeFRONTIER

• CST direct interface in modeFRONTIER

• Application case: optimization of wideband antenna

• Application case: optimization of Power Data Handling and Transmission (PDHT) antenna

Introducing modeFRONTIER

is an integration platform for multi-objective optimization, automation of design processes

and analytic decision making providing seamless coupling with engineering tools

within various disciplines

User’s Community and short company history ESTECO started in 1999 as a University spin-off.

modeFRONTIER was the first commercial tool that allowed a MULTI-OBJECTIVE

optimization applied to ANY engineering design area

Now modeFRONTIER is used worldwide

1999 2001 2003 2004 2008 2010 2013

modeFRONTIER v. 1

Esteco establishment

in Europe

modeFRONTIER v. 2

Expansion to Asian markets

modeFRONTIER v. 3

Opening of ESTECO

North America

modeFRONTIER v. 4

modeFRONTIER v. 5

Automotive

Research Inst. and Uni

Electronics

Aerospace

Energy

Materials

Appliances

Defence and Space

No

Yes

OK?

Initial Configuration

Simulate

Evaluate Results

Accept

Modify Configuration

Traditional Design Optimization Approach

Parametric models

Design Objectives and Constraints

Optimal trade-off Solution

The Concept behind modeFRONTIER

The Concept behind modeFRONTIER

The Black Box: (ADAMS, ANSYS, CST, GT-Suite, etc.)

Scheduler: (DOE, optimization algorithms,..)

Input Variables: Entities defining the design space.

Output Variables: Measures from the

system

modeFRONTIER can be coupled with most software (CAD, CAE or general application tools) and it enables the simultaneous use of a number of such software packages even

on different machines

Modules of modeFRONTIER

Process Integration

Statistical Analysis Multivariate Analysis Decision Making Response Surface Tool

Design of Experiments Optimization Algorithms Robust Design

CST interface in modeFRONTIER

• Existing I/O parameters are automatically introspected and listed after clicking apposite space

• Assign each one of them to mF workspace parameters

CST interface in modeFRONTIER

CST direct interface Preferences

• CST application can be selected

• CST solver can be specified

Application Examples:

1) Maximizing bandwidth for a wideband antenna with modeFRONTIER

2) Multi-objective optimization of an isoflux Antenna

from: F.Franchini, Multi-objectives optimization coupling modeFRONTIER and CST MICROWAVE STUDIO®, CST Workshop Series 2013 - 14 March 2013, Ankara, Turkey

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• Wideband antenna for mobile communications, e.g. in base stations

• Antenna performance is measured by return loss (low return loss desirable)

• Aim: design antenna with low return loss over the largest frequency range possible

– S11 - parameter used, equivalent to return loss

• Bandwidth for nominal design is 2.1 GHz

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Application example: Maximizing bandwidth for a wideband antenna

Results 1/2

• Bandwidth increased by 15% compared to original model

– Nominal design violated design constraint of S11 < -4.5 dB in range

– Automated process for antenna design created – Two-step optimization approach

Original design Final design

Bandwidth = 2.13 GHz Bandwidth = 2.43 GHz

S11 peak to -4.3 dB (above limit!) S11 peak below limit

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Engineering design process captured:

Easy adaptation for future projects (more parameters, different antenna model, additional result quantities …)

Relationship between input parameters and results clarified

Engineering time

CPU time

Defining modeFRONTIER workflow 1h

First optimization step (finding a starting point, 30 simulations, 20 minutes each): 10 h

Evaluation first step 30 min Second optimization step (20 simulations, 45 minutes each): 15 h

Evaluation of results 1 h

Total 2.5 h 25 h

Total time for project 1,5 days

Results 2/2

Estimated time to carry out a similar project

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PDHT Antenna Description • The Power Data Handling and Transmission (PDHT) antennas

are basic payloads on Low Earth Orbit (LEO) satellites. • These antennas play an important role in many mission of

earth Observation from Space, where high transmission rate is required to acquire Earth images in various spectral bands for several civilian and military applications.

• The basic PDHT Antenna architecture was developed more than ten years ago and It consists of a corrugated planar surfaces with cylindrical symmetry excited by quartz loaded launcher, so that the analysis is based on 2D Method of Moment Modeling, while the optimization is performed by a quasi-Newton technique.

F. Franchini, N.Baldecchi Enginsoft SpA, Firenze, Italy C. Iannicelli, Software System Engineering SpA, Roma, Italy

R. Ravanelli, Thales Alenia Italia SpA, Roma, Italy

Application example: Multi-objective optimization of an isoflux Antenna

PDHT Antenna Description

The new PDHT Antenna Structure conceived to meet new and more stringent requirements especially on cross-polarization discrimination XPD and operative frequency bandwidth has sets of slots in radial direction: 3D modeling is necessary with much more computation resources to perform electromagnetic analysis The slots are variables in number and geometry. New multi-objective evolutionary algorithms are required because of the new multi-variable and multi-objective nature of optimization problem

Electromagnetic Problem Formulation

-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90Theta (degs)

Am

plitu

de (

dBi)

MinimumGainMask

MaximumGainMask

R1

DR1 R2

DR2

DR….

DRn

R…

Rn

A2

A… An

D2

D…

Dn

A1

• Radio Frequency requirements are fixed on: • Gain achievement on desired mask defined on elevation angular range • Cross Polar Discrimination (XPD) on required angular and frequency • Amplitude and Phase Ripple with respect of required frequency • Return Loss requirement

• The Antenna Structure is described by a set of geometrical parameters

• The Antenna Performances Optimization consists in defining the best combination of geometrical variables

Minimum Gain Mask Maximum Gain Mask

Optimization Methodology

• First step: Design of Experiments (DOE) in modeFRONTIER and sensitivity analysis to reduce the variables from 100 to 25 variables

• Second step: Optimization phase performed by Genetic Algorithm and Game Theory on the reduced variables space

DOE – Sensitivity Analysis

• Uniform Latin Hypercube DOE approach allow to generate a set of uncorrelated designs in input such to avoid linear correlation between them.

• Sensitivity Analysis has been performed in order to better understand I/O correlations, and to reduce the optimization problem dimension

Optimization strategy

• A first screening of design space has been implemented combining the current DOE with MOGT (Game Theory algorithm)

• Starting from the Pareto Frontier of MOGT step, MOGAII (Multi –objective Genetic Algorithm) has been applied to extend the set of optimum solutions

• A good compromise of the most important requirements (Gain and XPD) has been found

Discussion of results 1/2

• Comparing the results between optimized design and original design the following improvements have been achieved: • Improvement of the gain at ±62°

(6.6 dB vs. 6 dB);

• The pattern widening on the enlarged coverage has been achieved so that the antenna can be used for the lower satellite position with 70 degs field of view;

• The XPD improve of 7 dB passing from 5 dB to 12 dB;

BASELINE

OPTIMIZED

Discussion of results 2/2

• The amplitude ripple in band of interest has been satisfied with 1 dB (peak to peak) variation. The new solution presents a equalized copular pattern over a large frequency bandwidth;

• The phase ripple in band of interest is satisfied with 3 deg (peak to peak) variation;

Conclusion

• The presentation highlights the powerful capabilities of modeFRONTIER couple with CST MICROWAVE STUDIO in the antenna development process (Optimization)

• In modeFRONTIER any numerical model can be integrated in the process, and a large variety of multi-objective optimization algorithms and pre/post-processing tools are available

Thank you!

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e-mail: engineering@esteco.com

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