Trawl-Door Shape Optimization by Space-Mapping- Corrected CFD Models and Kriging Surrogates

Trawl-doors are a large part of the fluid flow resistance of trawlers fishing gear and has considerable effect on the fuel consumption. A key factor in reducing that consumption is by implementing computational models in the design process. This study presents a robust two dimensional computational fluid dynamics models that is able to capture the nonlinear flow past multi-element hydrofoils. Efficient optimization algorithms are applied to the design of trawl-doors using problem formulation that captures true characteristics of the design space where lift-to-drag ratio is maximized. Four design variables are used in the optimization process to control the fluid flow angle of attack, as well as position and orientation of a leading-edge slat. The optimization process involves both multi-point space mapping, and mixed modeling techniques that utilize space mapping to create a physics-based surrogate model. The results demonstrate that lift-to-drag maximization is more appropriate than lift-constraint drag minimization in this case and that local search using multi-point space mapping can yield satisfactory design at low computational cost. By using global search with mixed modeling a solution with higher quality is obtained, but at a higher computational cost than local search.

[1]  John W. Bandler,et al.  Accurate modeling of microwave devices using kriging‐corrected space mapping surrogates , 2012 .

[2]  Slawomir Koziel,et al.  Optimal shape design of multi-element trawl-doors using local surrogate models , 2015, J. Comput. Sci..

[3]  D. Mavriplis UNSTRUCTURED GRID TECHNIQUES , 1997 .

[4]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[5]  Elvar Hermannsson Hydrodynamic Shape Optimization of Trawl Doors with Three-Dimensional Computational Fluid Dynamics Models and Local Surrogates , 2014 .

[6]  Slawomir Koziel,et al.  Reliable design optimization of microwave structures using multipoint‐response‐correction space mapping and trust regions , 2011 .

[7]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[8]  Slawomir Koziel,et al.  Knowledge-Based Airfoil Shape Optimization Using Space Mapping , 2012 .

[9]  Slawomir Koziel,et al.  Surrogate-Based Modeling and Optimization , 2013 .

[10]  S. Koziel,et al.  Microwave Device Modeling Using Space-Mapping and Radial Basis Functions , 2007, 2007 IEEE/MTT-S International Microwave Symposium.

[11]  Slawomir Koziel,et al.  Variable-resolution shape optimisation: low-fidelity model selection and scalability , 2015, Int. J. Math. Model. Numer. Optimisation.

[12]  S. Koziel,et al.  Theoretical Justification of Space-Mapping-Based Modeling Utilizing a Database and On-Demand Parameter Extraction , 2006, IEEE Transactions on Microwave Theory and Techniques.

[13]  Qi-Jun Zhang,et al.  Neural Network Inverse Modeling and Applications to Microwave Filter Design , 2008, IEEE Transactions on Microwave Theory and Techniques.

[14]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[15]  E. Jonsson Aerodynamic Optimization by Variable-Resolution Modeling and Space Mapping , 2012 .

[16]  Farrokh Mistree,et al.  Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization , 2001 .