Airfoil aerodynamic optimization for a high-altitude long-endurance aircraft using multi-objective genetic-algorithms

Zetina, Abel Mata; Jeong, Shinkyu; Obayashi, Shigeru

doi:10.1109/cec.2013.6557845

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Choice of solver and number of parameters have strong impact on the choice of optimization algorithm. Airfoil optimization is a problem typically tackled by global search methods, most commonly genetic algorithms (Zetina et al , 2013) and Particle Swarm Optimization. Local search is not appropriate in this problem area, due to the nature of search space, with possible existence of multiple local minima.…”

Section: Introductionmentioning

confidence: 99%

“…Airfoil parametrization can be done in many ways, most commonly used are PARSEC (Zetina et al , 2013) and non-uniform rational B-spline curve (NURBS; Ribeiro et al , 2012), which define top and bottom curves with at least ten parameters. Another way of defining airfoil parametrically is class-shape-transformation (CST) method which builds the shape by summing basis functions (Kulfan, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Robust design and optimization of UAV empennage

Klimczyk

Goraj

2017

AEAT

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Purpose This paper aims to address the issue of designing aerodynamically robust empennage. Aircraft design optimization often narrowed to analysis of cruise conditions does not take into account other flight phases (manoeuvres). These, especially in unmanned air vehicle sector, can be significant part of the whole flight. Empennage is a part of the aircraft, with crucial function for manoeuvres. It is important to consider robustness for highest performance. Design/methodology/approach Methodology for robust wing design is presented. Surrogate modelling using kriging is used to reduce the optimization cost for high-fidelity aerodynamic calculations. Analysis of varying flight conditions, angle of attack, is made to assess robustness of design for particular mission. Two cases are compared: global optimization of 11 parameters and optimization divided into two consecutive sub-optimizations. Findings Surrogate modelling proves its usefulness for cutting computational time. Optimum design found by splitting problem into sub-optimizations finds better design at lower computational cost. Practical implications It is demonstrated, how surrogate modelling can be used for analysis of robustness, and why it is important to consider it. Intuitive split of wing design into airfoil and planform sub-optimizations brings promising savings in the optimization cost. Originality/value Methodology presented in this paper can be used in various optimization problems, especially those involving expensive computations and requiring top quality design.

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Section: Introductionmentioning

confidence: 99%