Article
Energy & Fuels
Tao Zhang, Xianjin Zeng, Jianchun Guo, Fanhui Zeng, Ming Li
Summary: A computational fluid dynamics model was established in this study to investigate the transport and accumulation behavior of particles in fractures after water flooding. The results showed that injected particles mainly accumulated along the water phase channel in the main fracture, contributing to enhanced oil recovery.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Review
Physics, Multidisciplinary
Ming Li, Run-Ran Liu, Linyuan Lu, Mao-Bin Hu, Shuqi Xu, Yi-Cheng Zhang
Summary: In the last two decades, network science has flourished and influenced various fields from the perspective of the heterogeneous interaction patterns of complex systems. The percolation model, as a paradigm for random and semi-random connectivity, plays a key role in the development of network science and its applications. Concepts and analytical methods related to percolation theory are employed to quantify and solve core problems of networks, while insights into percolation theory also facilitate understanding of networked systems.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2021)
Article
Engineering, Electrical & Electronic
Rafiul K. Rasel, Benjamin J. Straiton, Qussai M. Marashdeh, Fernando L. Teixeira
Summary: Measurement of phase volume fractions in water-containing multiphase flows is necessary for optimization of industrial flow processes. A new approach based on electrical capacitance tomography (ECT) sensors and Hanai's mixture formula has shown potential for estimating water volume fractions in two-phase water-containing flows. However, the approach has only been investigated in controlled experiments under static conditions. In this study, the proposed ECT-based method is evaluated in a flow loop study for volume fraction estimation in oil-water two-phase flows.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Chemical
Dayang Wang, Ningde Jin, Lusheng Zhai, Yingyu Ren
Summary: In this study, a novel liquid film sensor is used to investigate the characteristics of the liquid film around Taylor bubble structure in gas, oil, and water three-phase flow, including structural and nonlinear dynamics characteristics. The structural characteristics, such as proportion, appearance frequency, and thickness of the liquid film, are obtained, and the effects of liquid and gas superficial velocities and oil content on them are investigated. The entropy analysis is introduced to successfully uncover and quantify the dynamic complexity of the liquid film behavior.
CHINESE JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Mechanics
Yohei Morii, Toshihiro Kawakatsu
Summary: A general multiscale and multiphysics simulation framework is proposed for inhomogeneous viscoelastic and elastoplastic complex flows, integrating macroscopic particle simulations with microscopic simulators to evaluate local stress. The platform combines SPH method and microscopic molecular simulators, allowing for simulation of complex flows with deformable objects. Dynamic switching of microscopic models and appropriate boundary conditions enable accurate simulations, demonstrating good quantitative agreement with experimental results.
Article
Computer Science, Information Systems
Lucas Guerreiro, Filipi N. Silva, Diego R. Amancio
Summary: Discovery processes in network science focus on knowledge acquisition through exploring nodes. Different learning strategies can lead to the same learning performance, indicating the need to combine learning curves with other sequence features for inferring network topology.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Lusheng Zhai, Yuqing Wang, Jie Yang, Ningde Jin
Summary: In this study, a measurement system is designed to accurately measure the flow parameters of oil-water-gas three-phase flows. By using a conductance wire-mesh sensor and distributed coaxial conductance sensors, the flow structures of oil-water-gas can be visualized effectively and real-time correction of the gas-phase distribution can be achieved, improving the accuracy of gas volume fraction measurement. The study reveals the structure and evolution characteristics of oil-water-gas flows and investigates the influence of dispersed oil phase on Taylor bubbles and liquid slugs.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Energy & Fuels
Ran Li, Zhangxin Chen, Keliu Wu, Xing Hao, Jinze Xu
Summary: This study introduces an analytical model for water-oil two-phase flow in inorganic nanopores of shale oil reservoirs, taking into account water and oil slippage at the solid substrate and the impact of pore dimensions on oil movement. The model better predicts oil transport behavior in actual reservoir conditions by incorporating nanopore properties and discusses the effects of surface wettability and oil properties on water-oil flow.
Article
Engineering, Electrical & Electronic
Bin Jiang, Ziyan Tang, Runsong Dai, Ziqu Wang, Landi Bai, Ningde Jin
Summary: Flows of two immiscible liquids in horizontal pipes are often encountered in the petroleum industry. The complicated flow structures in horizontal oil-water two-phase flow pose a challenge to flow measurement. In this study, the feasibility of double helix capacitance sensors for measuring water holdup in horizontal oil-water two-phase flow was investigated. The sensitivity distributions of sensors with helical angles of 180 degrees and 360 degrees were analyzed using the finite-element method (FEM). An experimental study was conducted to compare the water holdup measurement characteristics of the two different sensors. The 180 degrees double helix capacitance sensor was found to have better characteristics in water holdup measurement.
