Proper orthogonal decomposition for order reduction of permanent magnet machine model
Proper orthogonal decomposition for order reduction of permanent magnet machine model
复制标题
永磁电机模型降阶的适当正交分解
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
A. Belahcen
中科院分区：
文献类型：
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作者：
M. Farzamfar;P. Rasilo;F. Martin;A. Belahcen
模型降阶是一种降低数值模拟中数学模型的规模、复杂性和计算成本的方法。本征正交分解法是一种有效的模型降阶技术，本文介绍了它在永磁电机低维模型生成中的应用。在适当的正交分解，从高维数值模拟（称为快照）收集的数据投影到一组正交基函数。之后，将这些基函数与原始模型方程组合以构建降阶模型。将原模型与简化模型的计算结果进行比较，结果表明简化模型能够准确地再现被研究机器的局部和全局操作量。
Model order reduction is an approach for reducing size, complexity, and computation cost of mathematical models in numerical simulations. This paper describes the application of proper orthogonal decomposition method, as one of the most efficient model order reduction techniques, in generating lower dimensional model of a permanent magnet machine. In proper orthogonal decomposition, data collected from high-dimensional numerical simulations (called snapshots) are projected onto a set of orthonormal basis functions. Thereafter, these basis functions are combined with the original model equations to build a reduced order model. The comparison of computational results of the original model with the reduced model indicates that the reduced model is able to accurately reproduce both local and global operation quantities of the machine under investigation.