Computational modeling of flee-surface slurry flow problems using particle simulation method
蛋白质研究的进展英语作文
蛋白质研究的进展英语作文In recent years, the field of protein research has seen remarkable advancements that have revolutionized our understanding of biological processes and opened new avenues for medical treatments. This essay will discuss the latest developments in protein research, including structural analysis, functional insights, and therapeutic applications.Firstly, the advent of high-resolution imaging techniques such as cryo-electron microscopy (cryo-EM) has allowed scientists to visualize proteins at near-atomic resolutions. This has led to a more detailed understanding of protein structures, which is crucial for deciphering their functions within the cell. Cryo-EM has been instrumental in studying proteins that were previously difficult to crystallize for X-ray diffraction, thus expanding the database of known protein structures exponentially.Secondly, the field of proteomics, which involves the large-scale study of proteins, has made significant strides. Techniques such as mass spectrometry have become more sophisticated, enabling researchers to identify and quantify thousands of proteins in a single sample. This hasfacilitated the discovery of protein-protein interactions and the mapping of complex signaling pathways, which are vitalfor understanding cellular processes and disease mechanisms.Thirdly, the study of protein dynamics has gained momentum.Proteins are not static entities; they undergo conformational changes that are essential for their function. Researchers are now able to capture these dynamic processes through advanced computational modeling and single-molecule techniques, providing a more nuanced view of how proteins operate within biological systems.In the realm of therapeutics, protein research has yielded promising results. Monoclonal antibodies, for instance, have become a cornerstone of targeted cancer therapies. These antibodies are designed to specifically bind to proteins on the surface of cancer cells, leading to their destruction without harming healthy cells. Additionally, the development of protein-based drugs, such as insulin for diabetes and erythropoietin for anemia, has improved the quality of life for millions of patients.Lastly, the CRISPR-Cas9 system has emerged as a powerful tool for protein research and gene editing. By leveraging the protein's natural ability to target specific DNA sequences, scientists can now edit genes with unprecedented precision. This has profound implications for the study of protein function and the potential to correct genetic disorders at the molecular level.In conclusion, the progress in protein research has been nothing short of transformative. From structural elucidation to therapeutic applications, proteins remain at the forefront of biological discovery. As technology continues to advance, the horizon of what we can learn from and do with proteins isonly expanding, promising a future filled with new insights and breakthroughs.。
人工智能领域中英文专有名词汇总
名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域 二级分类 三级分类。
点云数据转换成实体模型通过基于点的立体像素化立体像素
点云数据转换成实体模型通过基于点的立体像素化立体像素PointCloudDataConversionintoSolidModelsviaPoint-BasedVoxelization1 2 3 4Tommy Hinks ; Hamish Carr ; Linh Truong-Hong ; and Debra F. Laefer, M.ASCEAbstract:Automatedconversionofpointclouddatafromlaserscanninginto formatsappropriateforstructuralengineeringholdsgreatprom- iseforexploitingincreasinglyavailableaeriallyandterrestriallybase dpixelizeddataforawiderangeofsurveying-relatedapplicationsfrom environmental modeling to disaster management. This paper introduces a point-based voxelization method to automatically transform pointclouddataintosolidmodelsforcomputationalmodeling.Thefundamentalvi abilityofthetechniqueisvisuallydemonstratedforbothaerial andterrestrialdata.Foraerialandterrestrialdata,thiswasachievedinl essthan30sfordatasetsupto650,000points.Inallcases,thesolid models converged without any user intervention when processed in a commercial ?nite-element method program. DOI: 10.1061/ASCESU.1943-5428.0000097 2013 American Society of Civil Engineers.CE Database subject headings: Data processing; Surveys; Finite element method; Information management.Author keywords: Terrestrial; Aerial; Laser scanning; LiDAR; Voxelization; Computational modeling; Solid models; Finite element.Introductionexist.Thispaperlaysthegroundworkforkeyadvancementinsucha pipeline. The procedure proposed herein to reconstruct buildingLaser scanning has achieved great prominence within the civil en- facadesfrompointcloud,whichisafundamentalstepforgenerating gineering community in recent years for topics as divergent as city-scale computational models.coastline monitoring Olsen et al. 2009, 2011, airport layout op- timization Parrish and Nowak 2009, and ground-displacementidenti?cation for water-system risk assessment Stewart et al.FacadeReconstruction2009. Additionally, there has been strong motivation to obtainfurther functionality from laser scanning and other remote-sensing Inrecentyears,developmentsinlaser-scanningtechnologyand?ight-data, including three-dimensional 3D volume estimation forpath planning have allowed aerial laser scanning ALS to acquire mining Mukherji 2012, road documentation Dong et al. 2007,pointclouddataquicklyandaccuratelyatacityscale,therebyhaving structuralidenti?cationShanandLee2005;Zhangetal.2012,and thepotentialforreconstructing3Dbuildingsurfacesacrossanentire emergency planning Laefer and Pradhan 2006. Furthermore,city in nearly real time. A number of approaches based on semi- computational responses of city-scale building groups are increas- automaticLangandForstner1996andautomaticHenricssonetal. inglyindemandforheightenedurbanization,disastermanagement,1996techniqueshavebeenproposedtoreconstructbuildingmodelsand microclimate modeling, but input data are typically too ex- from such data sets, but automatically extracting highly detailed, pensive as a result of the need for manual surveying. Additionally, accurate,andcomplexbuildingsstillremainsachallengeHaalaandcurrent software tools for transforming remote-sensing data into Kada 2010. The semiautomatic procedures need human operator computationalmodelshaveoneormoreofthefollowingproblems: intelligence.TheautomaticvisualmodelingofurbanareasfromALS alowdegreeofreliability,aninabilitytocapturepotentiallycritical data tends to extract sample points for an individual building by details,and/oraneedforahighdegreeofhumaninteraction.Todate, applying segmentation techniques and then reconstructing eacha seamless, automated, and robust transformation pipeline frombuilding individually. In such cases, vertical facade surfaces are notremote-sensing data into city-scale computational models does not portrayed in detail, and outlines may be of relatively low accuracy unless ground planes are integrated, which requires either a priori1 informationormanualintervention.Unfortunately,theeffectivenessDoctoralRecipient,SchoolofComputerScienceandInformatics,Univ.of engineering modeling often depends largely on the geometricCollege Dublin, Bel?eld, Dublin 4, Ireland. E-mail: ******************2accuracy and details of the building models?thus the currentSeniorLecturer,SchoolofComputing,FacultyofEngineering,Univ.ofmismatch.Leeds, Leeds LS2 9JT, U.K. E-mail: h.carr@//0>.3Post-doctoral Researcher, Urban Modelling Group, School of Civil, Presently, commercial products are generally semiautomatic StructuralandEnvironmentalEngineering,Univ.CollegeDublin,Bel?eld, Laefer et al. 2011, whereas in the computer graphics and photo- Dublin 4, Ireland. E-mail: linh.truonghong@gmailgrammetry communities, researchers have focused on automated4AssociateProfessor,LeadPI,UrbanModellingGroup,SchoolofCivil,surfacereconstructionfromdenseandregularsamplepointsHoppeStructuralandEnvironmentalEngineering,Univ.CollegeDublin,Bel?eld, 1994; Kazhdan et al. 2006. Unfortunately, ALS data are oftenDublin 4, Ireland corresponding author. E-mail: ******************* sparse and irregular, and may contain major occlusions on vertical Note.ThismanuscriptwassubmittedonNovember16,2011; approvedsurfaces owing to street- and self-shadowing Hinks et al. 2009.on September 10, 2012; published online on September 13, 2012. Discus- Dedicated urban modeling surface-reconstruction approachession period open until October 1, 2013; separate discussions must be generallyusethemajorbuildingplanesChenandChen2007andsubmitted for individual papers. This paper is part of the Journal ofcan be described as either model-driven or data-driven. Model-Surveying Engineering, Vol. 139, No. 2, May 1, 2013ASCE, ISSN0733-9453/2013/2-72?83/$25.00. driven techniques use a ?xed set of geometric primitives that are72 / JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13.Copyright ASCE. For personal use only; all rights reserved.Fig. 1. Work?ow of the proposed approach: *Collection and preparation of LiDAR data involve multiple steps outside the scope of this paper’s scienti?ccontribution;thesegenerallyincludeplanning,collection,re gistration,and?ltering;seeTruong-Hong2011andHinks2011forfurther detailsttedtothepointdata.Suchtechniquescanbeeffectivewhenadataset is sparse because the ?tting of geometric primitives does not require complete data. In contrast, data-driven techniques derive surfaces directly from the point data and are capable of modeling arbitrarilyshapedbuildings.Generally,data-drivenapproachesaremore?exiblethanmodel-drivenapproaches,butareoftensensitiveto noise in the input data.For strictly visual representation, model-driven approachescanbeeffective.Forexample,Haalaetal.1998 proposed four dif-ferent primitives and their combinations to automatically derive 3D building geometry of houses from ALS and existing ground planes.Similarly, Maas and Vosselman 1999 introduced an invariantmoment-basedalgorithmfortheparametersofastandardgabled-roofhouse type that allowed for modeling asymmetric elements such as dormers. However, these efforts assume homogeneous point dis-Fig. 2. Octree representationtributions, which is unrealistic. You et al. 2003 also adapted a set of geometric primitives and ?tting strategies to model complex buildings with irregular shapes, but the approach required user interventionandgeneratedonlylimitedwalldetails.Huetal.2004used a combination of linear and nonlinear ?tting primitives to SolidModelingreconstructacomplexbuilding,inwhichaerialimagerywasusedtore?ne the models. To generate building models directly from point cloud data forIncontrast,manydata-driventechniquesoperatingonALSdata engineering simulations [e.g., FEM], there are three dominant reconstruct roof shapes directly from sample points of roof planes. methods:1constructivesolidgeometryCSG,whereobjectsareSubsequently, the remainder of the building is simply extruded represented using Boolean combinations of simpler objects; 2 to the ground level from the roof-shape outlines. Vosselman and boundary representations B-reps, where object surfaces are rep- Dijkman2001usedaHoughtransformforextractionofplanefaces resentedeitherexplicitly orimplicitly;and3spatialsubdivision roofplanesfromtheALSdata,andthen3Dbuildingmodelswere representations,wherean objectdomain is decomposed intocells reconstructed by combining ground planes and the detected roof withsimple topologic and geometric structure, such as regular planes.Hofmannetal.2003introducedamethodtoextractplanar