Surrogate based modeling and optimization koziel slawomir leifsson leifur
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The chapters are self-contained with basic concepts and formulations along with applications and examples. We solve the problems using a computationally efficient physics-based surrogate approach exploiting space mapping. The low-fidelity model is based on the same governing fluid flow equations as the high-fidelity one, but uses coarser discretization and relaxed convergence criteria. Aerodynamic noise is an important criterion in the design of aircraft and wind turbines. Examples demonstrate that the optimized airfoil design can be obtained at a low computational cost. These and other problems can be alleviated by the development and employment of so-called surrogates that reliably represent the expensive, simulation-based model of the system or device of interest bu.

Comparison of the standard and the proposed approach is carried out in the context of aerodynamic design of a transonic airfoil using a multi-level optimization algorithm. The performance of the optimization algorithm strongly depends on the quality of the low-fidelity models. In the first stage, a Cauchy-based approximation model is constructed using coarsely discretized full-wave simulation. The number of high fidelity evaluations increases from 4 to 9, whereas the number of low fidelity evaluations increases more rapidly, from 600 to 2000. It is demonstrated that the proposed technique allows us to obtain satisfactory design at a computational cost of few full-wave simulations of the structure in question.

This approach allows us to obtain the final design at low computational costs and at a high-fidelity level of structure description. An alternative approach is to use physics-based models, where the surrogate is created by correcting an auxiliary low-fidelity model, e. We successfully illustrate our approach through a number of antenna modeling examples. We also present tuning space mapping, a powerful methodology, but one that requires extra engineering knowledge of the problem under investigation. A discussion of the advantages and disadvantages of the presented methods is also given.

Microwave Opt Technol Lett 52: 1807â€”1810, 2010; Published online in Wiley InterScience www. We illustrate yield analysis of a second-order tapped-line microstrip filter using our model. In this paper, we describe the details of the design optimization procedure of the contraction, which is a key to getting a high quality flow in the experimental section. Operation of the approach is illustrated with design optimization of a 7 by 7 microstrip antenna array. In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges. Â© 2013 by the American Institute of Aeronautics and Astronautics, Inc. Designs satisfying performance requirements are typically obtained at the cost of just a few evaluations of the high-fidelity model.

High flow quality is elemental for both research projects. Our approach is compared to two conventional methods of generating a family of variable-fidelity models. In all cases, the optimal design is obtained at a low computational cost corresponding to a few high-fidelity simulations of the structure. Our technique allows for simultaneous control of the radiation pattern and the reflection coefficients of the array. In our approach, the analysis of the entire response of the structure at hand e.

The final design is refined using a polynomial-based approximation model constructed from the coarse-discretization simulation data. Computational optimization is becoming a standard tool that is widely used in engineering design and industrial applications. A computationally efficient method for design optimization of antennas is discussed. Unfortunately, accurate simulations are often computationally very expensive with evaluation times as long as hours or even days per design, making design automation using conventional methods impractical. The results show that a satisfactory design can be obtained at the cost of a few iterations of the algorithm. Simulation and Modeling Methodologies, Technologies and Appl. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.

Â© 2010 Wiley Periodicals, Inc. All of them use the same prediction-correction scheme, however, they differ in the way the low-fidelity model information it utilized to construct the surrogate model. A technique for simulation-driven design of excitation tapers for planar antenna arrays is presented. On the one hand, we review recent adjoint methodologies, on the other we focus on space mapping implementations, including the original, aggressive, implicit, output, tuning, and related developments. We show that our approach obtains a modeling accuracy comparable to that provided by direct Cauchy approximation of the high-fidelity electromagnetic simulation data.

We present application examples of design optimization and statistical analysis of several microwave filters. The chapters are self-contained with basic concepts and formulations along with applications and examples. The found optimum is further refined using a response surface approximation model constructed from the coarse-discretization simulation data. Design and optimization of trawl-doors using computational models are a key factor in minimizing the fuel consumption. Convergence conditions for the chosen subclass of algorithms are discussed and explained using a synthetic example, the so-called generalized cheese-cutting problem. 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 sc. A simple yet efficient method for design optimization of omnidirectional antennas is presented.

Still, it is well known that space mapping algorithms may suffer from convergence issues, which are consequences of certain fundamental features. Copyright Â© 2012 by the American Institute of Aeronautics and Astronautics, Inc. Computational optimization forms an integrated part of modern computational science. These and other problems can be alleviated by the development and employment of so-called surrogates that reliably represent the expensive, simulation-based model of the system or device of interest but they are much more reasonable and analytically tractable. We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.

In multi-fidelity shape optimization, a computationally cheap surrogate model is used in lieu of the expensive, but accurate, high-fidelity model in an iterative fashion. Whether a coarse model is sufficiently similar to the fine model may be critical to the performance of the space mapping optimization algorithm and a poor coarse model may result in lack of convergence. The optimized designs are obtained at lower computational cost than that of two comparators. Based on contemporary research, it formulates multi-objective design tasks, highlights related challenges in the context of antenna design, and discusses solution approaches. Two examples are provided: a wideband microstrip antenna and a double ring antenna. We demonstrate, using two examples, that significant reduction of the modeling error is possible even though the location of the additional training points is constrained to the vicinity of the original training locations.