IEEE SENSORS JOURNAL
(2023)
Review
Physics, Multidisciplinary
Mengyao Zhang, Tao Huang, Zhaoxia Guo, Zhenggang He
Summary: This paper provides a comprehensive review of the complex network-based analysis and dynamics of traffic networks. It introduces the application of complex network theory in reducing congestion and improving traffic efficiency, discusses the characteristics of complex traffic networks, and presents congestion analysis and network robustness. The paper also offers guidance and research opportunities for future studies.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Ashesh Sharma, Shreyas Ananthan, Jayanarayanan Sitaraman, Stephen Thomas, Michael A. Sprague
Summary: This study investigates the use of overset meshes in predicting accurate wind farm aerodynamics involving large motions and complex geometry components. The study examines the influence of information exchange and compares different approaches for coupling overlapping meshes. Experimental results show that linear interpolation and few outer iterations are sufficient for achieving asymptotic convergence of engineering quantities of interest in incompressible flow simulations.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Acoustics
Weikai Ren, Ningde Jin, Jiachen Zhang
Summary: In this study, a novel gas holdup measurement method based on flow structure detection was proposed. A combined measurement system integrating ultrasonic and optical sensors was developed, which achieved a high level of measurement precision.
Article
Physics, Multidisciplinary
Michele Buzzicotti
Summary: In recent years, the fluid mechanics community has been actively exploring new machine learning approaches to solve long-standing problems. The exchange between ML and fluid mechanics has led to significant advancements in both fields. ML benefits from physics-inspired methods and a scientific environment for quantitative testing, while fluid mechanics benefits from tools that can handle big data, have flexible scope, and reveal unknown correlations. This paper reviews ML algorithms that are important in the current developments in fluid mechanics and discusses the open challenges for their application.
Article
Mechanics
Balachandra Suri
Summary: This theoretical study investigates spatial symmetries and bifurcations in a two-dimensional flow consisting of square vortices, revealing a sequence of symmetry-breaking bifurcations leading to the formation of asymmetric flows under different spatial symmetries. The analysis uncovers a range of pitchfork and Hopf bifurcations, resulting in steady or time-dependent asymmetric flows, as well as different types of flows emerging from symmetry-breaking bifurcations. The research provides new theoretical insights into experimental observations in quasi-two-dimensional square vortex flows.
Article
Mechanics
Piyush Mani Tripathi, Saptarshi Basu
Summary: A two-phase approach has been proposed to study supercritical flow with heat transfer deterioration (HTD), with density variation identified as the primary cause of HTD in supercritical flows. The study focuses on forces generated due to density variation and conceptualizes a theoretical expression for computing the phase boundary distance from the wall.
Article
Engineering, Electrical & Electronic
Weidong Dang, Mengyu Li, Dongmei Lv, Xinlin Sun, Zhongke Gao
Summary: This article proposes a novel convolutional neural network model to address the issue of fatigue in long-term use of brain-computer interface systems. The model achieves significant improvements in the classification accuracy of steady-state VEP and steady-state motion VEP signals.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Mathematics, Interdisciplinary Applications
Wei-Dong Dang, Dong-Mei Lv, Ru-Mei Li, Lin-Ge Rui, Zhuo-Yi Yang, Chao Ma, Zhong-Ke Gao
Summary: In this paper, a novel multilayer network-based convolutional neural network (CNN) model is proposed for emotion recognition from multi-channel nonlinear EEG signals. The model combines the concept of multilayer brain network and deep learning, and achieves good performance in recognizing emotions from EEG signals.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2022)
Article
Engineering, Biomedical
He Wang, Xinshan Zhu, Peiyin Chen, Yuxuan Yang, Chao Ma, Zhongke Gao
Summary: This paper proposes a model architecture for EEG signal analysis using a gradient-based neural architecture search algorithm. The results show that the proposed model achieves competitive accuracy and better standard deviation compared to existing methods in emotion recognition and driver drowsiness assessment tasks.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Engineering, Mechanical
Na Dong, Wenjin Lv, Shuo Zhu, Zhongke Gao, Celso Grebogi
Summary: This research explores the temperature tracking control problem of single-effect LiBr/H2O absorption chiller using model-free adaptive control strategy, and improves the control effect by adding output error rate to the objective function.
NONLINEAR DYNAMICS
(2022)
Article
Mathematics, Applied
Meng-Yu Li, Rui-Qi Wang, Jian-Bo Zhang, Zhong-Ke Gao
Summary: Gas-liquid two-phase flow is a challenging topic in the study of multiphase flow. In this research, dynamic experiments are conducted using a self-designed four-sector distributed conductivity sensor to obtain multi-channel signals. The adaptive optimal kernel time-frequency representation is used to characterize the evolution of the flow, and a complex network is built based on the time-frequency energy distribution. The results show that this approach allows effective analysis of multi-channel measurement information and reveals the evolutionary mechanisms of gas-liquid two-phase flow. Furthermore, a temporal-spatial convolutional neural network is proposed for flow structure recognition, achieving a classification accuracy of 95.83%.