gridsandoctreesGoldman2009;HoffmannandRossignac1996;roof faces by analyzing triangle mesh slopes and orientations from there are many extensive treatises available for in-depth consid-a triangular irregular network structure generated from ALS data. eration of this topic B?hm et al. 1984; Rossignac and Requicha More recently, Dorninger and Pfeifer 2008 used an a-shape ap- 1984, 1999.proach to determine a roof outline from point clouds of the roof Generating solid models automatically from point cloud data projectedontoahorizontalplane.Also,ZhouandNeumann2010 is particularly important because the cost of manually creating created impressive buildings for a large urban area by using a vol- solid models of existing objects is far greater than the associated umetric modeling approach in which roof planes were determined hardware,software,andtrainingcosts.Assuch,spatialsubdivision based on a normal vector obtained from analysis of grid cells be- representations are used extensively for creating solid models of longingtorooflayers.However,thesemodelsarealsoextrudedand buildings in which regular grids or octrees are employed to de- lack vertical-wall details. compose an entire object intononoverlapping 3D regions, com-Therefore, this paper presents an automated approach to con- monly referred to as voxels. Voxels are usually connected andverting point clouds of individual buildings into solid models for described a simple topologic and geometric structure. In grids, structural analysis by means of computational analysis in which avolumeissubdividedintosmallerregionsbyappropriateplanes thepointcloudthatweresemiautomaticallysegmentedfromLight parallel to the coordinate system axes,typically using aCartesian Detection and Ranging LiDAR data become the input Fig. 1. coordinate system. An initial voxel bounding all point data re-Notably, this proposed approach focuses on reconstructing solid cursively divides a volume into eight subvoxels, organized in modelsbyusingvoxelgridswiththecriticalparameteraseitherthe a hierarchical structure Samet 1989. Voxels may be labeled voxel size or the number of voxel grids; for more details on col- white,black,orgraybasedontheirpositionsFig.2.Blackvoxels lecting ALS and terrestrial laser scanning TLS data and on are completely inside the solid, whereas white voxels are com- segmenting point clouds, see Truong-Hong 2011andHinks pletelyoutside.Voxelswithbothblackandwhitechildrenaregray 2011. Hoffmann and Rossignac 1996.JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013 / 73J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13. Copyright ASCE. For personal use only; all rights reserved.Fig.3.Voxelgridspanningavolumeina3Dspaceboundedbyx ,x ,y ,y ,andz ,z ,whe reDx,Dy,andDzarevoxelsizes andmin min minN , N , and N are the number of voxels in each directionx y zIn an application of spatial subdivision for surface recon-struction,CurlessandLevoy1996presentedavolumetricmethodforintegratingrangeimagestoreconstruc tanobject’ssurfacebasedon acumulative weighted signed-distancefunction. Unfortunately,the approach is not suited for arbitrary objects. In related work, GuarnieriandPontin2005builtatriangulatedmeshofanobject’ssurfacebycombiningaconsensussurface[asproposedbyWheeleret al. 1998], an octree representation, and the marching-cubesalgorithm Lorensen and Cline 1987. This multifaceted algorithmFig. 4. Point-based voxelization avoids surface reconstruction and canreducetheeffectofthenoiseowingtosurfacesampling,sensoroperates directly on point datameasurements,andregistrationerrors.However,foroptimalresults,themethodrequiresmodi?cationofparametersthatdependheavilyon input-data characteristics such as the voxel size, the threshold value for the angle, and the distance between two consecutive neighbor-range viewpoints. z 2zminN? 1 ?3?zDzThevoxelhaseightlatticeverticesassociatedwithsixrectangular VoxelizationfacesFig.3.Eachinteriorvoxelhas26neighboringvoxels,witheight sharing a vertex,12 sharing an edge,and six sharing a face. Critical to octree/quadree representations for further processing is Conversely,anexteriororinteriorvoxelonahole’sboundaryoften voxelization. This term describes the conversion of any type of has only 17 neighboring voxels four sharing a vertex, eight geometric or volumetric object such as a curve, surface, solid, or sharinganedge,and?vesharingaface.Moreover,mostexisting computedtomographicdataintovolumetricdatastoredina3Darray voxelization techniques operate on surface representations ofof voxels Karabassi et al. 1999. Initially, a voxel grid divides objects, where a signi?cant part of the problem is to identifya bounded 3D region into a set of cells, which are referred to as throughwhichvoxelsthesurfacespass.Suchmethodsarereferredvoxels. The division is typically conducted in the axial directions to as surface-based voxelization Cohen-Or and Kaufman 1995of a Cartesian coordinate system. Before voxelization, three pairs [Fig.4a?c].Incontrast,thepoint-basedvoxelizationinthispaper ofcoordinatevalues??x , x , ?y , y , and ?z , z ? aremin min minoperates directly on the point data and does not require a derived createdalongthethreeaxesX, Y, and Zde?ningaglobalsystemsurface [Fig. 4a?c]. Point-based voxelization is conceptually Fig. 3. The basic idea of a voxelization algorithm is to examine much simpler than surface-based voxelization algorithms, and whethervoxelsbelongtotheobjectofinterestandtoassignavalue whereas the mechanisms are well known, they have not beenof 1 or 0,respectively Karabassi et al. 1999; a further description applied to generating solid modeling of buildings from LiDARof voxel grids is available in Cohen and Kaufman 1990.data.An initial voxel bounding all point cloud data in 3D Euclidean3Asmentionedearlier,eachvoxelisclassi?edasactiveorinactivespaceR is subdivided into subset voxels by grids along the x-, y-, corresponding to binary values based on the sample points within andz-coordinatesinaCartesiancoordinatesystem.Eachvoxelinthethat voxel [Eq. 4]subset is represented by an index v?i, j, k?, where i2?0; N 21 , xj2?0; N 21 , and k2?0; N 21 Fig. 3. With the dimensionsy zactive ifn$TnofindividualvoxelsDx, Dy, Dz,anumberofvoxelsN , N , Nx y zf n?4?valong each direction are given in Eqs. 1?3 inactive ifn,Tnwheretheargumentn5numberofpointsmapping to avoxel,andx 2xmin T 5user-speci?edthresholdvalue.Typically,T 51,whichmeansn nN? 1 ?1?xDxthat voxels containing at least one mapping point are classi?edasactiveandallothersasinactive.Moresophisticateddensity-basedy 2yminclassi?cation functions can be designed. An example is shown inN? 1 ?2?yDyFig. 5.74 / JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13. Copyright ASCE. For personal use only; all rights reserved.Fig. 5. Voxelization model of front building of Trinity College, Dublin, Ireland, created by a voxel grid: a input data set of 245,000 ALS points;bvoxelizationmodelwithvoxelsizeDx5Dy5Dz50:25m;cvoxelclassi?cationwiththethresholdT51andvoxelizationmodelwithaboutn5,000 active voxels n is the largest number of points mapping to asingle voxelFig. 6. Solid model componentsProposedConversionofVoxelizedModelsintoSolidModelsTo reconstruct vertical surfaces of building models, a voxel grid is used to divide data points in a bounded 3D region into smallervoxels. Important facade features such as windows and doors are subsequently detected basedon a voxel’s characteristics, where an inactive voxel represents the inside of an opening. Consequently, building models are converted into an appropriate format for com- putational processing.Anobjectisde?nedbyitssurfaceboundary,whichthenmustbeFig. 7. Face orientation as dictated by the right-hand ruleconvertedintoanappropriatesolidrepresentationcompatiblewithcommercialcomputationalpackages.Althoughmanyschemesareavailable,B-repsarehereinadoptedbecauseoftheircompatibilitywith commercial structural-analysis software e.g., ANSYS soft- Keypointsarerepresentedbya3Dcoordinateofasingularpoint.ware Laefer et al. 2011. The proposed method de?nes both the An edge is de?ned as the connection between exactly two keygeometry and topology of an object by a set of nonoverlappingpoints;forexample,theedgee 5fP, Pgistheedgewithstartingij i jandendingpointP.Notably,edgeshaveanorientation;asfaces approximate the boundary of the solid model. This section pointPi jsuch, e 52eThus, the edges e and e would be ?ipped. EdgepresentsabriefdescriptionoftheB-repschemeimplementedintheij ji ij jiproposed approach; for more details, see Goldman 2009. Ge- ?ipping is important when de?ning an orientable face for dis-ometry is de?ned by key singular points, with each point rep- tinguishing the inside from the outside.resenting a speci?c location in space. Topology is de?ned by Similarly, faces represent surfaces of a solid model that areconnections between key points. When used together, they can connections between edges. The faces are further connected de?neasolidmodelFig.6.DatastructuresfordescribingB-reps to form volumes. A face is de?ned as a list of edgesoften capture the incidence relations between a face and its f5fe ,e ,.,e g that involve closed paths. A face01 12 ?n22??n21?bounding edges and an edge and its bounding vertices, whichconsistingofthreekeypointsisatriangle,whereasqu。
CS专业研究方向详解(1)
美国大学CS专业十三大研究方向美国大学CS专业的研究分支也超级多，不同分支对学生的要求也会不同，因此，学生们要依照自己的条件选择适合自己的研究方向。
一、体系结构、编译器和并行计算 Architecture, Compilers and Parallel Computing 体系结构和编译器的研究要紧集中在硬件设计，编程语言和下一代编译器。
并行计算研究的包括范围很广，包括并行计算的计算模型，并行算法，并行编译器设计等。
二、系统与网络 Systems and Networking可细分为：(1)网络与散布式系统(Networking and distributed systems)：移动通信系统，无线网络协议(wireless protocols)，Ad-hoc网络，效劳质量治理(Quality of Service management，QoS)，多媒体网络，运算机对等联网(peer-to-peer networking, P2P)，路由，网络模拟，主动队列治理(active queue management, AQM)和传感器网络(sensor networks)。
(2)操作系统(Operating system)：散布式资源治理，普适计算(ubiquitous computing/pervasive computing)环境治理，反射中间件(reflective middleware)，中间件元级操作系统(middleware “meta-operating systems”)，面向对象操作系统设计，许诺单个用户与多运算机、对等操作系统效劳交互的用户设计，上下文灵敏的散布式文件系统，数据中心的电源治理，文件/存储系统，自主计算(autonomic computing)，软件健壮性的系统支持和数据库的系统支持。
(3)平安(Security): 隐私，普适计算，无线传感器(wireless sensors)，移动式和嵌入式运算机，标准，认证，验证策略，QoS保证和拒绝效劳爱惜，下一代通信，操作系统虚拟化和认证，关键基础设施系统，例如SCADA操纵系统和医疗，消息系统，平安网关，可用性平安。
光学自由曲面加工技术
