Research in Engineering Optimization & Modeling Center at Reykjavik University Slawomir Koziel Engineering Optimization & Modeling Center School of Science and Engineering Reykjavik University presented at Reykjavik University, March 17, 2011

Engineering Optimization & Modeling Center (EOMC) EOMC is a research group within the School of Science and Engineering, Reykjavik University Members: Slawomir KozielLeifur Leifsson Stanislav Ogurtsov Website:

EOMC: Background and Motivation Contemporary engineering is more and more dependent on computer simulation Increasing complexity of structures and systems and higher demand for accuracy make engineering design challenging due to: Lack of design applicable theoretical models High computational cost of accurate simulation Simulation-driven design becomes a must for growing number of engineering fields

EOMC: Research Outline Research outline: EOMC develops efficient optimization and modeling techniques for computationally expensive real-world engineering design problems Application areas: Microwave/RF engineering Aerospace design Aeroacoustics Hydrodynamics Ocean science

EOMC: Research Outline Selected topics: Algorithms for rapid optimization of expensive objective functions Surrogate-based and knowledge-based techniques Tuning methodologies High-performance distributed computing Interfacing major microwave/RF CAD software packages Selected applications: Simulation-based design of RF/microwave components and circuits Development of component models for CAD/EDA software Inverse design in electromagnetic and aerodynamics Aerodynamic and hydrodynamic optimization Multidisciplinary design and optimization Optimization of ocean models

Simulation-Driven Microwave Design Using Surrogate Models Traditional design methods employing EM solver in an optimization loop are impractical due to: High computational cost of EM simulation Poor analytical properties of EM-based objective functions Lack of sensitivity information or sensitivity expensive to compute Surrogate-based design replaces direct optimization by iterative re-optimization and updating of the surrogate:

Example: Design of Hairpin Filter Using Space Mapping Fine model: Simulation time 17 hours per design! Coarse model: Equivalent circuit – simulation time less than 0.1s Surrogate: Coarse model composed with auxiliary transformation

Example: Design of Microstrip Hairpin Filter Traditional design methods fail for this example Space Mapping: Optimal design obtained after 5 EM simulations! Initial responses and design specifications Responses of the optimized filter

Invasive Methods: Simulation-Based Tuning Tuning SM constructs the surrogate by replacing designable sub-sections of the structure with suitable circuit-based components Example: Microstrip filter with co-calibrated ports and its tuning model

Example: Box-Section Chebyshev Microstrip Bandpass Filter Filter structure with places Tuning model: for inserting the tuning ports: Coarse (- - -) and fine model ( ) Fine model response after response at the initial design one (!) TSM iteration

Aerodynamic Design Optimization Design wing shapes which provide the right combination of lift and drag. CFD models are essential design tools. CFD models are accurate but can be extremely computationally heavy. A simulation of steady flow past a wing can take up to several days on a typical workstation. Shock High- speed Mach contours Mach contours and streamlines Low-speed

Example: Inverse design of 2D airfoil sections Objective: Match a given pressure distribution by design of airfoil shape. Fine model: RANS equations with Spalart-Allmaras turbulence model. Coarse model: Same as fine, but with coarse grid and relaxed convergence criteria. Surrogate-based optimization gives 92% in CPU cost compared to direct optimization. Initial Target Initial Optimized

Optimization of Ocean Models Task: Calibration of the ocean model (model response: concentration of various components, e.g., zooplankton, versus time) R f : high-resolution time-domain simulation (integration using small time steps) R c : low-resolution time-domain simulation (integration using larger time steps) The surrogate: response-corrected low-fidelity model

Optimization of Ocean Models Multiplicative correction is suitable to create a surrogate model in this case: High-fidelity model response at: u d – target; u 0 – initial solution u * – result of direct R f optimization u d – result of direct R c optimization u d – result of surrogate-based optimization Surrogate-based optimization gives 84% savings in computational cost compared to direct R f optimization (60 versus 375 high-fidelity model evaluations)

Surrogate-Based Modeling and Optimization Software SMF system: in-house GUI-based Matlab toolbox (over code lines) for surrogate-based optimization. SMF implements: Major SBO algorithms and modeling schemes Sockets for major EM/ circuit simulators Internal scripting language Distributed computing capabilities

EOMC: International Collaboration Collaborating institutions: McMaster University (Canada) Stanford University (USA) Technical University of Denmark ITESO (Mexico) Carleton University (Canada) Gent University (Belgium) Christian Albrechts University (Germany) University of Pretoria (South Africa) North Carolina State University (USA) National Physical Laboratory (UK) Gdansk University of Technology (Poland) Sonnet Software Ltd. (USA) Computer Simulation Technology AG (Germany)

Research Opportunities with EOMC EOMC offers a number of research projects for students pursuing Masters/PhD degrees in Electrical or Mechanical Engineering Example projects in Electrical Engineering: Surrogate-based optimization techniques for computer-aided microwave design Simulation-based tuning for microwave design optimization Design of antennas for personal communication using surrogate models Example projects in Mechanical Engineering: Efficient aerodynamic shape optimization using physics-based models Development of flapping-wing unmanned air vehicles All the projects involve numerical simulations using both EM solvers and circuit simulators (Electrical Engineering projects) and computational-fluid dynamics solvers (Mechanical Engineering projects), Matlab programming, as well as working with various optimization and modeling techniques