Chenxu Yang

Dalian University of Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Jianrui Niu

Hebei University of Science and Technology

Junjun Ma

Hebei University of Science and Technology

Junzhou Huo

Dalian University of Technology

Yunke Li

China Agricultural University

Meng Chen

Lanzhou Jiaotong University

Situ Mu

Hebei University of Science and Technology

Chun Liu

Hebei University of Science and Technology

Jing Zhang

Shanghai Institute of Ceramics

Yonghuan Bian

Hebei University of Science and Technology

Jing Zhang

Yanbian University

Cooperative Institutions

Hebei University of Science and Technology

Dalian University of Technology

Tsinghua University

Chinese Academy of Sciences

China National Offshore Oil Corporation (China)

North China Electric Power University

Tianjin University

Chinese Academy of Medical Sciences & Peking Union Medical College

China National Petroleum Corporation (China)

University of Chinese Academy of Sciences

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

N-doped carbon achieved by pyrolyzing urea and active carbon for the capacitive deionization coupled PMS oxidation system

Desalination (2024)

Meng Chen Chenxu Yang Minghao Yu Mengyao Han Zihan Meng

Capacitive Deionization

Carbon fibers

Active carbon

10.1016/j.desal.2024.117918

Cite

Citations (3)

Optimization of the Intermediate Chamber Improves Desalination Performance in Flow Electrode Capacitive Deionization (Fcdi): A Comparative Study

Yunke Li Chenxu Yang Meng Chen Yonghuan Bian Jianrui Niu

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Capacitive Deionization

10.2139/ssrn.4772038

Cite

Citations (0)

Research on Advanced Prediction of Major Equipment Fatigue Load Based on Deep Learning

Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems (2024)

Bowen Yang Chenxu Yang Junzhou Huo X.T. Si

Abstract The fatigue life of complex service structures is predicted in real-time according to dynamic random loads. Real-time monitoring of stress/strain in structure key parts is important for practical engineering. Based on the deep learning theory, this paper proposes a long and short-term memory convolutional neural network (CNN- LSTM) prediction model with attention mechanism by using the actual workload data set of key components in major equipment. Firstly, convolutional neural networks (CNN) is used to extract the high-dimensional abstract representation from the preprocessed data, and attention mechanism is added to give each channel different attention scores. Then, long and short-term memory (LSTM) is used to process the time series data and output the final prediction results. In this study, data points in the future are predicted, and the final predicted results are root mean square error (RMSE) of 33.2626 and mean absolute percentage error (MAPE) of 15.3768, which can well fit the future load change trend. At the same time, the multi-condition identification module is added to the code, which can better adapt to the changing load of mechanical equipment in the actual service process. Finally, this paper also designed an ablation experiment, which proved the effectiveness of the overall structure of the prediction model designed in this study and the necessity of the three parts of CNN, ECA-Net and LSTM. It provides theoretical support and practical engineering application for real-time monitoring and predicting the fatigue life of important equipment.

10.1115/1.4065819

Cite

Citations (0)

PNN-Based Condition Monitoring Method for Shield Machine Roller Blades

Chongru Wang Qiang Gao Youfu Wang Chenxu Yang Junzhou Huo

Condition Monitoring

10.1109/acirs62330.2024.10684966

Cite

Citations (0)

Application of spectrum smoothing to improve the accuracy of atomic inner-shell ionization cross sections impacted by electron near the threshold energy

Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (2023)

Chenxu Yang Y. Wu Zhihong Liu H. Chen Y.D. Li

Smoothing

10.1016/j.nimb.2023.03.009

Cite

Citations (0)

Study on Nonlinear Parameter Inversion and Numerical Simulation in Condensate Reservoirs

Processes (2024)

Kuiqian Ma Chenxu Yang Zhennan Gao Xifeng Wang Xinrong Liu

The B6 metamorphic buried hill condensate gas reservoir exhibits a highly compact matrix, leading to a rapid decline in bottom-hole pressure during initial production. The minimal difference between formation and saturation pressures results in severe retrograde condensation, with multiphase flow further increasing resistance. Conventional numerical simulations often overestimate reservoir energy supply due to their failure to account for this additional resistance, leading to inaccuracies in bottom-hole pressure predictions and gas–oil ratio during history matching. To address these challenges, this study conducted research on nonlinear numerical simulation for buried hill condensate gas reservoirs and established a method for calculating a multiphase pressure sweep range based on the well testing theory. By correcting and fitting the pressure propagation boundaries with numerical simulation, the nonlinear flow parameters applicable to the B6 gas field were inversed. This study revealed that conventional Darcy flow is inadequate for predicting pressure propagation boundaries and that it is possible to reasonably characterize the pressure sweep range through nonlinear flow. This approach resulted in an improvement in the accuracy of historical matching for bottom-hole pressure and gas–oil ratio, which improve the historical fitting accuracy to 85%, providing valuable insights for the development of similar reservoirs.

Reservoir Simulation

Multiphase flow

10.3390/pr12091823

Cite

Citations (1)

Optimization of the intermediate chamber improves desalination performance in flow electrode capacitive deionization (FCDI): A comparative study

Desalination (2024)

Yunke Li Chenxu Yang Meng Chen Yonghuan Bian Jianrui Niu

Capacitive Deionization

10.1016/j.desal.2024.117743

Cite

Citations (6)

Experimental Study on Phase Characteristics of CO<sub>2</sub> Injection in BZ13-2 Strong Volatile Oil Reservoir in Bohai Sea Buried Hills

Open Journal of Geology (2023)

Zhennan Gao Lei Zhang Yong Jiang Jing‐Tao Wu Chenxu Yang

BZ13-2 oil field is a deep submerged strongly volatile reservoir in Bohai Sea. This oil reservoir has the characteristics of high gas oil ratio and small difference in formation pressure and saturation point pressure. It usually adopts gas injection development to avoid crude oil degassing and fast decreasing production capacity. However, the phase characteristics and miscibility mechanism of this high-temperature and high-pressure fluid after gas injection are not clear. Therefore, it is necessary to study the feasibility of CO2 injection to improve oil recovery in near critical volatile oil reservoirs through CO2 injection experiments. In the early stage of the depletion experiment, the content of heavy components in the remaining oil increased significantly, so the depletion method is not conducive to the development of such reservoirs. With the increase of CO2 injection, the volumetric expansion coefficient of formation crude oil increases significantly, while the saturation pressure and formation crude oil viscosity remain basically unchanged. The minimum miscible pressure experiment shows that CO2 injection under formation pressure conditions can achieve multiphase miscibility. Based on experimental research results, the BZ13-2 oilfield is suitable for early gas injection development and can significantly improve recovery.

Saturation (graph theory)

Miscibility

Gas oil ratio

Oil Production

Injection well

10.4236/ojg.2023.137033

Cite

Citations (0)

Design and Simulation of Electro-hydraulic Servo System of Shield Cutter-changing Robot for Shield Machine

Hao Tian Hao Chen Chenxu Yang Cheng Zhang Kaixuan Han

10.1109/acirs62330.2024.10684900

Cite