Concepts NREC

Concepts NREC是世界上唯一一個(gè)集設(shè)計(jì)、分析、加工于一體的研發(fā)平臺(tái)，可用于各種葉輪機(jī)械包括壓縮機(jī)、渦輪增壓器、膨脹機(jī)、葉片泵等產(chǎn)品。軟件集成了Concepts NREC公司50多年的工程設(shè)計(jì)經(jīng)驗(yàn)。主要功能包括：

a.總體方案、一維方案設(shè)計(jì)

b.三維詳細(xì)設(shè)計(jì)和全三元流CFD分析

c.有限元應(yīng)力和振動(dòng)分析

d.軸承設(shè)計(jì)和轉(zhuǎn)子動(dòng)力學(xué)分析?

e.多學(xué)科多目標(biāo)優(yōu)化設(shè)計(jì)軟件f.直紋面?zhèn)热屑庸ぁ⒆杂汕纥c(diǎn)加工和閉式葉輪整體銑削專業(yè)軟件

軟件具體模塊名稱及功能簡(jiǎn)介如下：

離心/斜流壓氣機(jī)設(shè)計(jì)點(diǎn)及非設(shè)計(jì)點(diǎn)平均流線性能預(yù)測(cè)程序:COMPAL

葉片泵設(shè)計(jì)點(diǎn)及非設(shè)計(jì)點(diǎn)平均流線性能預(yù)測(cè)程序:PUMPAL

風(fēng)機(jī)/風(fēng)扇設(shè)計(jì)點(diǎn)及非設(shè)計(jì)點(diǎn)平均流線性能預(yù)測(cè)程序:FANPAL

徑流渦輪設(shè)計(jì)及性能預(yù)測(cè)程序：RITAL

軸流壓氣機(jī)/渦輪設(shè)計(jì)點(diǎn)及非設(shè)計(jì)點(diǎn)平均流線性能預(yù)測(cè)程序:AXIAL

三維流道和葉片幾何設(shè)計(jì)，快速交互式流場(chǎng)分析和通流計(jì)算程序:AxCent·

從其它三維CAD軟件的葉輪數(shù)據(jù)輸入接口：CADTranslator·

快速設(shè)計(jì)級(jí)CFD程序:PushbuttonCFD

自動(dòng)FEA前后處理程序及解算程序:PushbuttonFEA

高溫渦輪氣冷葉片設(shè)計(jì)分析系統(tǒng):CTAADS

多學(xué)科自動(dòng)優(yōu)化接口程序:TurboOptII

轉(zhuǎn)子動(dòng)力學(xué)及軸承分析軟件:DyRoBeS·

葉輪零件整體數(shù)控加工自動(dòng)數(shù)控編程軟件：MAX-PAC

Concepts NREC軟件用戶群

ConceptsNREC公司業(yè)務(wù)遍布世界各地，客戶數(shù)量超過(guò)400家，包括知名的制造廠商、政府科研部門、工程協(xié)會(huì)、研究所和高校等。

應(yīng)用行業(yè)包括航空發(fā)動(dòng)機(jī)、燃?xì)廨啓C(jī)、汽輪機(jī)、火箭渦輪泵、渦輪增壓器、壓縮機(jī)、透平膨脹機(jī)、能量回收裝置、各種葉片泵和風(fēng)機(jī)等產(chǎn)品領(lǐng)域，產(chǎn)品類型可包括徑流、斜流、軸流或組合結(jié)構(gòu)，單級(jí)或多級(jí)設(shè)計(jì)。

Concepts NREC中國(guó)用戶

自1993年進(jìn)入中國(guó)以來(lái)，目前國(guó)內(nèi)軟件用戶已經(jīng)超過(guò)80家，涵蓋壓縮/氣機(jī)、渦輪增壓器、風(fēng)機(jī)/鼓風(fēng)機(jī)、透平膨脹機(jī)、葉片泵、汽輪機(jī)、機(jī)床廠、葉輪專業(yè)加工單位等領(lǐng)域。

如沈鼓、金通靈、重通、開山、杭氧、開封空分、寧波博格華納、上?；裟犴f爾、湖南天雁、山東富源、無(wú)錫威孚、萊恩電泵等領(lǐng)域內(nèi)的知名單位。2100433B

