检索结果-内蒙古大学图书馆

On thermomechanical problems in a topology optimisation method based on non-uniform rational basis spline entities

COMPUTERS & STRUCTURES 2024年 305卷

作者： Urso, Elisabetta Montemurro, Marco HESAM Univ Univ Bordeaux Arts & Metiers Inst Technol Bordeaux INPCNRSINRAI2M UMR 5295 F-33405 Talence France

This paper presents a new method to deal with thermomechanical topology optimisation (TO) problems based on a pseudo-density algorithm reformulated in the context of Non Uniform Rational Basis Spline (NURBS) entities. Specifically, a NURBS entity is used to represent the topological descriptor, providing an implicit filtering effect thanks to the local support propriety. The problem is formulated in the most general case of inhomogeneous Neumann-Dirichlet boundary conditions and design-dependent thermal sources and thermomechanical loads. In this context, a study on the combined effect of design-dependent heat sources, thermomechanical loads and applied forces and displacements on the optimal topologies is carried out. Furthermore, the influence of the penalisation schemes involved in the definition of the stiffness matrix, conductivity matrix, thermal loads and thermal sources on the optimised topology is investigated through a wide campaign of sensitivity analyses. Finally, sensitivity analyses are also conducted to investigate the influence of the integer parameters of the NURBS entity on the optimised solution. The effectiveness of the approach is tested on 2D and 3D benchmark problems.

关键词： Topology optimisation NURBS Design-dependent loads density-based algorithm Thermomechanics Penalisation scheme

来源：评论

学校读者我要写书评

暂无评论

Toward the implementation of a multi-component framework in a density-based flow solver for handling chemically reacting flows

引用

INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW 2021年第10期31卷 3169-3195页

作者： Kalkote, Nikhil Assam, Ashwani Eswaran, Vinayak Indian Inst Technol Hyderabad Dept Mech & Aerosp Engn Hyderabad India Indian Inst Technol Patna Dept Mech Engn Patna Bihar India

Purpose The purpose of this study is to present and demonstrate a numerical method for solving chemically reacting flows. These are important for energy conversion devices, which rely on chemical reactions as their operational mechanism, with heat generated from the combustion of the fuel, often gases, being converted to work. Design/methodology/approach The numerical study of such flows requires the set of Navier-Stokes equations to be extended to include multiple species and the chemical reactions between them. The numerical method implemented in this study also accounts for changes in the material properties because of temperature variations and the process to handle steep spatial fronts and stiff source terms without incurring any numerical instabilities. An all-speed numerical framework is used through simple low-dissipation advection upwind splitting (SLAU) convective scheme, and it has been extended in a multi-component species framework on the in-house density-based flow solver. The capability of solving turbulent combustion is also implemented using the Eddy Dissipation Concept (EDC) framework and the recent k-kl turbulence model. Findings The numerical implementation has been demonstrated for several stiff problems in laminar and turbulent combustion. The laminar combustion results are compared from the corresponding results from the Cantera library, and the turbulent combustion computations are found to be consistent with the experimental results. Originality/value This paper has extended the single gas density-based framework to handle multi-component gaseous mixtures. This paper has demonstrated the capability of the numerical framework for solving non-reacting/reacting laminar and turbulent flow problems. The all-speed SLAU convective scheme has been extended in the multi-component species framework, and the turbulent model k-kl is used for turbulent combustion, which has not been done previously. While the former method provides the capability of solv

关键词： Computational fluid dynamics density-based algorithm Laminar combustion Turbulent combustion

来源：评论

学校读者我要写书评

暂无评论

A topology optimization method for problems with design-dependent loads based on non-uniform rational basis spline entities

引用

MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第28期31卷 10671-10686页

作者： Urso, Elisabetta Zerrouq, Salah-eddine Montemurro, Marco Univ Bordeaux HESAM Univ Arts & Metiers Inst Technol CNRSINRABordeaux INP Talence France Ecole Natl Super Arts & Metiers Dept IMC Site ENSAM Lab I2M F-33405 Talence France

In this paper, topology optimization (TO) problems of structures subjected to design-dependent loads are formulated in the context of a special density-based TO approach wherein a Non Uniform Rational Basis Spline (NURBS) hyper-surface is used to represent the topological descriptor. Unlike classical density-based TO approaches, the NURBS-density-based method allows representing the pseudo-density field through a purely geometric computer-aided design compatible entity. In this context, the problem is formulated in the most general case of inhomogeneous Neumann-Dirichlet boundary conditions. Moreover, a thorough study of the penalty function of the design-dependent loads is carried out to investigate its effect on the optimized topologies and overcome the singularity effect related to the zones characterized by low values of the pseudo-density field. Finally, a wide campaign of sensitivity analyses is conducted to investigate the influence of the integer parameters of the NURBS entity, of the combination of design-depedent loads and inhomogeneous Neumann-Dirichlet boundary conditions, and of the concentrated load on the optimized topology. The effectiveness of the approach is tested on 2D and 3D benchmark problems.

