Concepts NREC是世界上最著名的叶轮机械专业服务公司(以下简称CN公司)。全世界唯一的既开发和推广叶轮机械设计/加工专用(CAE/CAM)软件,同时也提供叶轮机械样机开发和性能测试的全方位高端服务公司,当前员工总数130人。

公司前身源于美国麻省理工学院的3位科学家1956年成立的北方研究工程公司(NREC)和美国工程院院士DaveJapikse博士于1980年成立的ConceptsETI公司。公司分支机构和服务体系遍布全球各个主要工业国家。

2000年,集成两家公司原软件为全新的AgileEngineeringDesignSystem(AEDS)敏捷工程设计系统,致力于为业界提供“敏捷设计”和“敏捷制造”为宗旨的透平机械研发一体化解决方案。

CN具有一支经验十分丰富的专家队伍,当前公司专家团队曾在诸多著名大公司和研究机构承担过重要型号或产品研发,包括:GE、NASA、Honeywell、Pratt&Whitney、DR、IR、RR、SolarTurbines、Hamilton、Lycoming、Williams、ARL、AEDC、Flowsever等等。

数十年研发持续积累、强大的专家队伍、全球客户不断反馈是CN工程咨询和软件开发技术能力的核心知识库。

CN还具备非常先进的样机试制和试验台位等硬件条件,能够快速实现从先进设计到高精密制造以及性能试验的完整研发过程。每年承担诸多美国SBIR,STTR科研项目。公司每年在ASME等学术会议上发表诸多研究成果论文。

Concepts NREC造价信息

市场价 信息价 询价
材料名称 规格/型号 市场价
(除税)
工程建议价
(除税)
行情 品牌 单位 税率 供应商 报价日期
公司LOGO 不锈钢烤漆+双层亚克力UV印刷 查看价格 查看价格

恩奇

13% 上海恩奇数码科技有限公司
公司品牌布 品种:灯箱布;规格:5100×72000; 查看价格 查看价格

杰兴

13% 云南杰兴广告有限公司
物业公司标识牌 360*2000 查看价格 查看价格

13% 武汉奥哲利环卫科技有限公司
自来水公司专用井盖 ZSZF350C 球墨铸铁,350×200,防响胶条,弹性紧锁结构,方块防滑纹 C250 查看价格 查看价格

吉信

13% 河北省保定吉信金属制品有限公司
自来水公司专用井盖 ZSZF350D 球墨铸铁,350×200,防响胶条,弹性紧锁结构,方块防滑纹 D400 查看价格 查看价格

吉信

13% 河北省保定吉信金属制品有限公司
自来水公司专用井盖 ZSZF700C 球墨铸铁,700×450,防响胶条,弹性紧锁结构,方块防滑纹 C250 查看价格 查看价格

吉信

13% 河北省保定吉信金属制品有限公司
自来水公司专用井盖 ZSZF450C 球墨铸铁,450×450,防响胶条,弹性紧锁结构,方块防滑纹 C250 查看价格 查看价格

吉信

13% 河北省保定吉信金属制品有限公司
不锈钢公司招牌发光字 品种:不锈钢公司招牌发光字;规格(mm):1.0×10×10;材质:不锈钢标识字体;牌号:201;表面处理:镀钛、镀铜做旧、烤漆、蚀刻;说明 查看价格 查看价格

东方通顺

cn 13% 北京东方通顺不锈钢有限公司
材料名称 规格/型号 除税
信息价
含税
信息价
行情 品牌 单位 税率 地区/时间
门铃开关带指示窗(可标明住宅式公司) GKB5H8,lOA250V 查看价格 查看价格