光学自由曲面加工技术English Answer:Optical Freeform Surface Manufacturing Technology.Optical freeform surfaces are complex optical elements with surfaces that deviate significantly from traditional spherical or cylindrical shapes. Their ability to manipulate light in novel ways has led to advancements in numerous fields, including optics, lasers, and imaging systems.The manufacturing of optical freeform surfaces requires specialized techniques to achieve the desired surface form and optical properties. One such technique is diamond turning, which uses a single-point diamond cutting tool to remove material from the workpiece in a controlled manner. Another technique is ultra-precision grinding, which utilizes a grinding wheel with sub-micron abrasiveparticles to shape the workpiece surface. These techniquesallow for precise control over the surface form and roughness, leading to high-quality optical components.Advancements in Optical Freeform Surface Manufacturing.Recent advancements in optical freeform surface manufacturing have enabled the production of increasingly complex and precise optical elements. These advancements include:Improved machining techniques: New diamond turning and ultra-precision grinding techniques have significantly reduced加工误差and improved surface quality.Advanced metrology systems: Non-contact measurement techniques such as interferometry and laser scanning allow for accurate and efficient characterization of freeform surfaces.Computational modeling and simulation: Computer simulations can now predict the behavior of light interacting with freeform surfaces, aiding in the designand fabrication of optical systems.Applications of Optical Freeform Surfaces.Optical freeform surfaces find applications in a wide range of fields, including:Imaging systems: Freeform lenses and mirrors enable compact and high-performance imaging systems with improved resolution and field of view.Optical communications: Freeform surfaces can be used to create optical components for fiber optic networks, improving signal transmission and bandwidth.Laser systems: Freeform surfaces can enhance laser beam quality, stability, and power output.Precision optics: Optical freeform surfaces are usedin instruments for metrology, microscopy, and spectroscopy.Challenges and Future Directions.While significant progress has been made in optical freeform surface manufacturing, several challenges remain:High production cost: The fabrication of freeform surfaces can be time-consuming and expensive, limitingtheir widespread adoption.Complex design and modeling: The design and modeling of freeform surfaces can be complex and computationally intensive.Metrology challenges: Measuring the complex shapes of freeform surfaces accurately and efficiently remains a challenge.Future research and development efforts will focus on addressing these challenges and further advancing the field of optical freeform surface manufacturing.Chinese Answer:光学自由曲面加工技术。
Computational Fluid Dynamics Modeling of。。。Steelmaking Process外文翻译
学生毕业设计（论文）外文译文学院冶金与材料工程学院专业班级冶金工程学生姓名学号译文要求1.外文翻译必须使用签字笔，手工工整书写，或用A4纸打印。
2.所选的原文不少于10000印刷字符，其内容必须与课题或专业方向紧密相关，由指导教师提供，并注明详细出处。
3.外文翻译书文本后附原文（或复印件）。
文献出处：METALLURGICAL AND MATERIALS TRANSACTIONS B, 2010, 41B(6): 1354-1367.电弧炉炼钢过程中超音速聚流氧枪的流体动力学模拟MORSHED ALAM, JAMAL NASER, GEOFFREY BROOKS, andANDREA FONTANA摘要：超音速的气体射流现在广泛应用于电弧炉炼钢，其他许多工业用来增加气液混合，反应速率和能量效率。
然而，对于超音速聚流氧枪，已有的基本物理研究非常有限。
在本研究中，超音速射流流体动力学（CFD）在有火焰覆盖环境温度和室温中的实验数据进行验证。
数值结果表明，超音速氧、氮的射流在火焰覆盖的潜在的核心长度分别比无火焰覆盖的超过4倍和3倍，这是与实验数据相吻合。
使用火焰笼罩的超音速射流相比常规的超音速射流的扩展率显着下降。
本CFD模型被用于在大约1700K（1427℃）炼钢条件下研究连续超音速氧气射流的特性。
连续超音速氧气射流在炼钢条件的潜在的核心长度是在室温环境温度的1.4倍。
1 引言在碱性氧气转炉和电弧炉炼钢中，高速气体射流被广泛使用于熔炉中提纯铁液和搅拌溶液。
由于动高压与其联合使之具有更高更深的穿透力和能够更好的融合，所以超音速气体射流优于亚音速气流。
拉法儿喷嘴在炼钢中过去常被用来加快气体射流使之接近马赫数2.0的超音速速度[1]。
当一个超音速射流从拉法儿喷嘴喷出时，它便于周围的环境相互作用产生一个湍流混合的区域。
在与喷嘴距离加大的过程中，射流直径会增加，射流速度会减缓。
在吹氧期间，液面与喷嘴出口之间的距离越大，周围流体的夹带越多，反过来又降低了冲击速度以及渗透液面的深度。
Introduction to Finite Element Method
1. Mathematical Model
(1) odeling
Physical Problems Mathematica l Model Solution
Identify control variables Assumptions (empirical law)
(2) Types of solution Sol. Eq.
(b) total number of element (mesh) 1D: 2D: 3D:
b. Select a shape function 1D line element: u=ax+b c. Define the compatibility and constitutive law
d. Form the element stiffness matrix and equations (a) Direct equilibrium method (b) Work or energy method (c) Method of weight Residuals
Continuous system Time-independent PDE Time-dependent PDE Discrete system Linear algebraic eq. ODE
(2) Discretization Modeling a body by dividing it into an equivalent system of finite elements interconnected at a finite number of points on each element called nodes.
(2) Analysis procedures of linear static structural analysis
Geometric Modeling
Geometric ModelingGeometric modeling is a crucial aspect of computer graphics and design,playing a significant role in various industries such as architecture, engineering, animation, and manufacturing. It involves creating digital representations of objects and environments using mathematical and computational techniques. Geometric modeling allows designers and engineers to visualize and analyze complex structures, simulate real-world scenarios, and communicate their ideas effectively. However, it also presents several challenges and limitations that need to be addressed to ensure accurate and efficient modeling processes. One of the primary challenges in geometric modeling is achieving precision and accuracy in representing real-world objects and environments. Designers and engineers often need to create highly detailed and intricate models that accurately reflect the physical properties and behavior of the objects they are working with. This requires advanced mathematical algorithms and computational techniques to ensure that the digital models are as close to reality as possible. Inaccurate or imprecise geometric models can lead to design flaws, engineering errors, andcostly rework, highlighting the importance of addressing this challenge. Another significant challenge in geometric modeling is handling complex geometries and shapes. Many real-world objects and structures have irregular and non-standard shapes that are difficult to represent using traditional geometric primitives such as spheres, cubes, and cylinders. This complexity is further compounded in industries such as aerospace, automotive, and biomedical engineering, where the need for highly complex and organic shapes is prevalent. Overcoming this challenge requires the development of advanced modeling techniques, such as freeform modeling and surface reconstruction, to accurately capture and represent complex geometries. Furthermore, geometric modeling also faces challenges related to computational efficiency and performance. Creating and manipulating geometric models often involves complex mathematical operations and algorithms that can be computationally intensive. As the size and complexity of models increase, the computational requirements also escalate, leading to longer processing times and reduced interactivity. This can hinder the design and engineering process, makingit difficult for designers and engineers to work with large and intricate modelsin a timely manner. Addressing this challenge involves optimizing algorithms, leveraging parallel processing techniques, and utilizing hardware acceleration to improve computational efficiency and performance. In addition to technical challenges, geometric modeling also raises issues related to interoperability and data exchange. In today's collaborative and interconnected design environment, it is essential for geometric models to be compatible and interoperable with various software applications and systems. However, different software tools often use proprietary data formats and representations, making it challenging to exchange and work with geometric models across different platforms. This interoperability challenge can impede seamless collaboration and data exchange between designers, engineers, and other stakeholders, highlighting the need for standardized data formats and interoperability solutions. Moreover, geometric modeling also presents challenges related to modeling real-time interactive environments and simulations. In applications such as virtual reality, gaming, and simulation, it is essential to create geometric models that can be rendered and interacted with in real-time. Achieving real-time interactivity and visual fidelity requires optimizing geometric models, leveraging level-of-detail techniques, and utilizing advanced rendering algorithms. This challenge becomes even more pronounced as the demand for immersive and realistic virtual environments continues to grow, necessitating the development of innovative solutions to address this challenge. Furthermore, ethical considerations also come into play in geometric modeling, particularly in applications such as medical imaging and virtual reality. The use of geometric models in these contexts raises concerns about patient privacy, data security, and the potential misuse of digital representations. Designers and engineers must be mindful of these ethical considerations and ensure that the use of geometric models complies with ethical standards and regulations. This involves implementing secure data handling practices, obtaining informed consent for the use of patient data, and safeguarding the integrity and privacy of digital representations. In conclusion, geometric modeling is a critical component of computer graphics and design, enabling designers and engineers to create digital representations of objects and environments for various applications. However, it also presents several challenges and limitations that need to be addressed toensure accurate and efficient modeling processes. From achieving precision and accuracy to handling complex geometries, improving computational efficiency, addressing interoperability issues, enabling real-time interactivity, and considering ethical considerations, there are numerous aspects to consider in the realm of geometric modeling. Overcoming these challenges requires the development of advanced mathematical algorithms, computational techniques, and ethical frameworks to ensure that geometric modeling meets the needs of diverse industries while upholding high standards of accuracy, efficiency, and ethical responsibility.。
用Fluent液固相变模拟Liquid-Solid Phase Change Modeling Using Fluent
Liquid-Solid Phase Change Modeling Using FluentAnirudh and Joseph Lam 2002 Fluent Users’ Group MeetingSolidification Modelu uFLUENT can be used to solve fluid flow problems involving both solidification and melting for pure materials and alloys Instead of tracking the liquid-solid front explicitly, an enthalpyporosity formulation is used For pure materials, (Tsolidus and Tliquidus are equal), a method based on specific heat is used The energy equation is written in terms of enthalpy:∂ r (ρH )+ ∇.(ρ uH ) = ∇.(k∇T ) + S ∂tuuuThe liquid fraction, can be defined as:u uβ = 0 if T ≤ Tsolidus β = 1 if T ≥ Tliquidusβ= T − Tsolidus Tliquidus − Tsolidusuif Tsolidus< T <Tliquidus ,2UGM 2002ConfidentialSolidification ModeluuuuOther relationships between the liquid fraction and temperature (and species concentrations) are possible, but are not implemented yet The latent heat content/release can be written in terms of the latent heat and state of melting of the material, L: ∆ H = β L Instantaneous latent heat content/release can vary between zero (for a solid) and L (for a liquid) Solution for temperature is essentially an iteration between the energy and the liquid fraction equationUGM 2002Confidential3Treatment of Mushy Zone in Alloysu uuIn Mushy zone, liquid fraction lies between 0 and 1 Mushy zone is modeled as a ''pseudo'' porous medium: porosity decreases from 1 to 0 as the material solidifies Appropriate momentum sink terms are added to account for the pressure drop caused by the solid material(1 − β ) 2 r r S= 3 A mush ( v − v p ) (β + ε )uβ : ε : A r mush : vP :liquid volume fraction small number to prevent division by 0 is the mushy zone constant and the solid pull velocityuPorosity in a solidified cell is zero: velocities takes on the solid (pull) velocity Similar sinks are also added to the turbulence equations (1 − β ) 2 S= 3 A φ (β + ε ) mush4UGM 2002ConfidentialContact Resistance Due to ShrinkageuWall T Tw Near wall cellulTw Rc l/k TPresence of an air gap between the walls and the solidified material causes an additional heat transfer resistance between the cooling walls and adjacent fluid with liquid fraction less than 1 This contact resistance is accounted for by adding appropriate resistance in the wall heat