摘要
针对压缩空气储能系统储气装置内运行参量瞬态演化特性难以快速精准预测的问题,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,实现了储气装置内复杂非线性流场时空演化特征的解耦建模,建立了储气装置内全局运行参量精准表征与快速流场预测方法。采用动力学模态分解对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,重构高维时空流场并实现流场全阶参数的动态精准预测。实验结果表明,储气装置压力场预测的平均相对误差为0.05%,速度场预测的平均相对误差为0.19%,体积分数场预测的平均相对误差为0.83%,模型在保证预测精度的同时计算成本降低4个数量级。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了计算效率,为实现系统全工况灵活高效调控提供了参考。
To address the challenge of rapidly and accurately predicting the transient evolution of operational parameters in within the air storage tanks of compressed air energy storage(CAES)systems,this study proposes a reduced-order flow field prediction model based on dynamic mode decomposition(DMD)and long short-term memory(LSTM)network.This model achieves decoupled modeling of the spatiotemporal evolution features of the complex nonlinear flow fields inside storage tanks and establishes a method for accurate global parameter characterization and rapid flow field prediction.DMD decouples and reduces the full-order spatiotemporal datasets of key physical quantities(e.g.,velocity,temperature,and pressure)within the storage tanks,enabling accurate extraction of dominant modes governing flow field evolution.An LSTM network model is then constructed to predict the temporal evolution characteristics of the low-dimensional dynamical system,reconstruct high-dimensional spatiotemporal flow fields,and achieve dynamic and precise prediction of full-order flow parameters.The experimental results demonstrate that the average relative errors for pressure field prediction is 0.05%,0.19% for the velocity field,and 0.83% for the volume fraction field.The model reduces computational cost by four orders of magnitude while maintaining prediction accuracy.This method enables high-precision real-time flow field prediction for the air storage tanks of CAES systems,significantly improving computational efficiency without sacrificing prediction accuracy,and provides valuable insights for flexible and efficient regulation of such systems under all operating conditions.
作者
苏浩
王同生
邹瀚森
余柯宇
席光
姚尔人
SU Hao;WANG Tongsheng;ZOU Hansen;YU Keyu;XI Guang;YAO Erren(School of Energy and Power Engineering,Xi’an Jiaotong University,Xi’an 710049,China;School of Future Technology,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《西安交通大学学报》
北大核心
2026年第3期110-121,共12页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(52306050,52130603)
陕西省重点研发计划资助项目(2025CY-YBXM-171)。
关键词
压缩空气储能
储气装置
流场预测
解耦降阶
神经网络
compressed air energy storage
air storage tank
flow field prediction
data downgrading
neural network
