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Surface tension effects on Rayleigh-Taylor instability in nonideal fluids:A multiple-relaxation-time discrete Boltzmann study

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science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 87-105页

作者： Feng Chen Aiguo Xu Jiahui Song Yanbiao Gan Yudong Zhang Ning Guan School of Aeronautics Shan Dong Jiaotong UniversityJinan 250357China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing100871China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of TechnologyBeijing100081China National Key Laboratory of Shock Wave and Detonation Physics Mianyang621999China School of Aerospace Engineering Beijing Institute of TechnologyBeijing100081China Hebei Key Laboratory of Trans-Media Aerial Underwater Vehicle North China Institute of Aerospace EngineeringLangfang065000China School of Mechanics and Safety Engineering Zhengzhou UniversityZhengzhou450001China

A multi-relaxation-time discrete Boltzmann model for compressible non-ideal gases with adjustable specific heat ratio is proposed,and the impact of surface tension on Rayleigh-Taylor instability(RTI)is investigated from two perspectives:macroscopic and non-equilibrium *** terms of physical cognition,(1)it is found that there are two critical surface tensions:the upper critical value and the lower critical *** the surface tension is below the lower critical value,the RTI evolution aligns qualitatively with the case without surface *** the surface tension coefficient increases,the inhibitory effect on RTI evolution gradually *** the surface tension is greater than the upper critical value,the disturbance interface tends to be stable after multiple *** the surface tension is in between,the perturbation interface first reverses,or even multiple reverses,and then destabilizes and develops rapidly.(2)A series of new kinetic behavior characteristics are given,and it is found that some behavior characteristics have important reference value for the control of system *** example,the first peak in the growth rate of the global average non-organized momentum flux strength D_(2)corresponds to the onset of the regular nonlinear *** peak in the growth rate of the global average non-organized energy flux strength D_(3,1)marks the beginning of the re-acceleration *** onset of the uniform acceleration stage in the growth rate of the global average nonequilibrium strength D_(T NE)corresponds to the system transitioning into the regular nonlinear stage,while its terminal point(also the peak)corresponds to the system entering the re-acceleration *** insights enhance understanding of RTI mechanisms in complex fluids kinetically.

关键词： statistical mechanics of classical fluids flow instabilities kinetic theory

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Phase-field-based lattice Boltzmann method for containerless freezing

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Physical Review E 2024年第3期110卷 035301页

作者： Jiangxu Huang Lei Wang Zhenhua Chai Baochang Shi School of Mathematics and Statistics School of Mathematics and Physics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing

In this paper we first propose a phase-field model for the containerless freezing problems, in which the volume expansion or shrinkage of the liquid caused by the density change during the phase change process is considered by adding a mass source term to the continuum equation. Then a phase-field-based lattice Boltzmann (LB) method is further developed to simulate solid-liquid phase change phenomena in multiphase systems. We test the developed LB method by the problem of conduction-induced freezing in a semi-infinite space, the three-phase Stefan problem, and the droplet solidification on a cold surface, and the numerical results are in agreement with the analytical and experimental solutions. In addition, the LB method is also used to study the rising bubbles with solidification. The results of the present method not only accurately capture the effect of bubbles on the solidification process, but also are in agreement with the previous work. Finally, a parametric study is carried out to examine the influences of some physical parameters on the sessile droplet solidification, and it is found that the time of droplet solidification increases with the increase of droplet volume and contact angle.

关键词： Drops & bubbles Liquid-solid phase transition

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model 1

Emergency Transportation and Structural Prediction and Optim...

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1st IEEE International Conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Nie, Yiyang Chen, Xiang Wu, Xuanli Deng, Zihao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology Shcool of Mathematics and Computational Science Xiangtan China

ISBN: (纸本)9798350323795

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model;second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed;finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes. © 2023 IEEE.

关键词： Multiobjective optimization

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AN ACCELERATION STRATEGY FOR RANDOMIZE-THEN-OPTIMIZE SAMPLING VIA DEEP NEURAL NETWORKS

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Journal of computational mathematics 2021年第6期39卷 848-864页

作者： Liang Yan Tao Zhou School of Mathematics Southeast UniversityNanjing210096China Nanjing Center for Applied Mathematics Nanjing211135China LSEC Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China

Randomize-then-optimize (RTO) is widely used for sampling from posterior distributions in Bayesian inverse problems. However, RTO can be computationally intensive forcomplexity problems due to repetitive evaluations of the expensive forward model and itsgradient. In this work, we present a novel goal-oriented deep neural networks (DNN) surrogate approach to substantially reduce the computation burden of RTO. In particular,we propose to drawn the training points for the DNN-surrogate from a local approximatedposterior distribution – yielding a flexible and efficient sampling algorithm that convergesto the direct RTO approach. We present a Bayesian inverse problem governed by ellipticPDEs to demonstrate the computational accuracy and efficiency of our DNN-RTO approach, which shows that DNN-RTO can significantly outperform the traditional RTO.