湛江市2005年2月信息价
门窗成品均为中山市启泰门窗业有限公司产品; 查看价格 查看价格

中山市2009年1月信息价
250V门铃开关带指示窗"住宅名称""公司名称" B6-mC-250 查看价格 查看价格

佛山市南海区2005年4月信息价
门铃开关带指示窗"住宅名称""公司名称" 250V B6-mC-250 查看价格 查看价格

佛山市南海区2005年1月信息价
250V门铃开关带指示窗"住宅名称""公司名称" B6-mC-250 查看价格 查看价格

佛山市南海区2005年1月信息价
门铃开关带指示窗"住宅名称""公司名称" 250V B6-mC-250 查看价格 查看价格

佛山市南海区2005年4月信息价
门铃开关带指示窗"住宅名称""公司名称" 250V B6-mC-250 查看价格 查看价格

佛山市南海区2005年3月信息价
门铃开关带指示窗"住宅名称""公司名称" 250V B6-mC-250 查看价格 查看价格

佛山市南海区2005年2月信息价
材料名称 规格/需求量 报价数 最新报价
(元)
供应商 报价地区 最新报价时间
关于关于LNL1-3/S-16A价格 LNL1-3/S-16A|2个 1 查看价格 霍尼韦尔朗能电器系统技术(广东)有限公司 广东  云浮市 2010-07-30
公司铭牌 8mm厚铜板阴刻纹理,表面做旧处理|1个 3 查看价格 广州市和威工艺品有限公司 全国   2022-07-29
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公司logo字 1、公司背景墙logo字2、字体:1.2mm304 不锈钢围边精工立体字,字厚20mm边缘打磨抛光,中文字体高度:120mm(26个),英文字体高度:50mm(64个)3、logo:20mm亚克力切割打磨、表面烤漆.高度:268mm|10个 3 查看价格 中山市朗点标识有限公司 广东   2022-05-09
成品游船(专业公司二次深化设计) 成品游船(专业公司二次深化设计)|1套 3 查看价格 兴化市元丰木船厂 全国   2020-11-27
公司LOGO 1.5mm厚304#不锈钢折弯立体字,表面烤漆灰色|1个 3 查看价格 广州市和威工艺品有限公司 全国   2022-07-29
公司招牌 见原档图|1个 2 查看价格 深圳市源艺坊工艺品有限公司    2015-01-21
公司logo 可移背胶2.325×0.7|8张 3 查看价格 广州美端广告制作有限公司 广东   2018-12-28

Concepts NREC是世界上唯一一个集设计、分析、加工于一体的研发平台,可用于各种叶轮机械包括压缩机、涡轮增压器、膨胀机、叶片泵等产品。软件集成了Concepts NREC公司50多年的工程设计经验。主要功能包括:

a.总体方案、一维方案设计

b.三维详细设计和全三元流CFD分析

c.有限元应力和振动分析

d.轴承设计和转子动力学分析

e.多学科多目标优化设计软件f.直纹面侧刃加工、自由曲面点加工和闭式叶轮整体铣削专业软件

软件具体模块名称及功能简介如下:

离心/斜流压气机设计点及非设计点平均流线性能预测程序:COMPAL

叶片泵设计点及非设计点平均流线性能预测程序:PUMPAL

风机/风扇设计点及非设计点平均流线性能预测程序:FANPAL

径流涡轮设计及性能预测程序:RITAL

轴流压气机/涡轮设计点及非设计点平均流线性能预测程序:AXIAL

三维流道和叶片几何设计,快速交互式流场分析和通流计算程序:AxCent·

从其它三维CAD软件的叶轮数据输入接口:CADTranslator·

快速设计级CFD程序:PushbuttonCFD

自动FEA前后处理程序及解算程序:PushbuttonFEA

高温涡轮气冷叶片设计分析系统:CTAADS

多学科自动优化接口程序:TurboOptII

转子动力学及轴承分析软件:DyRoBeS·

叶轮零件整体数控加工自动数控编程软件:MAX-PAC

Concepts NREC软件用户群

ConceptsNREC公司业务遍布世界各地,客户数量超过400家,包括知名的制造厂商、政府科研部门、工程协会、研究所和高校等。

应用行业包括航空发动机、燃气轮机、汽轮机、火箭涡轮泵、涡轮增压器、压缩机、透平膨胀机、能量回收装置、各种叶片泵和风机等产品领域,产品类型可包括径流、斜流、轴流或组合结构,单级或多级设计。

Concepts NREC中国用户

自1993年进入中国以来,目前国内软件用户已经超过80家,涵盖压缩/气机、涡轮增压器、风机/鼓风机、透平膨胀机、叶片泵、汽轮机、机床厂、叶轮专业加工单位等领域。

如沈鼓、金通灵、重通、开山、杭氧、开封空分、宁波博格华纳、上海霍尼韦尔、湖南天雁、山东富源、无锡威孚、莱恩电泵等领域内的知名单位。2100433B

Concepts NREC关于公司常见问题

  • boconcept北欧风情怎么样?

