Combustion Engine Optimization

Transcription

1 Combustion Engine Optimization Stefan Jakobsson, Muhammad Saif Ul Hasnain,, Robert Rundqvist, Fredrik Edelvik, Michael Patriksson Mattias Ljungqvist Volvo Cars, Johan Wallesten and Dimitri Lortet Volvo Powertrain GMMC Scientific Board, January

2 Background The aim is to develop best practice for Diesel engine optimization that will lead to better performance and lower NOx and soot emissions Integrate commercial CFD solver STAR-CD with inhouse multi-objective optimization algorithms Project partners are FCC, Volvo Cars and Volvo Powertrain

3 Problem definition The objective is to improve IMEP and reduce NOx and soot emissions. The three conflicting objectives makes it a Multiobjective Optimization problem The design variables that are choosen for optimization are: Span Angle Nozzle Hole Diameter Tip Protrusion Swirl Number Injection timing

4 Diesel engine combustion simulation Transient simulation on a 72 degree sector of an engine cylinder Moving mesh with approx. 0.2 million cells Long simulation times (~20 h) on 4 processors using STAR- CD

5 Multi-objective optimization of Combustion engines Long simulation times (~20 h) Minimize fuel consumption while keeping emissions on low level Multi-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 other Both 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 emissions Several load cases of engine

6 Objectives NOx, Soot & IMEP Thermal NOx are considered NOx is evaluated by Extended Zeldovich mechanism. High temperature dependent NOx Soot in Diesel engines are usually formed by incomplete combustion Mauss soot Model is used IMEP is a measure of the work output from the engine Calculated by integrating the pressure on the piston over the compression/expansion cycle

7 Desirable properties of the optimization algorithm Should treat simulation software as black-box since e.g. no gradient information is available Use surrogate models to approximate all objectives A good balance between local and global search Not too sensitive to numerical errors Not overemphasize boundary regions Not cluster points in minimas Possible to run several simulations in parallel Possibility to run several load cases Efficient handling of expensive constraints

8 Interpolation / Approximation with Radial Basis Functions RBF 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 points The surrogate models are based on RBF approximations and possibly combined with transformation of the objectives Effect of different design variables on objectives can also be studied with the help of these response surfaces

9 The quality function

10 The qualsolve algorithm 1. Choose and evaluate initial points 2. Construct surrogate model. Find Pareto front for surrogate model. Create distance function (input to ω) 3. Construct and maximize quality function 4. Evaluate new point 5. Go to step 2 unless maximal number of function evaluations reached

11 Response surfaces for IMEP

12 Cross Validation Reference: J. S. U. Hjort, Computer Intensive Statistical Methods

13 Resulting Pareto fronts using qualsolve

14 Resulting Pareto fronts using qualsolve

15 Future research Improve the quality function concept and study similar variants Find reasonable convergence criteria for the algorithm Develop 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

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

17 Project continuation Develop a user-friendly software demonstrator that includes Routines for multi-objective optimization with radial basis functions An API to facilitate coupling to commercial CFD software such as STAR-CD Visualization of Pareto optimal solutions

18 Conclusions The significance of span angle and nozzle hole diameter is quite evident from response surfaces A significant improvement in objectives can be predicted The Pareto front gives a flexibility in design selection and trade-offs can be seen Easy to integrate different simulator with the optimization algorithm 2 Scientific publications under preparation

19 Antenna Optimization Stefan Jakobsson, Fredrik Edelvik, Björn Andersson, Michael Patriksson Prof. Anders Derneby, Anders Stjerman, Martin Johansson, Antenna Research Center Ericsson AB

20 Antenna design The antenna is one of the most critical components in a wireless communication network Some important characteritstics: Resonance frequency Radiation pattern Gain Bandwidth Efficiency Impedance Placement of antenna

21 GMMC - Multi-objective Antenna Optimization Develop new efficient optimization algorithms and a software demonstrator for the design of industrial antenna systems Objective: Study communication performance possibilities and limitations for multiple antennas within a limited area, such as a handheld terminal Partner Ericsson AB Antenna Research Centre in Göteborg

22 MIMO systems Capacity may be increased in mobile communication networks with the introduction of MIMO transmission schemes Multiple 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

23 Initial requirements Frequency bands: MHz and/or MHz Ground-plane size: 40 mm x 90 mm Number of antennas: one or two VSWR: 2 (50 ohm) Output parameters: - VSWR bandwidth - Antenna coupling - Radiation efficiency - Pattern correlation - Scattering parameter correlation - Pattern orthogonality

24 Antenna type The 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. L 2 Lf Df Short finger Long finger W 2 W 1 W t L 1 Shorting Feed point Ground-plane h Lt

25 Methodology For the electromagnetic simulations the software package efield will be used Software is partly developed at FCC and the source code is available Software includes state-of-the-art solvers in time domain and frequency domain Further develop qualsolve and couple to efield Investigate optimization algorithms that do not treat simulation software as black-box

26 Example: Minimization of return loss of a patch antenna at two frequencies (2600 and 3000 MHz). Object Description Constraint (mm) ε tol Tolerance Constant (2) L g Size of ground plane Constant (60) L Length of patch 2ε tol L 44(30) w Width of patch 2ε tol w 44(18) x Position of feed 0 x L/2-ε tol (4) t Slab height Constant (3) ε r Relative permittivity Constant (2.2)

27 Optimization problem

28 Results Pareto front

29 Decision making: Return loss as a function of frequency

30 Design variables at Pareto front

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

32 Gradient based optimization algorithms By using the adjoint equation the derivates with respect to the design parameters can be efficiently and accurately computed For Maxwell s equations the differential operator is self-adjoint. PEC boundary conditions are self-adjoint, other BCs need modification Source code is available which makes gradient based optimization possible Apply optimization methods such as e.g. SQP or Methods of Moving Asymptots that utilize gradient information Another option is to use RBF with generalized interpolation that utilize gradient information

33 Tight coupling of optimization and simulation Grid resolution is controlled by the optimizer Most simulations are performed on a coarse model to find interesting region Adaptive meshing Close collaboration with our partner Fraunhofer-ITWM who has much experience in such algorithms for various applications

34 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 process Long-term goal is to develop a tool-box for CEM-based optimal design