关键词： Bayesian inverse problems Deep neural network Markov chain Monte Carlo

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Bayesian Network-based Active Learning for Ontology-based Video Retrieval using Hidden Markov Logic Model with Edge Histogram Descriptor

Bayesian Network-based Active Learning for Ontology-based Vi...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Sathiyaprasad, B. Sreeji, S. Satheeshkumar, B. Menaka, D. Babu, K. Jones, G. Maria Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600 119 India Galgotias University School of Computing Science and Engineering UP Greater Noida India SRM Institute of Science and Technology Katankulathur Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9798350331790

A large amount of data is collected, analysed, and used on a regular basis by a large number of people for web pages, books, journals, and files which make up a significant portion of the data on the Internet. The burden on significant content-based retrieval and other applications is increasing as the volume of surveillance video data grows. Therefore to reduce the burden during retrieval and to solve the problem of video surveillance an ontology based video retrieval by feature extraction and classification techniques is proposed. In the Initial step the examined form of description files in OWL (Web Ontology Language is fed as input. Then the video pre-processed and segmented before extracting the features. The features of video has been extracted using "Hidden Markov Logic Model With Edge Histogram Descriptor (HMLM-EHD)"in which the spatio based features are extracted with Shape-based features that define object shapes in image is extracted from object contours or regions. After the extraction of features, classification is done using Bayesian network based active learning. The retrieval subsystem evaluates the provided query, performs similarity matching operations (which can be done using the correlation Distance Algorithm), and finally provides the result to the end user for these categorised video frames. The experimental results shows retrieved video frames and their parametric analysis in terms of accuracy of 94%, precision of 85%, recall of 71 % and F-1 score of 73%. © 2023 IEEE.

关键词： Feature extraction

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An Optimization Method for Renewable Energy and Energy Storage Capacity Allocation Based on Temporal Production Simulation 7

An Optimization Method for Renewable Energy and Energy Stora...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Du, Hongbo Li, Chi Chen, Liang Zhang, Jinping Kou, Caixia College of Sciences Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Renewable Energy Grid-Integration China Electric Power Research Institute Beijing China Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350345094

With the rapid expansion of large-scale renewable energy bases in China, optimizing the allocation of renewable energy and energy storage capacity is crucial for improving system efficiency. In this paper, a joint optimal configuration method of wind-photovoltaic-energy storage capacity based on time series production simulation is proposed for renewable energy transmission base. Its aim is to minimize the total investment cost of the system under certain constraints about the proportion of renewable energy power generation, renewable energy utilization rate, conventional thermal power operation, energy storage power station operation, ultra-high voltage direct current (UHVDC) regulation and power transmission. To handle the nonlinear constraints, linearization is achieved by introducing integer variables. For the complex operation constraints of conventional thermal power and UHVDC channel, a simplified modeling method of start-stop cycle and adjustment cycle is proposed, which can effectively reduce the number of integer variables and improve the solving efficiency. The effectiveness of the proposed method is tested on the data of a renewable energy base in Northwest China. © 2023 IEEE.

关键词： Energy utilization

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A Novel Approach of Disease Diagnostic Prediction Using SMOTE Ensemble Classification 2nd

A Novel Approach of Disease Diagnostic Prediction Using SMOT...

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2nd International Conference on Deep sciences for computing and Communications, IconDeepCom 2023

作者： Gajendran, Sudhakaran Arunarani, A.R. Nair, Akhil R. Logeswari, G. Elakkiya, R. School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9783031689079

Hepatitis is a liver inflammation caused primarily by viral infection. There are several methods that help with its diagnosis. However, the available datasets for Hepatitis research contain missing values that are imputed by median values. The Synthetic Minority Oversampling Technique (SMOTE) oversamples the minority class, while feature selection algorithms extract critical features from the actual data. One significant disadvantage of existing approaches is their inability to efficiently deal with missing data, resulting in lower statistical effectiveness. In this paper, a novel approach for predicting disease diagnostic is proposed. Firstly, recursive feature elimination (RFE) is used to remove features from the total number declared, followed by univariate analysis, which provides a score to each feature. The principal component analysis (PCA) compresses data. The mutual information score and ReliefF are two automatic feature selection approaches that restore key features. The synthetic minority oversampling technique (SMOTE) oversamples the minority class to obtain balanced data. Several experiments have been conducted using the hepatitis dataset and the framework has shown to predict the disease much more accurately than existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Kinetics of Rayleigh-Taylor instability in van der Waals fluid:the influence of compressibility

