基于动力学模态分解和LSTM神经网络的压缩空气储气装置流场动态预测方法研究

苏浩; 王同生; 邹瀚森; 余柯宇; 姚尔人; 席光

doi:10.7652/xjtuxb202208000

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基于动力学模态分解和LSTM神经网络的压缩空气储气装置流场动态预测方法研究

更新时间：2026-01-02

- 基于动力学模态分解和LSTM神经网络的压缩空气储气装置流场动态预测方法研究
- Study on Dynamic Prediction Method of Flow Field in Compressed Air Storage Device Based on Dynamic Mode Decomposition and LSTM Neural Network
- 默认刊物名称 2026年
- 作者机构：
  
  1.西安交通大学能源与动力工程学院，710049，西安
  2.西安交通大学未来技术学院，710049，西安
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202208000
  中图分类号： TK02
- 收稿：2025-05-14，
  
  修回：2025-07-16，
  
  录用：2025-08-29，
- 稿件说明：
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苏浩, 王同生, 邹瀚森, 等. 基于动力学模态分解和LSTM神经网络的压缩空气储气装置流场动态预测方法研究[J/OL]. 默认刊物名称, 2026.

SU Hao, WANG Tonsheng, ZOU Hansen, et al. Study on Dynamic Prediction Method of Flow Field in Compressed Air Storage Device Based on Dynamic Mode Decomposition and LSTM Neural Network[J/OL]. Moren Journal, 2026.
苏浩, 王同生, 邹瀚森, 等. 基于动力学模态分解和LSTM神经网络的压缩空气储气装置流场动态预测方法研究[J/OL]. 默认刊物名称, 2026. DOI： 10.7652/xjtuxb202208000.

SU Hao, WANG Tonsheng, ZOU Hansen, et al. Study on Dynamic Prediction Method of Flow Field in Compressed Air Storage Device Based on Dynamic Mode Decomposition and LSTM Neural Network[J/OL]. Moren Journal, 2026. DOI： 10.7652/xjtuxb202208000.

摘要

储气装置作为压缩空气储能系统关键的能量存储设备，其热力参量的精准预测是实现系统高效稳定运行的基础。为揭示储气装置在充气过程中流场动态变化的复杂非线性特性，实现储气装置内全局热力参量的精准捕捉与快速预测，本文提出了一种基于动力学模态分解和长短期记忆神经网络的流场降阶预测模型，通过动力学模态与神经网络耦合机制实现了储气装置内流场时空演化特征的解耦建模，为高维非线性动力系统的高效表征与预测提供了新的理论框架。首先通过数值仿真获取三维储气装置内工质合速度、温度和压力等关键物理量的时空全阶数据集，引入动力学模态分解对时空流场数据特征解耦降阶，实现表征流场演化特征主导模态的准确提取；通过构建长短期记忆神经网络模型预测低维动力系统的时序演化特性，进而重构高维时空流场，实现流场全阶参数的动态精准预测。实验结果表明，该方法可实现压缩空气储能系统储气装置内流场的高精度实时预测，在确保预测精度的同时显著提升计算效率，为实现系统全工况灵活高效调控提供理论支撑。

Abstract

As the key energy storage device of compressed air energy storage system

the accurate prediction of thermal parameters is the basis for the efficient and stable operation of the system. In order to reveal the complex nonlinear characteristics of the flow field dynamics during the filling process of the gas storage device

and to achieve the accurate capture and fast prediction of the global thermal parameters in the gas storage device

this paper proposes a flow field downscaling prediction model based on dynamic modal decomposition and long short-term memory neural networks. By coupling dynamic modes with neural networks

the model achieves decoupled modelling of the spatiotemporal evolution characteristics of the flow field within the storage device

providing a new theoretical framework for the efficient characterisation and prediction of high-dimensional nonlinear dynamic systems. First

a three-dimensional dataset of key physical quantities such as the total velocity

temperature

and pressure of the working fluid within the storage tank is obtained through numerical simulation. Dynamic mode decomposition is then introduced to decouple and reduce the dimensionality of the spatio-temporal flow field data

enabling the accurate extraction of the dominant modes characterising the evolution of the flow field. By constructing a long short-term memory neural network model to predict the temporal evolution characteristics of low-dimensional dynamical systems

the high-dimensional spatiotemporal flow field is reconstructed

enabling dynamic and precise prediction of all-order parameters of the flow field. Experimental results demonstrate that this method can achieve high-precision real-time prediction of the flow field within the storage tank of a compressed air energy storage system

significantly improving computational efficiency while ensuring prediction accuracy

thereby providing a theoretical foundation for achieving flexible and efficient control of the system under all operating conditions.

关键词

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references

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