...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Mechanical Design >Multidimensional Clustering Interpretation and Its Application to Optimization of Coolant Passages of a Turbine Blade
【24h】

Multidimensional Clustering Interpretation and Its Application to Optimization of Coolant Passages of a Turbine Blade

机译：多维聚类解释及其在涡轮叶片冷却水道优化中的应用

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

A data-clustering method can be a useful tool for engineering design that is based on numerical optimization. The clustering method is an effective way of producing representative designs, or clusters, from a large set of potential designs. The results presented here focus on the application of clustering to single-objective optimization results. In the case of single-objective optimization, the method is used to determine the clusters in a set of quasi-optimal feasible solutions generated by an optimizer. A data-clustering procedure based on an evolutionary method is briefly described. The number of clusters is determined automatically and need not be known a priori. The method is demonstrated by application to the results of a turbine blade coolant passage shape-optimization problem. The solutions are transformed to a lower-dimensional space for better understanding of their variance and character. Engineering information, such as the shapes and locations of the internal passages, is supported by the visualization of clustered solutions. The clustering, transformation, and visualization methods presented in this study might be applicable to the increasing interpretation demands of design optimization.
机译：数据聚类方法可以是基于数值优化的工程设计的有用工具。聚类方法是从大量潜在设计中生成代表性设计或聚类的有效方法。这里介绍的结果集中于聚类在单目标优化结果中的应用。在单目标优化的情况下，该方法用于确定优化器生成的一组拟最佳可行解中的聚类。简要描述了基于进化方法的数据聚类过程。聚类的数量是自动确定的，无需事先知道。通过将其应用于涡轮叶片冷却剂通道形状优化问题的结果，证明了该方法。将解决方案转换到较低维的空间，以更好地了解其方差和特征。群集解决方案的可视化支持工程信息，例如内部通道的形状和位置。本研究中提出的聚类，转换和可视化方法可能适用于设计优化不断增长的解释需求。

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