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Frontiers of physics 2025年第1期20卷 269-297页

作者： Jie Chen Aiguo Xu Yudong Zhang Dawei Chen Zhihua Chen National Key Laboratory of Transient Physics Nanjing University of Science and TechnologyNanjing 210094China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsP.O.Box 8009-26Beijing 100088China State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of TechnologyBeijing 100081China HEDPS Center for Applied Physics and Technologyand College of EngineeringPeking UniversityBeijing 100871China National Key Laboratory of Shock Wave and Detonation Physics Mianyang 621999China School of Mechanics and Safety Engineering Zhengzhou UniversityZhengzhou 450001China

Early studies on Rayleigh−Taylor instability(RTI)primarily relied on the Navier−Stokes(NS)*** research progresses,it becomes increasingly evident that the kinetic information that the NS model failed to capture is of great value for identifying and even controlling the RTI process;simultaneously,the lack of analysis techniques for complex physical fields results in a significant waste of data *** addition,early RTI studies mainly focused on the incompressible case and the weakly compressible *** the case of strong compressibility,the density of the fluid from the upper layer(originally heavy fluid)may become smaller than that of the surrounding(originally light)fluid,thus invalidating the early method of distinguishing light and heavy fluids based on *** this paper,tracer particles are incorporated into a single-fluid discrete Boltzmann method(DBM)model that considers the van der Waals *** using tracer particles to label the matter-particle sources,a careful study of the matter-mixing and energy-mixing processes of the RTI evolution is realized in the single-fluid *** effects of compressibility on the evolution of RTI are examined mainly through the analysis of bubble and spike velocities,the ratio of area occupied by heavy fluid,and various entropy generation rates of the *** is demonstrated that:(i)compressibility has a suppressive effect on the spike velocity,and this suppressive impact diminishes as the Atwood number(At)*** influence of compressibility on bubble velocity shows a staged behavior with increasing.(ii)The impact of compressibility on the entropy production rate associated with the heat flow(S_(NOEF))is related to the stages of RTI ***,this staged impact of compressibility on S_(NOEF)varies with *** exhibits an inhibitory effect on the entropy production rate associated with viscous stresses(S_(NOEF)).(iii)By incorporating the morphological parameter of the pr

关键词： discrete Boltzmann method Rayleigh-Taylor instability compressibility effect tracer particles

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Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach 5

Enhancing Diabetic Retinopathy Diagnosis with Inception v4: ...

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5th International Conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Athilakshmi, R. Jansi, R. Upadhyay, Roshan Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350321425

An automated system for the classification of Diabetic Retinopathy (DR) is introduced in this study. DR is a prominent cause of blindness globally, primarily impacting individuals aged 20 to 65. The proposed system can be an essential tool in the sustainable healthcare industry, providing timely and accurate diagnosis and treatment to patients with DR. Current manual screening by ophthalmologists can be time-consuming. Accurate categorization of DR is crucial to provide appropriate care and reduce the likelihood of permanent blindness. The use of big data and deep learning models in DR detection has the potential to revolutionize screening and diagnosis, leading to improved patient outcomes and reduced healthcare costs. This study compares traditional and CNN models, including Decision Trees, Random Forest, KNN, SVM, and Inception V4, to identify the best model for classifying retinal images into five stages of DR severity: No DR, Mild, Moderate, Severe, and Proliferative DR. Among the models tested, Inceptionv4 demonstrated the highest accuracy of 92.83% in classifying DR data. These findings demonstrate the potential of an automated system to improve DR diagnosis and management. © 2023 IEEE.

关键词： Computer vision

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Designing architected materials for mechanical compression via simulation, deep learning, and experimentation

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npj computational Materials 2023年第1期9卷 1534-1542页

作者： Andrew J.Lew Kai Jin Markus J.Buehler Laboratory for Atomistic and Molecular Mechanics(LAMM) Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Department of Chemistry Massachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA Center for Computational Science and Engineering Schwarzman College of ComputingMassachusetts Institute of Technology77 Massachusetts Ave.CambridgeMA 02139USA

Architected materials can achieve enhanced properties compared to their plain *** architecting serves as a powerful design lever to achieve targeted behavior without changing the base ***,the connection between architected structure and resultant properties remains an open field of great interest to many fields,from aerospace to civil to automotive ***,we focus on properties related to mechanical compression,and design hierarchical honeycomb structures to meet specific values of stiffness and compressive *** do so,we employ a combination of techniques in a singular workflow,starting with molecular dynamics simulation of the forward design problem,augmenting with data-driven artificial intelligence models to address the inverse design problem,and verifying the behavior of de novo structures with experimentation of additively manufactured *** thereby demonstrate an approach for architected design that is generalizable to multiple material properties and agnostic to the identity of the base material.

关键词： thereby additive starting

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