关键词： Topology optimization NURBS hyper-surfaces design-dependent loads density-based algorithm inertial loads penalization schemes

来源：评论

学校读者我要写书评

暂无评论

Enhanced Image Reconstruction in Electrical Impedance Tomography using Radial Basis Function Neural Networks

引用

MEASUREMENT SCIENCE REVIEW 2024年第6期24卷 200-210页

作者： Nomvussi, Serge Ayme Kouakouo Mikulka, Jan Brno Univ Technol Dept Theoret & Expt Elect Engn Antoninska 548-1 Brno 60190 Czech Republic

This paper presents a novel cascade algorithm for image reconstruction in electrical impedance tomography (EIT) using radial basis function neural networks. The first subnetwork applies a density-based algorithm and k-nearest neighbors (KNN) to determine the center and width of the radial basis function neural networks, with the aim of preventing ill-conditioned connection weights between the hidden and output layers. The second subnetwork is a generalized regression neural network dedicated to functional approximation. The combined subnetworks result in a reduced mean square error and achieve an accuracy of 89.54 % without noise and an accuracy between 82.90 % and 89.53 % with noise levels ranging from 30 to 60 dB. In comparison, the original radial basis function neural networks (RBFNN) method achieves an accuracy of 85.44 % without noise and between 80.90 % and 85.31 % under similar noise conditions. The total variation (TV) method achieves 83.13 % without noise, with noise-influenced accuracy ranging from 34.28 % to 45.15 %. The Gauss-Newton method achieves 82.35 % accuracy without noise, with accuracy ranging from 33.21 % to 46.15 % in the presence of noise. The proposed method proves to be resilient to various types of noise, including white Gaussian noise, impulsive noise, and contact noise, and consistently delivers superior performance. It also outperforms the other methods in noise-free conditions. The reliability of the method in noisy environments supports its potential application in the development of new modular systems for electrical impedance tomography.

关键词： Artificial neural network density-based algorithm electrical impedance tomography k-nearest neighbors radial basis function neural networks EIDORS framework

来源：评论

学校读者我要写书评

暂无评论

Computational analysis in underexpanded jets simulations

引用

FLUID DYNAMICS RESEARCH 2022年第6期54卷 065502-065502页

作者： Martins, J. R. Barbosa, J., V Santos, F. P. Silva, L. F. L. R. Univ Fed Rio de Janeiro UFRJ Escola Quim BR-21941909 Rio De Janeiro RJ Brazil

Flammable gases leaks at high pressures are hazardous, especially the free underexpanded jets. Therefore, understanding their behavior is fundamental to guarantee a safe environment. Due to the difficulty to obtain experimental data from free under expanded jets, related to the great speeds and gradients, computational fluid dynamics (CFD) has become an essential tool. Simulations for free underexpanded jets to identify algorithms that better represent this problem were performed in this work. The analysis involved different algorithms for fluid flow solution and a local time stepping approach using both OpenFOAM and ANSYS Fluent CFD packages. Our study concluded that all algorithms and software studied had a satisfactory result compared to experimental data. However, we observed that Fluent had a considerable advantage because the simulation was faster compared with other solvers in OpenFOAM, for instance.

关键词： underexpanded jets pressure-based algorithm density-based algorithm OpenFOAM Fluent

来源：评论

学校读者我要写书评

暂无评论

Novel density-based and hierarchical density-based clustering algorithms for uncertain data

引用

NEURAL NETWORKS 2017年 93卷 240-255页

作者： Zhang, Xianchao Liu, Han Zhang, Xiaotong Dalian Univ Technol Dalian 116024 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116024 Peoples R China

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN;(2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing algo

关键词： Clustering Uncertain data density-based algorithm Hierarchical density-based algorithm

来源：评论

学校读者我要写书评

暂无评论

Study and Implementation of Clustering algorithms in R 3

Study and Implementation of Clustering Algorithms in R

引用

International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC)

作者： Meena, Gaurav Chauhan, Pradeep Singh Choudhary, Ravi Raj Cent Univ Rajasthan Dept Comp Sci Ajmer 305917 Rajasthan India