    这种家具挺好的,价格不等,不过宜家的性价比还是挺高的,您可以看一下。

  • concept2划船器噪音大么

    据我了解的话,不管哪个牌子,划船器多多少少都是有噪音的哦,只要在挑选的时候注意一下,concept2划船器噪音一般般,中等水平哦,给你介绍一下吧  第一,划船的话可以室内可以室外的,其实个人觉得除了c...

  • concept2划船器噪音大么

    据我了解的话,不管哪个牌子,划船器多多少少都是有噪音的哦,只要在挑选的时候注意一下,concept2划船器噪音一般般,中等水平哦,给你介绍一下吧第一,划船的话可以室内可以室外的,其实个人觉得除了con...

Concepts NREC关于公司文献

CONCEPTc计量泵操作手册-中文 CONCEPTc计量泵操作手册-中文

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关于公司介绍的ppt 关于公司介绍的ppt

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英文标准名称: Industrial systems,installations and equipment and industrial products-Structuring principles and reference designations-Part 4:Discussion of concepts

发布日期 IssuanceDate: 2005-3-3

实施日期 ExecuteDate: 2005-8-1

首次发布日期 FirstIssuance Date: 1985-4-18

标准状态 StandardState: 现行

复审确认日期 ReviewAffirmance Date:

计划编号 Plan No: 20030927-T-524

代替国标号 ReplacedStandard:

被代替国标号 ReplacedStandard:

废止时间 RevocatoryDate:

采用国际标准号 AdoptedInternational Standard No: IEC 61346-4:1998

采标名称 AdoptedInternational Standard Name:

采用程度 ApplicationDegree: IDT

采用国际标准 AdoptedInternational Standard: IEC

国际标准分类号(ICS): 29.020

中国标准分类号(CCS): K04

标准类别 StandardSort: 基础

标准页码 Number ofPages: 18

标准价格(元) Price(¥): 13

主管部门 Governor: 国家标准化管理委员会

归口单位 TechnicalCommittees: 全国电气信息结构、文件编制和图形符号标准化技术委员会

起草单位 DraftingCommittee:2100433B

Contents

part one Foundations

chapter one Models and Concepts of Life and Intelligence 3

The Mechanics of Life and Thought 4

Stochastic Adaptation: Is Anything Ever Really Random"para" label-module="para">

The “Two Great Stochastic Systems” 12

The Game of Life: Emergence in Complex Systems 16

The Game of Life 17

Emergence 18

Cellular Automata and the Edge of Chaos 20

Artificial Life in Computer Programs 26

Intelligence: Good Minds in People and Machines 30

Intelligence in People: The Boring Criterion 30

Intelligence in Machines: The Turing Criterion 32

chapter two Symbols, Connections, and Optimization by Trial and Error 35

Symbols in Trees and Networks 36

Problem Solving and Optimization 48

A Super-Simple Optimization Problem 49

Three Spaces of Optimization 51

Fitness Landscapes 52

High-Dimensional Cognitive Space and Word Meanings 55

Two Factors of Complexity: NK Landscapes 60

Combinatorial Optimization 64

Binary Optimization 67

Random and Greedy Searches 71

Hill Climbing 72

Simulated Annealing 73

Binary and Gray Coding 74

Step Sizes and Granularity 75

Optimizing with Real Numbers 77

Summary 78

chapter three On Our Nonexistence as Entities: The Social Organism 81

Views of Evolution 82

Gaia: The Living Earth 83

Differential Selection 86

Our Microscopic Masters"para" label-module="para">

Looking for the Right Zoom Angle 92

Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization 94

Accomplishments of the Social Insects 98

Optimizing with Simulated Ants: Computational Swarm Intelligence 105

Staying Together but Not Colliding: Flocks, Herds, and Schools 109

Robot Societies 115

Shallow Understanding 125

Agency 129

Summary 131

chapter four Evolutionary Computation Theory and Paradigms 133

Introduction 134

Evolutionary Computation History 134

The Four Areas of Evolutionary Computation 135

Genetic Algorithms 135

Evolutionary Programming 139

Evolution Strategies 140

Genetic Programming 141

Toward Unification 141

Evolutionary Computation Overview 142

EC Paradigm Attributes 142

Implementation 143

Genetic Algorithms 146

An Overview 146

A Simple GA Example Problem 147

A Review of GA Operations 152

Schemata and the Schema Theorem 159

Final Comments on Genetic Algorithms 163

Evolutionary Programming 164

The Evolutionary Programming Procedure 165

Finite State Machine Evolution 166

Function Optimization 169

Final Comments 171

Evolution Strategies 172

Mutation 172

Recombination 174

Selection 175

Genetic Programming 179

Summary 185

chapter five Humans—Actual, Imagined, and Implied 187

Studying Minds 188

The Fall of the Behaviorist Empire 193

The Cognitive Revolution 195

Bandura’s Social Learning Paradigm 197

Social Psychology 199

Lewin’s Field Theory 200

Norms, Conformity, and Social Influence 202

Sociocognition 205

Simulating Social Influence 206

Paradigm Shifts in Cognitive Science 210

The Evolution of Cooperation 214

Explanatory Coherence 216

Networks in Groups 218

Culture in Theory and Practice 220

Coordination Games 223

The El Farol Problem 226

Sugarscape 229

Tesfatsion’s ACE 232

Picker’s Competing-Norms Model 233

Latané’s Dynamic Social Impact Theory 235

Boyd and Richerson’s Evolutionary Culture Model 240

Memetics 245

Memetic Algorithms 248

Cultural Algorithms 253

Convergence of Basic and Applied Research 254

Culture—and Life without It 255

Summary 258

chapter six Thinking Is Social 261

Introduction 262

Adaptation on Three Levels 263

The Adaptive Culture Model 263

Axelrod’s Culture Model 265

Experiment One: Similarity in Axelrod’s Model 267

Experiment Two: Optimization of an Arbitrary Function 268

Experiment Three: A Slightly Harder and More Interesting Function 269

Experiment Four: A Hard Function 271

Experiment Five: Parallel Constraint Satisfaction 273

Experiment Six: Symbol Processing 279

Discussion 282

Summary 284

part two The Particle Swarm and Collective Intelligence

chapter seven The Particle Swarm 287

Sociocognitive Underpinnings: Evaluate, Compare, and Imitate 288

Evaluate 288

Compare 288

Imitate 289

A Model of Binary Decision 289

Testing the Binary Algorithm with the De Jong Test Suite 297

No Free Lunch 299

Multimodality 302

Minds as Parallel Constraint Satisfaction Networks in Cultures 307

The Particle Swarm in Continuous Numbers 309

The Particle Swarm in Real-Number Space 309

Pseudocode for Particle Swarm Optimization in Continuous Numbers 313

Implementation Issues 314

An Example: Particle Swarm Optimization of Neural Net Weights 314

A Real-World Application 318

The Hybrid Particle Swarm 319

Science as Collaborative Search 320

Emergent Culture, Immergent Intelligence 323

Summary 324

chapter eight Variations and Comparisons 327

Variations of the Particle Swarm Paradigm 328

Parameter Selection 328

Controlling the Explosion 337

Particle Interactions 342

Neighborhood Topology 343

Substituting Cluster Centers for Previous Bests 347

Adding Selection to Particle Swarms 353

Comparing Inertia Weights and Constriction Factors 354

Asymmetric Initialization 357

Some Thoughts on Variations 359

Are Particle Swarms Really a Kind of Evolutionary Algorithm"para" label-module="para">

