Stefan Jakobsson, Muhammad Muhammad SaifSaif

UlUl

HasnainHasnain, Robert , Robert RundqvistRundqvist,, Fredrik Edelvik, Michael Patriksson

Mattias Ljungqvist Volvo Cars, Johan Wallesten

and Dimitri Lortet Volvo Powertrain

GMMC Scientific Board, January 9 2008

Combustion Engine Optimization

Background

The aim is to develop best practice for Diesel engine optimization that will lead to better performance and lower NOx and soot emissionsIntegrate commercial CFD solver STAR-CD with in-house multi-objective optimization algorithmsProject partners are FCC, Volvo Cars and Volvo Powertrain

The objective is to improve IMEP and reduce NOx and soot emissions. The three conflicting objectives makes it a Multiobjective Optimization problemThe design variables that are choosen for optimization are:

Span AngleNozzle Hole DiameterTip ProtrusionSwirl NumberInjection timing

Problem definition

Diesel engine combustion simulation

Transient simulation on a 72 degree sector of an engine cylinderMoving mesh with approx. 0.2 million cellsLong simulation times (~20 h) on 4 processors using STAR-CD

Multi-objective

optimization

of Combustion

engines

Long simulation times (~20 h)Minimize fuel consumption while keeping emissions on low levelMulti-criteria optimization. The trade-off between soot and NOx is referred to as the Diesel dilemma one value is reduced only at the expense of otherBoth cheap and expensive constraints. Cheap constraints include upper and lower bounds on the input parameters and geometrical constraints. Expensive constraints can be maximal levels of fuel consumption and emissionsSeveral load cases of engine

Thermal NOx are consideredNOx is evaluated by Extended Zeldovich mechanism. High temperature dependent NOx

Soot in Diesel engines are usually formed by incomplete combustion

Mauss soot Model is usedIMEP is a measure of the work output from the engine

Calculated by integrating the pressure on the piston over the compression/expansion cycle

Objectives NOx, Soot & IMEP

Desirable

properties

of the optimization

algorithm

Should treat simulation software as black-box since e.g. no gradient information is availableUse surrogate models to approximate all objectivesA good balance between local and global searchNot too sensitive to numerical errorsNot overemphasize boundary regions Not cluster points in minimasPossible to run several simulations in parallelPossibility to run several load casesEfficient handling of expensive constraints

Interpolation / Approximation with Radial Basis FunctionsRBF expansions approximates a set of numerically evaluated design data points

In order to optimize an expensive black box function it is helpful to create a surrogate model or response surface and utilize it in order to find new evaluation pointsThe surrogate models are based on RBF approximations and possibly combined with transformation of the objectivesEffect of different design variables on objectives can also be studied with the help of these response surfaces

The quality

function

The qualSolve

algorithm

1.

Choose and evaluate initial points2.

Construct surrogate model. Find Pareto front for surrogate model. Create distance function (input to ω)

3.

Construct and maximize quality function4.

Evaluate new point

5.

Go to step 2 unless maximal number of function evaluations reached

Response surfaces for IMEP

Cross Validation

Reference: J. S. U. Hjort, ”Computer Intensive Statistical

Methods”

Resulting Pareto fronts using qualSolve

Resulting Pareto fronts using qualSolve

Future

research

Improve the quality function concept and study similar variantsFind reasonable convergence criteria for the algorithmDevelop the approximation method for RBF further: Currently we use cross validation to find a free parameter for the approximation. Some problems show up when the density of evaluated points is low and in combination with transformations of the objective functions.Tune the algorithm so that it focuses on the more on the interesting areas of the Pareto front. We have experienced that the low NOx region of the Pareto front is overemphasized in our optimization

Future

research cont’d

Utilize cross validation to compare different surrogate models and transformations. Might also be used to investigate different scalings of design parametersCurrently the distance to the Pareto front is the measure used in the quality function. Many other alternatives for measuring the relevance exist

Project continuation

Develop a user-friendly software demonstrator that includesRoutines for multi-objective optimization with radial basis functionsAn API to facilitate coupling to commercial CFD software such as STAR-CDVisualization of Pareto optimal solutions