ISBN: (纸本)9781538632437

clustering is a process in which we group the data by finding similarities between data based on their characteristics. These groups are called cluster. In clustering, there is a division of data into groups of similar objects. These groups are the clusters, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. Clustering is unsupervised learning technique, based on the concept of maximize infra-clustering and minimize inter- clustering. Nowadays, clustering of biological dataset is the widely researched topic among computer science. Bio- informatics has become area that receive most of the attention of data mining techniques. Generally, bio- informatics targets to solve complicated problems like gene categorization and its functionality, gene expression analysis of data obtained from micro- array experiments etc. These clustering techniques are addressed with R. Clustering techniques are used to analyze the structure of biological data. There are many different methods but we study k- means, Hierarchical and density- based clustering algorithm for Biological Data using R programming tool.

关键词： k- means algorithm Hierarchical algorithm density-based algorithm

来源：评论

学校读者我要写书评

暂无评论

Study and Implementation of Clustering algorithms in R

Study and Implementation of Clustering Algorithms in R

引用

International Conference on Current Trends in Computer, Electrical, Electronics and Communication

作者： Gaurav Meena Pradeep Singh Chauhan Ravi Raj Choudhary Department of Computer Science Central University of Rajasthan Ajmer Rajasthan 305917

ISBN: (纸本)9781538632444

clustering is a process in which we group the data by finding similarities between data based on their characteristics. These groups are called cluster. In clustering, there is a division of data into groups of similar objects. These groups are the clusters, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. Clustering is unsupervised learning technique, based on the concept of maximize intra-clustering and minimize inter- clustering. Nowadays, clustering of biological dataset is the widely researched topic among computer science. Bio-informatics has become area that receive most of the attention of data mining techniques. Generally, bio-informatics targets to solve complicated problems like gene categorization and its functionality, gene expression analysis of data obtained from micro-array experiments etc. These clustering techniques are addressed with R. Clustering techniques are used to analyze the structure of biological data. There are many different methods but we study k- means, Hierarchical and density-based clustering algorithm for Biological Data using R programming tool.

关键词： K-means algorithm Hierarchical algorithm density-based algorithm

来源：评论

学校读者我要写书评

暂无评论

density-based trajectory outlier detection algorithm

引用

Journal of Systems Engineering and Electronics 2013年第2期24卷 335-340页

作者： Zhipeng Liu Dechang Pi Jinfeng Jiang College of Information Science and Technology Nanjing University of Aeronautics and Astronautics College of Computer Science and Techonology Nanjing University of Posts and Telecommunications

With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory *** the field of data mining for moving objects,the problem of anomaly detection is a hot *** on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and *** results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.

关键词： density-based algorithm trajectory outlier detection（TRAOD） partition-and-detect framework Hausdorff distance

来源：评论

学校读者我要写书评

暂无评论

Using Twitter to Enhance Traffic Incident Awareness

Using Twitter to Enhance Traffic Incident Awareness

引用

International IEEE Conference on Intelligent Transportation Systems

作者： Shen Zhang Sch. of Transp. Sci. & Eng. Harbin Inst. of Technol. Harbin China

ISBN: (纸本)9781467365970

Automatic incident detection is an important component of intelligent transportation management systems that provides information for emergency traffic control and management purposes. Social media are rapidly emerging as a novel avenue for the contribution and dissemination of information that has immense value for increasing awareness of traffic incidents. In this paper, we endeavor to assess the potential of the use of harvested tweets for traffic incident awareness. A hybrid mechanism based on Latent Dirichlet Allocation (LDA) and document clustering is proposed to model incident-level semantic information, while spatial point pattern analysis is applied to explore the spatial patterns. A global Monte Carlo K-test indicates that the incident-topic tweets are significantly clustered at different scales up to 600m. Then a density-based algorithm successfully detects the clusters of tweets posted spatially close to traffic incidents. The experiments support the notion that social media feeds act as sensors, which allow enhancing awareness of traffic incidents and their potential disturbances.

关键词： Monte Carlo methods emergency management information dissemination intelligent transportation systems pattern clustering road accidents road traffic control social networking (online) statistical testing text analysis traffic information systems LDA Twitter automatic incident detection density-based algorithm document clustering emergency traffic control information global Monte Carlo K-test hybrid mechanism incident-level semantic information incident-topic tweets intelligent transportation management system latent Dirichlet allocation social media feeds spatial point pattern analysis traffic incident awareness enhancement tweet cluster detection Clustering algorithms Media Semantics Sensors Sociology Statistics Incident Detection Latent Dirichlet Allocation Social Media Spatial Point Pattern

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：