The purpose of this paper is to present a novel turbine balancing using Ant Colony Optimization method. Results are compared against well known optimization methods available at open literature. With the new approach, turbine blade set can be separated in to two blade sets as heavy and light blades. This approach makes possible the application of Ant Colony Optimization methodology. ACO methodology is compared with Steepest Descent and Exchange Heuristic methods using nine different initial blade placements. And results are presented. Performance of the three evaluated methods is affected by the initial blade placement. Exchange Heuristics method was quick and provided good results in most of the cases. Ant colony optimization was able find better results than the Steepest Descent method. The approach of separating blades into two sets decreased the solution time of Steepest Descent algorithm. Ant colony optimization method can be used for turbine blade assembly and balancing for aircraft gas turbine applications. This approach is used for the first time in this area and not seen at the open literature.

中文翻译：

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基于蚁群优化的涡轮叶片组件转子平衡在航空发动机应用中的应用

本文的目的是提出一种使用蚁群优化方法的新型涡轮机平衡。将结果与公开文献中提供的众所周知的优化方法进行比较。使用新方法，涡轮机叶片组可以分为重型和轻质叶片两个叶片组。这种方法使蚁群优化方法学的应用成为可能。使用9种不同的初始叶片位置，将ACO方法与最速下降法和交换启发法进行了比较。并给出了结果。三种评估方法的性能受初始刀片放置的影响。Exchange Heuristics方法快速且在大多数情况下提供了良好的结果。与最速下降法相比，蚁群优化能够找到更好的结果。将叶片分成两组的方法减少了“最速下降”算法的求解时间。蚁群优化方法可用于涡轮叶片的组装和飞机燃气轮机应用的平衡。这种方法是该领域中第一次使用，公开文献中未见。