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Computer Modeling of the Eddy Current Losses of Metal Fasteners in Rotor Slots of a Large Nuclear Steam Turbine Generator Based on Finite-Element Method and Deep Gaussian Process Regression

机译:基于有限元法和深层高斯过程回归的大型核汽轮发电机转子槽涡流损耗涡流损耗的计算机建模

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摘要

Eddy current analysis is a key issue for large turbine generators. The finite-element method (FEM) is a computational tool for obtaining the electromagnetic characteristics of electrical machines. In this article, we propose a computer model of the eddy current losses of metal fasteners in the rotor slots of a large turbine generator. The electromagnetic properties of the rotor fasteners and the outer diameter of the rotor are taken as the input, and the eddy current loss of the rotor fasteners is taken as the output. A prediction model is constructed using the FEM and deep learning. The analysis results show that compared with the independent finite-element analysis, this method reduces the design cycle time and improves the design efficiency for a large-capacity turbine generator. Compared with other machine learning models, the error is smaller and the accuracy is higher. This method provides a new way to accurately predict the eddy current loss of a generator under complex nonlinear conditions.
机译:涡流分析是大型涡轮发电机的关键问题。有限元方法(FEM)是用于获得电机电磁特性的计算工具。在本文中,我们提出了一种计算机模型在大型涡轮发电机的转子槽中的金属紧固件的涡流损耗的计算机模型。转子紧固件的电磁特性和转子的外径被用作输入,转子紧固件的涡流损耗被视为输出。使用FEM和深度学习构建预测模型。分析结果表明,与独立有限元分析相比,该方法减少了设计循环时间并提高了大容量涡轮发电机的设计效率。与其他机器学习模型相比,误差较小,准确性更高。该方法提供了一种在复杂的非线性条件下准确地预测发电机的涡流损耗的新方法。

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