Article
Engineering, Biomedical
Weixin Niu, Chao Ma, Xinlin Sun, Mengyu Li, Zhongke Gao
Summary: This article proposes a double way deep residual neural network combined with brain network analysis, allowing for the classification of multiple emotional states. The emotional EEG signals are transformed into frequency bands and brain networks are constructed. The features extracted from the two pathways are concatenated for classification, and the proposed model achieves high accuracy in emotion recognition tasks.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Editorial Material
Mathematics, Applied
Z. Gao, D. Ghosh, H. A. Harrington, J. G. Restrepo, D. Taylor
Article
Mathematics, Applied
Meng Du, Jie Wei, Meng-Yu Li, Zhong-ke Gao, Juergen Kurths
Summary: In this paper, a novel interconnected ordinal pattern network is proposed to investigate the spatial coupling behaviors of gas-liquid two-phase flow patterns using multivariate fluctuation signals as observations. Two network indices, global subnetwork mutual information (I) and global subnetwork clustering coefficient (C), are used to quantify the spatial coupling strength of different flow patterns. The evolutionary behaviors of gas-liquid two-phase flow patterns are further characterized by calculating the proposed coupling indices under different flow conditions. The proposed interconnected ordinal pattern network provides a new tool for understanding the evolutional mechanisms of multi-phase flow systems and can be applied to investigate coupling behaviors in other complex systems with multiple observations.
Article
Computer Science, Artificial Intelligence
Biao Sun, Beida Song, Jiajun Lv, Peiyin Chen, Xinlin Sun, Chao Ma, Zhongke Gao
Summary: This paper proposes a multiscale feature extraction network (MSFEnet) based on channel-spatial attention for decoding electromyographic (EMG) signals in gesture recognition classification tasks. By fusing the spatiotemporal characteristics and different scales of the EMG signal, feature channel attention module and feature-spatial attention module are constructed to capture more key channel and spatial features. Experimental results show that MSFEnet performs well in extracting temporal and spatial fused features, and achieves higher classification accuracy.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Mathematics, Applied
Nan Yao, Qian-Yun Zhang, De-Yi Ren, You-Jun Li, Chun-Wang Su, Zhong-Ke Gao, Juergen Kurths
Summary: Through numerical analysis, we have investigated the critical behavior of spatiotemporal dynamical systems towards chimera or synchronization as final stable states. The transient time and critical values of chimeras with different numbers of clusters have been measured and analyzed, revealing a power-law variation process with increasing perturbation strengths. Additionally, a phenomenological model has been proposed based on the observation of alternating convergence of critical values for odd and even clusters.
Article
Engineering, Electrical & Electronic
Jianbo Zhang, Chao Ma, Peiyin Chen, Mengyu Li, Ruiqi Wang, Zhongke Gao
Summary: This article proposes a novel multitask learning method for predicting gas void fraction. By conducting experiments and designing a co-attention-based cross-stitch network, the measurement and classification of gas void fraction are simultaneously processed. The results show that this method achieves superior performance in gas void fraction prediction.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Lili Xia, Zhiyong Qu, Jianpeng An, Zhongke Gao
Summary: Nuclei segmentation is performed using a weakly supervised method based on convolutional neural network and point annotations. The proposed method maximizes the image's inherent features through dual input with boundaries and color information. Two types of coarse labels generated from point annotations provide constraint information for the segmentation task. Incorporating colorization as an auxiliary task improves network performance by guiding the extraction of effective features. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
He Wang, Peiyin Chen, Meng Zhang, Jianbo Zhang, Xinlin Sun, Mengyu Li, Xiong Yang, Zhongke Gao
Summary: A robust decoding model is urgently needed in order to efficiently utilize brain-computer interface (BCI) systems in the presence of subject and period variation. Most electroencephalogram (EEG) decoding models depend on specific subject and period characteristics, requiring calibration and training with annotated data prior to application. This poses challenges, especially in the rehabilitation process of disability based on motor imagery (MI), where extended data collection periods are difficult. To address this issue, an unsupervised domain adaptation framework called ISMDA is proposed, which focuses on the offline MI task.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Biomedical
Peiyin Chen, He Wang, Xinlin Sun, Haoyu Li, Celso Grebogi, Zhongke Gao
Summary: In this study, a novel domain adaptation method with optimal transport and frequency mixup is proposed for cross-subject transfer learning in motor imagery BCIs. The method maps preprocessed EEG signals from source and target domains into latent space and aligns their distribution using optimal transport. Experimental results show that the proposed method outperforms previous state-of-the-art domain adaptation approaches.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Electrical & Electronic
Xinlin Sun, Chao Ma, Peiyin Chen, Mengyu Li, He Wang, Weidong Dang, Chaoxu Mu, Zhongke Gao
Summary: The research developed a method combining complex networks and graph convolutional networks to detect major depressive disorder (MDD) and achieved good detection accuracy on a public dataset.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
