用机器学习加速头灯散热器数字孪生，实时预测温度变化。
Predictive Digital Twins for Thermal Management Using Machine Learning and Reduced-Order Models
- 结合物理降阶模型与机器学习，快速构建热管理数字孪生。
- 神经网络预测误差均值仅54.240，优于其他模型。
- 适合汽车热设计优化与预测性维护场景。
数字孪生技术可实现实时仿真与预测。本文提出一种新型预测型数字孪生框架，用于头灯散热器的热管理，融合基于计算流体动力学（CFD）的物理降阶模型（ROM）与监督式机器学习。通过本征正交分解（POD）构建组件化ROM库，高效捕捉热动态特性。采用决策树、K近邻、支持向量回归（SVR）和神经网络等模型，预测最优ROM配置，实现数字孪生的快速更新。其中神经网络达到54.240的平均绝对误差（MAE），优于其他模型。定量对比显示预测值与原始值高度吻合。该可扩展、可解释的框架显著提升汽车系统热管理能力，支持稳健设计与预测性维护。
原文摘要 · Abstract (English)
Digital twins enable real-time simulation and prediction in engineering systems. This paper presents a novel framework for predictive digital twins of a headlamp heatsink, integrating physics-based reduced-order models (ROMs) from computational fluid dynamics (CFD) with supervised machine learning. A component-based ROM library, derived via proper orthogonal decomposition (POD), captures thermal dynamics efficiently. Machine learning models, including Decision Trees, k-Nearest Neighbors, Support Vector Regression (SVR), and Neural Networks, predict optimal ROM configurations, enabling rapid digital twin updates. The Neural Network achieves a mean absolute error (MAE) of 54.240, outperforming other models. Quantitative comparisons of predicted and original values demonstrate high accuracy. This scalable, interpretable framework advances thermal management in automotive systems, supporting robust design and predictive maintenance.
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