flux calculation:Contact Thermal Resistance Near the Wall( T − Tw ) q= ( l / k + R c ( 1 − β ))k : thermal conductivity β : liquid volume fraction, and Rc : contact resistance[m 2K / W]UGM 2002 Confidential5Pull Velocity for Continuous CastinguIn continuous casting processes, the solidified material is “continuously” pulled out from the computational domainu uuSolid material has a finite velocity that influences solidification front The exact computation of the pull velocity for the solid material is dependent on the E and σ of the solid and the forces acting on it In FLUENT, an approximated Laplacian equation is used:r ∇ vp = 02uAppropriate Dirichlet boundary conditions are used on walls and outlets and the fluid velocity is picked up at the interface mold Solidified Shell PullHorizontal Continuous Casting of IronUGM 2002 Confidential6Solidification/Melting Model InputsuDefine/models/ solidification and melting ...uDefine/materials...UGM 2002Confidential7Continuous Casting of Round BilletSlag (oil) InletInput Parameters : Parameters:u uMoldLiquid Symmetry axis Moving wallu u u u u u u uDiameter of the mold : 0.115 m mold: Diameter of the nozzle : 0.03 m nozzle: Mold length : 0.50 m length: Casting speed: 0.03 m/s speed: Solidus temperature : 1490 °C temperature: Liquidus temperature : 1530 °C temperature: Melt superheat: 20 °C superheat: Mold heat transfer coefficient : 1270W/m2/°C coefficient: Spray heat transfer coefficient: 1080W/m2/°C coefficient: Simulated length : 3.0 m length:Casting speedSchematic configurationUGM 2002Confidential8Continuous Casting of Round BilletInput Parameters (continued): (continued):Density: Density: 7020 kg/m3 u Viscosity: Viscosity: 6.2x10 -3 J/kg u Latent heat of fusion: 270x103 J/kg fusion: u Specific heat : 680 J/kg/ °C heat: u Thermal conductivity : 34 W/m/ °C conductivity: u Thermal expansion coefficient: 1x10-4/K coefficient: u Permeability coefficient: 5.0x10 -11 m 2 coefficient: u Mesh size : 227x48 cells (10,896 total) size: u Near wall treatment: enhanced wall treatment treatment:Solidified shell thickness (mm)0 0.0 Expt. of Ushijima (1962) Computed profile 0 20 30 40 50uPressure-outletuComputational mesh in the upper regionSolidification profile is obtained based on 30% liquid fraction (after Aboutalebi et al, 1995) Comparison of predicted solidified shell thickness with the experiments of Ushijima (1962) shows good agreement9UGM 2002ConfidentialDistance from the meniscus (m)0.51.01.52.02.53.03.5Melting of Pure Material: GalliumInsulated wall gCold wallt = 5 min (current) t = 8 min (current) t = 12 min (current) t = 5 min (Exp.*) t = 8 min (Exp.*) t = 12 min (Exp.*) t = 5 min (Rep. Comp.) t = 8 min (Rep. Comp.) t = 12 min (Rep. Comp.) *(Webb and Viskanta, NHT Vol. 9, pp. 539-558, 1986)Hot wall4.5 cmInsulated wall 9.0 cmMaterial: Gallium(Pure) Property Units Density kg/m3 Specific Heat j/kg-k Therm. Cond. w/m-k Viscosity kg/m-s Therm. Exp. Coeff. 1/k Melting Heat j/kg Solidus Temperature Liquidus Temperature1.2Validation of Pure Metal Melting (Gallium)Value 5904 371.5 29 0.001639 9.9999997e-05 80000 k 302 k 3021.0 0.8 0.6 0.4 0.2 0.0 0Y-CoordinateSte = 0.042, Pr = 0.021, Ra = 2.2 x 105UGM 2002 Confidential0.20.40.6 0.8 1 X-Coordinate1.21.410Solidification with VOFu uuuA liquid metal droplet spreads and solidifies on a cooled substrate The VOF (free surface) and phase change models in FLUENT are used together to simulate the process Results are useful for applications of liquid metal jetting, used in electronics manufacturing Also useful for printing, painting, spray gluing applicationsConfidentialUGM 200211Liquid Metal Solidificationt = 50 µs solidification (black)uVOF model uses single set of fluid equationsutracks liquid/gas volume fraction and location and shape of gasliquid interface tracks liquid-solid interface within the liquid phase source terms in energy equation depend on liquid fraction and available latent heatuPhase change model uses enthalpy-porosity methodu uUGM 2002Confidential12Liquid Metal Solidification ...t = 250 µsu usolidification (black)uSolid layer is colored black Spread factor, dfinal/dinitial = 3.2 as predicted by FLUENT is in good agreement with published numerical finding, 2.971 Simulation shows that FLUENT can capture combined phenomena of free surface flows and phase changet = 750 µssolidification (black)t = 1.9 mssolidification (complete)R. Holt and Albert Y. Tong, “The normal incidence impact and solidification phenomena of a liquid metal droplet onto a rigid substrate”, ASME FED-Vol. 234, 1995 IMECE, 215-224.UGM 2002 Confidential1Brendon13Vibrating Mold with SolidificationuA 3D round billet case is studied with mold oscillationsu uThe mold stroke: 0.025m and frequency: 1Hz The average free-board is 0.15mu uSolidification in the mold is analyzed Free-surface of the steel is assumed shearfree and not dynamically recalculated as with a VOF analysislThis is *not* a limitation but a simplification adopted hereuAll other process parameters are same as the 2D studyConfidentialUGM 200214Vibrating Mold with SolidificationSome instantaneous solution fields Velocity vectors PathlinesuTemperatureLiquid FractionUGM 2002Confidential15Vibrating Mold with Solidificationu uLiquid FractionTemperatureuThese animations on a diameter-plane The mold water region contours are showing velocity magnitude in both cases The dynamics of the flow-fields are slower than the mold-oscillation rateuuIn a couple of mold-cycles, no significant change in the flow-field should be expected Oscillation frequency in the actual mold can be slowerUGM 2002Confidential16Mold Flow with Meniscus Atmospheric Pressure and SolidificationuSEN (V-in)A slab caster moldu u uuuFlow and heat transfer Solidification Mold top is open to atmosphere Free surface at the meniscus No chemistry is consideredSEN-jet (internal) Mold wall GeometryOpen slab Wall Pressure OutletUGM 2002Confidential17Mold Flow with Meniscus and SolidificationuMold top is open to atmosphereu uuNo mold powder is considered Air on top of the mold maintains a mild recirculating flow Outlet pressure reflects the force of the support-rolls which would determine the free-board in the molduSolidified shell on mold wall develops depending on the process parametersu u u uCasting speed Incoming superheat of liquid steel The jet penetration and SEN design Mold water flow rateConfidentialVelocity Vectors Liquid FractionUGM 200218Mold Flow with Meniscus and SolidificationuuuContact resistance is automatically introduced based on the local state of solidification Similarly, pull velocity is assigned on the solidified material only Figure shows shell thickness, free surface (meniscus) and the velocity field in a slab caster mold on the wide, symmetry faceUGM 2002Confidential19Summary of Melting/Solidification ModeluFeaturesu u u u u u uEnthalpy-Porosity formulation Accessible through GUI interface Contact resistance at walls (customizable) Marangoni convection at "walls" Solid pull velocities (both user-specified and computed) VOF-compatible Sink terms for momentum & turbulent quantities in mush & solid zones Lack of species transports model User-specified H-T curveuLimitationu uuBoth species model and custom-H-T curve should be available in the next release - macro-segregation can be modeledUGM 2002Confidential20。
Fluent用户手册
The FLUENT User's Guide tells you what you need to know to use FLUENT. At the end of the User's Guide, you will find a Reference Guide, a nomenclature list, a bibliography, and an index.!! Under U.S. and international copyright law, Fluent is unable to distribute copies of the papers listed in the bibliography, other than those published internally by Fluent. Please use your library or a document delivery service to obtain copies of copyrighted papers.A brief description of what's in each chapter follows:•Chapter 1, Getting Started, describes the capabilities of FLUENT and the way in which it interacts with other Fluent Inc. and third-party programs. It also advises you on how to choose the appropriate solverformulation for your application, gives an overview of the problem setup steps, and presents a samplesession that you can work through at your own pace. Finally, this chapter provides information aboutaccessing the FLUENT manuals on CD-ROM or in the installation area.•Chapter 2, User Interface, describes the mechanics of using the graphical user interface, the text interface, and the on-line help. It also provides instructions for remote and batch execution. (See the separate Text Command List for information about specific text interface commands.)•Chapter 3, Reading and Writing Files, contains information about the files that FLUENT can read and write, including hardcopy files.•Chapter 4, Unit Systems, describes how to use the standard and custom unit systems available in FLUENT.•Chapter 5, Reading and Manipulating Grids, describes the various sources of computational grids and explains how to obtain diagnostic information about the grid and how to modify it by scaling, translating, and other methods. This chapter also contains information about the use of non-conformal grids.•Chapter 6, Boundary Conditions, explains the different types of boundary conditions available in FLUENT, when to use them, how to define them, and how to define boundary profiles and volumetric sources and fix the value of a variable in a particular region. It also contains information about porousmedia and lumped parameter models.•Chapter 7, Physical Properties, explains how to define the physical properties of materials and the equations that FLUENT uses to compute the properties from the information that you input.•Chapter 8, Modeling Basic Fluid Flow, describes the governing equations and physical models used by FLUENT to compute fluid flow (including periodic flow, swirling and rotating flows, compressibleflows, and inviscid flows), as well as the inputs you need to provide to use these models.•Chapter 9, Modeling Flows in Moving Zones, describes the use of single rotating reference frames, multiple moving reference frames, mixing planes, and sliding meshes in FLUENT.•Chapter 10, Modeling Turbulence, describes FLUENT's models for turbulent flow and when and how to use them.•Chapter 11, Modeling Heat Transfer, describes the physical models used by FLUENT to compute heat transfer (including convective and conductive heat transfer, natural convection, radiative heat transfer,and periodic heat transfer), as well as the inputs you need to provide to use these models.•Chapter 12, Introduction to Modeling Species Transport and Reacting Flows, provides an overview of the models available in FLUENT for species transport and reactions, as well as guidelines for selectingan appropriate model for your application.•Chapter 13, Modeling Species Transport and Finite-Rate Chemistry, describes the finite-rate chemistry models in FLUENT and how to use them. This chapter also provides information about modeling species transport in non-reacting flows.•Chapter 14, Modeling Non-Premixed Combustion, describes the non-premixed combustion model and how to use it. This chapter includes details about using prePDF.•Chapter 15, Modeling Premixed Combustion, describes the premixed combustion model and how to use it.•Chapter 16, Modeling Partially Premixed Combustion, describes the partially premixed combustion model and how to use it.