This work introduces the use of multivariate global sensitivity analysis for assessing the impact of uncertain electric machine design parameters on efficiency maps and profiles. Contrary to the common approach of applying variance-based (Sobol') sensitivity analysis elementwise, multivariate sensitivity analysis provides a single sensitivity index per parameter, thus allowing for a holistic estimation of parameter importance over the full efficiency map or profile. Its benefits are demonstrated on permanent magnet synchronous machine models of different fidelity. Computations based on Monte Carlo sampling and polynomial chaos expansions are compared in terms of computational cost. The sensitivity analysis results are subsequently used to simplify the models, by fixing non-influential parameters to their nominal values and allowing random variations only for influential parameters. Uncertainty estimates obtained with the full and reduced models confirm the validity of model simplification guided by multivariate sensitivity analysis.

翻译：本文引入了多变量全局灵敏度分析方法，用于评估不确定的电机设计参数对效率图谱及特性曲线的影响。与常见的基于方差的Sobol'灵敏度分析逐点应用方法不同，多变量灵敏度分析为每个参数提供一个统一的灵敏度指标，从而能够对参数在整个效率图谱或特性曲线上的重要性进行整体评估。该方法在不同保真度的永磁同步电机模型上展示了其优势。基于蒙特卡洛采样和多项式混沌展开的计算方法在计算成本方面进行了比较。随后，灵敏度分析结果被用于简化模型：将非影响参数固定为其名义值，仅允许影响参数存在随机变化。通过完整模型和简化模型获得的不确定性估计验证了基于多变量灵敏度分析进行模型简化的有效性。