Aircraft Air Inlet Design Optimization via Surrogate-Assisted Evolutionary Computation

被引：6

作者：

Lombardil, Andre ^{[1
]}

Ferrari, Denise ^{[2
]}

Santos, Luis ^{[3
]}

机构：

[1] Embraer, Sao Paulo, SP, Brazil

[2] Inst Tecnol Aeronaut, Sao Paulo, SP, Brazil

[3] Univ Sao Paulo, Sao Paulo, SP, Brazil

来源：

EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT II | 2015年 / 9019卷

关键词：

Design optimization; Genetic algorithm; Surrogate modeling; Air inlet; CFD; Aerodynamics;

D O I：

10.1007/978-3-319-15892-1_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In aviation, the performance impact of auxiliary air inlets used for system ventilation is significant. The flow phenomena and consequently the numerical model, is highly non-linear, leading to a compromise between pressure recovery and drag for a given mass flow condition. This work follows a step-by-step approach which highlights the important issues related to solving such complex optimization problem, using surrogate methods coupled to evolutionary algorithms. Its conclusions can be used as a guideline to similar industrial applications.

引用

页码：313 / 327

页数：15

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