面向数字孪生应用的燃气轮机金属温度快速预测模型研究

李甲珊; 阙晓斌; 闫学慧; 王晓博; 王旭

doi:10.19805/j.cnki.jcspe.2026.250660

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面向数字孪生应用的燃气轮机金属温度快速预测模型研究

更新时间：2026-03-11

- 面向数字孪生应用的燃气轮机金属温度快速预测模型研究
- Fast Prediction Model for Gas Turbine Metal Temperature in Digital Twin Applications
- 动力工程学报 2026年46卷第3期页码：190-200
- 作者机构：
  
  中国联合重型燃气轮机技术有限公司,北京,100016
- 作者简介：
  
  [ "李甲珊(1994—),女,陕西安康人,工程师,硕士研究生,研究方向为重型燃气轮机热分析,E-mail:daisy_ljs33@163.com" ]
- 基金信息：
  
  基础研究资助项目(J2019-I-0009-0009)
- DOI：10.19805/j.cnki.jcspe.2026.250660
  中图分类号：
- 网络首发：2026-03-10，
  
  纸质出版：2026-03-10
- 稿件说明：
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李甲珊,阙晓斌,闫学慧,王晓博,王旭. 面向数字孪生应用的燃气轮机金属温度快速预测模型研究动力工程学报, 2026, 46(3): 190-200 https://doi.

org/10.19805/j.cnki.jcspe.2026.250660
李甲珊,阙晓斌,闫学慧,王晓博,王旭. 面向数字孪生应用的燃气轮机金属温度快速预测模型研究动力工程学报, 2026, 46(3): 190-200 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.250660.

org/10.19805/j.cnki.jcspe.2026.250660 DOI：

摘要

针对燃气轮机数字孪生应用中的金属温度实时预测需求

提出了一种基于循环神经网络(RNN)的燃气轮机金属温度快速预测方法。传统基于运行曲线工况驱动的整机温度场物理模型存在复杂度高、计算耗时长等缺陷

难以满足燃气轮机试验、运行过程中实时预测与故障预警等应用需求。为解决这一问题

基于物理模型仿真数据集和整机试验数据集训练了2组模型

且经优化后2组模型的保真度分别达到99.14%和94.32%。系统分析了神经网络超参数对模型性能的影响规律

并提出了有效的模型调优和降低过拟合的方法。最后通过测试数据集验证了模型泛化能力

并基于功能模型接口(FMI)标准实现了燃气轮机金属温度快速预测模型在自主F级重型燃气轮机试验样机中的部署应用。研究成果为燃气轮机数字孪生系统的实时温度预测提供了有效的解决方案。

Abstract

To address the real-time prediction requirements of gas turbine metal temperature in digital twin applications

a fast prediction method of gas turbine metal temperature based on recurrent neural networks (RNN) was proposed. Conventional physics-based whole-engine temperature field models driven by operational curves suffer from high complexity and long computation time

failing to meet the demands of real-time prediction and fault warning in gas turbine testing and operation. Two models were trained using datasets from physics-model simulations and whole-engine experiments

achieving fidelity of 99.14% and 94.32% after optimization. The influence of neural network hyperparameters on model performance was systematically analyzed

and effective tuning methods for model optimization and overfitting reduction were proposed. Model generalization capability was verified through test datasets. Deployment was implemented in a prototype F-class heavy-duty gas turbine using functional mock-up interface (FMI) standards

providing an effective solution for real-time temperature prediction in gas turbine digital twin systems.

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