Multi-objective optimization coupling modeFRONTIER and CST ...

Multi-objective optimization coupling modeFRONTIER and CST MICROWAVE STUDIO A. Clarich, Z. Wen* ESTECO, Trieste, (Italy)

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 modeFRONTIER v. 1

modeFRONTIER v. 2

modeFRONTIER v. 3

modeFRONTIER v. 4

1999

2001

2003

2008

Esteco establishment in Europe

Expansion to Asian markets

2004

Opening of ESTECO North America

Automotive Research Inst. and Uni Electronics Aerospace Energy Materials Appliances Defence and Space

modeFRONTIER v. 5

2010

2013

The Concept behind modeFRONTIER

Traditional Design

Optimization Approach Parametric models

Initial Configuration

Design Objectives and Constraints

Simulate

Evaluate Results

Modify Configuration

No OK?

Yes Accept

Optimal trade-off Solution

The Concept behind modeFRONTIER

Scheduler: (DOE, optimization algorithms,..)

Input Variables: Entities defining the design space.

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

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

Design of Experiments

Optimization Algorithms

Robust Design

Response Surface Tool

Statistical Analysis

Multivariate Analysis

Decision Making

CST interface in modeFRONTIER

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 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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Application example: Maximizing bandwidth for a wideband antenna

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

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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

Results 1/2 Original design

S11 peak to -4.3 dB (above limit!)

Bandwidth = 2.13 GHz

Final design S11 peak below limit

Bandwidth = 2.43 GHz

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 –

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Two-step optimization approach

Results 2/2 Engineering design process captured: 

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



Relationship between input parameters and results clarified

Estimated time to carry out a similar project Engineering time Defining modeFRONTIER workflow

1h

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

10 h 30 min

Second optimization step (20 simulations, 45 minutes each): Evaluation of results Total Total time for project

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CPU time

15 h 1h 2.5 h 1,5 days

25 h

Application example: Multi-objective optimization of an isoflux Antenna

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

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 10

D2

8

D…

6

Dn

4 2

DRn

Amplitude (dBi)

0 -2

Rn

DR….

-4

R…

DR2

-6 -8

DR1

-10

R1

MinimumGainMask Minimum Gain Mask Maximum Gain Mask MaximumGainMask

-12 -14 -16

R2

A1 A2 A…

-18

An

-20 -90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

70

80

90

Theta (degs)

•

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

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The Antenna Structure is described by a set of geometrical parameters

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

Optimization Methodology

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

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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.

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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)

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

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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: BASELINE

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Improvement of the gain at ±62° (6.6 dB vs. 6 dB);

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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;

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

OPTIMIZED

Discussion of results 2/2

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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;

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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! ESTECO Area Science Park Padriciano, 99 34149 Trieste - Italy e-mail: [email protected]

www.esteco.com