Evolutionary auto-design for aircraft engine cycle

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xudong Feng, Zhening Liu, Feng Wu, Handing Wang
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引用次数: 0

Abstract

Traditional engine cycle innovation is limited by human experiences, imagination, and currently available engine component performance expectations. Thus, the engine cycle innovation process is quite slow for the past 90 years. In this work, we propose a mixed variable multi-objective evolutionary optimization method for automatic engine cycle design. In the first, a simulation toolkit is developed for performance evaluation of potentially viable engine cycle solutions. Then, the engine cycle solutions are mixed encoded by the pins and the parameters of different engine components. The new engine cycle solutions are generated through the mutation operator. Finally, we construct two optimization objectives to drive the optimization process. Through the experimental research, new engine cycle solutions are discovered that exceed the performance of known turbojet and turbofan engines.

Abstract Image

航空发动机循环演化自动设计
传统的发动机循环创新受到人类经验、想象力和现有发动机部件性能预期的限制。因此,在过去的90年里,发动机循环创新的过程相当缓慢。本文提出了一种用于自动发动机循环设计的混合变量多目标进化优化方法。首先,开发了一个模拟工具包,用于评估潜在可行的发动机循环解决方案的性能。然后,利用引脚和不同发动机部件的参数对发动机循环解进行混合编码。通过变异算子生成新的发动机循环解。最后,我们构建了两个优化目标来驱动优化过程。通过实验研究,发现了超越已知涡喷发动机和涡扇发动机性能的新的发动机循环解决方案。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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