•Chapter 17, Modeling Pollutant Formation, describes the models for the formation of NOx and soot and how to use them.•Chapter 18, Introduction to Modeling Multiphase Flows, provides an overview of the models for multiphase flow (including the discrete phase, VOF, mixture, and Eulerian models), as well as guidelines for selecting an appropriate model for your application.•Chapter 19, Discrete Phase Models, describes the discrete phase models available in FLUENT and how to use them.•Chapter 20, General Multiphase Models, describes the general multiphase models available in FLUENT (VOF, mixture, and Eulerian) and how to use them.•Chapter 21, Modeling Solidification and Melting, describes FLUENT's model for solidification and melting and how to use it.•Chapter 22, Using the Solver, describes the FLUENT solvers and how to use them.•Chapter 23, Grid Adaption, explains the solution-adaptive mesh refinement feature in FLUENT and how to use it.•Chapter 24, Creating Surfaces for Displaying and Reporting Data, explains how to create surfaces in the domain on which you can examine FLUENT solution data.•Chapter 25, Graphics and Visualization, describes the graphics tools that you can use to examine your FLUENT solution.•Chapter 26, Alphanumeric Reporting, describes how to obtain reports of fluxes, forces, surface integrals, and other solution data.•Chapter 27, Field Function Definitions, defines the flow variables that appear in the variable selection drop-down lists in FLUENT panels, and tells you how to create your own custom field functions. •Chapter 28, Parallel Processing, explains the parallel processing features in FLUENT and how to use them. This chapter also provides information about partitioning your grid for parallel processing.18. Introduction to Modeling Multiphase FlowsA large number of flows encountered in nature and technology are a mixture of phases. Physical phases of matter are gas, liquid, and solid, but the concept of phase in a multiphase flow system is applied in a broader sense. In multiphase flow, a phase can be defined as an identifiable class of material that has a particular inertial response to and interaction with the flow and the potential field in which it is immersed. For example, different-sized solid particles of the same material can be treated as different phases because each collection of particles with the same size will have a similar dynamical response to the flow field.This chapter provides an overview of multiphase modeling in FLUENT, and Chapters 19 and 20 provide details about the multiphase models mentioned here. Chapter 21 provides information about melting and solidification.18.1 Multiphase Flow RegimesMultiphase flow can be classified by the following regimes, grouped into four categories:gas-liquid or liquid-liquid flowsbubbly flow: discrete gaseous or fluid bubbles in a continuous fluiddroplet flow: discrete fluid droplets in a continuous gasslug flow: large bubbles in a continuous fluidstratified/free-surface flow: immiscible fluids separated by a clearly-defined interfacegas-solid flowsparticle-laden flow: discrete solid particles in a continuous gaspneumatic transport: flow pattern depends on factors such as solid loading, Reynolds numbers, and particle properties. Typical patterns are dune flow, slug flow, packed beds, and homogeneous flow.fluidized beds: consist of a vertical cylinder containing particles where gas is introduced through a distributor. The gas rising through the bed suspends the particles. Depending on the gas flow rate, bubbles appear and rise through the bed, intensifying the mixing within the bed.liquid-solid flowsslurry flow: transport of particles in liquids. The fundamental behavior of liquid-solid flows varies with the properties of the solid particles relative to those of the liquid. In slurry flows, the Stokes number (seeEquation 18.4-4) is normally less than 1. When the Stokes number is larger than 1, the characteristic of the flow is liquid-solid fluidization.hydrotransport: densely-distributed solid particles in a continuous liquidsedimentation: a tall column initially containing a uniform dispersed mixture of particles. At the bottom, the particles will slow down and form a sludge layer. At the top, a clear interface will appear, and in the middle a constant settling zone will exist.three-phase flows (combinations of the others listed above)Each of these flow regimes is illustrated in Figure 18.1.1.Figure 18.1.1: Multiphase Flow Regimes18.2 Examples of Multiphase SystemsSpecific examples of each regime described in Section 18.1 are listed below:Bubbly flow examples: absorbers, aeration, air lift pumps, cavitation, evaporators, flotation, scrubbersDroplet flow examples: absorbers, atomizers, combustors, cryogenic pumping, dryers, evaporation, gas cooling, scrubbersSlug flow examples: large bubble motion in pipes or tanksStratified/free-surface flow examples: sloshing in offshore separator devices, boiling and condensation in nuclear reactorsParticle-laden flow examples: cyclone separators, air classifiers, dust collectors, and dust-laden environmental flowsPneumatic transport examples: transport of cement, grains, and metal powdersFluidized bed examples: fluidized bed reactors, circulating fluidized bedsSlurry flow examples: slurry transport, mineral processingHydrotransport examples: mineral processing, biomedical and physiochemical fluid systemsSedimentation examples: mineral processing18.3 Approaches to Multiphase ModelingAdvances in computational fluid mechanics have provided the basis for further insight into the dynamics of multiphase flows. Currently there are two approaches for the numerical calculation of multiphase flows: the Euler-Lagrange approach and the Euler-Euler approach.18.3.1 The Euler-Lagrange ApproachThe Lagrangian discrete phase model in FLUENT (described in Chapter 19) follows the Euler-Lagrange approach. The fluid phase is treated as a continuum by solving the time-averaged Navier-Stokes equations, while the dispersed phase is solved by tracking a large number of particles, bubbles, or droplets through the calculated flow field. The dispersed phase can exchange momentum, mass, and energy with the fluid phase.A fundamental assumption made in this model is that the dispersed second phase occupies a low volume fraction, even though high mass loading ( ) is acceptable. The particle or droplet trajectories are computed individually at specified intervals during the fluid phase calculation. This makes the model appropriate for the modeling of spray dryers, coal and liquid fuel combustion, and some particle-laden flows, but inappropriate for the modeling of liquid-liquid mixtures, fluidized beds, or any application where the volume fraction of the second phase is not negligible.18.3.2 The Euler-Euler ApproachIn the Euler-Euler approach, the different phases are treated mathematically as interpenetrating continua. Since the volume of a phase cannot be occupied by the other phases, the concept of phasic volume fraction is introduced. These volume fractions are assumed to be continuous functions of space and time and their sum is equal to one. Conservation equations for each phase are derived to obtain a set of equations, which have similar structure for all phases. These equations are closed by providing constitutive relations that are obtained from empirical information, or, in the case of granular flows , by application of kinetic theory.In FLUENT, three different Euler-Euler multiphase models are available: the volume of fluid (VOF) model, the mixture model, and the Eulerian model.The VOF ModelThe VOF model (described in Section 20.2) is a surface-tracking technique applied to a fixed Eulerian mesh. It is designed for two or more immiscible fluids where the position of the interface between the fluids is of interest. In the VOF model, a single set of momentum equations is shared by the fluids, and the volume fraction of each of the fluids in each computational cell is tracked throughout the domain. Applications of the VOF model include stratified flows , free-surface flows, filling, sloshing , the motion of large bubbles in a liquid, the motion of liquid after a dam break, the prediction of jet breakup (surface tension), and the steady or transient tracking of any liquid-gas interface.The Mixture ModelThe mixture model (described in Section 20.3) is designed for two or more phases (fluid or particulate). As in the Eulerian model, the phases are treated as interpenetrating continua. The mixture model solves for the mixture momentum equation and prescribes relative velocities to describe the dispersed phases. Applications of the mixture model include particle-laden flows with low loading, bubbly flows, sedimentation , and cyclone separators. The mixture model can also be used without relative velocities for the dispersed phases to model homogeneous multiphase flow.The Eulerian ModelThe Eulerian model (described in Section 20.4) is the most complex of the multiphase models in FLUENT. It solves a set of n momentum and continuity equations for each phase. Coupling is achieved through the pressure and interphase exchange coefficients. The manner in which this coupling is handled depends upon the type of phases involved; granular (fluid-solid) flows are handled differently than non-granular (fluid-fluid) flows. For granular flows , the properties are obtained from application of kinetic theory. Momentum exchange between the phases is also dependent upon the type of mixture being modeled. FLUENT's user-defined functions allow you tocustomize the calculation of the momentum exchange. Applications of the Eulerian multiphase model include bubble columns , risers , particle suspension, and fluidized beds .18.4 Choosing a Multiphase ModelThe first step in solving any multiphase problem is to determine which of the regimes described inSection 18.1 best represents your flow. Section 18.4.1 provides some broad guidelines for determining appropriate models for each regime, and Section 18.4.2 provides details about how to determine the degree of interphase coupling for flows involving bubbles, droplets, or particles, and the appropriate model for different amounts of coupling.18.4.1 General GuidelinesIn general, once you have determined the flow regime that best represents your multiphase system, you can select the appropriate model based on the following guidelines. Additional details and guidelines for selecting the appropriate model for flows involving bubbles, droplets, or particles can be found in Section 18.4.2.For bubbly, droplet, and particle-laden flows in which the dispersed-phase volume fractions are less than or equal to 10%, use the discrete phase model. See Chapter 19 for more information about the discrete phase model.For bubbly, droplet, and particle-laden flows in which the phases mix and/or dispersed-phase volume fractions exceed 10%, use either the mixture model (described in Section 20.3) or the Eulerian model (described in Section 20.4). See Sections 18.4.2 and 20.1 for details about how to determine which is more appropriate for your case.For slug flows, use the VOF model. See Section 20.2 for more information about the VOF model.For stratified/free-surface flows, use the VOF model. See Section 20.2 for more information about the VOF model.For pneumatic transport, use the mixture model for homogeneous flow (described in Section 20.3) or the Eulerian model for granular flow (described in Section 