用AI专家库自动设计电机，5秒出方案，效率超传统方法数倍。
Data Driven Automatic Electrical Machine Preliminary Design with Artificial Intelligence Expert Guidance
- 基于2D有限元生成数据，训练可预测性能的智能代理模型。
- 5秒生成高功率密度设计（2.21 kVA/kg），较传统方法提升9%。
- 适合需要快速电机原型的工程师或自动化设计系统开发者。
本文提出一种数据驱动的电机设计框架，以绕线转子同步发电机（WRSG）为例。不同于依赖经验的传统初步设计流程，该框架利用基于人工智能的专家数据库，直接根据用户需求生成设计方案。通过2D有限元模型扫描关键设计变量（如长度和直径），生成初始数据并记录每种设计的机器几何与性能，用于训练基于最优预测元模型（MOP）的代理模型，实现设计变量到关键性能指标（KPI）的映射。模型训练完成后，结合元启发式算法，可在数秒内生成数千种可扩展几何设计，覆盖10–60kVA功率范围，构建AI专家数据库。以30kVA WRSG为例验证框架有效性，从数据库中选取设计编号1138，其功率密度达2.21 kVA/kg，仅需5秒完成，优于传统方法的2.02 kVA/kg（耗时数天）。该数据库还可作为高质量数据源，进一步支持自动化电机设计的AI模型开发。
原文摘要 · Abstract (English)
This paper presents a data-driven electrical machine design (EMD) framework using wound-rotor synchronous generator (WRSG) as a design example. Unlike traditional preliminary EMD processes that heavily rely on expertise, this framework leverages an artificial-intelligence based expert database, to provide preliminary designs directly from user specifications. Initial data is generated using 2D finite element (FE) machine models by sweeping fundamental design variables including machine length and diameter, enabling scalable machine geometry with machine performance for each design is recorded. This data trains a Metamodel of Optimal Prognosis (MOP)-based surrogate model, which maps design variables to key performance indicators (KPIs). Once trained, guided by metaheuristic algorithms, the surrogate model can generate thousands of geometric scalable designs, covering a wide power range, forming an AI expert database to guide future preliminary design. The framework is validated with a 30kVA WRSG design case. A prebuilt WRSG database, covering power from 10 to 60kVA, is validated by FE simulation. Design No.1138 is selected from database and compared with conventional design. Results show No.1138 achieves a higher power density of 2.21 kVA/kg in just 5 seconds, compared to 2.02 kVA/kg obtained using traditional method, which take several days. The developed AI expert database also serves as a high-quality data source for further developing AI models for automatic electrical machine design.
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