Evolution beyond Darwin 362

Selection and Self-Organization 363

Ergodicity: Where Can It Get from Here"para" label-module="para">

Convergence of Evolutionary Computation and Particle Swarms 367

Summary 368

chapter nine Applications 369

Evolving Neural Networks with Particle Swarms 370

Review of Previous Work 370

Advantages and Disadvantages of Previous Approaches 374

The Particle Swarm Optimization Implementation Used Here 376

Implementing Neural Network Evolution 377

An Example Application 379

Conclusions 381

Human Tremor Analysis 382

Data Acquisition Using Actigraphy 383

Data Preprocessing 385

Analysis with Particle Swarm Optimization 386

Summary 389

Other Applications 389

Computer Numerically Controlled Milling Optimization 389

Ingredient Mix Optimization 391

Reactive Power and Voltage Control 391

Battery Pack State-of-Charge Estimation 391

Summary 392

chapter ten Implications and Speculations 393

Introduction 394

Assertions 395

Up from Social Learning: Bandura 398

Information and Motivation 399

Vicarious versus Direct Experience 399

The Spread of Influence 400

Machine Adaptation 401

Learning or Adaptation"para" label-module="para">

Cellular Automata 403

Down from Culture 405

Soft Computing 408

Interaction within Small Groups: Group Polarization 409

Informational and Normative Social Influence 411

Self-Esteem 412

Self-Attribution and Social Illusion 414

Summary 419

chapter eleven And in Conclusion . . . 421

Appendix A Statistics for Swarmers 429

Appendix B Genetic Algorithm Implementation 451

Glossary 457

References 475

Index 4972100433B

part one Foundations

chapter one Models and Concepts of Life and Intelligence 3

The Mechanics of Life and Thought 4

Stochastic Adaptation: Is Anything Ever Really Random"para" label-module="para">

The “Two Great Stochastic Systems” 12

The Game of Life: Emergence in Complex Systems 16

The Game of Life 17

Emergence 18

Cellular Automata and the Edge of Chaos 20

Artificial Life in Computer Programs 26

Intelligence: Good Minds in People and Machines 30

Intelligence in People: The Boring Criterion 30

Intelligence in Machines: The Turing Criterion 32

chapter two Symbols, Connections, and Optimization by Trial and Error 35

Symbols in Trees and Networks 36

Problem Solving and Optimization 48

A Super-Simple Optimization Problem 49

Three Spaces of Optimization 51

Fitness Landscapes 52

High-Dimensional Cognitive Space and Word Meanings 55

Two Factors of Complexity: NK Landscapes 60

Combinatorial Optimization 64

Binary Optimization 67

Random and Greedy Searches 71

Hill Climbing 72

Simulated Annealing 73

Binary and Gray Coding 74

Step Sizes and Granularity 75

Optimizing with Real Numbers 77

Summary 78

chapter three On Our Nonexistence as Entities: The Social Organism 81

Views of Evolution 82

Gaia: The Living Earth 83

Differential Selection 86

Our Microscopic Masters"para" label-module="para">

Looking for the Right Zoom Angle 92

Flocks, Herds, Schools, and Swarms: Social Behavior as Optimization 94

Accomplishments of the Social Insects 98

Optimizing with Simulated Ants: Computational Swarm Intelligence 105

Staying Together but Not Colliding: Flocks, Herds, and Schools 109

Robot Societies 115

Shallow Understanding 125

Agency 129

Summary 131

chapter four Evolutionary Computation Theory and Paradigms 133

Introduction 134

Evolutionary Computation History 134

The Four Areas of Evolutionary Computation 135

Genetic Algorithms 135

Evolutionary Programming 139

Evolution Strategies 140

Genetic Programming 141

Toward Unification 141

Evolutionary Computation Overview 142

EC Paradigm Attributes 142

Implementation 143

Genetic Algorithms 146

An Overview 146

A Simple GA Example Problem 147

A Review of GA Operations 152

Schemata and the Schema Theorem 159

Final Comments on Genetic Algorithms 163

Evolutionary Programming 164

The Evolutionary Programming Procedure 165

Finite State Machine Evolution 166

Function Optimization 169

Final Comments 171

Evolution Strategies 172

Mutation 172

Recombination 174

Selection 175

Genetic Programming 179

Summary 185

chapter five Humans—Actual, Imagined, and Implied 187

Studying Minds 188

The Fall of the Behaviorist Empire 193