20.4). See Sections 18.4.2 and 20.1 for details about how to determine which is more appropriate for your case.For fluidized beds, use the Eulerian model for granular flow. See Section 20.4 for more information about the Eulerian model.For slurry flows and hydrotransport , use the mixture or Eulerian model (described, respectively, inSections 20.3 and 20.4). See Sections 18.4.2 and 20.1 for details about how to determine which is more appropriate for your case.For sedimentation, use the Eulerian model. See Section 20.4 for more information about the Eulerian model.For general, complex multiphase flows that involve multiple flow regimes, select the aspect of the flow that is of most interest, and choose the model that is most appropriate for that aspect of the flow. Note that the accuracy of results will not be as good as for flows that involve just one flow regime, since the model you use will be valid for only part of the flow you are modeling.18.4.2 Detailed GuidelinesFor stratified and slug flows, the choice of the VOF model, as indicated in Section 18.4.1, is straightforward. Choosing a model for the other types of flows is less straightforward. As a general guide, there are some parameters that help to identify the appropriate multiphase model for these other flows: the particulate loading, , and the Stokes number, St. (Note that the word ``particle'' is used in this discussion to refer to a particle, droplet, or bubble.)The Effect of Particulate LoadingParticulate loading has a major impact on phase interactions. The particulate loading is defined as the mass density ratio of the dispersed phase ( d) to that of the carrier phase ( c):The material density ratiois greater than 1000 for gas-solid flows, about 1 for liquid-solid flows, and less than 0.001 for gas-liquid flows. Using these parameters it is possible to estimate the average distance between the individual particles of the particulate phase. An estimate of this distance has been given by Crowe et al. [ 42]:where . Information about these parameters is important for determining how the dispersed phase shouldbe treated. For example, for a gas-particle flow with aparticulate loading of 1, the interparticle space is about 8; the particle can therefore be treated as isolated (i.e., very low particulate loading).Depending on the particulate loading, the degree of interaction between the phases can be divided into three categories:For very low loading, the coupling between the phases is one-way; i.e., the fluid carrier influences the particles via drag and turbulence, but the particles have no influence on the fluid carrier. The discrete phase, mixture, and Eulerian models can all handle this type of problem correctly. Since the Eulerian model is the most expensive, the discrete phase or mixture model is recommended.For intermediate loading, the coupling is two-way; i.e., the fluid carrier influences the particulate phase via drag and turbulence, but the particles in turn influence the carrier fluid via reduction in mean momentum and turbulence. The discrete phase, mixture, and Eulerian models are all applicable in this case, but you need to take into account other factors in order to decide which model is more appropriate. See below for information about using the Stokes number as a guide.For high loading, there is two-way coupling plus particle pressure and viscous stresses due to particles (four-way coupling). Only the Eulerian model will handle this type of problem correctly.The Significance of the Stokes NumberFor systems with intermediate particulate loading, estimating the value of the Stokes number can help you select the most appropriate model. The Stokes number can be defined as the relation between the particle response time and the system response time:where and t s is based on the characteristic length ( L s) and the characteristic velocity ( V s) of the system under investigation: .For , the particle will follow the flow closely and any of the three models (discrete phase, mixture, or Eulerian) is applicable; you can therefore choose the least expensive (the mixture model, in most cases), or themost appropriate considering other factors. For , the particles will move independently of the flowand either the discrete phase model or the Eulerian model is applicable. For , again any of the three models is applicable; you can choose the least expensive or the most appropriate considering other factors. ExamplesFor a coal classifier with a characteristic length of 1 m and a characteristic velocity of 10 m/s, the Stokes number is 0.04 for particles with a diameter of 30 microns, but 4.0 for particles with a diameter of 300 microns. Clearly the mixture model will not be applicable to the latter case.For the case of mineral processing, in a system with a characteristic length of 0.2 m and a characteristic velocity of 2 m/s, the Stokes number is 0.005 for particles with a diameter of 300 microns. In this case, you can choose between the mixture and Eulerian models. (The volume fractions are too high for the discrete phase model, as noted below.)Other ConsiderationsKeep in mind that the use of the discrete phase model is limited to low volume fractions. Also, the discrete phase model is the only multiphase model that allows you to specify the particle distribution or include combustion modeling in your simulation.。
第一章 FLOW-3D使用简介
12 mm
sand casting
die casting 3 mm
0.003
180 ms
30 s
Solution for one-dimensional heat flow into semi-infinite medium from Casting: An Analytical Approach, Reikher, Barkhudarov, Springer 2007.
1975年，Dr. Hirt & Dr. Nichols发表VOF技术 1.定义流体的液面动作状态 2.追踪流体液面流动时的变化 3.定义流体流动时的边界条件设定 所有的CFD软件，关于自由液面的定义，均Follow此一准则。
利用FAVOR技术，使曲面造型的 Model也能够顺利的以矩形网格加以 描述，使分析模型不会失真。
Heat Penetration Depth: Examples
Tilt pour, filling time 20 seconds （倾斜铸造，充型时间为 20 秒）: • total initial cell count: 2.2 million cells • with 40 mm thermal shell: 930k active cells = 740k in Байду номын сангаасold + 185k in metal
New Output Quantities
distance travelled by fluid
fluid residence time
Finite Element Solver For Structural Analysis
FLOW-3D 将提供与结
构分析软件相接之有限元 网格，以预测铸件应力分 析与变形问题。
冲压模具 英文论文
Computational published quarterly by the Association
Materials Science of Computational Materials Science
The optimal design of micro-punching
die by using abductive and SA methods
J.-Ch. Lin a, K.-S. Lee b, W.-S. Lin c,*
a Department of Mechanical Design Engineering, National Formosa University,
64 Wunhua Road, Huwei, Yunlin ,Taiwan
of the punch and die has been a common topic for scholars.
Design/methodology/approach: The input parameters (punching times, clearance) and output results (wear)
MANUFACTURING AND PROCESSING OF ENGINEERING MATERIALS
92 (C) Copyright by International OCSCO World Press. All rights reserved. 2009
As a result, the mathematics model is difficult to converge and the neural network will inaccurately predict wear.
Flow3d软件简介
Flow3d软件简介Flow3d software profilePublished: 2009-5-20 11:21:43 source: China - build China die casting web casting industry and Trade Information Center flagship online text and FLOW-3D] [simulation tool of high efficient, engineers can according to self define various physical models, used in various engineering fields. By accurately predicting free surface flow (free-surface, flows), the FLOW-3D can assist you in improving the existing process in the engineering field.FLOW-3D is a full set of software that does not require additional grid generation modules or post-processing modules.A fully integrated graphical user interface allows users to quickly complete simulation project settings to result output.Mesh and geometry Meshing & Geometry:Structured finite difference method meshMulti block grid technology supports embedded or connected grid blocks.Fractional areas/volumes (FAVOR) technology enables efficient and precise definition of geometric appearanceFree mesh settingsBuilt in basic geometry generatorYou can read various CAD format filesFlow type options Flow, Type, Options:In pipe flow, pipe outflow, and free surface flow modelSupport three-dimensional, two-dimensional or one-dimensional problem calculationTransient flow calculationSupport Cartesian coordinate system or cylindrical coordinate systemSupports non viscous, viscous, laminar, and turbulent flows Multiple quantitative values, specified calculations Coordinate axis calculationTwo phase flowHeat transfer calculation (including phase change) Saturated and unsaturated porous materialFlow definition options Flow, Definition, Options:General initial conditionsBoundary conditionsSymmetryRigid wallContinuousCycleSpecified pressureSpecify speedOutflowMesh overlapStill waterReboot optionsContinuation simulation calculationFrom the previous simulation, the overlapping data is computed . add, remove or change the model parametersNumeric model options Numerical, Modeling, Options:.Volume-of-Fluid (VOF) method tracing fluid boundary --TruVOFEfficient geometric definition of.Fractional areas/volumes (FAVOR). one order, two order and three order flow calculation advection.Sharp fluid interface trackingImplicit solution and explicit solution calculationSupport Point, line, relaxation, and GMRES pressure solversUser defined variables, sub programs, and outputsA computational iteration tool for executing programsFluid model options Fluid, Modeling, Options:A single incompressible fluid - confined or with free surfacesTwo types of incompressible fluids - miscible, or, with, sharp, interfaces, etc.Compressible fluids - subsonic, transonic, supersonicSaturated fluidAcoustic phenomenaMass particles of different density / diameterHot model option Thermal Modeling Options:Natural convectionForced convectionFluid and solid heat conductionFluid and solid heat transferThermal conduction.Designated heat fluxSpecified temperatureHeat transfer from fluid / object to spaceEnergy distribution / concentration in a fluid or solid The heat radiation of the Yi.Viscous heatPhysical model option Physical Modeling Options: Erosion and erosion depositsCavitationPhase change (liquid gas, liquid solid)Surface tensionThermosyphon phenomenonAdhesion of contact surfaceRoughness of contact surfaceSteam and bubblesCuring and melting (heat-of-transformation, table)Mass / momentum / energy generation settingDistributed mass / energy generatorShear change, viscosity model of density change and temperature dependenceThixotropic viscosityElastic tensionElectric fieldInsulation phenomenonElectroosmosisElectrostatic particlesElectric drive phenomenonJoule heatingCoil gasMolecular and turbulent diffusionSpecial physical model Special, Physical, Models:Six degrees of freedom, general moving objects, --user, specified, motion, or, fully-coupled, with, rigid, motion, body, etc.Rotating objectsLinear and, quadratic, flow, losses)Collision modelMetal casting model Metal, Casting, Models:Curing / melting (heat-of-transformation, table)Curing shrinkageTwo element segregation during solidificationThe rate of cure affected by latent heat release Thermal cyclingDefect trackingCavitation modelLost foam casting modelSemi solid material modelSand mould moisturePlunger head movementBack pressure and exhaustSand core blowingTurbulence model Turbulence, Models:.Prandtl mixing length.One-equation transport.Two-equation, k-, epsilon, transport.RNG (renormalized, group, theory).Large eddy simulationPorous material model Porous, Media, Models:Variable pore settingDirectional