Simulation-Driven Modeling and Optimization

1. Front Matter

   Pages i-viii

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2. Numerical Aspects of Model Order Reduction for Gas Transportation Networks

   • Sara Grundel, Nils Hornung, Sarah Roggendorf

   Pages 1-28

3. Parameter Studies for Energy Networks with Examples from Gas Transport

   • Tanja Clees

   Pages 29-54

4. Fast Multi-Objective Aerodynamic Optimization Using Space-Mapping-Corrected Multi-Fidelity Models and Kriging Interpolation

   • Leifur Leifsson, Slawomir Koziel, Yonatan Tesfahunegn, Adrian Bekasiewicz

   Pages 55-73

5. Assessment of Inverse and Direct Methods for Airfoil and Wing Design

   • Mengmeng Zhang, Arthur William Rizzi

   Pages 75-109

6. Performance Optimization of EBG-Based Common Mode Filters for Signal Integrity Applications

   • Carlo Olivieri, Francesco de Paulis, Antonio Orlandi, Slawomir Koziel

   Pages 111-133

7. Unattended Design of Wideband Planar Filters Using a Two-Step Aggressive Space Mapping (ASM) Optimization Algorithm

   • Marc Sans, Jordi Selga, Ana Rodríguez, Paris Vélez, Vicente E. Boria, Jordi Bonache et al.

   Pages 135-159

8. Two-Stage Gaussian Process Modeling of Microwave Structures for Design Optimization

   • J. Pieter Jacobs, Slawomir Koziel

   Pages 161-184

9. Efficient Reconfigurable Microstrip Patch Antenna Modeling Exploiting Knowledge Based Artificial Neural Networks

   • Murat Simsek, Ashrf Aoad

   Pages 185-206

10. Expedited Simulation-Driven Multi-Objective Design Optimization of Quasi-Isotropic Dielectric Resonator Antenna

    • Adrian Bekasiewicz, Slawomir Koziel, Wlodzimierz Zieniutycz, Leifur Leifsson

    Pages 207-231

11. Optimal Design of Photonic Crystal Nanostructures

    • Abdel-Karim S. O. Hassan, Nadia H. Rafat, Ahmed S. A. Mohamed

    Pages 233-260

12. Design Optimization of LNAs and Reflectarray Antennas Using the Full-Wave Simulation-Based Artificial Intelligence Models with the Novel Metaheuristic Algorithms

    • Filiz Güneş, Salih Demirel, Selahattin Nesil

    Pages 261-298

13. Stochastic Decision-Making in Waste Management Using a Firefly Algorithm-Driven Simulation-Optimization Approach for Generating Alternatives

    • Raha Imanirad, Xin-She Yang, Julian Scott Yeomans

    Pages 299-323

14. Linear and Nonlinear System Identification Using Evolutionary Optimisation

    • K. Worden, I. Antoniadou, O. D. Tiboaca, G. Manson, R. J. Barthorpe

    Pages 325-345

15. A Surrogate-Model-Assisted Evolutionary Algorithm for Computationally Expensive Design Optimization Problems with Inequality Constraints

    • Bo Liu, Qingfu Zhang, Georges Gielen

    Pages 347-370

16. Sobol Indices for Dimension Adaptivity in Sparse Grids

    • Richard P. Dwight, Stijn G. L. Desmedt, Pejman Shoeibi Omrani

    Pages 371-395

17. Back Matter

    Pages 397-404

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This edited volume is devoted to the now-ubiquitous use of computational models across most disciplines of engineering and science, led by a trio of world-renowned researchers in the field. Focused on recent advances of modeling and optimization techniques aimed at handling computationally-expensive engineering problems involving simulation models, this book will be an invaluable resource for specialists (engineers, researchers, graduate students) working in areas as diverse as electrical engineering, mechanical and structural engineering, civil engineering, industrial engineering, hydrodynamics, aerospace engineering, microwave and antenna engineering, ocean science and climate modeling, and the automotive industry, where design processes are heavily based on CPU-heavy computer simulations. Various techniques, such as knowledge-based optimization, adjoint sensitivity techniques, and fast replacement models (to name just a few) are explored in-depth along withan array of the latest techniques to optimize the efficiency of the simulation-driven design process.

High-fidelity simulation models allow for accurate evaluations of the devices and systems, which is critical in the design process, especially to avoid costly prototyping stages. Despite this and other advantages, the use of simulation tools in the design process is quite challenging due to associated high computational cost. The steady increase of available computational resources does not always translate into the shortening of the design cycle because of the growing demand for higher accuracy and necessity to simulate larger and more complex systems. For this reason, automated simulation-driven design—while highly desirable—is difficult when using conventional numerical optimization routines which normally require a large number of system simulations, each one already expensive.

• computational modeling
• computer simulation
• engineering optimization
• numerical optimization
• simulating complex systems
• computationally expensive simulation
• iterative simulation design processes
• surrogate-based modelling
• simulation-driven design optimization
• metaheuristic optimization
• efficient optimization algorithms
• aerodynamic optimization
• gas transport network design
• antenna design
• microwave structure design
• crystal nanostructures
• algorithms
• modeling
• optimization

• Engin. Optimization & Modeling Cent, Reykjavik University, Reykjavik, Iceland

  Slawomir Koziel

• College of Engineering, Iowa State University, Ames, USA

  Leifur Leifsson

• School of Science and Technology, Middlesex University, London, United Kingdom

  Xin-She Yang

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