The Cognitive Revolution 195

Bandura’s Social Learning Paradigm 197

Social Psychology 199

Lewin’s Field Theory 200

Norms, Conformity, and Social Influence 202

Sociocognition 205

Simulating Social Influence 206

Paradigm Shifts in Cognitive Science 210

The Evolution of Cooperation 214

Explanatory Coherence 216

Networks in Groups 218

Culture in Theory and Practice 220

Coordination Games 223

The El Farol Problem 226

Sugarscape 229

Tesfatsion’s ACE 232

Picker’s Competing-Norms Model 233

Latané’s Dynamic Social Impact Theory 235

Boyd and Richerson’s Evolutionary Culture Model 240

Memetics 245

Memetic Algorithms 248

Cultural Algorithms 253

Convergence of Basic and Applied Research 254

Culture—and Life without It 255

Summary 258

chapter six Thinking Is Social 261

Introduction 262

Adaptation on Three Levels 263

The Adaptive Culture Model 263

Axelrod’s Culture Model 265

Experiment One: Similarity in Axelrod’s Model 267

Experiment Two: Optimization of an Arbitrary Function 268

Experiment Three: A Slightly Harder and More Interesting Function 269

Experiment Four: A Hard Function 271

Experiment Five: Parallel Constraint Satisfaction 273

Experiment Six: Symbol Processing 279

Discussion 282

Summary 284

part two The Particle Swarm and Collective Intelligence

chapter seven The Particle Swarm 287

Sociocognitive Underpinnings: Evaluate, Compare, and Imitate 288

Evaluate 288

Compare 288

Imitate 289

A Model of Binary Decision 289

Testing the Binary Algorithm with the De Jong Test Suite 297

No Free Lunch 299

Multimodality 302

Minds as Parallel Constraint Satisfaction Networks in Cultures 307

The Particle Swarm in Continuous Numbers 309

The Particle Swarm in Real-Number Space 309

Pseudocode for Particle Swarm Optimization in Continuous Numbers 313

Implementation Issues 314

An Example: Particle Swarm Optimization of Neural Net Weights 314

A Real-World Application 318

The Hybrid Particle Swarm 319

Science as Collaborative Search 320

Emergent Culture, Immergent Intelligence 323

Summary 324

chapter eight Variations and Comparisons 327

Variations of the Particle Swarm Paradigm 328

Parameter Selection 328

Controlling the Explosion 337

Particle Interactions 342

Neighborhood Topology 343

Substituting Cluster Centers for Previous Bests 347

Adding Selection to Particle Swarms 353

Comparing Inertia Weights and Constriction Factors 354

Asymmetric Initialization 357

Some Thoughts on Variations 359

Are Particle Swarms Really a Kind of Evolutionary Algorithm"para" label-module="para">

Evolution beyond Darwin 362

Selection and Self-Organization 363

Ergodicity: Where Can It Get from Here"para" label-module="para">

Convergence of Evolutionary Computation and Particle Swarms 367

Summary 368

chapter nine Applications 369

Evolving Neural Networks with Particle Swarms 370

Review of Previous Work 370

Advantages and Disadvantages of Previous Approaches 374

The Particle Swarm Optimization Implementation Used Here 376

Implementing Neural Network Evolution 377

An Example Application 379

Conclusions 381

Human Tremor Analysis 382

Data Acquisition Using Actigraphy 383

Data Preprocessing 385

Analysis with Particle Swarm Optimization 386

Summary 389

Other Applications 389

Computer Numerically Controlled Milling Optimization 389

Ingredient Mix Optimization 391

Reactive Power and Voltage Control 391

Battery Pack State-of-Charge Estimation 391

Summary 392

chapter ten Implications and Speculations 393

Introduction 394

Assertions 395

Up from Social Learning: Bandura 398

Information and Motivation 399

Vicarious versus Direct Experience 399

The Spread of Influence 400

Machine Adaptation 401

Learning or Adaptation"para" label-module="para">

Cellular Automata 403

Down from Culture 405

Soft Computing 408

Interaction within Small Groups: Group Polarization 409

Informational and Normative Social Influence 411

Self-Esteem 412

Self-Attribution and Social Illusion 414

Summary 419

chapter eleven And in Conclusion . . . 421

Appendix A Statistics for Swarmers 429

Appendix B Genetic Algorithm Implementation 451

Glossary 457

References 475

Index 497

……2100433B

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