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Thermal Optimization of a High-Power Salient-Pole Electrical Machine

机译:大功率凸极电机的热优化

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摘要

An optimization of the thermal behavior of a high-power salient-pole electrical machine is presented. Temperatures are calculated with the lumped method, which provides the thermal trends with relatively low computational cost. This model is used to define an aggregated objective function of our nonlinear thermal optimization problem by combining the mean solid temperature with the maximum temperature criteria. The 13 design variables correspond to the main volumetric flow rates in the electrical machine, which are bounded and subjected to a nonlinear constraint, assuming a fixed geometry. Two MATLAB optimization algorithms were tested: the Active-set (FMINCON solver) and the genetic algorithm (GA). Due to the strong nonlinearities of the model and the resulting nonconvex optimization problem, the GA is likely to give better results. Minimizing the mean solid temperature was demonstrated to be more important than the maximum temperature criterion. A strategic flow configuration is found to send fresh air to the second half of the cooling circuit, where air usually arrives heated. This optimal configuration provides better cooling than its current modeled configuration. This methodology should be of interest during the development phase.
机译:提出了大功率凸极电机热性能的优化方法。使用集总方法计算温度,该方法以相对较低的计算成本提供了热趋势。该模型用于通过将平均固体温度与最高温度标准相结合来定义非线性热优化问题的汇总目标函数。这13个设计变量对应于电机中的主要体积流量,假设几何形状固定,则这些变量受限制并受到非线性约束。测试了两种MATLAB优化算法:活动集(FMINCON求解器)和遗传算法(GA)。由于模型的强非线性和由此产生的非凸优化问题,遗传算法可能会给出更好的结果。事实证明,最小化平均固体温度比最大温度标准更为重要。找到了一种战略性的流量配置,可以将新鲜空气发送到冷却回路的后半部,在此通常会加热空气。这种最佳配置比其当前的建模配置提供了更好的散热。在开发阶段应该对此方法感兴趣。

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