AI 中文总结
该研究提出用于感应电机瞬态热模拟的降基（RB）方法，其精度可媲美有限元（FE）模型，支持全自动构造且计算效率接近集总参数热网络（LPTN），为电机热瞬态模拟提供高效低维近似方案。
AI 中文摘要
降基（RB）方法可为参数化偏微分方程提供精度可控的低维近似。我们讨论感应电机瞬态热模拟的RB近似构造，将其性能与校准至测量值后的有限元（FE）模型及集总参数热网络（LPTN）对比。数值结果表明，RB模型可达到FE精度，支持全自动构造，且计算效率与传统LPTN相当。
英文摘要
Reduced Basis (RB) methods provide low-dimensional approximations of parametrized partial differential equations with controllable accuracy. We discuss the construction of RB approximations for transient thermal simulation of an induction motor and compare their performance to Finite Element (FE) models and Lumped Parameter Thermal Networks (LPTNs) after calibration to measurements. Numerical results demonstrate that RB models can achieve FE accuracy, allow for a fully automatic construction, and offer computational efficiency comparable to traditional LPTNs.
