• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Aerospace science and technology >Data-driven constraint approach to ensure low-speed performance in transonic aerodynamic shape optimization
【24h】

Data-driven constraint approach to ensure low-speed performance in transonic aerodynamic shape optimization

机译：数据驱动的约束方法，以确保跨音速空气动力形状优化的低速性能

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Aerodynamic shape optimization based on computational fluid dynamics has the potential to become more widely used in the industry; however, the optimized shapes are often criticized for not being practical. Techniques seeking more practical results, such as multipoint optimization and geometric constraints, are either ineffective or too time consuming because they require trial and error. We propose a data-driven constraint for the aerodynamic shape optimization of aircraft wings that ensures the overall practicality of the optimum shape, with a focus on achieving a good low-speed performance. The constraint is formulated by extracting the relevant features from an airfoil database via modal analysis, correlation analysis, and Gaussian mixture models. The optimization results demonstrated that this approach addresses the thin leading edge issue that had plagued previous optimization results, and further analysis demonstrated that this data-driven constraint ensures good low-speed off-design performance without sacrificing the transonic on-design performance. The proposed approach can use other airfoil databases and can even be generalized to other shape optimization and engineering design problems. (C) 2019 Elsevier Masson SAS. All rights reserved.
机译：基于计算流体动力学的空气动力学形状优化具有在行业中更广泛使用的潜力;然而，优化的形状通常是批评而不是实用的。寻求更实际结果的技术，例如多点优化和几何约束，无论是无效还是太耗量，因为它们需要试验和错误。我们为飞机翼的空气动力形状优化提出了一种数据驱动的限制，从而确保了最佳形状的整体实用性，重点是实现良好的低速性能。通过模态分析，相关性分析和高斯混合模型从翼型数据库中提取相关特征来制定约束。优化结果表明，这种方法解决了困扰先前的优化结果的薄前沿问题，进一步的分析表明，这种数据驱动的约束确保了良好的低速偏移设计性能而不牺牲跨音速开启设计性能。所提出的方法可以使用其他翼型数据库，甚至可以推广到其他形状优化和工程设计问题。 （c）2019年Elsevier Masson SAS。版权所有。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号