pore settingThe general fluid loss. Yi (linear and quadratic)Capillary pressureUnsaturated fluidThermal transmission of porous materialsTwo phase fluid combined with more than one material object model Two-phase, and, Two-component, Models:,Liquid / liquid and gas / liquid interfaceTwo phase flow mixingThe mixing of a single compressible fluid with a dispersed incompressible fluidTwo-phase drift fluxPhase change between gas liquid and liquid gasAdiabatic bubblePhase change bubbleContinuous fluid with discontinuous particlesScalar transportDiscontinuous particle model Discrete, Particle, Models:Massless particles are indicatedMass particles of size / weight can be specifiedFluid power drag calculations for one-dimensional and two dimensions.Monte-Carlo diffusionParticle fluid momentum coupling calculationCoefficient of resistance of cohesive particlesPoint or mass particle generatorCharged particlesParticle tracking, particle trackingShallow fluid model Shallow, Flow, Models:Shallow water modelGeneral shallow layerWetting and dryingWind shearSurface roughness effectChemical model Chemistry Models:.Chemical rate equation solver.Stationary or advected speciesAutomation features Automatic Features:Mesh and initial conditions are generatedTime stepping control for precision and stability calculation Automatically limiting fluid compressibilityConvergence and relaxation calculation by FLOW-3D control Automatic prompt for optimum calculationInterfaces with other software Options, for, Coupling, with, Other, Programs:General input format: Stereolithography (STL), files--binary, or, ASCIIFrom ANSYS or I is DEAS to tetrahedral dataDirect data connection port with Tecplot, Ensight, and, FieldViewOutput format to PLOT3D-compatible visualization programs.Neutral file format outputCustomized computing tools are added.Topgraphic dataData manipulation options Data, Processing, Options:Full automatic or customized production chartGraphics support OpenGL-based graphicsColor or black and white vectors, contour maps, 3D, and particle image outputVariable records over timeBy the calculation of force and force the tickets.Animation output.PostScript, JPEG, and Bitmap image outputStreamline output.STL geometry file viewMultiprocessor computing Multi-Processor Computing:Memory sharing calculations (SMP version, support for multicore CPU, support for Windows/Linux systems)Computer cluster system (MP version should be installed on Linux system)。
北京理工大学-姓名范群波
主要论文 •Li Guoju, Zhang Xu, Fan Qunbo(通讯作者）, Wang Linlin, Zhang Hongmei, Wang Fuchi,Wang Yangwei. Simulation of Damage and Failure Processes of Interpenetrating SiC/Al Composites Subjected to Dynamic Compressive Loading. Acta Materialia,2014, 78: 190-202 •Li Rongting, Fan Qunbo(通讯作者）, Gao Ruihua, Huo Lirui, Wang Fuchi, Wang Yangwei. Effects of dynamic mechanical properties on the ballistic performance of a new near-β titanium alloy Ti684.Materials & Design. 2014,62:233-240 •Wang Linlin, Fan Qunbo(通讯作者）, Li Guoju, Zhang Hongmei, Wang Fuchi. Experimental observation and numerical simulation of SiC3D/Al interpenetrating phase composite material subjected to a three-point bending load. Computational Materials Science. 2014, 95: 408-413 •高瑞华，范群波（通讯作者），王富耻，张毅鹏，霍利瑞，裴传虎.钛合金装甲材料动态力学性能及其抗弹 能力关系研究.稀有金属材料与工程.2014（录用） •李荣婷，范群波（通讯作者），王富耻，高瑞华.一种新型近β钛合金 Ti684 动态力学性能与抗弹性能的研 究.稀有金属材料与工程.2014（录用） •Wang Fuchi, Huo Dongmei, Li Shukui, Fan Qunbo(通讯作者). Inducing TiAl3 in titanium alloys by electric pulse heat treatment improves mechanical properties. Journal of Alloys and Compounds 550 (2013) 133–136 •Shen Wei, Wang Fuchi, Fan Qunbo(通讯作者), Ma Zhuang. Lifetime prediction of plasma-sprayed thermal barrier coating systems. Surface and Coatings Technology. 2013， 217，2013：39-45 •Liu Jintao, Cai Hongnian, Wang Fuchi, Fan Qunbo(通讯作者). Multiscale Numerical Simulation of the Shaped Charge Jet Generated from Tungsten-Copper Powder Liner. Journal of Physics: Conference Series 419 (2013) 012045 •Li Guoju, Fan Qunbo(通讯作者), Wu Zheng, Zhang Xu, Wang Yangwei, Wang Fuchi. Modeling the Dynamic Damage Process of the SiC3d/Al Interpenetrating Phase Composites. Journal of Physics: Conference Series 419 (2013) 012024 •Gao Ruihua, Fan Qunbo(通讯作者), Wang Fuchi. Numerical Simulation in relation to Adiabatic Shearing Behaviors in Titanium Alloy Journal of Physics: Conference Series 419 (2013) 012020 •Shen Wei, Fan Qunbo, Wang Fuchi, Ma Zhuang. The influence of defects on the effective Young’s
abaqus拉伸模拟力位移曲线负值
abaqus拉伸模拟力位移曲线负值abaqus是一种用于有限元分析的通用商业软件，可用于模拟和分析各种力学行为。
在拉伸模拟中，我们可以通过abaqus来分析材料在拉伸载荷下的力位移曲线。
本文将介绍abaqus拉伸模拟力位移曲线的相关参考内容。
在abaqus中进行拉伸模拟时，需要定义材料的力学性质，如弹性模量、屈服强度、断裂韧性等。
对于线弹性材料，可以使用线性弹性模型，而对于非线弹性材料，则需要使用合适的本构模型。
以下是一些常用的材料本构模型和其相关参考内容：1. 线性弹性模型：- 小应变线性弹性模型（Linear Elastic）：该模型假设材料在拉伸过程中的应变很小，从而保持材料的线弹性行为。
- 参考内容：Kuhl, E., et al. (2002). "Computational modelingof collagen networks: Simulation of mesoscale structures and mechanical behavior." Journal of biomechanics 35(12): 1663-1671.2. 非线性弹性模型：- 全塑性模型（Perfect Plasticity）：该模型假设材料在拉伸过程中会发生完全塑性变形，即超过屈服点后，材料不再恢复到原始形态。
- 参考内容：Zienkiewicz, O. C., et al. (2002). "Finite element analysis of incompressible solids and structures." Solid mechanics and its applications 95: 93-95.- 屈服准则模型（Yield Criteria）：该模型用于描述材料的屈服行为，常用的模型有Von Mises屈服准则、Tresca屈服准则等。
OffshoreWindTurbineHydrodynamics：海上风机的流体力学
Offshore Wind Turbine Hydrodynamics Modeling in SIMPACKAs the offshore wind energy sector expands, so too does the demand for advanced simulation environments that are able to accurately model these com-plex systems. The latest trend is floating offshore wind turbines which can be installed in deep water and hold great economic potential. To accurately simu-late offshore wind turbines, the S tutt-gart Chair of Wind Energy(SWE) at the Universityof S tuttgart has ex-tended S IMPACK with a coupling to the hydrodynamicpackage HydroDyn developedby NREL. A Morison force element and dynamic MBS mooring system model were also introduced. By taking advan-tage of these hydrodynamic extensions plus existing advanced drivetrain and aerodynamic submodels, a full dynamic coupled simulation of fixed-bottom and floating offshore wind turbines is pos-sible with SIMPACK.HYDRODYNAMICS FOR OFFSHORE WIND TURBINESOffshore wind turbine support structure types include:• monopile (gravity-based and suction bucket foundations for shallow sites)• jacket and tripod structures for depths up to 50 m• floating structures for deeper locations In general, hydrodynamic and hydrostatic loads on offshore structures subject to waves and currents are an effect of the inte-grated pressure distribution on the wetted surface. In offshore terminology, the various load contributions are separated into:• buoyancy force (hydrostatic restoring)• radiation force:a. inertia force from added massb. viscous damping force • wave excitation force:a. diffraction (incident-wave scattering)b. Froude-Kriloff (undisturbed pressure field forces)• sea current force and • nonlinear higher order forces (slow, mean drift and sum-fre-quency forces).Some substructures for wind turbines consist of slender axisymmetric cylindricalωd dsfluidI s /2zxu kr syxyu tvu k = u t + ωd I s /2ωdWAMIT8 | SIMPACK News | July 2013elements. This enables the use of the simple and efficient semi-empirical Morison Equa-tion which is valid if the flow acceleration can be assumed uniform at the location of the cylinder thus simplifying the diffraction problem. This requires that the diameter of the cylinder D be much smaller than the wavelength L — typically D/L values of less than 0.15–0.2. It is also assumed that rela-tive motions are small so that viscous drag dominates the damping; radiation damping can be neglected; and that off-diagonal added-mass terms are negligible, as in the case of axisymmetric structures. Since the equation contains empirical coefficients for added mass, inertia and drag (which de-pend on the Keulegan-Carpenter number, Reynolds number and surface roughness), careful attention to these is required to obtain viable results.For structures with larger diameters and larger motions—typically tripods or float-ing structures—effects from hydrodynamic radiation and diffraction (not considered by Morison’s Equation) become important. For such structures, linear hydrodynamicFig 1: Calculation of Morison forces on mooring line segmenttheory is currently most commonly used. It is based on potential theory, and includes effects from linear hydrostatic restoring, added mass and damping contributions from linear wave radiation (including free-surface memory effects), and incident wave excitation from linear diffraction. Typically, nonlinear viscous drag contributions areFig 2: HydroDyn calculation procedure and interface to SIMPACK (image source: NREL)Mooring-System3 DOF3 DOF2 DOF1 DOF3 DOF3 DOF3 DOF2 DOF1 DOF3 DOF3 DOF3 DOF2 DOF1 DOF3 DOFy α, β, γy α, β, γy α, β, γα, γ, y α, γ, y α, γx, y, zα, γ, y α, γ, y x, y, zα, γ, y α, γ, y x, y, zα, γα, γ0 DOF6 DOFanchorseabed rigid BodyJointfairlead spar buoy3 DOFc t ,d t c r , d ru y φx φzd sc s SIMPACK News | July 2013 | 9added from Morison’s equation. However, nonlinear steep and/or breaking waves, vortex-induced vibrations, second-order effects of mean-drift, slow-drift and sum-frequency excitation, and any other higher order effects, are neglected within Hydro-Dyn. To overcome this limitation, a coupling between SIMPACK and the Computational Fluid Dynamics (CFD) tool ANSYS CFX is currently being developed at SWE (Beyer, Arnold & Cheng, 2013). The incorporation of second-order hydrodynamic effects is planned for future releases of HydroDyn.To enable modeling of offshore wind tur-bines in SIMPACK, the two hydrodynamic Fig 3: Topology of dynamic nonlinear MBS mooring system Fig 4: Topology of floating offshore wind turbinemodeling methodologies described have been implemented. Currently, most other commercial codes only ap-ply Morison’s equation and are, therefore, limited to afore-mentioned slender structures where radia-tion damping and off-diagonal added-mass terms are negligible.MORISON FORCE ELEMENT For cylindrical fixed-bottom structures and mooring systems, a SIMorison user Force Element was implemented at SWE into SIMPACK 9. It uses the relative formula-tion of the Morison equation according to Östergaard and Schellin, and also includesan option to directly account for buoyancyif the body is always completely submerged. Due to the relative simplicity of the Morison Equation, the user only needs to supplyvalues for the two empirical coefficients: inertia C m and drag C D . A Reynolds depen-dency of these coefficients can be added.Water density, kinematic viscosity, effective cylindrical diameter (to determine the cross sectional area) and length of the body where the Force Element is applied also need to be defined. The desired discretiza-tion of a mooring system can be achieved by using multiple Morison Force Elementsalong cylindrical structures with differentdiameters and lengths (Fig. 1).Since the Morison equation in its relativeformulation features an added mass term depending on the relative fluid acceleration, the routine requires the structure to accelerate at eachtime step. In MBS, the acceleration is usually not solvedduring integration, thus making the imple-mentation of Morison’s Equation complex. Here, SIMPACK’s ability to use algebraic states (q-states) is utilized, "anticipating" acceleration results of the Right-Hand Side, i.e., making them available before they areactually calculated.