Self-adaptive reduced order thermal modeling of underwater propulsion motors

LI Ruiye; CHENG Peng; LAN Hai

doi:10.15938/j.emc.2024.07.009

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Self-adaptive reduced order thermal modeling of underwater propulsion motors

更新时间：2025-06-26

- Self-adaptive reduced order thermal modeling of underwater propulsion motors
- Electric Machines and Control Vol. 28, Issue 7, Pages: 88-100(2024)
- 作者机构：
  
  哈尔滨工程大学智能科学与工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.15938/j.emc.2024.07.009
  CLC： TM301.4
- Received：06 September 2023，
  
  Published：2024-07
- 稿件说明：
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LI Ruiye, CHENG Peng, LAN Hai. Self-adaptive reduced order thermal modeling of underwater propulsion motors[J]. Electric Machines and Control, 2024, 28(7): 88-100.
DOI：

LI Ruiye, CHENG Peng, LAN Hai. Self-adaptive reduced order thermal modeling of underwater propulsion motors[J]. Electric Machines and Control, 2024, 28(7): 88-100. DOI： 10.15938/j.emc.2024.07.009.

摘要

为了提升水下推进电机的综合性能，提出自适应降阶模型来分析电机的动态多模态热场域性能特性，解决现有算法在处理高维复杂问题时资源耗费大和易陷入局部最优的问题。在时域中模型采用“无方程”的非侵入式动态模态分解方法，从时间序列数据中提取主要动态模态，实现系统数据的降维和模态分解，精确描述系统行为和预测未来状态；在空间域中，结合基于改善期望的自适应策略和径向基函数进行参数化近似和降阶。通过评估设计空间中各候选点的预测不可信度，迭代提升模型质量，平衡空间全局探索与局部探究间的关系。多步骤协作建模框架能够从有限的大规模模拟数据集中进行精确的场域解预测。通过实验系统验证了模型在常规和异常状态下温度变化规律的有效性和可靠性，结果表现出高准确性和稳定性。

Abstract

To enhance the comprehensive performance of underwater propulsion motors，an adaptive reduced-order model was developed to analyze the dynamic multimodal thermal field performance characteristics. This approach addresses the issues of high resource consumption and local optimum traps encountered by existing algorithms in handling high-dimensional complex problems. In the time domain，an‘equation-free' non-intrusive dynamic mode decomposition method was employed to extract the primary dynamic modes from time series data，achieving dimensionality reduction and mode decomposition for precise system behavior description and future state prediction. In the spatial domain，a combination of an expectation-improvement-based adaptive strategy and radial basis functions was used for parametric approximation and order reduction. By evaluating the prediction uncertainty of each candidate point in the design space，the model quality was iteratively improved，balancing global exploration and local exploitation. This multi-step collaborative modeling framework enables accurate field solution predictions from limited large-scale simulation datasets. The model's effectiveness and reliability in predicting temperature variations under both normal and abnormal conditions were validated through experimental systems，demonstrating high accuracy and stability.

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