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Active and switchable thermal control

Science China(Physics,Mechanics & Astronomy) 2022年第11期65卷 80-81页

作者： Chenhan Liu Yunfei Chen Micro-and Nano-scale Thermal Measurement and Thermal Management Laboratory School of Energy and Mechanical EngineeringNanjing Normal UniversityNanjing210046China Jiangsu Key Laboratory for Numerical Simulation of Large-Scale Complex Systems Nanjing Normal UniversityNanjing210046China Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments School of Mechanical EngineeringSoutheast UniversityNanjing211100China

In the past century, actively controlling electron transport has led to unprecedented advances in the digital world. Controlling phonon(i.e., thermal) transport like electron transport is a current topic of interest. To realize active and switchable thermal control(Figure 1), numerous studies have been conducted recently due to the emerging challenges in the fields of thermal management, heating and refrigeration.

关键词： thermal transport Figure

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Improving the Performance of Lattice Boltzmann Method with Pipelined Algorithm on A Heterogeneous Multi-zone Processor 23rd

Improving the Performance of Lattice Boltzmann Method with...

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23rd International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Zhang, Qingyang Xu, Lei Chen, Rongliang Chen, Lin Chen, Xinhai Wang, Qinglin Liu, Jie Yang, Bo Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410000 China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha410000 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031299261

Lattice Boltzmann method (LBM) has become a powerful method in computational fluid dynamics and has drawn more and more attention in high-performance computing due to its particulate nature and local dynamics, especially on recent multi-core or many-core platforms. This paper develops a parallel software framework for 3D LBM simulation on a heterogeneous multi-zone processor, MT-3000. An improved pipelined algorithm named Pencil-H is proposed, which can not only fully exploit the advantages of each component of MT-3000 but also overlap the time of calculation and communication. Moreover, an architecture-aware multi-level parallelization algorithm is developed to fully utilize the computational performance of MT-3000. A benchmark test is performed to verify the reliability and test the performance of the LBM code. Experimental results show that the optimized code achieves a 32.02 × speedup compared with using 16 CPU cores and achieves a performance of 286.03MLUPS which reaches 72.3% of the theoretical peak performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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Energy spectrum of two-dimensional isotropic rapidly rotating turbulence

arXiv

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arXiv 2024年

作者： Li, Peiyang Xie, Jin-Han Department of Mechanics and Engineering Science at College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China

We study a two-dimensional isotropic rotating system and obtain both theoretically and numerically a K-2 energy spectrum under the rapidly rotating condition (Ro 1), which was initially obtained by Zeman [1] and Zhou [2]. In rotating turbulence, the K-2 energy spectrum was proposed under the assumption of isotropy, however, the direction selectivity of rotation breaks isotropy, making this K-2 spectrum not easily observable. To fill the gap between theoretical assumptions and realizability, we study the turbulence of inertial waves in an artificial two-dimensional isotropic rotating turbulence system. In the limit of a small Rossby number, we asymptotically derive a nonlinear amplitude equation for inertial waves, which gives the K-2 spectrum using a strong turbulence argument. This scaling is justified by numerical simulations of both the amplitude equation and the original system. © 2024, CC BY.

关键词： Nonlinear equations

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A Reliability Prediction Method for AUTOSAR Architecture Considering Unreliable Platforms

A Reliability Prediction Method for AUTOSAR Architecture Con...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Cangzhou Yuan Hongliang Niu Yang Zhang Yilong Yang Qiangwei Li State Key Laboratory of Complex & Critical Software Environment School of Software Beihang University Beijing China School of Transportaton Science and Engineering Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

With the trend of intelligence, automobile archi-tecture has become more complex. It is necessary to predict and discover reliability-related issues to reduce the cost of correction in the later period. In AUTOSAR-based automotive architecture design, software and hardware interaction, mid-dleware platform behavior, physical environment, and system usage profile affect the system's reliability. It is necessary to comprehensively consider these factors to predict the system's reliability reasonably. However, existing methods often overlook the influence of some factors, especially oversimplifying the control flow of the middleware platform in the system. Resulting in difficulty in effectively modeling the behavior of the AUTOSAR middleware platform in error propagation and its impact on system failure behavior. To analyze the impact of middleware platforms on failure behavior, this paper analyzes the impact of the AutoSAR middleware platform on application software faults based on error propagation methods. Then, expand the AUTOSAR meta-model to model reliability parameters and automatically convert the architecture model into a formal model for reliability prediction. Finally, the effectiveness of considering unreliable platform behavior modeling was verified through a car headlight design case study.

关键词： Processor scheduling Computational modeling Merging Computer architecture Predictive models Reliability engineering Hardware software reliability Automobiles Middleware

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V-AUTOSAR: Graphical Modeling Language for AUTOSAR Architecture and Resource Modeling

V-AUTOSAR: Graphical Modeling Language for AUTOSAR Architect...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Yilong Yang Yang Zhang Hongliang Niu Cangzhou Yuan Qiangwei Li School of Software State Key Laboratory of Complex & Critical Software Environment Beihang University Beijing China School of Transportaton Science and Engineering Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

AUTOSAR enhances the management of complex automotive electrical and electronic architectures by improving the reusability and interchangeability of software modules between OEMs and suppliers. However, existing AUTOSAR modeling tools need help with non-intuitive representation and complicated architectural relationship modeling processes. In this paper, we propose a visual architecture modeling language to represent based on model-driven development of the system architecture design of vehicles. Our approach addresses these issues by implementing multi-dimensional visualization capabilities, incorporating two-dimensional graphical representations and detailed one-dimensional tabular displays. Furthermore, we introduce a practical'AUTOSAR meeting in the middle modeling method, which allows for separate modeling at different levels. This approach effectively harnesses the expertise of detailed bottom-level designers and high-level architects, improving efficiency in automotive system design. A detailed case study and evaluation substantiate the effectiveness of our modeling language in describing the system architecture.

