To assess the performance of electrification in an aircraft, multiphysics modeling becomes a good choice for the design of more-electric equipment. However, the high computational cost and huge design space of this complex model lead to difficulties in the optimal design of the electrical power system, thus model simplification is mandatory. For this purpose, this article first proposes a novel model simplification approach based on data mining, and the design of a small electrical power generation system is investigated to demonstrate it. According to the formulated multiphysics model of the system, this article uses the optimal Latin Hypercube-based design of experiment to generate data for the analysis. Based on the generated data, a fusion algorithm integrating multiple feature selection methods is presented to facilitate the dimensionality reduction of the problem's design space. Also, machine learning algorithms are applied to the surrogate model establishment, allowing the reduction of computational time. The investigation of various optimization routines with various multiobjective genetic algorithms shows that the proposed practices improve the system-level optimization efficiency with low computational complexity, ease of search, and high accuracy, which is competitive compared with state-of-the-arts.

中文翻译：

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基于数据挖掘的发电系统模型简化和优化


为了评估飞机的电气化性能，多物理场建模成为设计多电气化设备的不错选择。然而，这种复杂模型的高计算成本和巨大的设计空间导致电力系统的优化设计困难，因此必须进行模型简化。为此，本文首先提出了一种基于数据挖掘的新型模型简化方法，并通过研究小型发电系统的设计来证明它。根据系统的多物理场模型，本文采用基于拉丁超立方体的最优实验设计来生成分析数据。基于生成的数据，提出了一种集成多种特征选择方法的融合算法，以促进问题设计空间的降维。此外，机器学习算法应用于代理模型的建立，可以减少计算时间。对各种多目标遗传算法的各种优化例程的研究表明，所提出的实践提高了系统级优化效率，计算复杂度低，易于搜索，精度高，与现有技术相比具有竞争力。