ConclusionsThe significance of span angle and nozzle hole diameter is quite evident from response surfacesA significant improvement in objectives can be predictedThe Pareto front gives a flexibility in design selection and trade-offs can be seenEasy to integrate different simulator with the optimization algorithm2 Scientific publications under preparation

Stefan Jakobsson, Fredrik Edelvik, Björn Andersson, Michael Patriksson

Prof. Anders Derneby, Anders Stjerman, Martin Johansson, Antenna

Research Center Ericsson AB

Antenna Optimization

Antenna

design

The antenna is one of the mostcritical components in a wireless communication networkSome important characteritstics:

Resonance frequencyRadiation patternGainBandwidthEfficiencyImpedance

Placement of antenna

GMMC -

Multi-objective

Antenna

Optimization

Develop new efficient optimization algorithms and a software demonstrator for the design of industrial antennasystemsObjective: Study communication performance possibilitiesand limitations for multiple antennas within a limited area, such as a handheld terminalPartner Ericsson AB – Antenna Research Centre in Göteborg

MIMO systems

Capacity may be increased in mobile communication networks with the introduction of MIMO transmission schemesMultiple antennas will be introduced both at the base-station and the terminal sides (Multiple Input Multiple Output)

Multi-beam base-station antenna Multi-path propagation environment

Multi-antenna terminal

Initial requirements

Frequency bands: 880-960 MHz and/or 2500-2690 MHzGround-plane size: 40 mm x 90 mmNumber of antennas: one or twoVSWR: ≤2 (50 ohm)

Output parameters:-

VSWR bandwidth

-

Antenna

coupling-

Radiation

efficiency

-

Pattern

correlation-

Scattering

parameter correlation

-

Pattern

orthogonality

Antenna

typeThe PIFA (Planar Inverted F-Antenna) is a commonly used antenna element consisting of a dielectric slab (or air), one or two antenna fingers, a coaxial feed, and a shorting pin connected to the ground-plane.

Long finger

Feed point Ground-plane Shorting

2L

1L

2W

1W

h tL

tWShort finger

fL fD

Methodology

For the electromagnetic simulations the software package efield will be used

Software is partly developed at FCC and the source code is availableSoftware includes state-of-the-art solvers in time domain and frequency domain

Further develop qualSolve and couple to efieldInvestigate optimization algorithms that do not treat simulation software as black-box

Example: Minimization of return loss of a patch antenna at two frequencies (2600 and 3000 MHz).

Object Description Constraint (mm)

εtol Tolerance Constant

(2)

Lg Size

of ground

plane Constant

(60)

L Length

of patch 2εtol

· L · 44 (30)

w Width

of patch 2εtol

· w · 44 (18)

x Position of feed 0 · x · L/2 - εtol

(4)

t Slab

height Constant

(3)

εr Relative permittivity Constant

(2.2)

Optimization

problem

Results

–

Pareto

front

Decision

making: Return

loss as a function

of frequency

Design variables at Pareto

front

Optimization

algorithms

Black-box algorithms have the advantage that they can be used for very different problems, such as combustion engine and antenna optimizationBut, since we have full access to the simulation software a tighter coupling between simulation and optimization should be investigatedThis includes gradient-based methods and methods for which the grid resolution is controlled by the optimization algorithm

Gradient based

optimization

algorithms

By using the adjoint equation the derivates with respect to the design parameters can be efficiently and accurately computedFor Maxwell’s equations the differential operator is self-adjoint. PEC boundary conditions are self-adjoint, other BCs need modificationSource code is available which makes gradient based optimizationpossibleApply optimization methods such as e.g. SQP or Methods of MovingAsymptots that utilize gradient informationAnother option is to use RBF with generalized interpolation that utilize gradient information

Tight coupling

of optimization

and simulation

Grid resolution is controlled by the optimizerMost simulations are performed on a coarse model to find interesting regionAdaptive meshingClose collaboration with our partner Fraunhofer-ITWM who has much experience in such algorithms for various applications

Summary

The knowledge and software built up in the earlier optimization projects combined with the strong CEM tradition at FCC constitute a strong platform for performing research on antenna optimization Virtual prototyping based on optimization with simulation assists antenna engineers in the design processLong-term goal is to develop a tool-box for CEM-based optimal design