关键词： Visualization systems architecture Computer architecture software Informatics System analysis and design Automotive engineering Model-driven development

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Task Assignment Scheme Designed for Online Urban Sensing Based on Sparse Mobile Crowdsensing

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IEEE Internet of Things Journal 2025年第11期12卷 17791-17806页

作者： Zeng, Hongjian Xiong, Yonghua She, Jinhua Yu, Anjun China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Tokyo University of Technology School of Engineering Hachioji Tokyo192-0982 Japan Jiangxi Ganyue Expressway Company Ltd. Operation Management Department Jiangxi Nanchang330000 China

Sparse mobile crowdsensing (SMCS) achieves urban-scale environmental sensing by assigning tasks to workers in specific subareas and inferring global data from the collected information. However, the effectiveness of SMCS is often limited because many studies overlook workers’ mobility and data collection time during subarea selection, as well as the time constraints of the sensing cycle in task assignment. This may affect the task completion timeliness and data quality. To address these issues, we develop a subarea evaluation method based on deep reinforcement learning, considering both the temporal effectiveness of sensing tasks and the importance of subarea selection for data inference. Using the subarea evaluation values derived from this method, we establish an online urban sensing task assignment model which is subject to constraints of sensing cycle time and cost budget. This model aims to find the task assignment result that minimizes data inference error by maximizing the comprehensive utility value. Considering the characteristics of the task assignment model, we propose an evolutionary algorithm named OTA-EA, which is based on an improved genetic algorithm. Its enhanced evolutionary operators can avoid generating infeasible solutions while maintaining robust search and optimization performance. Lastly, we conduct experimental evaluations of these methods on the real-world datasets. The results demonstrate that our subarea evaluation method can significantly reduce the data inference error, and our evolutionary task assignment algorithm can achieve better task assignment results than the baseline algorithms. © 2014 IEEE.

关键词： Genetic algorithms

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An Improved Machine Learning Model for Pure Component Property Estimation

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engineering 2024年第8期39卷 61-73页

作者： Xinyu Cao Ming Gong Anjan Tula Xi Chen Rafiqul Gani Venkat Venkatasubramanian State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Bard College at Simon’s RockGreat BarringtonMA 01230USA PSE for SPEED Company Charlottenlund DK-2920Denmark Sustainable Energy and Environment Thrust The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 510000China Department of Applied Sustainability Széchenyi István UniversityGyör 9026Hungary Complex Resilient Intelligent Systems Laboratory Department of Chemical EngineeringColumbia UniversityNew YorkNJ 10027USA

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product ***,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these ***,accuracy and predictive capabilities still limit the scope and applicability of most property estimation *** paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different *** selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research *** framework is assessed using datasets of varying sizes for 20 pure component *** 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

关键词： Group contribution Gaussian process Warping function Prior predictive checking

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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Long-term Solar Irradiance Forecasting Based on TimesNet with Multiple Weather Parameters

Long-term Solar Irradiance Forecasting Based on TimesNet wit...

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Chinese Control and Decision Conference, CCDC

作者： Fei Gong Shuo Shan Haikun Wei Kanjian Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In renewable energy grids, solar irradiance prediction is significant for electric operation planning. The emphasis of our work is forecasting irradiance changes over multiple days ahead using hourly irradiance data in association with weather parameters such as temperature, humidity, barometric pressure, wind speed, and cloud cover. Specifically, a framework based on TimesNet was proposed for 4-day global horizontal irradiance (GHI) forecasting and a comparative experiment was conduct with three well-performing models in long-term forecasting tasks. The experimental results indicate that the suggested method outperformed the accuracy when compared to other models, with MSE 0.198, MAE 0.259, RMSE 0.445 and R 2 0.787.

关键词： Solar irradiance Temperature distribution Computational modeling Wind speed Time series analysis Weather forecasting Predictive models

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Research Progress on Thermal Shock Behavior of Porous Ceramics

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China's Refractories 2022年第1期31卷 24-29页

作者： YAN Mingwei LIU Kaiqi ZHANG Jiayu SUN Guangchao LI Xiang SI Kaikai State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100019China University of Chinese Academy of Sciences Beijing 100049China

Thermal-mechanical coupling effect causes a large stress within porous ceramics at high temperatures,resulting in strength attenuation and product reliability *** shock resistance is one of the key factors to characterize the reliability of ceramic materials under thermal-mechanical coupling *** is important to study the thermal shock resistance of the porous ceramics to evaluate their service performance and improve their service *** better evaluate the effect of the thermal shock resistance on properties of the porous ceramics,the evaluation theories,experimental characterization methods and influencing factors about the thermal shock performance of the porous ceramics were reviewed in this paper,and some future research directions were prospected.

关键词： porous ceramics thermal-mechanical coupling thermal shock resistance evaluation theory influence factor

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