Knowledge-Based Airfoil Shape Optimization Using Space Mapping

2012 ◽  
Author(s):  
Slawomir Koziel ◽  
Leifur Leifsson
Keyword(s):  
Shape Optimization ◽  
Space Mapping ◽  
Knowledge Based

Aerodynamic Shape Optimization by Space Mapping

2013 ◽  
pp. 213-245 ◽  
Author(s):  
Leifur Leifsson ◽  
Slawomir Koziel ◽  
Eirikur Jonsson ◽  
Stanislav Ogurtsov
Keyword(s):  
Shape Optimization ◽  
Space Mapping ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape

scholarly journals Shape Optimization of Trawl-doors Using Variable-fidelity Models and Space Mapping

2015 ◽  
Vol 51 ◽  
pp. 905-913 ◽  
Author(s):  
Ingi M. Jonsson ◽  
Leifur Leifsson ◽  
Slawomir Koziel ◽  
Yonatan A. Tesfahunegn ◽  
Adrian Bekasiewicz
Keyword(s):  
Shape Optimization ◽  
Space Mapping ◽  
Variable Fidelity

scholarly journals Blade shape optimization of an aircraft propeller using space mapping surrogates

2019 ◽  
Vol 11 (7) ◽  
pp. 168781401986507
Author(s):  
Usama T Toman ◽  
Abdel-Karim SO Hassan ◽  
Farouk M Owis ◽  
Ahmed SA Mohamed
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shape Optimization ◽  
Stokes Equations ◽  
Optimization Method ◽  
Space Mapping ◽  
High Fidelity ◽  
Blade Shape ◽  
Computational Fluid Dynamics Simulations ◽  
Dynamics Simulations

Propeller performance greatly influences the overall efficiency of the turboprop engines. The aim of this study is to perform a propeller blade shape optimization for maximum aerodynamic efficiency with a minimal number of high-fidelity model evaluations. A physics-based surrogate approach exploiting space mapping is employed for the design process. A space mapping algorithm is utilized, for the first time in the field of propeller design, to link two of the most common propeller analysis models: the classical blade-element momentum theory to be the coarse model; and the high-fidelity computational fluid dynamics tool as the fine model. The numerical computational fluid dynamics simulations are performed using the finite-volume discretization of the Reynolds-averaged Navier–Stokes equations on an adaptive unstructured grid. The optimum design is obtained after few iterations with only 56 computationally expensive computational fluid dynamics simulations. Furthermore, an optimization method based on design of experiments and kriging response surface is used to validate the results and compare the computational efficiency of the two techniques. The results show that space mapping is more computationally efficient.

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