“For cylindrical fixed-bottom structures and mooring systems, a SIMorison user Force Element was implementedat SWE into SIMPACK 9.”10 | SIMPACK News | July 2013The wave generator can generate either periodic waves or random irregular Airy waves with user-defined significant wave height and peak spectral period based on a defined wave spectrum (the JONSWAP and Pierson-Moskovitz spectra are predefined). Kinematic stretching (Vertical, Extrapolation,Wheeler) is also implemented to provide predictions of wave kinematics above the mean water level; an option used only for Morison calculations since it is inconsistent with linear hydrodynamic theory.The Morison Equa-tion implementa-tion of HydroDyn is equivalent to the previously described Morison Force Element. It accounts for the current fraction of wetted surface dependent on instantaneous wave elevation. Currently, it is applicable for monopile structures, and the upcoming HydroDyn version 2 (already avail-able in an alpha version) will then be able to simulate multi-member fixed-bottom and floating substructures such as jackets or semi-submersibles with the Morison Equation.The third feature of HydroDyn is its linear hydrodynamic model. It computes loading contributions from:• linear hydrostatic restoring• nonlinear viscous drag contributions from Morison’s Equation• added mass and damping contributions from linear wave radiation (including free-surface memory effects)• incident wave excitation from linear diffraction The linear hydrodynamic option in Hydro-Dyn requires the user to enter frequency-dependent hydrodynamic vectors and matrices. These must be pre-calculated by external offshore panel-based codes such as WAMIT ® or ANSYS ® AQWA TM , which solve the linearized radiation and diffrac-tion problems in the frequency domain. Full details of HydroDyn’s theory are given in J onkman (J onkman, 2007). The upcoming HydroDyn version 2 release will also feature the possibility of Morison elements with linear hydrodynamics which can be used to model the hydrodynamic forces on the main pontoons of a semi-submersible with linear theory and on the braces with Morison’s.The fourth module within HydroDyn pro-vides a quasi-static mooring line model to efficiently calculate mooring line loads on floating platforms. At SWE, a dynamic nonlinear mooring line model has been developed within SIMPACK to overcome the drawbacks of the quasi-static approach (Fig. 3, 4). More details on this MBS moor-ing line model are given by Matha (Matha, Fechter, Kühn, Cheng, 2011).The original input file for HydroDyn has been modified for usage in SIMPACK and allows the user to define the incoming waves, to select between the Morison and linear hydrodynamic module, and define the properties of the mooring system.VALIDATION WITH OC3 & OC4The SIMHydro coupling was first validatedwith results from phase four of the IEA Annex 23 Offshore Code Comparison Col-laboration (OC3) project (Fig. 5), and is cur-rently used in phase two of the follow-upOC4 project. Exemplary results from OC4 load cases 1.3, representing free decaytests where the semi-submersible platform(Fig. 6) is released at an initial displacementin still water without wind loads, are shownin Fig. 7 and Fig. 8.The presented platform surge and pitch displacement show very good agreementbetween SIMPACK and other participants applying linear hydrodynamic theory like FAST (NREL) and DeepLinesWT (Principia). Compared to codes using Morison’s equa-tion for modeling the hydrodynamics — likeHAWC2 (DTU) and Bladed (GH) — distinct At SWE, the SIMorison Force Element is primarily used and validated by modeling the hydrodynamic loads on mooring lines. The regular or irregular Airy wave kinematics used by this element are computed by the SIMHydro element which is described next.SIMHYDRO — COUPLING TO NREL’S HYDRODYN The SIMHydro Force Element couples NREL’s HydroDyn module with SIMPACK (Fig. 2). HydroDyn was developed by J ason onkman at NREL (J onkman, 2007) and has since been used to model monopiles and various floating structures. The current release of Hy-droDyn offers four important features: • a wave generator for periodic and regu-lar/irregular Airy waves (J ONSWAP, PM spectra) including stretching • the Morison equation module for hydro-dynamic load calculation • a linear hydrodynamics module for load calculation on non-slender (floating) bodies • a quasi-static mooring line module for mooring system load calculation of float-ing platforms Fig 5: OC3 spar-buoy floating wind turbine model with MBS mooring system“At SWE, a dynamic nonlinear mooring line model has been developed within SIMPACK to overcome the drawbacks of the quasi-static approach.”HAWC2BladedDeepLinesWT FAST SIMPACKP l a t f o r m p i t c h [º]0 50 100 150 200 250 3001086420-2-4-6-8-10Simulation time [s]HAWC2BladedDeepLinesWT FAST SIMPACKP l a t f o r m s u r g e [m ]0 100 200 300 400 500 6002520151050-5-10-15-20-25Simulation time [s]SIMPACK News | July 2013 | 11differences in load and motion predictions are evident depending on the load case. This is due to the differences in the semi-empiric approach of a Morison-only formulation. USAGE OF SIMPACK OFFSHORE SWE uses SIMPACK to model offshore floating wind turbines in the European research projects OFFWINDTECH, Innwind,AFOSP and FLOATGEN. The latter is cur-rently the largest EU-funded offshore wind energy research project and will deploy two multi-MW floating wind turbine systems in Mediterranean waters over 40 m deep. With this project, the SWE will have the opportu-nity to compare the SIMPACK floating wind turbine model with measured scale and full-scale prototype data, analyze the differ-ences, validate the predictions and improve the models where required.SUMMARYThe implementation of SIMorison and SIMHydro Force Elements makes it possible to simulate fixed-bottom and floating wind turbines with SIMPACK. The coupling is vali-dated by OC3 and OC4. SIMPACK offshore wind turbine models have already been successfully applied in a number of research projects, and show excellent potential for future applications.REFERENCESBeyer, F., Arnold, M., Cheng, P. W. (2013). Analysis of Floating O ffshore Wind Turbine Hy-drodynamics using coupled CFD and Multibody Methods. ISOPE. Anchorage, USA.Jonkman, J. (2007). Dynamics Modeling and Loads Analysis of an O ffshore Floating Wind Turbine. NREL/TP-500-41958. Golden, US-CO :National Renewable Energy Laboratory.Matha, D., Fechter, U., Kühn, M., Cheng, P. W.(2011). Non-linear Multi-Body Mooring System Model for Floating O ffshore Wind Turbines.University of Stuttgart, OFFSHORE 2011, Amster-dam, Netherlands.Fig 6: OC4 semi-submersible floating wind turbine with quasi-static mooring system (only nodes displayed)Fig 7: OC4 LC 1.3a: Platform translation in surge direction Fig 8: OC4 LC 1.3c: Platform rotation in pitch direction。
美国国家自然科学基金
Fluent流体数值模拟软件中英对照
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光学曲面拟合误差量级
光学曲面拟合误差量级Optical surface fitting refers to the process of accurately modeling and representing the shape of anoptical surface. This is crucial in various fields such as optics, astronomy, and lens design, where the performance of optical systems heavily relies on the quality of the surface fitting. However, even with advanced technologies and techniques, there are still inherent errors and limitations in the fitting process that need to be considered.One of the main factors contributing to the fitting errors is the inherent complexity of optical surfaces. Optical surfaces can have intricate shapes and features, such as aspheric or freeform surfaces, which are challenging to accurately represent using traditional mathematical models. These complex surfaces often require more sophisticated algorithms and higher-order mathematical functions to achieve a better fit, but even then, there can still be residual errors due to the limitations of thefitting method.Another source of fitting errors is the measurement and characterization of the optical surface itself. In many cases, the surface profile is obtained through various measurement techniques such as interferometry or profilometry, which inherently have their own measurement uncertainties. These uncertainties can introduce errors in the measured data, leading to inaccuracies in the fitting process. Additionally, the sampling density and resolution of the measured data can also affect the accuracy of the fitting, as low-density or low-resolution data may not capture all the details of the surface.Furthermore, the choice of fitting algorithm and optimization method also plays a significant role in the accuracy of the fitting process. Different algorithms have different strengths and weaknesses, and the choice of the most suitable algorithm depends on the specific characteristics of the optical surface and the desired accuracy. However, even with the most advanced algorithms, there can still be limitations in terms of convergence,stability, or computational efficiency, which can affectthe overall fitting accuracy.The materials used for the optical surface can also introduce errors in the fitting process. Optical surfaces are often made of materials with certain physical properties, such as glass or plastics, which can have inherent imperfections or variations in their refractive index. These material properties can affect the behavior of light interacting with the surface, leading to deviations between the fitted model and the actual surface. Additionally, factors such as temperature variations or mechanical stresses can also induce deformations in the optical surface, further contributing to the fitting errors.Moreover, the manufacturing process of the optical surface can introduce errors that need to be considered in the fitting process. Manufacturing techniques such as polishing or molding can introduce surface irregularitiesor imperfections that may not be accurately captured by the fitting model. These manufacturing errors can cause discrepancies between the fitted model and the actualsurface, affecting the overall performance of the optical system.In conclusion, despite advancements in technology and techniques, there are still inherent errors and limitations in the fitting process of optical surfaces. The complexity of the surfaces, measurement uncertainties, choice offitting algorithms, material properties, and manufacturing errors all contribute to the overall fitting accuracy. It is important to consider these factors and continuously improve fitting methods to minimize errors and achieve higher levels of accuracy in optical surface fitting.。
直接使用CAD几何的蒙特卡罗粒子输运方法研究
摘要：传统的蒙特卡罗程序通常使用构造实体几何(CSG)的方法进行几何建模$然而对于某些复杂的
高阶曲面使用CSG进行精确建模非常困难且会耗费大量的时间$为解决这一问题，国际上提出了一种 直接使用CAD几何建模的蒙特卡罗(DAGMC)方法来进行粒子输运计பைடு நூலகம்$本文基于反应堆蒙特卡罗
本文基于堆用蒙特卡罗程序RMC⑺开发 使用DAGMC几何的DAG-RMC程序。通过 对方块组合体、燃料棒和犹他州茶壶3种模型 分别使用不同几何建模方法得到的临界计算结 果分析，验证DAG-RMC程序临界计算功能的 正确性。
1 RMC中DAGMC的实现
将RMC中原有的Cell类扩展为CSGCell 和DAGMCCell两类9 Surface类扩展为CSGSurface 和 DAGMCSurface 两类，并在 RMC 源 代码中调用DAGMC第3方库中的几何处理 函数。首先，利用DAGMC库函数将CAD几 何文件（h5m）所包含的栅元、曲面和材料信息 读取并转换为RMC的栅元、曲面和材料信息, 并把材料分配给对应的栅元，给各曲面定义对 应的边界条件。DAGMC几何的材料分配有两 种方式:一种是读取RMC输入文件的材料卡 信息；另一种是将材料信息嵌入到DAGMC文 件（h5m）中，使用UWUW模式读取材料信息$
然后，利用DAGMC库函数实现粒子定 位、计算粒子到边界距离、穿面后栅元判断等功 能,从而使RMC可基于CAD几何进行粒子输 运$添加DAGMC几何后的DAG-RMC程序
粒子输运模拟流程如图1所示，相关库函数的 作用列于表1$
图1 DAG-RMC粒子输运模拟流程
Fig. 1 DAG-RMC particle transport simulation process