Concepts NREC是世界上最著名的葉輪機(jī)械專業(yè)服務(wù)公司（以下簡(jiǎn)稱CN公司）。全世界唯一的既開發(fā)和推廣葉輪機(jī)械設(shè)計(jì)/加工專用（CAE/CAM）軟件，同時(shí)也提供葉輪機(jī)械樣機(jī)開發(fā)和性能測(cè)試的全方位高端服務(wù)公司，當(dāng)前員工總數(shù)130人。

公司前身源于美國(guó)麻省理工學(xué)院的3位科學(xué)家1956年成立的北方研究工程公司（NREC）和美國(guó)工程院院士DaveJapikse博士于1980年成立的ConceptsETI公司。公司分支機(jī)構(gòu)和服務(wù)體系遍布全球各個(gè)主要工業(yè)國(guó)家。

2000年，集成兩家公司原軟件為全新的AgileEngineeringDesignSystem（AEDS）敏捷工程設(shè)計(jì)系統(tǒng)，致力于為業(yè)界提供“敏捷設(shè)計(jì)”和“敏捷制造”為宗旨的透平機(jī)械研發(fā)一體化解決方案。

CN具有一支經(jīng)驗(yàn)十分豐富的專家隊(duì)伍，當(dāng)前公司專家團(tuán)隊(duì)曾在諸多著名大公司和研究機(jī)構(gòu)承擔(dān)過(guò)重要型號(hào)或產(chǎn)品研發(fā)，包括：GE、NASA、Honeywell、Pratt&Whitney、DR、IR、RR、SolarTurbines、Hamilton、Lycoming、Williams、ARL、AEDC、Flowsever等等。

數(shù)十年研發(fā)持續(xù)積累、強(qiáng)大的專家隊(duì)伍、全球客戶不斷反饋是CN工程咨詢和軟件開發(fā)技術(shù)能力的核心知識(shí)庫(kù)。

CN還具備非常先進(jìn)的樣機(jī)試制和試驗(yàn)臺(tái)位等硬件條件，能夠快速實(shí)現(xiàn)從先進(jìn)設(shè)計(jì)到高精密制造以及性能試驗(yàn)的完整研發(fā)過(guò)程。每年承擔(dān)諸多美國(guó)SBIR,STTR科研項(xiàng)目。公司每年在ASME等學(xué)術(shù)會(huì)議上發(fā)表諸多研究成果論文。

英文標(biāo)準(zhǔn)名稱： Industrial systems,installations and equipment and industrial products-Structuring principles and reference designations-Part 4:Discussion of concepts

發(fā)布日期 IssuanceDate： 2005-3-3

實(shí)施日期 ExecuteDate： 2005-8-1

首次發(fā)布日期 FirstIssuance Date： 1985-4-18

標(biāo)準(zhǔn)狀態(tài) StandardState： 現(xiàn)行

復(fù)審確認(rèn)日期 ReviewAffirmance Date：

計(jì)劃編號(hào) Plan No： 20030927-T-524

代替國(guó)標(biāo)號(hào) ReplacedStandard：

被代替國(guó)標(biāo)號(hào) ReplacedStandard:

廢止時(shí)間 RevocatoryDate：

采用國(guó)際標(biāo)準(zhǔn)號(hào) AdoptedInternational Standard No： IEC 61346-4:1998

采標(biāo)名稱 AdoptedInternational Standard Name：

采用程度 ApplicationDegree： IDT

采用國(guó)際標(biāo)準(zhǔn) AdoptedInternational Standard： IEC

國(guó)際標(biāo)準(zhǔn)分類號(hào)(ICS)： 29.020

中國(guó)標(biāo)準(zhǔn)分類號(hào)(CCS)： K04

標(biāo)準(zhǔn)類別 StandardSort： 基礎(chǔ)

標(biāo)準(zhǔn)頁(yè)碼 Number ofPages: 18

標(biāo)準(zhǔn)價(jià)格(元) Price(￥)： 13

主管部門 Governor： 國(guó)家標(biāo)準(zhǔn)化管理委員會(huì)

歸口單位 TechnicalCommittees： 全國(guó)電氣信息結(jié)構(gòu)、文件編制和圖形符號(hào)標(biāo)準(zhǔn)化技術(shù)委員會(huì)

起草單位 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