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Graph convolutional networks modeling for aerodynamic system stability with spatiotemporal topology fusion based on digital twin 认领 引用
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作者 Mingming Zhang Yating Zhen +4 位作者 Simeng Bai Xi Nan Ning Ma Ruoyang Liu Quan Wen 《Propulsion and Power Research》 SCIE CSCD 2026年第1期32-47,共16页
Digital twin,as a key direction in the digital transformation,is becoming an important supporting technology for intelligent operation and predictive maintenance of aeroengines.In response to the stability identificat... Digital twin,as a key direction in the digital transformation,is becoming an important supporting technology for intelligent operation and predictive maintenance of aeroengines.In response to the stability identification of the aerodynamic system,this study proposes a digital twin model based on the flow field topological structure graph convolution,focusing on the topology fusion modeling methods and the recognition strategies of unsteady flow characteristics.This method utilizes the Mapper algorithm in variational mode decomposition(VMD)to extract topological features from high-dimensional time-series data,constructing an adaptive graph structure generation module that reflects the complex dependencies in spatiotemporal sequences.Combining the advantages of time convolutional networks(TCN)and long short-term memory networks(LSTM),a TCN-LSTM time feature extraction module is designed.A spatiotemporal information fusion prediction model is constructed by the graph convolutional networks(GCN)module for the aerodynamic system.The model represents the root mean square error reduction approximately by 81.6%over the benchmark TCN_GCN model.With a prediction performance metric R 2 of 0.9955,the compressor stability state is well achieved by the high-precision prediction.In addition,the study applies spatial domain topological structure,topological entropy,and measure entropy analysis methods to provide an in-depth description of the aerodynamic instability process of dynamic system.The system instability warning can be detected in advance at 251.6 r,211.6 r,and 3.6 r by instances.This research combines topological theory with graph neural networks,developing a collaborative intelligent data processing framework.It aims at extracting the spatiotemporal structure and predicting the state of dynamic systems,providing multidimensional information for system recognition. 展开更多
关键词 Digital twin Topological structure Time convolution networks Graph neural networks Topological entropy Measure entropy
A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants 认领 引用
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作者 Shaoxiong Wu Ruoxin Li +6 位作者 Xiaofeng Tao Hailong Wu Ping Miao Yang Lu Yanyan Lu Qi Liu Li Pan 《Computers, Materials & Continua》 SCIE EI 2024年第11期3063-3077,共15页
Time series prediction has always been an important problem in the field of machine learning.Among them,power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulati... Time series prediction has always been an important problem in the field of machine learning.Among them,power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies.Traditional power load forecasting often has poor feature extraction performance for long time series.In this paper,a new deep learning framework Residual Stacked Temporal Long Short-Term Memory(RST-LSTM)is proposed,which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences.The network framework of RST-LSTM consists of two parts:one is a stacked time convolutional memory unit module for global and local feature extraction,and the other is a residual combination optimization module to reduce model redundancy.Finally,this paper demonstrates through various experimental indicators that RST-LSTM achieves significant performance improvements in both overall and local prediction accuracy compared to some state-of-the-art baseline methods. 展开更多
关键词 Times series forecasting long short term memory network(LSTM) time convolutional network(TCN) wavelet decomposition
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