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Effect of Square Pin-Fin Twist on Thermo-Hydraulic Performance of a Rectangular Channel in Forced Convection

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SSRN

SSRN 2024年

作者： Li, Yong Zhang, Jin Zhang, Yingchun Zhang, Jiajie Wei, Jiansheng Ma, Suxia Sunden, Bengt Xie, Gongnan College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Cleaner Intelligent Control on Coal & Electricity Ministry of Education Shanxi Taiyuan030024 China Shanxi Provincial Key Laboratory of High Efficiency Heat Storage and Low Carbon Heat Supply Shanxi Taiyuan030021 China School of Energy and Power Engineering North University of China Shanxi Taiyuan030051 China Academy of Space Information Systems Shaanxi Xi'An710000 China Lund University BS Heat Transfer and Fluid Flow AngelholmSE 22653 Sweden School of Marine Science and Technology Northwestern Polytechnical University P.O. Box 24 Xi’an710072 China

This study dealt with an experimental/numerical investigation to manage the thermo-hydraulic performance of a heat sink with square pin-fins at various twist angles and operating conditions. An experimental test platform, which utilized low-pressure air as cooling medium, was established. Steady-state liquid crystal thermography was employed to determine the Nusselt number of the heated surface. Through numerical simulations and experiments at Re = 11400-19000, it was observed that the overall performance of rectangular channels with square pin-fins was significantly better than that of flat rectangular channels. As the Reynolds number increases, the thermal resistance of the rectangular channel with square pin-fins decreased. In addition, the Nusselt number increased with an increase of the twist angle. For large twist angles (θ = 90° and θ = 180°), the friction factor reached a minimum value at Re = 15200, while the performance factor attained a maximum value at Re = 15200. For high Reynolds number (Re = 19000) and large twist angle (θ = 180°), the cooling channel with square pin-fins demonstrated optimal heat transfer and heat dissipation effects. Furthermore, twist fins at α = 0° and α = 45° exhibited distinct local heat transfer performances within the cooling channel. © 2024, The Authors. All rights reserved.

关键词： Nusselt number

Fast ADMM algorithm with efficient communication for distributed optimization of multi-agent systems

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Fast ADMM algorithm with efficient communication for distrib...

Chinese Automation Congress (CAC)

作者： Shuang Cao Xiaowei Jiang Ming Chi Xianhe Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9781665426480

This paper proposes a distributed optimization algorithm based on alternating direction method of multipliers (ADMM) for the distributed optimization problem of multi-agent systems, called ADMM with adaptive penalty terms and conditional communication (for short, AAPCC), by using the local information of agents to update the adaptive penalty terms to speed up the convergence speed while by restricting the communication conditions of agents to reduce the communication cost of agents in the iterative process. Thus, the communication cost of the system is reduced while the convergence speed is ensured. Numerical simulation experiments show that in a multi-agent system, compared with the classical ADMM, AAPCC can better balance the convergence speed and communication cost, suitable for multi-agent systems that require fast convergence speed and less communication cost.

关键词： Costs Automation Numerical simulation Convex functions Stability analysis Iterative algorithms Optimization

The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

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SSRN

SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

An Integral Sliding-Mode Parallel control Approach for General Nonlinear systems via Piecewise Affine Linear Models

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arXiv 2021年

作者： Zhang, Chunyang Gao, Qing Deng, Yue Qiu, Jianbin School of Automation Science and Electrical Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Astraunotics Beihang University Beijing China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

The fundamental problem of stabilizing a general non-affine continuous-time nonlinear system is investigated via piecewise affine linear models (PALMs) in this paper. A novel integral sliding-mode parallel control (ISMPC) approach is developed, where an uncertain piecewise affine system (PWA) is constructed to model a non-affine continuous-time nonlinear system equivalently on a compact region containing the origin. A piecewise sliding-mode parallel controller is designed to globally stabilize the PALM and, consequently, to semi-globally stabilize the original nonlinear system. The proposed scheme enjoys three favorable features: i) some restrictions on the system input channel are eliminated, thus the developed method is more relaxed compared with the published approaches;ii) it is convenient to be used to deal with both matched and unmatched uncertainties of the system;and (iii) the proposed piecewise parallel controller generates smooth control signals even around the boundaries between different subspaces, which makes the developed control strategy more implementable and reliable. Moreover, we provide discussions about the universality analysis of the developed control strategy for two kinds of typical nonlinear systems. Simulation results from two numerical examples further demonstrate the performance of the developed control approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Nonlinear systems

A novel LMI-based method for robust stabilization of fractional-order interval systems with 1 ≤ α < 2

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arXiv 2020年

作者： Badri, Pouya Sojoodi, Mahdi Advanced Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

This paper deals with the problem of robust dynamic output feedback stabilization of interval fractional-order linear time invariant (FO-LTI) systems with the fractional order 1 ≤ α Copyright © 2020, The Author... 详细信息

关键词： Linear matrix inequalities

A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

Fault diagnosis method for ship power-system DC–DC converter using augmented WPD and multi-branch CNN-BiLSTM network

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Journal of the Franklin Institute 2025年第9期362卷

作者： Ren, Yuanjie Zhang, Lanyong Shi, Peng Peng, Huimin College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China School of Electrical and Mechanical Engineering The University of Adelaide SA5000 Australia State Key Laboratory of Smart Grid Protection and Control Nari Group Corporation Jiangsu Nanjing211106 China

Direct current (DC) –DC converters are indispensable energy conversion devices in medium-voltage DC power systems of ships. Herein, the fault diagnosis problem of bidirectional DC–DC converters is investigated. Considering that the fault diagnosis of bidirectional DC–DC converters requires the effective combination of information from multiple diagnostic signals and differences in characteristic data in different time scales, a parallel stacking of multiple branches based on enhanced wavelet packet decomposition technology is proposed as a hybrid model of a convolutional neural network and bidirectional long short-term memory network. First, a multi-input method is used to extract effective features in the sequence data. Second, a multi-model fusion processing framework is used to perform fault diagnosis analysis on the DC–DC converter. Finally, the squeeze and excitation attention mechanism is used to weight all types of fault features and adaptively obtain them. The important weights of the classification feature maps learned by different branches generate fault classification results. Experimental results reveal that the proposed method can extract highly discriminative features from raw data and exhibits robustness in noisy environments. © 2025 The Franklin Institute

关键词： Convolutional neural networks

A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

Networked Integrated Sensing and Communications for 6G Wireless systems

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arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming